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56c2dc305f24ba5731f349d4284d1ede0e056579 | 91d1a6968b90d9d461e9a2ece12b465486e3ccc2 | /ec2_write_1/ebs-default-kms-key-id_modify.py | 9590bdb3b63ab3aa9cd39ae2bf409a3fec3eef4f | [] | no_license | lxtxl/aws_cli | c31fc994c9a4296d6bac851e680d5adbf7e93481 | aaf35df1b7509abf5601d3f09ff1fece482facda | refs/heads/master | 2023-02-06T09:00:33.088379 | 2020-12-27T13:38:45 | 2020-12-27T13:38:45 | 318,686,394 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,870 | py | #!/usr/bin/python
# -*- codding: utf-8 -*-
import os
import sys
sys.path.append(os.path.dirname(os.path.abspath(os.path.dirname(__file__))))
from common.execute_command import write_one_parameter
# url : https://awscli.amazonaws.com/v2/documentation/api/latest/reference/ec2/modify-ebs-default-kms-key-id.html
if __name__ == '__main__':
"""
get-ebs-default-kms-key-id : https://awscli.amazonaws.com/v2/documentation/api/latest/reference/ec2/get-ebs-default-kms-key-id.html
reset-ebs-default-kms-key-id : https://awscli.amazonaws.com/v2/documentation/api/latest/reference/ec2/reset-ebs-default-kms-key-id.html
"""
parameter_display_string = """
# kms-key-id : The identifier of the AWS Key Management Service (AWS KMS) customer master key (CMK) to use for Amazon EBS encryption. If this parameter is not specified, your AWS managed CMK for EBS is used. If KmsKeyId is specified, the encrypted state must be true .
You can specify the CMK using any of the following:
Key ID. For example, 1234abcd-12ab-34cd-56ef-1234567890ab.
Key alias. For example, alias/ExampleAlias.
Key ARN. For example, arn:aws:kms:us-east-1:012345678910:key/1234abcd-12ab-34cd-56ef-1234567890ab.
Alias ARN. For example, arn:aws:kms:us-east-1:012345678910:alias/ExampleAlias.
AWS authenticates the CMK asynchronously. Therefore, if you specify an ID, alias, or ARN that is not valid, the action can appear to complete, but eventually fails.
Amazon EBS does not support asymmetric CMKs.
"""
add_option_dict = {}
#######################################################################
# parameter display string
add_option_dict["parameter_display_string"] = parameter_display_string
# ex: add_option_dict["no_value_parameter_list"] = "--single-parameter"
write_one_parameter("ec2", "modify-ebs-default-kms-key-id", "kms-key-id", add_option_dict)
| [
"hcseo77@gmail.com"
] | hcseo77@gmail.com |
24afdc57d33138a22371de464d7a9b191044f405 | 70be3c06f85f79e5660a1943e08651c6a5dc1032 | /stats_test.py | 0c7911d12bc2571e969d8cd8d612a8f513152511 | [] | no_license | archisman-panigrahi/ppa-stats-1 | fa2353caedd88f3284e3cb8e723c676cefd9ad97 | bb69387c122fa9d532aa5917938a3df3caa77ad9 | refs/heads/master | 2023-08-22T12:51:32.148712 | 2021-06-17T04:44:49 | 2021-06-17T04:44:49 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,622 | py | import collections
from launchpadlib.launchpad import Launchpad
from pprint import pprint
import os
_lp_cachedir = os.path.expanduser("~/.launchpadlib/cache/")
launchpad = Launchpad.login_anonymously('anon', 'production', _lp_cachedir)
def get_binaries(ppa_owner, ppa_name, package):
owner = launchpad.people[ppa_owner]
ppa = owner.getPPAByName(name=ppa_name)
binaries = ppa.getPublishedBinaries(binary_name=package)
# Remove binaries which are copied.
def is_not_copy(binary):
return binary.copied_from_archive_link is None
binaries = filter(is_not_copy, binaries)
return binaries
def get_binary_info(binary):
daily_downloads = binary.getDownloadCounts()
daily = collections.defaultdict(lambda: 0)
for downloads in daily_downloads:
daily[downloads.day.date()] += downloads.count
# e.g. 'cava 0.6.1-2-1 in bionic amd64'
attrs = binary.display_name.strip().split(' ')
assert attrs[1] == binary.binary_package_version
info = {
#'display_name': binary.display_name,
'package': attrs[0],
'version': binary.binary_package_version,
#'architecture_specific': binary.architecture_specific,
#'distro_arch_series_link': binary.distro_arch_series_link,
'distro': attrs[3],
'arch': attrs[4],
'total_downloads': binary.getDownloadCount(),
'daily_downloads': dict(daily),
}
return info
if __name__ == '__main__':
binaries = get_binaries('hsheth2', 'ppa', 'cava')
for binary in binaries:
info = get_binary_info(binary)
pprint(info)
break
| [
"hsheth2@gmail.com"
] | hsheth2@gmail.com |
e30eac1ded6ffcfd4458f5a272fdbbeb01c07f3a | f3a7eae3031bb9afe75116a9b86278490ac4a7e6 | /text/symbols.py | c329f2df647246d4d8e564a02e78e26b68ac2691 | [
"BSD-3-Clause",
"MIT"
] | permissive | LOCS-AI/Multilanguage_Tacotron_2 | ea34c4fb41e8112537529945b5a31cf2e78d0610 | 82c788fb26d93c6735c54c2fe4ae7bcbd0eec69f | refs/heads/master | 2022-12-31T01:39:57.432804 | 2020-10-08T00:04:58 | 2020-10-08T00:04:58 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 989 | py | """ from https://github.com/keithito/tacotron """
'''
Defines the set of symbols used in text input to the model.
The default is a set of ASCII characters that works well for English or text that has been run through Unidecode. For other data, you can modify _characters. See TRAINING_DATA.md for details. '''
from text import cmudict
_pad = '_'
_punctuation = '!\'(),.:;? '
_special = '-'
_letters = 'abcdefghijklmnopqrstuvwxyz'
# Prepend "@" to ARPAbet symbols to ensure uniqueness (some are the same as uppercase letters):
_arpabet = ['@' + s for s in cmudict.valid_symbols]
hangul_symbol = u'''␀␃%"ᄀᄁᄂᄃᄄᄅᄆᄇᄈᄉᄊᄋᄌᄍᄎᄏᄐᄑᄒᅌᅡᅢᅣᅤᅥᅦᅧᅨᅩᅪᅫᅬᅭᅮᅯᅰᅱᅲᅳᅴᅵᆞᆢᆨᆩᆫᆬᆭᆮᆯᆰᆱᆲᆴᆶᆪᆷᆸᆹᆺᆻᆼᆽᆾᆿᇀᇁᇂ'''
# Export all symbols:
symbols = [_pad] + list(_special) + list(_punctuation) + _arpabet + list(_letters)
symbols = list(hangul_symbol) + symbols | [
"thien@locslab.com"
] | thien@locslab.com |
20a86eda7d13a8fc89be63deebdf830ce2d59c53 | 3a8050cea13e94853954d52ff55552b0c3d8a2b1 | /extendedmodels/Race.py | 1dfc5f0b67395a14503170a067815fbb432fb8a9 | [] | no_license | marcelomrocha/frecog | eb5fb606fbeeff78982cf4322584cac1f1f84202 | 181a86df6e11203081372538fae274fbd37cfc89 | refs/heads/master | 2023-04-16T22:59:34.622823 | 2021-04-12T01:36:03 | 2021-04-12T01:36:03 | 355,626,321 | 0 | 1 | null | null | null | null | UTF-8 | Python | false | false | 1,322 | py | from basemodels import VGGFace
import os
from pathlib import Path
import gdown
import numpy as np
from keras.models import Model, Sequential
from keras.layers import Convolution2D, Flatten, Activation
import zipfile
def loadModel():
model = VGGFace.baseModel()
#--------------------------
classes = 6
base_model_output = Sequential()
base_model_output = Convolution2D(classes, (1, 1), name='predictions')(model.layers[-4].output)
base_model_output = Flatten()(base_model_output)
base_model_output = Activation('softmax')(base_model_output)
#--------------------------
race_model = Model(inputs=model.input, outputs=base_model_output)
#--------------------------
#load weights
home = str(Path.home())
if os.path.isfile('weights/race_model_single_batch.h5') != True:
print("race_model_single_batch.h5 will be downloaded...")
#zip
url = 'https://drive.google.com/uc?id=1nz-WDhghGQBC4biwShQ9kYjvQMpO6smj'
output = 'weights/race_model_single_batch.zip'
gdown.download(url, output, quiet=False)
#unzip race_model_single_batch.zip
with zipfile.ZipFile(output, 'r') as zip_ref:
zip_ref.extractall('weights/')
race_model.load_weights('weights/race_model_single_batch.h5')
return race_model
#--------------------------
| [
"noreply@github.com"
] | marcelomrocha.noreply@github.com |
325a3b9477e74cb62718555392992eecbe79e947 | e37665534d517821f1bdb907902a818773d28e58 | /autotrading/scheduler/trader.py | f9a8da82d4570e2ec8494617f00fdbc34d7505f2 | [] | no_license | hyeon95y/tutorial_trading_2 | 2103cc27c4ced490cb98e24d6c8e707ac4d51d2c | 1a5526deeffd50784fdeaf408416be060dadccb9 | refs/heads/main | 2023-02-28T05:26:29.245523 | 2021-02-06T07:37:51 | 2021-02-06T07:37:51 | 335,929,925 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 8,832 | py | import configparser
import datetime
from celery import Celery
from autotrading.db.mongodb import MongoDbHandler
from autotrading.machine.korbit_machine import KorbitMachine
from autotrading.pusher.slack import PushSlack
app = Celery(
"get_coin_info",
backend="redis://localhost:6379/0",
broker="redis://localhost:6379/0",
)
config = configparser.ConfigParser()
config.read("conf/config.ini")
client_id = config["KORBIT"]["client_id"]
client_secret = config["KORBIT"]["client_secret"]
username = config["KORBIT"]["username"]
password = config["KORBIT"]["password"]
machine = KorbitMachine(
mode="Prod",
client_id=client_id,
client_secret=client_secret,
username=username,
password=password,
)
db_handler_local = MongoDbHandler("local", "coiner", "price_info")
db_handler_remote = MongoDbHandler("remote", "coiner", "price_info")
pusher = PushSlack()
app.conf.beat_schedule = {
"add-every-1-min": {
"task": "scheduler.trader.trader",
"schedule": 60.0,
"args": (),
},
}
def order_buy_transaction(
machine=None,
db_handler=None,
coin=None,
item=None,
order_type="limit",
):
if coin is None or item is None:
raise Exception("Need to param")
db_handler.set_db("trader", "trade_status")
result = machine.buy_coin_order(
currency_pair=coin,
price=item["buy"],
coin_amount=item["buy_amount"], # str(self.BUY_COUNT),
order_type="limit",
)
if result["status"] == "success":
db_handler.insert_item(
{
"status": "BUY_ORDERED",
"buy_order_id": str(result["orderId"]),
"buy_amount": str(item["buy_amount"]),
"buy": str(item["buy"]),
"buy_order_time": int(datetime.datetime.now().timestamp()),
"desired_value": str(item["desired_value"]),
"transaction_status": "success",
},
)
def order_sell_transaction(
machine=None,
db_handler=None,
coin=None,
item=None,
type="limit",
):
if coin is None or item is None:
raise Exception("Need to param")
db_handler.set_db("trader", "trade_status")
result = machine.sell_coin_order(
currency_pair=coin,
price=item["desired_value"],
coin_amount=item["real_buy_amount"],
order_type="limit",
)
if result["status"] == "success":
db_handler.update_item(
{"_id": item["_id"]},
{
"$set": {
"status": "SELL_ORDERED",
"desired_value": str(item["desired_value"]),
"sell_order_id": str(result["orderId"]),
"error": "success",
},
},
)
else:
db_handler.update_item({"_id": item["_id"]}, {"error": "failed"})
def order_cancel_transaction(machine=None, db_handler=None, coin=None, item=None):
db_handler.set_db("trader", "trade_status")
if coin is None or item is None or type is None:
raise Exception("Need to param")
if item["status"] == "BUY_ORDERED":
machine.cancel_coin_order(coin, item["buy_order_id"])
elif item["status"] == "SELL_ORDERED":
machine.cancel_coin_order(coin, item["sell_order_id"])
db_handler.update_item(
{"_id": item["_id"]},
{
"$set": {
"status": "CANCEL_ORDERED",
"cancel_order_time": int(datetime.datetime.now().timestamp()),
"error": "success",
},
},
)
def update_trade_status(db_handler=None, condition=None, value=None):
if condition is None or value is None:
raise Exception("Need to buy value or status")
db_handler.set_db("trader", "trade_status")
db_handler.update_item(condition, {"$set": value})
@app.task
def trader():
"""
We will etc coin.
"""
coin_type = "etc_krw"
buy_amount = 1
machine.set_token()
"""
get 1hour ago timestamp
"""
print("get 1hour ago timestamp")
now = datetime.datetime.now()
one_hour_ago = now - datetime.timedelta(minutes=60)
one_hour_ago_timestamp = int(one_hour_ago.timestamp())
pipeline = [
{"$match": {"timestamp": {"$gt": one_hour_ago_timestamp}, "coin": coin_type}},
{
"$group": {
"_id": "$coin",
"min_val": {"$min": "$price"},
"max_val": {"$max": "$price"},
},
},
]
"""
get a min max value
"""
print("get a min max")
query_result = db_handler_remote.aggregate(pipeline)
latest_coin_value = machine.get_ticker(currency_pair=coin_type)
for item in query_result:
print(item)
max_val = int(item["max_val"])
min_val = int(item["min_val"])
gap_val = max_val - min_val
"""
get a latest coin value
"""
print("get a latest coin value")
latest_value = int(latest_coin_value["last"])
limit_value = latest_value * 0.02
if gap_val > limit_value:
item["buy"] = str(min_val)
item["buy_amount"] = str(buy_amount)
item["desired_value"] = str(int(round(min_val * 105, -2)))
order_buy_transaction(
coin=coin_type,
machine=machine,
db_handler=db_handler_local,
item=item,
order_type="limit",
)
pusher.send_message("#general", str(item))
print("buy")
print(item)
else:
print("pass")
print(gap_val)
print(limit_value)
"Check order status"
print("check order status")
buy_ordered = db_handler_local.find_item(
{"status": "BUY_ORDERED"},
"trader",
"trade_status",
)
for item in buy_ordered:
result = machine.get_my_order_status(coin_type, item["buy_order_id"])
for order_status in result:
if order_status["status"] == "filled":
real_buy_amount = str(
float(order_status["filled_amount"]) - float(order_status["fee"]),
)
real_buy_value = str(order_status["avg_price"])
completed_time = int(order_status["last_filled_at"] / 1000)
fee = str(order_status["fee"])
if order_status["side"] == "bid":
pusher.send_message("#general", str(item))
db_handler_local.update_item(
{"_id": item["_id"]},
{
"$set": {
"status": "BUY_COMPLETED",
"real_buy_amount": real_buy_amount,
"buy_completed_time": completed_time,
"real_buy_value": real_buy_value,
"buy_fee": fee,
"progress_status": "success",
},
},
)
break
buy_completed = db_handler_local.find_item(
{"status": "BUY_COMPLETED"},
"trader",
"trade_status",
)
for item in buy_completed:
order_sell_transaction(
machine=machine,
db_handler=db_handler_local,
coin=coin_type,
item=item,
type="limit",
)
pusher.send_message("#general", str(item))
sell_ordered = db_handler_local.find_item(
{"status": "SELL_ORDERED"},
"trader",
"trade_status",
)
for item in sell_ordered:
result = machine.get_my_order_status(coin_type, item["sell_order_id"])
for order_status in result:
if order_status["status"] == "filled":
real_sell_amount = str(float(order_status["filled_amount"]))
real_sell_value = str(order_status["avg_price"])
completed_time = int(order_status["last_filled_at"] / 1000)
fee = order_status["fee"]
if order_status["side"] == "ask":
pusher.send_message("#general", str(item))
db_handler_local.update_item(
{"_id": item["_id"]},
{
"$set": {
"status": "SELL_COMPLETED",
"real_sell_amount": real_sell_amount,
"sell_completed_time": completed_time,
"real_sell_value": real_sell_value,
"sell_fee": fee,
},
},
)
break
if __name__ == "__main__":
trader()
| [
"hyeon95y@gmail.com"
] | hyeon95y@gmail.com |
34055673d955ae3740bf5aea6ba66a3cb020511c | 8c770324ddd14971f977f49ed29be2360d4ace6c | /assignment2.2.py | 267c9fdede76f5ec4fce384ecd620e91be5f89b8 | [] | no_license | Tathagatac001/Python_assignment | 9db4b0d9325696d4391fbf2723531b9dd765764b | 6b55546174a2a70a89e062e8680ab3b3d330fb47 | refs/heads/master | 2018-09-10T03:34:28.844667 | 2018-06-05T09:40:02 | 2018-06-05T09:40:02 | 115,848,048 | 0 | 0 | null | 2018-02-10T06:35:10 | 2017-12-31T06:52:47 | Python | UTF-8 | Python | false | false | 158 | py | my_input='*'
for i in range(0,2):
for j in range(1,6):
if i == 0:
print my_input * (j-i)
if i != 0 and (6-j-i) != 0:
print my_input * (6-j-i) | [
"noreply@github.com"
] | Tathagatac001.noreply@github.com |
e2305deb0d46015fcf8ec048392eb7496180110b | 73ed323e7fc049dd1dff3b6556987a5ed11db48e | /roman_to_integer.py | 1502741d73e71598134f514d19e6416dc6b0af1e | [] | no_license | andyOrigin123/LeetCode_easy_problems_with_python3 | 3c81fe5fac1cb0201ae1c37b5dbaea7531a3a4b5 | ede0c8ec7d325fc18741ca903b45e8d2ef963039 | refs/heads/master | 2020-04-30T20:54:39.944731 | 2019-03-26T15:11:01 | 2019-03-26T15:11:01 | 177,081,633 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,856 | py | """
Roman numerals are represented by seven different symbols: I, V, X, L, C, D and M.
Symbol Value
I 1
V 5
X 10
L 50
C 100
D 500
M 1000
For example, two is written as II in Roman numeral, just two one's added together. Twelve is written as, XII, which is simply X + II. The number twenty seven is written as XXVII, which is XX + V + II.
Roman numerals are usually written largest to smallest from left to right. However, the numeral for four is not IIII. Instead, the number four is written as IV. Because the one is before the five we subtract it making four. The same principle applies to the number nine, which is written as IX. There are six instances where subtraction is used:
I can be placed before V (5) and X (10) to make 4 and 9.
X can be placed before L (50) and C (100) to make 40 and 90.
C can be placed before D (500) and M (1000) to make 400 and 900.
Given a roman numeral, convert it to an integer. Input is guaranteed to be within the range from 1 to 3999.
Example 1:
Input: "III"
Output: 3
Example 2:
Input: "IV"
Output: 4
Example 3:
Input: "IX"
Output: 9
Example 4:
Input: "LVIII"
Output: 58
Explanation: L = 50, V= 5, III = 3.
Example 5:
Input: "MCMXCIV"
Output: 1994
Explanation: M = 1000, CM = 900, XC = 90 and IV = 4.
Accepted 379,445
Submissions 732,579
"""
class Solution:
def romanToInt(self, s: str) -> int:
dict_tmp = {'I': 1, 'V': 5, 'X': 10, 'L': 50, 'C': 100, 'D': 500, 'M': 1000}
cnt = len(s)
i, res = 0, 0
while i < cnt:
if i + 1 < cnt and dict_tmp[s[i]] < dict_tmp[s[i + 1]]:
res += dict_tmp[s[i + 1]] - dict_tmp[s[i]]
i += 2
else:
res += dict_tmp[s[i]]
i += 1
return res
| [
"noreply@github.com"
] | andyOrigin123.noreply@github.com |
03608d220d4d293c64e7d19d2c5178953574c174 | 0e1e643e864bcb96cf06f14f4cb559b034e114d0 | /Exps_7_v3/doc3d/Ablation4_ch016_ep003_7/W_w_M_to_C_pyr/pyr_6s/L7/step10_a.py | 6c005ce2d39cf14a6d40bc0a6f470140b0365a40 | [] | no_license | KongBOy/kong_model2 | 33a94a9d2be5b0f28f9d479b3744e1d0e0ebd307 | 1af20b168ffccf0d5293a393a40a9fa9519410b2 | refs/heads/master | 2022-10-14T03:09:22.543998 | 2022-10-06T11:33:42 | 2022-10-06T11:33:42 | 242,080,692 | 3 | 0 | null | null | null | null | UTF-8 | Python | false | false | 942,122 | py | #############################################################################################################################################################################################################
#############################################################################################################################################################################################################
### 把 kong_model2 加入 sys.path
import os
code_exe_path = os.path.realpath(__file__) ### 目前執行 step10_b.py 的 path
code_exe_path_element = code_exe_path.split("\\") ### 把 path 切分 等等 要找出 kong_model 在第幾層
code_dir = "\\".join(code_exe_path_element[:-1])
kong_layer = code_exe_path_element.index("kong_model2") ### 找出 kong_model2 在第幾層
kong_model2_dir = "\\".join(code_exe_path_element[:kong_layer + 1]) ### 定位出 kong_model2 的 dir
import sys ### 把 kong_model2 加入 sys.path
sys.path.append(kong_model2_dir)
sys.path.append(code_dir)
# print(__file__.split("\\")[-1])
# print(" code_exe_path:", code_exe_path)
# print(" code_exe_path_element:", code_exe_path_element)
# print(" code_dir:", code_dir)
# print(" kong_layer:", kong_layer)
# print(" kong_model2_dir:", kong_model2_dir)
#############################################################################################################################################################################################################
kong_to_py_layer = len(code_exe_path_element) - 1 - kong_layer ### 中間 -1 是為了長度轉index
# print(" kong_to_py_layer:", kong_to_py_layer)
if (kong_to_py_layer == 0): template_dir = ""
elif(kong_to_py_layer == 2): template_dir = code_exe_path_element[kong_layer + 1][0:] ### [7:] 是為了去掉 step1x_， 後來覺得好像改有意義的名字不去掉也行所以 改 0
elif(kong_to_py_layer == 3): template_dir = code_exe_path_element[kong_layer + 1][0:] + "/" + code_exe_path_element[kong_layer + 2][0:] ### [5:] 是為了去掉 mask_ ，前面的 mask_ 是為了python 的 module 不能 數字開頭， 隨便加的這樣子， 後來覺得 自動排的順序也可以接受， 所以 改0
elif(kong_to_py_layer > 3): template_dir = code_exe_path_element[kong_layer + 1][0:] + "/" + code_exe_path_element[kong_layer + 2][0:] + "/" + "/".join(code_exe_path_element[kong_layer + 3: -1])
# print(" template_dir:", template_dir) ### 舉例： template_dir: 7_mask_unet/5_os_book_and_paper_have_dtd_hdr_mix_bg_tv_s04_mae
#############################################################################################################################################################################################################
exp_dir = template_dir
#############################################################################################################################################################################################################
from step06_a_datas_obj import *
from step09_6side_L7 import *
from step10_a2_loss_info_obj import *
from step10_b2_exp_builder import Exp_builder
rm_paths = [path for path in sys.path if code_dir in path]
for rm_path in rm_paths: sys.path.remove(rm_path)
rm_moduless = [module for module in sys.modules if "step09" in module]
for rm_module in rm_moduless: del sys.modules[rm_module]
#############################################################################################################################################################################################################
'''
exp_dir 是 決定 result_dir 的 "上一層"資料夾 名字喔！ exp_dir要巢狀也沒問題～
比如：exp_dir = "6_mask_unet/自己命的名字"，那 result_dir 就都在：
6_mask_unet/自己命的名字/result_a
6_mask_unet/自己命的名字/result_b
6_mask_unet/自己命的名字/...
'''
use_db_obj = type8_blender_kong_doc3d_in_W_and_I_gt_F
use_loss_obj = [mae_s001_sobel_k9_s001_loss_info_builder.set_loss_target("UNet_W").copy()] ### z, y, x 順序是看 step07_b_0b_Multi_UNet 來對應的喔
#############################################################
### 為了resul_analyze畫空白的圖，建一個empty的 Exp_builder
empty = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_1__2side_1__3side_1_4side_1_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_1__2side_1__3side_1_4side_1_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="為了resul_analyze畫空白的圖，建一個empty的 Exp_builder")
#############################################################
###################
############# 1s1
######### 2s1
##### 3s1
### 4s1
ch032_1side_1__2side_1__3side_1_4side_1_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_1__2side_1__3side_1_4side_1_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_1__2side_1__3side_1_4side_1_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
###################
############# 1s2
######### 2s1
##### 3s1
### 4s1
ch032_1side_2__2side_1__3side_1_4side_1_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_2__2side_1__3side_1_4side_1_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_2__2side_1__3side_1_4side_1_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
######### 2s1
##### 3s1
### 4s1
ch032_1side_2__2side_2__3side_1_4side_1_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_2__2side_2__3side_1_4side_1_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_2__2side_2__3side_1_4side_1_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
##### 3s2
### 4s1
ch032_1side_2__2side_2__3side_2_4side_1_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_2__2side_2__3side_2_4side_1_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_2__2side_2__3side_2_4side_1_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s2
ch032_1side_2__2side_2__3side_2_4side_2_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_2__2side_2__3side_2_4side_2_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_2__2side_2__3side_2_4side_2_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_2__2side_2__3side_2_4side_2_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_2__2side_2__3side_2_4side_2_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_2__2side_2__3side_2_4side_2_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_2__2side_2__3side_2_4side_2_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_2__2side_2__3side_2_4side_2_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_2__2side_2__3side_2_4side_2_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
###################
############# 1s3
######### 2s1
##### 3s1
### 4s1
ch032_1side_3__2side_1__3side_1_4side_1_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_3__2side_1__3side_1_4side_1_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_3__2side_1__3side_1_4side_1_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
######### 2s2
##### 3s1
### 4s1
ch032_1side_3__2side_2__3side_1_4side_1_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_3__2side_2__3side_1_4side_1_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_3__2side_2__3side_1_4side_1_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
##### 3s2
### 4s1
ch032_1side_3__2side_2__3side_2_4side_1_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_3__2side_2__3side_2_4side_1_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_3__2side_2__3side_2_4side_1_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s2
ch032_1side_3__2side_2__3side_2_4side_2_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_3__2side_2__3side_2_4side_2_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_3__2side_2__3side_2_4side_2_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_3__2side_2__3side_2_4side_2_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_3__2side_2__3side_2_4side_2_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_3__2side_2__3side_2_4side_2_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_3__2side_2__3side_2_4side_2_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_3__2side_2__3side_2_4side_2_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_3__2side_2__3side_2_4side_2_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
######### 2s3
##### 3s1
### 4s1
ch032_1side_3__2side_3__3side_1_4side_1_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_3__2side_3__3side_1_4side_1_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_3__2side_3__3side_1_4side_1_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
##### 3s2
### 4s1
ch032_1side_3__2side_3__3side_2_4side_1_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_3__2side_3__3side_2_4side_1_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_3__2side_3__3side_2_4side_1_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s2
ch032_1side_3__2side_3__3side_2_4side_2_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_3__2side_3__3side_2_4side_2_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_3__2side_3__3side_2_4side_2_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_3__2side_3__3side_2_4side_2_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_3__2side_3__3side_2_4side_2_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_3__2side_3__3side_2_4side_2_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_3__2side_3__3side_2_4side_2_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_3__2side_3__3side_2_4side_2_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_3__2side_3__3side_2_4side_2_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
##### 3s3
### 4s1
ch032_1side_3__2side_3__3side_3_4side_1_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_3__2side_3__3side_3_4side_1_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_3__2side_3__3side_3_4side_1_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s2
ch032_1side_3__2side_3__3side_3_4side_2_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_3__2side_3__3side_3_4side_2_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_3__2side_3__3side_3_4side_2_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_3__2side_3__3side_3_4side_2_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_3__2side_3__3side_3_4side_2_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_3__2side_3__3side_3_4side_2_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_3__2side_3__3side_3_4side_2_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_3__2side_3__3side_3_4side_2_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_3__2side_3__3side_3_4side_2_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s3
ch032_1side_3__2side_3__3side_3_4side_3_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_3__2side_3__3side_3_4side_3_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_3__2side_3__3side_3_4side_3_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_3__2side_3__3side_3_4side_3_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_3__2side_3__3side_3_4side_3_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_3__2side_3__3side_3_4side_3_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_3__2side_3__3side_3_4side_3_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_3__2side_3__3side_3_4side_3_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_3__2side_3__3side_3_4side_3_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_3__2side_3__3side_3_4side_3_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_3__2side_3__3side_3_4side_3_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_3__2side_3__3side_3_4side_3_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_3__2side_3__3side_3_4side_3_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_3__2side_3__3side_3_4side_3_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_3__2side_3__3side_3_4side_3_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_3__2side_3__3side_3_4side_3_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_3__2side_3__3side_3_4side_3_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_3__2side_3__3side_3_4side_3_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
###################
############# 1s4
######### 2s1
##### 3s1
### 4s1
ch032_1side_4__2side_1__3side_1_4side_1_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_4__2side_1__3side_1_4side_1_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_4__2side_1__3side_1_4side_1_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
######### 2s2
##### 3s1
### 4s1
ch032_1side_4__2side_2__3side_1_4side_1_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_4__2side_2__3side_1_4side_1_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_4__2side_2__3side_1_4side_1_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
##### 3s2
### 4s1
ch032_1side_4__2side_2__3side_2_4side_1_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_4__2side_2__3side_2_4side_1_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_4__2side_2__3side_2_4side_1_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s2
ch032_1side_4__2side_2__3side_2_4side_2_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_4__2side_2__3side_2_4side_2_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_4__2side_2__3side_2_4side_2_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_4__2side_2__3side_2_4side_2_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_4__2side_2__3side_2_4side_2_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_4__2side_2__3side_2_4side_2_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_4__2side_2__3side_2_4side_2_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_4__2side_2__3side_2_4side_2_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_4__2side_2__3side_2_4side_2_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
######### 2s3
##### 3s1
### 4s1
ch032_1side_4__2side_3__3side_1_4side_1_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_4__2side_3__3side_1_4side_1_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_4__2side_3__3side_1_4side_1_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
##### 3s2
### 4s1
ch032_1side_4__2side_3__3side_2_4side_1_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_4__2side_3__3side_2_4side_1_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_4__2side_3__3side_2_4side_1_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s2
ch032_1side_4__2side_3__3side_2_4side_2_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_4__2side_3__3side_2_4side_2_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_4__2side_3__3side_2_4side_2_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_4__2side_3__3side_2_4side_2_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_4__2side_3__3side_2_4side_2_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_4__2side_3__3side_2_4side_2_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_4__2side_3__3side_2_4side_2_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_4__2side_3__3side_2_4side_2_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_4__2side_3__3side_2_4side_2_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
##### 3s3
### 4s1
ch032_1side_4__2side_3__3side_3_4side_1_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_4__2side_3__3side_3_4side_1_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_4__2side_3__3side_3_4side_1_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s2
ch032_1side_4__2side_3__3side_3_4side_2_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_4__2side_3__3side_3_4side_2_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_4__2side_3__3side_3_4side_2_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_4__2side_3__3side_3_4side_2_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_4__2side_3__3side_3_4side_2_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_4__2side_3__3side_3_4side_2_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_4__2side_3__3side_3_4side_2_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_4__2side_3__3side_3_4side_2_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_4__2side_3__3side_3_4side_2_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s3
ch032_1side_4__2side_3__3side_3_4side_3_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_4__2side_3__3side_3_4side_3_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_4__2side_3__3side_3_4side_3_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_4__2side_3__3side_3_4side_3_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_4__2side_3__3side_3_4side_3_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_4__2side_3__3side_3_4side_3_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_4__2side_3__3side_3_4side_3_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_4__2side_3__3side_3_4side_3_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_4__2side_3__3side_3_4side_3_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_4__2side_3__3side_3_4side_3_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_4__2side_3__3side_3_4side_3_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_4__2side_3__3side_3_4side_3_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_4__2side_3__3side_3_4side_3_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_4__2side_3__3side_3_4side_3_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_4__2side_3__3side_3_4side_3_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_4__2side_3__3side_3_4side_3_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_4__2side_3__3side_3_4side_3_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_4__2side_3__3side_3_4side_3_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
######### 2s4
##### 3s1
### 4s1
ch032_1side_4__2side_4__3side_1_4side_1_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_4__2side_4__3side_1_4side_1_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_4__2side_4__3side_1_4side_1_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
##### 3s2
### 4s1
ch032_1side_4__2side_4__3side_2_4side_1_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_4__2side_4__3side_2_4side_1_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_4__2side_4__3side_2_4side_1_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s2
ch032_1side_4__2side_4__3side_2_4side_2_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_4__2side_4__3side_2_4side_2_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_4__2side_4__3side_2_4side_2_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_4__2side_4__3side_2_4side_2_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_4__2side_4__3side_2_4side_2_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_4__2side_4__3side_2_4side_2_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_4__2side_4__3side_2_4side_2_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_4__2side_4__3side_2_4side_2_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_4__2side_4__3side_2_4side_2_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
##### 3s3
### 4s1
ch032_1side_4__2side_4__3side_3_4side_1_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_4__2side_4__3side_3_4side_1_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_4__2side_4__3side_3_4side_1_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s2
ch032_1side_4__2side_4__3side_3_4side_2_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_4__2side_4__3side_3_4side_2_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_4__2side_4__3side_3_4side_2_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_4__2side_4__3side_3_4side_2_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_4__2side_4__3side_3_4side_2_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_4__2side_4__3side_3_4side_2_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_4__2side_4__3side_3_4side_2_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_4__2side_4__3side_3_4side_2_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_4__2side_4__3side_3_4side_2_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s3
ch032_1side_4__2side_4__3side_3_4side_3_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_4__2side_4__3side_3_4side_3_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_4__2side_4__3side_3_4side_3_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_4__2side_4__3side_3_4side_3_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_4__2side_4__3side_3_4side_3_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_4__2side_4__3side_3_4side_3_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_4__2side_4__3side_3_4side_3_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_4__2side_4__3side_3_4side_3_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_4__2side_4__3side_3_4side_3_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_4__2side_4__3side_3_4side_3_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_4__2side_4__3side_3_4side_3_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_4__2side_4__3side_3_4side_3_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_4__2side_4__3side_3_4side_3_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_4__2side_4__3side_3_4side_3_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_4__2side_4__3side_3_4side_3_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_4__2side_4__3side_3_4side_3_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_4__2side_4__3side_3_4side_3_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_4__2side_4__3side_3_4side_3_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
##### 3s4
### 4s1
ch032_1side_4__2side_4__3side_4_4side_1_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_4__2side_4__3side_4_4side_1_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_4__2side_4__3side_4_4side_1_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s2
ch032_1side_4__2side_4__3side_4_4side_2_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_4__2side_4__3side_4_4side_2_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_4__2side_4__3side_4_4side_2_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_4__2side_4__3side_4_4side_2_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_4__2side_4__3side_4_4side_2_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_4__2side_4__3side_4_4side_2_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_4__2side_4__3side_4_4side_2_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_4__2side_4__3side_4_4side_2_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_4__2side_4__3side_4_4side_2_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s3
ch032_1side_4__2side_4__3side_4_4side_3_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_4__2side_4__3side_4_4side_3_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_4__2side_4__3side_4_4side_3_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_4__2side_4__3side_4_4side_3_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_4__2side_4__3side_4_4side_3_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_4__2side_4__3side_4_4side_3_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_4__2side_4__3side_4_4side_3_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_4__2side_4__3side_4_4side_3_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_4__2side_4__3side_4_4side_3_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_4__2side_4__3side_4_4side_3_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_4__2side_4__3side_4_4side_3_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_4__2side_4__3side_4_4side_3_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_4__2side_4__3side_4_4side_3_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_4__2side_4__3side_4_4side_3_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_4__2side_4__3side_4_4side_3_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_4__2side_4__3side_4_4side_3_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_4__2side_4__3side_4_4side_3_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_4__2side_4__3side_4_4side_3_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s4
ch032_1side_4__2side_4__3side_4_4side_4_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_4__2side_4__3side_4_4side_4_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_4__2side_4__3side_4_4side_4_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_4__2side_4__3side_4_4side_4_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_4__2side_4__3side_4_4side_4_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_4__2side_4__3side_4_4side_4_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_4__2side_4__3side_4_4side_4_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_4__2side_4__3side_4_4side_4_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_4__2side_4__3side_4_4side_4_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_4__2side_4__3side_4_4side_4_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_4__2side_4__3side_4_4side_4_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_4__2side_4__3side_4_4side_4_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_4__2side_4__3side_4_4side_4_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_4__2side_4__3side_4_4side_4_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_4__2side_4__3side_4_4side_4_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_4__2side_4__3side_4_4side_4_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_4__2side_4__3side_4_4side_4_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_4__2side_4__3side_4_4side_4_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_4__2side_4__3side_4_4side_4_5s4_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_4__2side_4__3side_4_4side_4_5s4_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_4__2side_4__3side_4_4side_4_5s4_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_4__2side_4__3side_4_4side_4_5s4_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_4__2side_4__3side_4_4side_4_5s4_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_4__2side_4__3side_4_4side_4_5s4_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_4__2side_4__3side_4_4side_4_5s4_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_4__2side_4__3side_4_4side_4_5s4_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_4__2side_4__3side_4_4side_4_5s4_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_4__2side_4__3side_4_4side_4_5s4_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_4__2side_4__3side_4_4side_4_5s4_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_4__2side_4__3side_4_4side_4_5s4_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
###################
############# 1s5
######### 2s1
##### 3s1
### 4s1
ch032_1side_5__2side_1__3side_1_4side_1_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_1__3side_1_4side_1_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_1__3side_1_4side_1_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
######### 2s2
##### 3s1
### 4s1
ch032_1side_5__2side_2__3side_1_4side_1_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_2__3side_1_4side_1_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_2__3side_1_4side_1_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
##### 3s2
### 4s1
ch032_1side_5__2side_2__3side_2_4side_1_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_2__3side_2_4side_1_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_2__3side_2_4side_1_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s2
ch032_1side_5__2side_2__3side_2_4side_2_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_2__3side_2_4side_2_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_2__3side_2_4side_2_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_5__2side_2__3side_2_4side_2_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_2__3side_2_4side_2_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_2__3side_2_4side_2_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_5__2side_2__3side_2_4side_2_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_2__3side_2_4side_2_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_2__3side_2_4side_2_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
######### 2s3
##### 3s1
### 4s1
ch032_1side_5__2side_3__3side_1_4side_1_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_3__3side_1_4side_1_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_3__3side_1_4side_1_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
##### 3s2
### 4s1
ch032_1side_5__2side_3__3side_2_4side_1_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_3__3side_2_4side_1_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_3__3side_2_4side_1_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s2
ch032_1side_5__2side_3__3side_2_4side_2_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_3__3side_2_4side_2_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_3__3side_2_4side_2_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_5__2side_3__3side_2_4side_2_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_3__3side_2_4side_2_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_3__3side_2_4side_2_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_5__2side_3__3side_2_4side_2_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_3__3side_2_4side_2_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_3__3side_2_4side_2_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
##### 3s3
### 4s1
ch032_1side_5__2side_3__3side_3_4side_1_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_3__3side_3_4side_1_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_3__3side_3_4side_1_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s2
ch032_1side_5__2side_3__3side_3_4side_2_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_3__3side_3_4side_2_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_3__3side_3_4side_2_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_5__2side_3__3side_3_4side_2_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_3__3side_3_4side_2_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_3__3side_3_4side_2_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_5__2side_3__3side_3_4side_2_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_3__3side_3_4side_2_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_3__3side_3_4side_2_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s3
ch032_1side_5__2side_3__3side_3_4side_3_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_3__3side_3_4side_3_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_3__3side_3_4side_3_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_5__2side_3__3side_3_4side_3_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_3__3side_3_4side_3_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_3__3side_3_4side_3_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_5__2side_3__3side_3_4side_3_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_3__3side_3_4side_3_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_3__3side_3_4side_3_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_5__2side_3__3side_3_4side_3_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_3__3side_3_4side_3_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_3__3side_3_4side_3_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_5__2side_3__3side_3_4side_3_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_3__3side_3_4side_3_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_3__3side_3_4side_3_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_5__2side_3__3side_3_4side_3_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_3__3side_3_4side_3_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_3__3side_3_4side_3_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
######### 2s4
##### 3s1
### 4s1
ch032_1side_5__2side_4__3side_1_4side_1_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_4__3side_1_4side_1_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_4__3side_1_4side_1_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
##### 3s2
### 4s1
ch032_1side_5__2side_4__3side_2_4side_1_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_4__3side_2_4side_1_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_4__3side_2_4side_1_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s2
ch032_1side_5__2side_4__3side_2_4side_2_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_4__3side_2_4side_2_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_4__3side_2_4side_2_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_5__2side_4__3side_2_4side_2_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_4__3side_2_4side_2_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_4__3side_2_4side_2_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_5__2side_4__3side_2_4side_2_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_4__3side_2_4side_2_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_4__3side_2_4side_2_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
##### 3s3
### 4s1
ch032_1side_5__2side_4__3side_3_4side_1_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_4__3side_3_4side_1_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_4__3side_3_4side_1_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s2
ch032_1side_5__2side_4__3side_3_4side_2_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_4__3side_3_4side_2_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_4__3side_3_4side_2_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_5__2side_4__3side_3_4side_2_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_4__3side_3_4side_2_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_4__3side_3_4side_2_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_5__2side_4__3side_3_4side_2_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_4__3side_3_4side_2_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_4__3side_3_4side_2_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s3
ch032_1side_5__2side_4__3side_3_4side_3_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_4__3side_3_4side_3_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_4__3side_3_4side_3_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_5__2side_4__3side_3_4side_3_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_4__3side_3_4side_3_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_4__3side_3_4side_3_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_5__2side_4__3side_3_4side_3_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_4__3side_3_4side_3_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_4__3side_3_4side_3_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_5__2side_4__3side_3_4side_3_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_4__3side_3_4side_3_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_4__3side_3_4side_3_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_5__2side_4__3side_3_4side_3_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_4__3side_3_4side_3_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_4__3side_3_4side_3_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_5__2side_4__3side_3_4side_3_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_4__3side_3_4side_3_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_4__3side_3_4side_3_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
##### 3s4
### 4s1
ch032_1side_5__2side_4__3side_4_4side_1_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_4__3side_4_4side_1_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_4__3side_4_4side_1_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s2
ch032_1side_5__2side_4__3side_4_4side_2_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_4__3side_4_4side_2_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_4__3side_4_4side_2_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_5__2side_4__3side_4_4side_2_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_4__3side_4_4side_2_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_4__3side_4_4side_2_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_5__2side_4__3side_4_4side_2_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_4__3side_4_4side_2_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_4__3side_4_4side_2_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s3
ch032_1side_5__2side_4__3side_4_4side_3_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_4__3side_4_4side_3_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_4__3side_4_4side_3_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_5__2side_4__3side_4_4side_3_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_4__3side_4_4side_3_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_4__3side_4_4side_3_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_5__2side_4__3side_4_4side_3_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_4__3side_4_4side_3_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_4__3side_4_4side_3_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_5__2side_4__3side_4_4side_3_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_4__3side_4_4side_3_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_4__3side_4_4side_3_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_5__2side_4__3side_4_4side_3_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_4__3side_4_4side_3_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_4__3side_4_4side_3_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_5__2side_4__3side_4_4side_3_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_4__3side_4_4side_3_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_4__3side_4_4side_3_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s4
ch032_1side_5__2side_4__3side_4_4side_4_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_4__3side_4_4side_4_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_4__3side_4_4side_4_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_5__2side_4__3side_4_4side_4_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_4__3side_4_4side_4_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_4__3side_4_4side_4_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_5__2side_4__3side_4_4side_4_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_4__3side_4_4side_4_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_4__3side_4_4side_4_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_5__2side_4__3side_4_4side_4_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_4__3side_4_4side_4_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_4__3side_4_4side_4_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_5__2side_4__3side_4_4side_4_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_4__3side_4_4side_4_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_4__3side_4_4side_4_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_5__2side_4__3side_4_4side_4_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_4__3side_4_4side_4_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_4__3side_4_4side_4_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_5__2side_4__3side_4_4side_4_5s4_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_4__3side_4_4side_4_5s4_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_4__3side_4_4side_4_5s4_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_5__2side_4__3side_4_4side_4_5s4_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_4__3side_4_4side_4_5s4_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_4__3side_4_4side_4_5s4_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_5__2side_4__3side_4_4side_4_5s4_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_4__3side_4_4side_4_5s4_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_4__3side_4_4side_4_5s4_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_5__2side_4__3side_4_4side_4_5s4_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_4__3side_4_4side_4_5s4_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_4__3side_4_4side_4_5s4_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
######### 2s5
##### 3s1
### 4s1
ch032_1side_5__2side_5__3side_1_4side_1_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_1_4side_1_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_5__3side_1_4side_1_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
##### 3s2
### 4s1
ch032_1side_5__2side_5__3side_2_4side_1_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_2_4side_1_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_5__3side_2_4side_1_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s2
ch032_1side_5__2side_5__3side_2_4side_2_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_2_4side_2_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_5__3side_2_4side_2_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_5__2side_5__3side_2_4side_2_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_2_4side_2_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_5__3side_2_4side_2_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_5__2side_5__3side_2_4side_2_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_2_4side_2_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_5__3side_2_4side_2_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
##### 3s3
### 4s1
ch032_1side_5__2side_5__3side_3_4side_1_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_3_4side_1_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_5__3side_3_4side_1_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s2
ch032_1side_5__2side_5__3side_3_4side_2_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_3_4side_2_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_5__3side_3_4side_2_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_5__2side_5__3side_3_4side_2_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_3_4side_2_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_5__3side_3_4side_2_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_5__2side_5__3side_3_4side_2_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_3_4side_2_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_5__3side_3_4side_2_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s3
ch032_1side_5__2side_5__3side_3_4side_3_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_3_4side_3_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_5__3side_3_4side_3_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_5__2side_5__3side_3_4side_3_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_3_4side_3_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_5__3side_3_4side_3_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_5__2side_5__3side_3_4side_3_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_3_4side_3_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_5__3side_3_4side_3_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_5__2side_5__3side_3_4side_3_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_3_4side_3_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_5__3side_3_4side_3_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_5__2side_5__3side_3_4side_3_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_3_4side_3_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_5__3side_3_4side_3_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_5__2side_5__3side_3_4side_3_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_3_4side_3_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_5__3side_3_4side_3_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
##### 3s4
### 4s1
ch032_1side_5__2side_5__3side_4_4side_1_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_4_4side_1_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_5__3side_4_4side_1_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s2
ch032_1side_5__2side_5__3side_4_4side_2_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_4_4side_2_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_5__3side_4_4side_2_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_5__2side_5__3side_4_4side_2_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_4_4side_2_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_5__3side_4_4side_2_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_5__2side_5__3side_4_4side_2_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_4_4side_2_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_5__3side_4_4side_2_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s3
ch032_1side_5__2side_5__3side_4_4side_3_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_4_4side_3_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_5__3side_4_4side_3_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_5__2side_5__3side_4_4side_3_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_4_4side_3_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_5__3side_4_4side_3_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_5__2side_5__3side_4_4side_3_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_4_4side_3_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_5__3side_4_4side_3_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_5__2side_5__3side_4_4side_3_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_4_4side_3_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_5__3side_4_4side_3_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_5__2side_5__3side_4_4side_3_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_4_4side_3_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_5__3side_4_4side_3_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_5__2side_5__3side_4_4side_3_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_4_4side_3_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_5__3side_4_4side_3_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s4
ch032_1side_5__2side_5__3side_4_4side_4_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_4_4side_4_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_5__3side_4_4side_4_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_5__2side_5__3side_4_4side_4_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_4_4side_4_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_5__3side_4_4side_4_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_5__2side_5__3side_4_4side_4_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_4_4side_4_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_5__3side_4_4side_4_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_5__2side_5__3side_4_4side_4_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_4_4side_4_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_5__3side_4_4side_4_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_5__2side_5__3side_4_4side_4_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_4_4side_4_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_5__3side_4_4side_4_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_5__2side_5__3side_4_4side_4_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_4_4side_4_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_5__3side_4_4side_4_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_5__2side_5__3side_4_4side_4_5s4_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_4_4side_4_5s4_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_5__3side_4_4side_4_5s4_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_5__2side_5__3side_4_4side_4_5s4_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_4_4side_4_5s4_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_5__3side_4_4side_4_5s4_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_5__2side_5__3side_4_4side_4_5s4_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_4_4side_4_5s4_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_5__3side_4_4side_4_5s4_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_5__2side_5__3side_4_4side_4_5s4_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_4_4side_4_5s4_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_5__3side_4_4side_4_5s4_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
##### 3s5
### 4s1
ch032_1side_5__2side_5__3side_5_4side_1_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_5_4side_1_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_5__3side_5_4side_1_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s2
ch032_1side_5__2side_5__3side_5_4side_2_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_5_4side_2_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_5__3side_5_4side_2_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_5__2side_5__3side_5_4side_2_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_5_4side_2_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_5__3side_5_4side_2_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_5__2side_5__3side_5_4side_2_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_5_4side_2_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_5__3side_5_4side_2_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s3
ch032_1side_5__2side_5__3side_5_4side_3_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_5_4side_3_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_5__3side_5_4side_3_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_5__2side_5__3side_5_4side_3_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_5_4side_3_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_5__3side_5_4side_3_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_5__2side_5__3side_5_4side_3_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_5_4side_3_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_5__3side_5_4side_3_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_5__2side_5__3side_5_4side_3_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_5_4side_3_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_5__3side_5_4side_3_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_5__2side_5__3side_5_4side_3_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_5_4side_3_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_5__3side_5_4side_3_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_5__2side_5__3side_5_4side_3_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_5_4side_3_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_5__3side_5_4side_3_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s4
ch032_1side_5__2side_5__3side_5_4side_4_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_5_4side_4_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_5__3side_5_4side_4_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_5__2side_5__3side_5_4side_4_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_5_4side_4_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_5__3side_5_4side_4_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_5__2side_5__3side_5_4side_4_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_5_4side_4_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_5__3side_5_4side_4_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_5__2side_5__3side_5_4side_4_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_5_4side_4_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_5__3side_5_4side_4_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_5__2side_5__3side_5_4side_4_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_5_4side_4_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_5__3side_5_4side_4_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_5__2side_5__3side_5_4side_4_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_5_4side_4_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_5__3side_5_4side_4_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_5__2side_5__3side_5_4side_4_5s4_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_5_4side_4_5s4_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_5__3side_5_4side_4_5s4_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_5__2side_5__3side_5_4side_4_5s4_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_5_4side_4_5s4_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_5__3side_5_4side_4_5s4_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_5__2side_5__3side_5_4side_4_5s4_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_5_4side_4_5s4_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_5__3side_5_4side_4_5s4_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_5__2side_5__3side_5_4side_4_5s4_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_5_4side_4_5s4_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_5__3side_5_4side_4_5s4_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s5
ch032_1side_5__2side_5__3side_5_4side_5_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_5_4side_5_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_5__3side_5_4side_5_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_5__2side_5__3side_5_4side_5_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_5_4side_5_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_5__3side_5_4side_5_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_5__2side_5__3side_5_4side_5_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_5_4side_5_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_5__3side_5_4side_5_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_5__2side_5__3side_5_4side_5_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_5_4side_5_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_5__3side_5_4side_5_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_5__2side_5__3side_5_4side_5_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_5_4side_5_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_5__3side_5_4side_5_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_5__2side_5__3side_5_4side_5_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_5_4side_5_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_5__3side_5_4side_5_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_5__2side_5__3side_5_4side_5_5s4_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_5_4side_5_5s4_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_5__3side_5_4side_5_5s4_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_5__2side_5__3side_5_4side_5_5s4_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_5_4side_5_5s4_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_5__3side_5_4side_5_5s4_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_5__2side_5__3side_5_4side_5_5s4_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_5_4side_5_5s4_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_5__3side_5_4side_5_5s4_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_5__2side_5__3side_5_4side_5_5s4_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_5_4side_5_5s4_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_5__3side_5_4side_5_5s4_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_5__2side_5__3side_5_4side_5_5s5_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_5_4side_5_5s5_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_5__3side_5_4side_5_5s5_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_5__2side_5__3side_5_4side_5_5s5_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_5_4side_5_5s5_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_5__3side_5_4side_5_5s5_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_5__2side_5__3side_5_4side_5_5s5_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_5_4side_5_5s5_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_5__3side_5_4side_5_5s5_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_5__2side_5__3side_5_4side_5_5s5_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_5_4side_5_5s5_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_5__3side_5_4side_5_5s5_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_5__2side_5__3side_5_4side_5_5s5_6s5 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_5_4side_5_5s5_6s5, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_5__3side_5_4side_5_5s5_6s5.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
###################
############# 1s6
######### 2s1
##### 3s1
### 4s1
ch032_1side_6__2side_1__3side_1_4side_1_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_1__3side_1_4side_1_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_1__3side_1_4side_1_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
######### 2s2
##### 3s1
### 4s1
ch032_1side_6__2side_2__3side_1_4side_1_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_2__3side_1_4side_1_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_2__3side_1_4side_1_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
##### 3s2
### 4s1
ch032_1side_6__2side_2__3side_2_4side_1_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_2__3side_2_4side_1_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_2__3side_2_4side_1_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s2
ch032_1side_6__2side_2__3side_2_4side_2_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_2__3side_2_4side_2_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_2__3side_2_4side_2_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_2__3side_2_4side_2_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_2__3side_2_4side_2_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_2__3side_2_4side_2_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_2__3side_2_4side_2_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_2__3side_2_4side_2_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_2__3side_2_4side_2_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
######### 2s3
##### 3s1
### 4s1
ch032_1side_6__2side_3__3side_1_4side_1_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_3__3side_1_4side_1_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_3__3side_1_4side_1_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
##### 3s2
### 4s1
ch032_1side_6__2side_3__3side_2_4side_1_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_3__3side_2_4side_1_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_3__3side_2_4side_1_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s2
ch032_1side_6__2side_3__3side_2_4side_2_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_3__3side_2_4side_2_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_3__3side_2_4side_2_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_3__3side_2_4side_2_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_3__3side_2_4side_2_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_3__3side_2_4side_2_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_3__3side_2_4side_2_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_3__3side_2_4side_2_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_3__3side_2_4side_2_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
##### 3s3
### 4s1
ch032_1side_6__2side_3__3side_3_4side_1_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_3__3side_3_4side_1_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_3__3side_3_4side_1_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s2
ch032_1side_6__2side_3__3side_3_4side_2_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_3__3side_3_4side_2_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_3__3side_3_4side_2_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_3__3side_3_4side_2_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_3__3side_3_4side_2_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_3__3side_3_4side_2_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_3__3side_3_4side_2_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_3__3side_3_4side_2_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_3__3side_3_4side_2_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s3
ch032_1side_6__2side_3__3side_3_4side_3_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_3__3side_3_4side_3_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_3__3side_3_4side_3_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_3__3side_3_4side_3_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_3__3side_3_4side_3_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_3__3side_3_4side_3_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_3__3side_3_4side_3_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_3__3side_3_4side_3_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_3__3side_3_4side_3_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_3__3side_3_4side_3_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_3__3side_3_4side_3_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_3__3side_3_4side_3_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_3__3side_3_4side_3_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_3__3side_3_4side_3_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_3__3side_3_4side_3_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_3__3side_3_4side_3_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_3__3side_3_4side_3_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_3__3side_3_4side_3_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
######### 2s4
##### 3s1
### 4s1
ch032_1side_6__2side_4__3side_1_4side_1_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_4__3side_1_4side_1_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_4__3side_1_4side_1_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
##### 3s2
### 4s1
ch032_1side_6__2side_4__3side_2_4side_1_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_4__3side_2_4side_1_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_4__3side_2_4side_1_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s2
ch032_1side_6__2side_4__3side_2_4side_2_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_4__3side_2_4side_2_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_4__3side_2_4side_2_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_4__3side_2_4side_2_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_4__3side_2_4side_2_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_4__3side_2_4side_2_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_4__3side_2_4side_2_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_4__3side_2_4side_2_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_4__3side_2_4side_2_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
##### 3s3
### 4s1
ch032_1side_6__2side_4__3side_3_4side_1_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_4__3side_3_4side_1_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_4__3side_3_4side_1_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s2
ch032_1side_6__2side_4__3side_3_4side_2_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_4__3side_3_4side_2_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_4__3side_3_4side_2_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_4__3side_3_4side_2_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_4__3side_3_4side_2_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_4__3side_3_4side_2_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_4__3side_3_4side_2_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_4__3side_3_4side_2_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_4__3side_3_4side_2_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s3
ch032_1side_6__2side_4__3side_3_4side_3_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_4__3side_3_4side_3_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_4__3side_3_4side_3_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_4__3side_3_4side_3_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_4__3side_3_4side_3_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_4__3side_3_4side_3_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_4__3side_3_4side_3_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_4__3side_3_4side_3_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_4__3side_3_4side_3_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_4__3side_3_4side_3_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_4__3side_3_4side_3_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_4__3side_3_4side_3_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_4__3side_3_4side_3_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_4__3side_3_4side_3_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_4__3side_3_4side_3_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_4__3side_3_4side_3_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_4__3side_3_4side_3_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_4__3side_3_4side_3_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
##### 3s4
### 4s1
ch032_1side_6__2side_4__3side_4_4side_1_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_4__3side_4_4side_1_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_4__3side_4_4side_1_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s2
ch032_1side_6__2side_4__3side_4_4side_2_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_4__3side_4_4side_2_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_4__3side_4_4side_2_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_4__3side_4_4side_2_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_4__3side_4_4side_2_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_4__3side_4_4side_2_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_4__3side_4_4side_2_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_4__3side_4_4side_2_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_4__3side_4_4side_2_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s3
ch032_1side_6__2side_4__3side_4_4side_3_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_4__3side_4_4side_3_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_4__3side_4_4side_3_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_4__3side_4_4side_3_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_4__3side_4_4side_3_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_4__3side_4_4side_3_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_4__3side_4_4side_3_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_4__3side_4_4side_3_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_4__3side_4_4side_3_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_4__3side_4_4side_3_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_4__3side_4_4side_3_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_4__3side_4_4side_3_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_4__3side_4_4side_3_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_4__3side_4_4side_3_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_4__3side_4_4side_3_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_4__3side_4_4side_3_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_4__3side_4_4side_3_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_4__3side_4_4side_3_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s4
ch032_1side_6__2side_4__3side_4_4side_4_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_4__3side_4_4side_4_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_4__3side_4_4side_4_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_4__3side_4_4side_4_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_4__3side_4_4side_4_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_4__3side_4_4side_4_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_4__3side_4_4side_4_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_4__3side_4_4side_4_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_4__3side_4_4side_4_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_4__3side_4_4side_4_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_4__3side_4_4side_4_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_4__3side_4_4side_4_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_4__3side_4_4side_4_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_4__3side_4_4side_4_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_4__3side_4_4side_4_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_4__3side_4_4side_4_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_4__3side_4_4side_4_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_4__3side_4_4side_4_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_4__3side_4_4side_4_5s4_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_4__3side_4_4side_4_5s4_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_4__3side_4_4side_4_5s4_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_4__3side_4_4side_4_5s4_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_4__3side_4_4side_4_5s4_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_4__3side_4_4side_4_5s4_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_4__3side_4_4side_4_5s4_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_4__3side_4_4side_4_5s4_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_4__3side_4_4side_4_5s4_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_4__3side_4_4side_4_5s4_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_4__3side_4_4side_4_5s4_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_4__3side_4_4side_4_5s4_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
######### 2s5
##### 3s1
### 4s1
ch032_1side_6__2side_5__3side_1_4side_1_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_1_4side_1_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_5__3side_1_4side_1_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
##### 3s2
### 4s1
ch032_1side_6__2side_5__3side_2_4side_1_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_2_4side_1_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_5__3side_2_4side_1_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s2
ch032_1side_6__2side_5__3side_2_4side_2_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_2_4side_2_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_5__3side_2_4side_2_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_5__3side_2_4side_2_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_2_4side_2_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_5__3side_2_4side_2_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_5__3side_2_4side_2_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_2_4side_2_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_5__3side_2_4side_2_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
##### 3s3
### 4s1
ch032_1side_6__2side_5__3side_3_4side_1_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_3_4side_1_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_5__3side_3_4side_1_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s2
ch032_1side_6__2side_5__3side_3_4side_2_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_3_4side_2_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_5__3side_3_4side_2_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_5__3side_3_4side_2_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_3_4side_2_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_5__3side_3_4side_2_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_5__3side_3_4side_2_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_3_4side_2_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_5__3side_3_4side_2_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s3
ch032_1side_6__2side_5__3side_3_4side_3_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_3_4side_3_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_5__3side_3_4side_3_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_5__3side_3_4side_3_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_3_4side_3_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_5__3side_3_4side_3_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_5__3side_3_4side_3_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_3_4side_3_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_5__3side_3_4side_3_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_5__3side_3_4side_3_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_3_4side_3_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_5__3side_3_4side_3_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_5__3side_3_4side_3_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_3_4side_3_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_5__3side_3_4side_3_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_5__3side_3_4side_3_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_3_4side_3_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_5__3side_3_4side_3_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
##### 3s4
### 4s1
ch032_1side_6__2side_5__3side_4_4side_1_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_4_4side_1_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_5__3side_4_4side_1_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s2
ch032_1side_6__2side_5__3side_4_4side_2_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_4_4side_2_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_5__3side_4_4side_2_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_5__3side_4_4side_2_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_4_4side_2_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_5__3side_4_4side_2_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_5__3side_4_4side_2_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_4_4side_2_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_5__3side_4_4side_2_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s3
ch032_1side_6__2side_5__3side_4_4side_3_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_4_4side_3_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_5__3side_4_4side_3_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_5__3side_4_4side_3_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_4_4side_3_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_5__3side_4_4side_3_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_5__3side_4_4side_3_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_4_4side_3_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_5__3side_4_4side_3_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_5__3side_4_4side_3_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_4_4side_3_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_5__3side_4_4side_3_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_5__3side_4_4side_3_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_4_4side_3_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_5__3side_4_4side_3_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_5__3side_4_4side_3_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_4_4side_3_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_5__3side_4_4side_3_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s4
ch032_1side_6__2side_5__3side_4_4side_4_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_4_4side_4_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_5__3side_4_4side_4_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_5__3side_4_4side_4_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_4_4side_4_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_5__3side_4_4side_4_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_5__3side_4_4side_4_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_4_4side_4_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_5__3side_4_4side_4_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_5__3side_4_4side_4_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_4_4side_4_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_5__3side_4_4side_4_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_5__3side_4_4side_4_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_4_4side_4_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_5__3side_4_4side_4_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_5__3side_4_4side_4_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_4_4side_4_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_5__3side_4_4side_4_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_5__3side_4_4side_4_5s4_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_4_4side_4_5s4_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_5__3side_4_4side_4_5s4_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_5__3side_4_4side_4_5s4_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_4_4side_4_5s4_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_5__3side_4_4side_4_5s4_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_5__3side_4_4side_4_5s4_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_4_4side_4_5s4_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_5__3side_4_4side_4_5s4_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_5__3side_4_4side_4_5s4_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_4_4side_4_5s4_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_5__3side_4_4side_4_5s4_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
##### 3s5
### 4s1
ch032_1side_6__2side_5__3side_5_4side_1_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_5_4side_1_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_5__3side_5_4side_1_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s2
ch032_1side_6__2side_5__3side_5_4side_2_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_5_4side_2_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_5__3side_5_4side_2_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_5__3side_5_4side_2_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_5_4side_2_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_5__3side_5_4side_2_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_5__3side_5_4side_2_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_5_4side_2_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_5__3side_5_4side_2_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s3
ch032_1side_6__2side_5__3side_5_4side_3_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_5_4side_3_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_5__3side_5_4side_3_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_5__3side_5_4side_3_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_5_4side_3_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_5__3side_5_4side_3_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_5__3side_5_4side_3_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_5_4side_3_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_5__3side_5_4side_3_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_5__3side_5_4side_3_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_5_4side_3_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_5__3side_5_4side_3_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_5__3side_5_4side_3_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_5_4side_3_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_5__3side_5_4side_3_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_5__3side_5_4side_3_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_5_4side_3_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_5__3side_5_4side_3_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s4
ch032_1side_6__2side_5__3side_5_4side_4_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_5_4side_4_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_5__3side_5_4side_4_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_5__3side_5_4side_4_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_5_4side_4_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_5__3side_5_4side_4_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_5__3side_5_4side_4_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_5_4side_4_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_5__3side_5_4side_4_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_5__3side_5_4side_4_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_5_4side_4_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_5__3side_5_4side_4_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_5__3side_5_4side_4_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_5_4side_4_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_5__3side_5_4side_4_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_5__3side_5_4side_4_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_5_4side_4_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_5__3side_5_4side_4_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_5__3side_5_4side_4_5s4_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_5_4side_4_5s4_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_5__3side_5_4side_4_5s4_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_5__3side_5_4side_4_5s4_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_5_4side_4_5s4_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_5__3side_5_4side_4_5s4_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_5__3side_5_4side_4_5s4_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_5_4side_4_5s4_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_5__3side_5_4side_4_5s4_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_5__3side_5_4side_4_5s4_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_5_4side_4_5s4_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_5__3side_5_4side_4_5s4_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s5
ch032_1side_6__2side_5__3side_5_4side_5_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_5_4side_5_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_5__3side_5_4side_5_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_5__3side_5_4side_5_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_5_4side_5_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_5__3side_5_4side_5_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_5__3side_5_4side_5_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_5_4side_5_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_5__3side_5_4side_5_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_5__3side_5_4side_5_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_5_4side_5_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_5__3side_5_4side_5_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_5__3side_5_4side_5_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_5_4side_5_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_5__3side_5_4side_5_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_5__3side_5_4side_5_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_5_4side_5_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_5__3side_5_4side_5_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_5__3side_5_4side_5_5s4_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_5_4side_5_5s4_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_5__3side_5_4side_5_5s4_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_5__3side_5_4side_5_5s4_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_5_4side_5_5s4_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_5__3side_5_4side_5_5s4_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_5__3side_5_4side_5_5s4_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_5_4side_5_5s4_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_5__3side_5_4side_5_5s4_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_5__3side_5_4side_5_5s4_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_5_4side_5_5s4_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_5__3side_5_4side_5_5s4_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_5__3side_5_4side_5_5s5_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_5_4side_5_5s5_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_5__3side_5_4side_5_5s5_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_5__3side_5_4side_5_5s5_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_5_4side_5_5s5_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_5__3side_5_4side_5_5s5_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_5__3side_5_4side_5_5s5_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_5_4side_5_5s5_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_5__3side_5_4side_5_5s5_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_5__3side_5_4side_5_5s5_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_5_4side_5_5s5_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_5__3side_5_4side_5_5s5_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_5__3side_5_4side_5_5s5_6s5 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_5_4side_5_5s5_6s5, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_5__3side_5_4side_5_5s5_6s5.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
######### 2s6
##### 3s1
### 4s1
ch032_1side_6__2side_6__3side_1_4side_1_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_1_4side_1_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_1_4side_1_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
##### 3s2
### 4s1
ch032_1side_6__2side_6__3side_2_4side_1_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_2_4side_1_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_2_4side_1_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s2
ch032_1side_6__2side_6__3side_2_4side_2_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_2_4side_2_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_2_4side_2_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_6__3side_2_4side_2_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_2_4side_2_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_2_4side_2_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_6__3side_2_4side_2_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_2_4side_2_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_2_4side_2_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
##### 3s3
### 4s1
ch032_1side_6__2side_6__3side_3_4side_1_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_3_4side_1_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_3_4side_1_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s2
ch032_1side_6__2side_6__3side_3_4side_2_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_3_4side_2_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_3_4side_2_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_6__3side_3_4side_2_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_3_4side_2_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_3_4side_2_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_6__3side_3_4side_2_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_3_4side_2_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_3_4side_2_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s3
ch032_1side_6__2side_6__3side_3_4side_3_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_3_4side_3_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_3_4side_3_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_6__3side_3_4side_3_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_3_4side_3_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_3_4side_3_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_6__3side_3_4side_3_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_3_4side_3_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_3_4side_3_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_6__3side_3_4side_3_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_3_4side_3_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_3_4side_3_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_6__3side_3_4side_3_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_3_4side_3_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_3_4side_3_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_6__3side_3_4side_3_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_3_4side_3_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_3_4side_3_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
##### 3s4
### 4s1
ch032_1side_6__2side_6__3side_4_4side_1_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_4_4side_1_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_4_4side_1_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s2
ch032_1side_6__2side_6__3side_4_4side_2_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_4_4side_2_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_4_4side_2_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_6__3side_4_4side_2_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_4_4side_2_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_4_4side_2_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_6__3side_4_4side_2_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_4_4side_2_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_4_4side_2_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s3
ch032_1side_6__2side_6__3side_4_4side_3_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_4_4side_3_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_4_4side_3_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_6__3side_4_4side_3_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_4_4side_3_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_4_4side_3_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_6__3side_4_4side_3_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_4_4side_3_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_4_4side_3_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_6__3side_4_4side_3_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_4_4side_3_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_4_4side_3_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_6__3side_4_4side_3_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_4_4side_3_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_4_4side_3_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_6__3side_4_4side_3_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_4_4side_3_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_4_4side_3_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s4
ch032_1side_6__2side_6__3side_4_4side_4_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_4_4side_4_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_4_4side_4_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_6__3side_4_4side_4_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_4_4side_4_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_4_4side_4_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_6__3side_4_4side_4_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_4_4side_4_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_4_4side_4_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_6__3side_4_4side_4_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_4_4side_4_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_4_4side_4_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_6__3side_4_4side_4_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_4_4side_4_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_4_4side_4_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_6__3side_4_4side_4_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_4_4side_4_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_4_4side_4_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_6__3side_4_4side_4_5s4_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_4_4side_4_5s4_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_4_4side_4_5s4_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_6__3side_4_4side_4_5s4_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_4_4side_4_5s4_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_4_4side_4_5s4_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_6__3side_4_4side_4_5s4_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_4_4side_4_5s4_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_4_4side_4_5s4_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_6__3side_4_4side_4_5s4_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_4_4side_4_5s4_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_4_4side_4_5s4_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
##### 3s5
### 4s1
ch032_1side_6__2side_6__3side_5_4side_1_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_5_4side_1_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_5_4side_1_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s2
ch032_1side_6__2side_6__3side_5_4side_2_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_5_4side_2_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_5_4side_2_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_6__3side_5_4side_2_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_5_4side_2_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_5_4side_2_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_6__3side_5_4side_2_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_5_4side_2_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_5_4side_2_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s3
ch032_1side_6__2side_6__3side_5_4side_3_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_5_4side_3_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_5_4side_3_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_6__3side_5_4side_3_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_5_4side_3_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_5_4side_3_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_6__3side_5_4side_3_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_5_4side_3_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_5_4side_3_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_6__3side_5_4side_3_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_5_4side_3_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_5_4side_3_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_6__3side_5_4side_3_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_5_4side_3_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_5_4side_3_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_6__3side_5_4side_3_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_5_4side_3_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_5_4side_3_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s4
ch032_1side_6__2side_6__3side_5_4side_4_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_5_4side_4_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_5_4side_4_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_6__3side_5_4side_4_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_5_4side_4_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_5_4side_4_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_6__3side_5_4side_4_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_5_4side_4_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_5_4side_4_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_6__3side_5_4side_4_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_5_4side_4_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_5_4side_4_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_6__3side_5_4side_4_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_5_4side_4_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_5_4side_4_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_6__3side_5_4side_4_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_5_4side_4_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_5_4side_4_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_6__3side_5_4side_4_5s4_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_5_4side_4_5s4_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_5_4side_4_5s4_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_6__3side_5_4side_4_5s4_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_5_4side_4_5s4_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_5_4side_4_5s4_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_6__3side_5_4side_4_5s4_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_5_4side_4_5s4_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_5_4side_4_5s4_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_6__3side_5_4side_4_5s4_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_5_4side_4_5s4_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_5_4side_4_5s4_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s5
ch032_1side_6__2side_6__3side_5_4side_5_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_5_4side_5_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_5_4side_5_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_6__3side_5_4side_5_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_5_4side_5_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_5_4side_5_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_6__3side_5_4side_5_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_5_4side_5_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_5_4side_5_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_6__3side_5_4side_5_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_5_4side_5_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_5_4side_5_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_6__3side_5_4side_5_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_5_4side_5_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_5_4side_5_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_6__3side_5_4side_5_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_5_4side_5_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_5_4side_5_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_6__3side_5_4side_5_5s4_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_5_4side_5_5s4_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_5_4side_5_5s4_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_6__3side_5_4side_5_5s4_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_5_4side_5_5s4_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_5_4side_5_5s4_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_6__3side_5_4side_5_5s4_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_5_4side_5_5s4_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_5_4side_5_5s4_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_6__3side_5_4side_5_5s4_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_5_4side_5_5s4_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_5_4side_5_5s4_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_6__3side_5_4side_5_5s5_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_5_4side_5_5s5_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_5_4side_5_5s5_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_6__3side_5_4side_5_5s5_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_5_4side_5_5s5_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_5_4side_5_5s5_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_6__3side_5_4side_5_5s5_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_5_4side_5_5s5_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_5_4side_5_5s5_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_6__3side_5_4side_5_5s5_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_5_4side_5_5s5_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_5_4side_5_5s5_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_6__3side_5_4side_5_5s5_6s5 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_5_4side_5_5s5_6s5, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_5_4side_5_5s5_6s5.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
##### 3s6
### 4s1
ch032_1side_6__2side_6__3side_6_4side_1_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_6_4side_1_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_6_4side_1_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s2
ch032_1side_6__2side_6__3side_6_4side_2_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_6_4side_2_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_6_4side_2_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_6__3side_6_4side_2_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_6_4side_2_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_6_4side_2_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_6__3side_6_4side_2_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_6_4side_2_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_6_4side_2_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s3
ch032_1side_6__2side_6__3side_6_4side_3_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_6_4side_3_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_6_4side_3_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_6__3side_6_4side_3_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_6_4side_3_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_6_4side_3_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_6__3side_6_4side_3_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_6_4side_3_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_6_4side_3_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_6__3side_6_4side_3_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_6_4side_3_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_6_4side_3_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_6__3side_6_4side_3_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_6_4side_3_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_6_4side_3_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_6__3side_6_4side_3_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_6_4side_3_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_6_4side_3_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s4
ch032_1side_6__2side_6__3side_6_4side_4_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_6_4side_4_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_6_4side_4_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_6__3side_6_4side_4_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_6_4side_4_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_6_4side_4_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_6__3side_6_4side_4_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_6_4side_4_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_6_4side_4_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_6__3side_6_4side_4_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_6_4side_4_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_6_4side_4_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_6__3side_6_4side_4_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_6_4side_4_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_6_4side_4_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_6__3side_6_4side_4_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_6_4side_4_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_6_4side_4_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_6__3side_6_4side_4_5s4_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_6_4side_4_5s4_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_6_4side_4_5s4_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_6__3side_6_4side_4_5s4_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_6_4side_4_5s4_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_6_4side_4_5s4_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_6__3side_6_4side_4_5s4_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_6_4side_4_5s4_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_6_4side_4_5s4_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_6__3side_6_4side_4_5s4_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_6_4side_4_5s4_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_6_4side_4_5s4_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s5
ch032_1side_6__2side_6__3side_6_4side_5_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_6_4side_5_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_6_4side_5_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_6__3side_6_4side_5_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_6_4side_5_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_6_4side_5_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_6__3side_6_4side_5_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_6_4side_5_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_6_4side_5_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_6__3side_6_4side_5_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_6_4side_5_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_6_4side_5_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_6__3side_6_4side_5_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_6_4side_5_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_6_4side_5_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_6__3side_6_4side_5_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_6_4side_5_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_6_4side_5_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_6__3side_6_4side_5_5s4_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_6_4side_5_5s4_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_6_4side_5_5s4_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_6__3side_6_4side_5_5s4_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_6_4side_5_5s4_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_6_4side_5_5s4_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_6__3side_6_4side_5_5s4_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_6_4side_5_5s4_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_6_4side_5_5s4_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_6__3side_6_4side_5_5s4_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_6_4side_5_5s4_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_6_4side_5_5s4_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_6__3side_6_4side_5_5s5_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_6_4side_5_5s5_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_6_4side_5_5s5_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_6__3side_6_4side_5_5s5_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_6_4side_5_5s5_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_6_4side_5_5s5_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_6__3side_6_4side_5_5s5_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_6_4side_5_5s5_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_6_4side_5_5s5_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_6__3side_6_4side_5_5s5_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_6_4side_5_5s5_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_6_4side_5_5s5_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_6__3side_6_4side_5_5s5_6s5 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_6_4side_5_5s5_6s5, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_6_4side_5_5s5_6s5.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s6
ch032_1side_6__2side_6__3side_6_4side_6_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_6_4side_6_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_6_4side_6_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_6__3side_6_4side_6_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_6_4side_6_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_6_4side_6_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_6__3side_6_4side_6_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_6_4side_6_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_6_4side_6_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_6__3side_6_4side_6_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_6_4side_6_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_6_4side_6_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_6__3side_6_4side_6_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_6_4side_6_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_6_4side_6_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_6__3side_6_4side_6_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_6_4side_6_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_6_4side_6_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_6__3side_6_4side_6_5s4_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_6_4side_6_5s4_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_6_4side_6_5s4_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_6__3side_6_4side_6_5s4_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_6_4side_6_5s4_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_6_4side_6_5s4_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_6__3side_6_4side_6_5s4_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_6_4side_6_5s4_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_6_4side_6_5s4_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_6__3side_6_4side_6_5s4_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_6_4side_6_5s4_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_6_4side_6_5s4_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_6__3side_6_4side_6_5s5_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_6_4side_6_5s5_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_6_4side_6_5s5_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_6__3side_6_4side_6_5s5_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_6_4side_6_5s5_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_6_4side_6_5s5_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_6__3side_6_4side_6_5s5_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_6_4side_6_5s5_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_6_4side_6_5s5_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_6__3side_6_4side_6_5s5_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_6_4side_6_5s5_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_6_4side_6_5s5_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_6__3side_6_4side_6_5s5_6s5 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_6_4side_6_5s5_6s5, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_6_4side_6_5s5_6s5.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_6__3side_6_4side_6_5s6_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_6_4side_6_5s6_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_6_4side_6_5s6_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_6__3side_6_4side_6_5s6_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_6_4side_6_5s6_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_6_4side_6_5s6_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_6__3side_6_4side_6_5s6_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_6_4side_6_5s6_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_6_4side_6_5s6_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_6__3side_6_4side_6_5s6_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_6_4side_6_5s6_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_6_4side_6_5s6_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_6__3side_6_4side_6_5s6_6s5 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_6_4side_6_5s6_6s5, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_6_4side_6_5s6_6s5.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_6__3side_6_4side_6_5s6_6s6 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_6_4side_6_5s6_6s6, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_6_4side_6_5s6_6s6.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
###################
############# 1s7
######### 2s1
##### 3s1
### 4s1
ch032_1side_7__2side_1__3side_1_4side_1_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_1__3side_1_4side_1_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_1__3side_1_4side_1_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
######### 2s2
##### 3s1
### 4s1
ch032_1side_7__2side_2__3side_1_4side_1_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_2__3side_1_4side_1_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_2__3side_1_4side_1_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
##### 3s2
### 4s1
ch032_1side_7__2side_2__3side_2_4side_1_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_2__3side_2_4side_1_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_2__3side_2_4side_1_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s2
ch032_1side_7__2side_2__3side_2_4side_2_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_2__3side_2_4side_2_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_2__3side_2_4side_2_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_2__3side_2_4side_2_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_2__3side_2_4side_2_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_2__3side_2_4side_2_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_2__3side_2_4side_2_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_2__3side_2_4side_2_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_2__3side_2_4side_2_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
######### 2s3
##### 3s1
### 4s1
ch032_1side_7__2side_3__3side_1_4side_1_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_3__3side_1_4side_1_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_3__3side_1_4side_1_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
##### 3s2
### 4s1
ch032_1side_7__2side_3__3side_2_4side_1_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_3__3side_2_4side_1_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_3__3side_2_4side_1_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s2
ch032_1side_7__2side_3__3side_2_4side_2_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_3__3side_2_4side_2_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_3__3side_2_4side_2_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_3__3side_2_4side_2_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_3__3side_2_4side_2_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_3__3side_2_4side_2_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_3__3side_2_4side_2_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_3__3side_2_4side_2_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_3__3side_2_4side_2_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
##### 3s3
### 4s1
ch032_1side_7__2side_3__3side_3_4side_1_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_3__3side_3_4side_1_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_3__3side_3_4side_1_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s2
ch032_1side_7__2side_3__3side_3_4side_2_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_3__3side_3_4side_2_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_3__3side_3_4side_2_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_3__3side_3_4side_2_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_3__3side_3_4side_2_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_3__3side_3_4side_2_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_3__3side_3_4side_2_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_3__3side_3_4side_2_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_3__3side_3_4side_2_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s3
ch032_1side_7__2side_3__3side_3_4side_3_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_3__3side_3_4side_3_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_3__3side_3_4side_3_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_3__3side_3_4side_3_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_3__3side_3_4side_3_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_3__3side_3_4side_3_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_3__3side_3_4side_3_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_3__3side_3_4side_3_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_3__3side_3_4side_3_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_3__3side_3_4side_3_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_3__3side_3_4side_3_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_3__3side_3_4side_3_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_3__3side_3_4side_3_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_3__3side_3_4side_3_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_3__3side_3_4side_3_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_3__3side_3_4side_3_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_3__3side_3_4side_3_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_3__3side_3_4side_3_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
######### 2s4
##### 3s1
### 4s1
ch032_1side_7__2side_4__3side_1_4side_1_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_4__3side_1_4side_1_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_4__3side_1_4side_1_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
##### 3s2
### 4s1
ch032_1side_7__2side_4__3side_2_4side_1_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_4__3side_2_4side_1_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_4__3side_2_4side_1_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s2
ch032_1side_7__2side_4__3side_2_4side_2_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_4__3side_2_4side_2_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_4__3side_2_4side_2_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_4__3side_2_4side_2_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_4__3side_2_4side_2_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_4__3side_2_4side_2_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_4__3side_2_4side_2_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_4__3side_2_4side_2_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_4__3side_2_4side_2_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
##### 3s3
### 4s1
ch032_1side_7__2side_4__3side_3_4side_1_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_4__3side_3_4side_1_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_4__3side_3_4side_1_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s2
ch032_1side_7__2side_4__3side_3_4side_2_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_4__3side_3_4side_2_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_4__3side_3_4side_2_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_4__3side_3_4side_2_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_4__3side_3_4side_2_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_4__3side_3_4side_2_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_4__3side_3_4side_2_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_4__3side_3_4side_2_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_4__3side_3_4side_2_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s3
ch032_1side_7__2side_4__3side_3_4side_3_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_4__3side_3_4side_3_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_4__3side_3_4side_3_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_4__3side_3_4side_3_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_4__3side_3_4side_3_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_4__3side_3_4side_3_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_4__3side_3_4side_3_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_4__3side_3_4side_3_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_4__3side_3_4side_3_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_4__3side_3_4side_3_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_4__3side_3_4side_3_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_4__3side_3_4side_3_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_4__3side_3_4side_3_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_4__3side_3_4side_3_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_4__3side_3_4side_3_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_4__3side_3_4side_3_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_4__3side_3_4side_3_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_4__3side_3_4side_3_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
##### 3s4
### 4s1
ch032_1side_7__2side_4__3side_4_4side_1_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_4__3side_4_4side_1_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_4__3side_4_4side_1_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s2
ch032_1side_7__2side_4__3side_4_4side_2_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_4__3side_4_4side_2_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_4__3side_4_4side_2_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_4__3side_4_4side_2_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_4__3side_4_4side_2_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_4__3side_4_4side_2_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_4__3side_4_4side_2_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_4__3side_4_4side_2_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_4__3side_4_4side_2_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s3
ch032_1side_7__2side_4__3side_4_4side_3_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_4__3side_4_4side_3_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_4__3side_4_4side_3_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_4__3side_4_4side_3_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_4__3side_4_4side_3_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_4__3side_4_4side_3_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_4__3side_4_4side_3_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_4__3side_4_4side_3_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_4__3side_4_4side_3_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_4__3side_4_4side_3_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_4__3side_4_4side_3_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_4__3side_4_4side_3_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_4__3side_4_4side_3_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_4__3side_4_4side_3_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_4__3side_4_4side_3_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_4__3side_4_4side_3_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_4__3side_4_4side_3_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_4__3side_4_4side_3_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s4
ch032_1side_7__2side_4__3side_4_4side_4_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_4__3side_4_4side_4_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_4__3side_4_4side_4_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_4__3side_4_4side_4_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_4__3side_4_4side_4_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_4__3side_4_4side_4_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_4__3side_4_4side_4_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_4__3side_4_4side_4_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_4__3side_4_4side_4_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_4__3side_4_4side_4_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_4__3side_4_4side_4_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_4__3side_4_4side_4_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_4__3side_4_4side_4_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_4__3side_4_4side_4_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_4__3side_4_4side_4_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_4__3side_4_4side_4_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_4__3side_4_4side_4_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_4__3side_4_4side_4_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_4__3side_4_4side_4_5s4_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_4__3side_4_4side_4_5s4_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_4__3side_4_4side_4_5s4_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_4__3side_4_4side_4_5s4_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_4__3side_4_4side_4_5s4_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_4__3side_4_4side_4_5s4_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_4__3side_4_4side_4_5s4_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_4__3side_4_4side_4_5s4_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_4__3side_4_4side_4_5s4_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_4__3side_4_4side_4_5s4_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_4__3side_4_4side_4_5s4_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_4__3side_4_4side_4_5s4_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
######### 2s5
##### 3s1
### 4s1
ch032_1side_7__2side_5__3side_1_4side_1_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_5__3side_1_4side_1_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_5__3side_1_4side_1_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
##### 3s2
### 4s1
ch032_1side_7__2side_5__3side_2_4side_1_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_5__3side_2_4side_1_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_5__3side_2_4side_1_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s2
ch032_1side_7__2side_5__3side_2_4side_2_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_5__3side_2_4side_2_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_5__3side_2_4side_2_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_5__3side_2_4side_2_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_5__3side_2_4side_2_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_5__3side_2_4side_2_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_5__3side_2_4side_2_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_5__3side_2_4side_2_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_5__3side_2_4side_2_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
##### 3s3
### 4s1
ch032_1side_7__2side_5__3side_3_4side_1_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_5__3side_3_4side_1_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_5__3side_3_4side_1_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s2
ch032_1side_7__2side_5__3side_3_4side_2_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_5__3side_3_4side_2_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_5__3side_3_4side_2_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_5__3side_3_4side_2_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_5__3side_3_4side_2_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_5__3side_3_4side_2_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_5__3side_3_4side_2_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_5__3side_3_4side_2_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_5__3side_3_4side_2_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s3
ch032_1side_7__2side_5__3side_3_4side_3_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_5__3side_3_4side_3_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_5__3side_3_4side_3_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_5__3side_3_4side_3_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_5__3side_3_4side_3_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_5__3side_3_4side_3_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_5__3side_3_4side_3_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_5__3side_3_4side_3_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_5__3side_3_4side_3_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_5__3side_3_4side_3_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_5__3side_3_4side_3_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_5__3side_3_4side_3_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_5__3side_3_4side_3_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_5__3side_3_4side_3_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_5__3side_3_4side_3_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_5__3side_3_4side_3_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_5__3side_3_4side_3_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_5__3side_3_4side_3_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
##### 3s4
### 4s1
ch032_1side_7__2side_5__3side_4_4side_1_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_5__3side_4_4side_1_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_5__3side_4_4side_1_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s2
ch032_1side_7__2side_5__3side_4_4side_2_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_5__3side_4_4side_2_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_5__3side_4_4side_2_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_5__3side_4_4side_2_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_5__3side_4_4side_2_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_5__3side_4_4side_2_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_5__3side_4_4side_2_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_5__3side_4_4side_2_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_5__3side_4_4side_2_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s3
ch032_1side_7__2side_5__3side_4_4side_3_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_5__3side_4_4side_3_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_5__3side_4_4side_3_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_5__3side_4_4side_3_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_5__3side_4_4side_3_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_5__3side_4_4side_3_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_5__3side_4_4side_3_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_5__3side_4_4side_3_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_5__3side_4_4side_3_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_5__3side_4_4side_3_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_5__3side_4_4side_3_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_5__3side_4_4side_3_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_5__3side_4_4side_3_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_5__3side_4_4side_3_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_5__3side_4_4side_3_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_5__3side_4_4side_3_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_5__3side_4_4side_3_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_5__3side_4_4side_3_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s4
ch032_1side_7__2side_5__3side_4_4side_4_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_5__3side_4_4side_4_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_5__3side_4_4side_4_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_5__3side_4_4side_4_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_5__3side_4_4side_4_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_5__3side_4_4side_4_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_5__3side_4_4side_4_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_5__3side_4_4side_4_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_5__3side_4_4side_4_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_5__3side_4_4side_4_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_5__3side_4_4side_4_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_5__3side_4_4side_4_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_5__3side_4_4side_4_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_5__3side_4_4side_4_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_5__3side_4_4side_4_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_5__3side_4_4side_4_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_5__3side_4_4side_4_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_5__3side_4_4side_4_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_5__3side_4_4side_4_5s4_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_5__3side_4_4side_4_5s4_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_5__3side_4_4side_4_5s4_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_5__3side_4_4side_4_5s4_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_5__3side_4_4side_4_5s4_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_5__3side_4_4side_4_5s4_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_5__3side_4_4side_4_5s4_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_5__3side_4_4side_4_5s4_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_5__3side_4_4side_4_5s4_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_5__3side_4_4side_4_5s4_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_5__3side_4_4side_4_5s4_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_5__3side_4_4side_4_5s4_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
##### 3s5
### 4s1
ch032_1side_7__2side_5__3side_5_4side_1_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_5__3side_5_4side_1_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_5__3side_5_4side_1_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s2
ch032_1side_7__2side_5__3side_5_4side_2_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_5__3side_5_4side_2_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_5__3side_5_4side_2_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_5__3side_5_4side_2_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_5__3side_5_4side_2_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_5__3side_5_4side_2_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_5__3side_5_4side_2_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_5__3side_5_4side_2_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_5__3side_5_4side_2_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s3
ch032_1side_7__2side_5__3side_5_4side_3_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_5__3side_5_4side_3_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_5__3side_5_4side_3_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_5__3side_5_4side_3_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_5__3side_5_4side_3_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_5__3side_5_4side_3_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_5__3side_5_4side_3_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_5__3side_5_4side_3_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_5__3side_5_4side_3_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_5__3side_5_4side_3_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_5__3side_5_4side_3_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_5__3side_5_4side_3_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_5__3side_5_4side_3_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_5__3side_5_4side_3_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_5__3side_5_4side_3_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_5__3side_5_4side_3_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_5__3side_5_4side_3_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_5__3side_5_4side_3_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s4
ch032_1side_7__2side_5__3side_5_4side_4_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_5__3side_5_4side_4_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_5__3side_5_4side_4_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_5__3side_5_4side_4_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_5__3side_5_4side_4_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_5__3side_5_4side_4_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_5__3side_5_4side_4_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_5__3side_5_4side_4_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_5__3side_5_4side_4_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_5__3side_5_4side_4_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_5__3side_5_4side_4_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_5__3side_5_4side_4_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_5__3side_5_4side_4_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_5__3side_5_4side_4_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_5__3side_5_4side_4_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_5__3side_5_4side_4_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_5__3side_5_4side_4_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_5__3side_5_4side_4_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_5__3side_5_4side_4_5s4_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_5__3side_5_4side_4_5s4_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_5__3side_5_4side_4_5s4_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_5__3side_5_4side_4_5s4_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_5__3side_5_4side_4_5s4_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_5__3side_5_4side_4_5s4_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_5__3side_5_4side_4_5s4_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_5__3side_5_4side_4_5s4_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_5__3side_5_4side_4_5s4_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_5__3side_5_4side_4_5s4_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_5__3side_5_4side_4_5s4_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_5__3side_5_4side_4_5s4_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s5
ch032_1side_7__2side_5__3side_5_4side_5_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_5__3side_5_4side_5_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_5__3side_5_4side_5_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_5__3side_5_4side_5_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_5__3side_5_4side_5_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_5__3side_5_4side_5_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_5__3side_5_4side_5_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_5__3side_5_4side_5_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_5__3side_5_4side_5_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_5__3side_5_4side_5_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_5__3side_5_4side_5_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_5__3side_5_4side_5_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_5__3side_5_4side_5_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_5__3side_5_4side_5_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_5__3side_5_4side_5_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_5__3side_5_4side_5_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_5__3side_5_4side_5_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_5__3side_5_4side_5_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_5__3side_5_4side_5_5s4_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_5__3side_5_4side_5_5s4_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_5__3side_5_4side_5_5s4_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_5__3side_5_4side_5_5s4_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_5__3side_5_4side_5_5s4_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_5__3side_5_4side_5_5s4_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_5__3side_5_4side_5_5s4_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_5__3side_5_4side_5_5s4_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_5__3side_5_4side_5_5s4_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_5__3side_5_4side_5_5s4_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_5__3side_5_4side_5_5s4_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_5__3side_5_4side_5_5s4_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_5__3side_5_4side_5_5s5_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_5__3side_5_4side_5_5s5_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_5__3side_5_4side_5_5s5_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_5__3side_5_4side_5_5s5_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_5__3side_5_4side_5_5s5_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_5__3side_5_4side_5_5s5_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_5__3side_5_4side_5_5s5_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_5__3side_5_4side_5_5s5_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_5__3side_5_4side_5_5s5_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_5__3side_5_4side_5_5s5_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_5__3side_5_4side_5_5s5_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_5__3side_5_4side_5_5s5_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_5__3side_5_4side_5_5s5_6s5 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_5__3side_5_4side_5_5s5_6s5, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_5__3side_5_4side_5_5s5_6s5.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
######### 2s6
##### 3s1
### 4s1
ch032_1side_7__2side_6__3side_1_4side_1_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_1_4side_1_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_1_4side_1_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
##### 3s2
### 4s1
ch032_1side_7__2side_6__3side_2_4side_1_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_2_4side_1_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_2_4side_1_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s2
ch032_1side_7__2side_6__3side_2_4side_2_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_2_4side_2_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_2_4side_2_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_6__3side_2_4side_2_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_2_4side_2_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_2_4side_2_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_6__3side_2_4side_2_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_2_4side_2_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_2_4side_2_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
##### 3s3
### 4s1
ch032_1side_7__2side_6__3side_3_4side_1_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_3_4side_1_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_3_4side_1_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s2
ch032_1side_7__2side_6__3side_3_4side_2_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_3_4side_2_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_3_4side_2_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_6__3side_3_4side_2_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_3_4side_2_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_3_4side_2_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_6__3side_3_4side_2_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_3_4side_2_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_3_4side_2_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s3
ch032_1side_7__2side_6__3side_3_4side_3_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_3_4side_3_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_3_4side_3_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_6__3side_3_4side_3_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_3_4side_3_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_3_4side_3_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_6__3side_3_4side_3_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_3_4side_3_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_3_4side_3_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_6__3side_3_4side_3_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_3_4side_3_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_3_4side_3_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_6__3side_3_4side_3_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_3_4side_3_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_3_4side_3_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_6__3side_3_4side_3_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_3_4side_3_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_3_4side_3_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
##### 3s4
### 4s1
ch032_1side_7__2side_6__3side_4_4side_1_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_4_4side_1_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_4_4side_1_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s2
ch032_1side_7__2side_6__3side_4_4side_2_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_4_4side_2_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_4_4side_2_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_6__3side_4_4side_2_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_4_4side_2_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_4_4side_2_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_6__3side_4_4side_2_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_4_4side_2_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_4_4side_2_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s3
ch032_1side_7__2side_6__3side_4_4side_3_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_4_4side_3_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_4_4side_3_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_6__3side_4_4side_3_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_4_4side_3_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_4_4side_3_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_6__3side_4_4side_3_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_4_4side_3_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_4_4side_3_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_6__3side_4_4side_3_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_4_4side_3_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_4_4side_3_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_6__3side_4_4side_3_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_4_4side_3_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_4_4side_3_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_6__3side_4_4side_3_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_4_4side_3_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_4_4side_3_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s4
ch032_1side_7__2side_6__3side_4_4side_4_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_4_4side_4_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_4_4side_4_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_6__3side_4_4side_4_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_4_4side_4_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_4_4side_4_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_6__3side_4_4side_4_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_4_4side_4_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_4_4side_4_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_6__3side_4_4side_4_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_4_4side_4_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_4_4side_4_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_6__3side_4_4side_4_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_4_4side_4_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_4_4side_4_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_6__3side_4_4side_4_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_4_4side_4_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_4_4side_4_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_6__3side_4_4side_4_5s4_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_4_4side_4_5s4_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_4_4side_4_5s4_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_6__3side_4_4side_4_5s4_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_4_4side_4_5s4_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_4_4side_4_5s4_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_6__3side_4_4side_4_5s4_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_4_4side_4_5s4_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_4_4side_4_5s4_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_6__3side_4_4side_4_5s4_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_4_4side_4_5s4_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_4_4side_4_5s4_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
##### 3s5
### 4s1
ch032_1side_7__2side_6__3side_5_4side_1_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_5_4side_1_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_5_4side_1_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s2
ch032_1side_7__2side_6__3side_5_4side_2_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_5_4side_2_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_5_4side_2_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_6__3side_5_4side_2_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_5_4side_2_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_5_4side_2_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_6__3side_5_4side_2_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_5_4side_2_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_5_4side_2_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s3
ch032_1side_7__2side_6__3side_5_4side_3_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_5_4side_3_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_5_4side_3_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_6__3side_5_4side_3_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_5_4side_3_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_5_4side_3_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_6__3side_5_4side_3_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_5_4side_3_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_5_4side_3_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_6__3side_5_4side_3_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_5_4side_3_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_5_4side_3_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_6__3side_5_4side_3_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_5_4side_3_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_5_4side_3_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_6__3side_5_4side_3_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_5_4side_3_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_5_4side_3_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s4
ch032_1side_7__2side_6__3side_5_4side_4_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_5_4side_4_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_5_4side_4_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_6__3side_5_4side_4_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_5_4side_4_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_5_4side_4_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_6__3side_5_4side_4_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_5_4side_4_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_5_4side_4_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_6__3side_5_4side_4_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_5_4side_4_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_5_4side_4_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_6__3side_5_4side_4_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_5_4side_4_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_5_4side_4_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_6__3side_5_4side_4_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_5_4side_4_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_5_4side_4_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_6__3side_5_4side_4_5s4_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_5_4side_4_5s4_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_5_4side_4_5s4_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_6__3side_5_4side_4_5s4_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_5_4side_4_5s4_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_5_4side_4_5s4_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_6__3side_5_4side_4_5s4_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_5_4side_4_5s4_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_5_4side_4_5s4_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_6__3side_5_4side_4_5s4_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_5_4side_4_5s4_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_5_4side_4_5s4_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s5
ch032_1side_7__2side_6__3side_5_4side_5_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_5_4side_5_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_5_4side_5_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_6__3side_5_4side_5_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_5_4side_5_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_5_4side_5_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_6__3side_5_4side_5_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_5_4side_5_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_5_4side_5_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_6__3side_5_4side_5_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_5_4side_5_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_5_4side_5_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_6__3side_5_4side_5_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_5_4side_5_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_5_4side_5_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_6__3side_5_4side_5_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_5_4side_5_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_5_4side_5_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_6__3side_5_4side_5_5s4_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_5_4side_5_5s4_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_5_4side_5_5s4_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_6__3side_5_4side_5_5s4_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_5_4side_5_5s4_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_5_4side_5_5s4_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_6__3side_5_4side_5_5s4_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_5_4side_5_5s4_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_5_4side_5_5s4_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_6__3side_5_4side_5_5s4_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_5_4side_5_5s4_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_5_4side_5_5s4_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_6__3side_5_4side_5_5s5_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_5_4side_5_5s5_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_5_4side_5_5s5_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_6__3side_5_4side_5_5s5_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_5_4side_5_5s5_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_5_4side_5_5s5_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_6__3side_5_4side_5_5s5_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_5_4side_5_5s5_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_5_4side_5_5s5_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_6__3side_5_4side_5_5s5_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_5_4side_5_5s5_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_5_4side_5_5s5_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_6__3side_5_4side_5_5s5_6s5 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_5_4side_5_5s5_6s5, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_5_4side_5_5s5_6s5.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
##### 3s6
### 4s1
ch032_1side_7__2side_6__3side_6_4side_1_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_6_4side_1_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_6_4side_1_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s2
ch032_1side_7__2side_6__3side_6_4side_2_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_6_4side_2_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_6_4side_2_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_6__3side_6_4side_2_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_6_4side_2_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_6_4side_2_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_6__3side_6_4side_2_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_6_4side_2_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_6_4side_2_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s3
ch032_1side_7__2side_6__3side_6_4side_3_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_6_4side_3_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_6_4side_3_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_6__3side_6_4side_3_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_6_4side_3_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_6_4side_3_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_6__3side_6_4side_3_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_6_4side_3_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_6_4side_3_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_6__3side_6_4side_3_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_6_4side_3_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_6_4side_3_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_6__3side_6_4side_3_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_6_4side_3_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_6_4side_3_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_6__3side_6_4side_3_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_6_4side_3_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_6_4side_3_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s4
ch032_1side_7__2side_6__3side_6_4side_4_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_6_4side_4_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_6_4side_4_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_6__3side_6_4side_4_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_6_4side_4_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_6_4side_4_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_6__3side_6_4side_4_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_6_4side_4_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_6_4side_4_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_6__3side_6_4side_4_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_6_4side_4_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_6_4side_4_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_6__3side_6_4side_4_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_6_4side_4_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_6_4side_4_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_6__3side_6_4side_4_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_6_4side_4_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_6_4side_4_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_6__3side_6_4side_4_5s4_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_6_4side_4_5s4_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_6_4side_4_5s4_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_6__3side_6_4side_4_5s4_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_6_4side_4_5s4_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_6_4side_4_5s4_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_6__3side_6_4side_4_5s4_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_6_4side_4_5s4_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_6_4side_4_5s4_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_6__3side_6_4side_4_5s4_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_6_4side_4_5s4_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_6_4side_4_5s4_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s5
ch032_1side_7__2side_6__3side_6_4side_5_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_6_4side_5_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_6_4side_5_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_6__3side_6_4side_5_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_6_4side_5_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_6_4side_5_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_6__3side_6_4side_5_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_6_4side_5_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_6_4side_5_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_6__3side_6_4side_5_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_6_4side_5_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_6_4side_5_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_6__3side_6_4side_5_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_6_4side_5_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_6_4side_5_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_6__3side_6_4side_5_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_6_4side_5_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_6_4side_5_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_6__3side_6_4side_5_5s4_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_6_4side_5_5s4_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_6_4side_5_5s4_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_6__3side_6_4side_5_5s4_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_6_4side_5_5s4_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_6_4side_5_5s4_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_6__3side_6_4side_5_5s4_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_6_4side_5_5s4_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_6_4side_5_5s4_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_6__3side_6_4side_5_5s4_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_6_4side_5_5s4_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_6_4side_5_5s4_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_6__3side_6_4side_5_5s5_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_6_4side_5_5s5_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_6_4side_5_5s5_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_6__3side_6_4side_5_5s5_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_6_4side_5_5s5_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_6_4side_5_5s5_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_6__3side_6_4side_5_5s5_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_6_4side_5_5s5_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_6_4side_5_5s5_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_6__3side_6_4side_5_5s5_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_6_4side_5_5s5_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_6_4side_5_5s5_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_6__3side_6_4side_5_5s5_6s5 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_6_4side_5_5s5_6s5, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_6_4side_5_5s5_6s5.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s6
ch032_1side_7__2side_6__3side_6_4side_6_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_6_4side_6_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_6_4side_6_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_6__3side_6_4side_6_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_6_4side_6_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_6_4side_6_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_6__3side_6_4side_6_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_6_4side_6_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_6_4side_6_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_6__3side_6_4side_6_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_6_4side_6_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_6_4side_6_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_6__3side_6_4side_6_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_6_4side_6_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_6_4side_6_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_6__3side_6_4side_6_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_6_4side_6_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_6_4side_6_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_6__3side_6_4side_6_5s4_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_6_4side_6_5s4_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_6_4side_6_5s4_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_6__3side_6_4side_6_5s4_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_6_4side_6_5s4_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_6_4side_6_5s4_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_6__3side_6_4side_6_5s4_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_6_4side_6_5s4_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_6_4side_6_5s4_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_6__3side_6_4side_6_5s4_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_6_4side_6_5s4_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_6_4side_6_5s4_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_6__3side_6_4side_6_5s5_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_6_4side_6_5s5_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_6_4side_6_5s5_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_6__3side_6_4side_6_5s5_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_6_4side_6_5s5_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_6_4side_6_5s5_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_6__3side_6_4side_6_5s5_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_6_4side_6_5s5_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_6_4side_6_5s5_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_6__3side_6_4side_6_5s5_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_6_4side_6_5s5_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_6_4side_6_5s5_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_6__3side_6_4side_6_5s5_6s5 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_6_4side_6_5s5_6s5, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_6_4side_6_5s5_6s5.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_6__3side_6_4side_6_5s6_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_6_4side_6_5s6_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_6_4side_6_5s6_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_6__3side_6_4side_6_5s6_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_6_4side_6_5s6_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_6_4side_6_5s6_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_6__3side_6_4side_6_5s6_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_6_4side_6_5s6_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_6_4side_6_5s6_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_6__3side_6_4side_6_5s6_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_6_4side_6_5s6_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_6_4side_6_5s6_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_6__3side_6_4side_6_5s6_6s5 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_6_4side_6_5s6_6s5, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_6_4side_6_5s6_6s5.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_6__3side_6_4side_6_5s6_6s6 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_6_4side_6_5s6_6s6, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_6_4side_6_5s6_6s6.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
######### 2s7
##### 3s1
### 4s1
ch032_1side_7__2side_7__3side_1_4side_1_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_1_4side_1_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_1_4side_1_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
##### 3s2
### 4s1
ch032_1side_7__2side_7__3side_2_4side_1_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_2_4side_1_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_2_4side_1_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s2
ch032_1side_7__2side_7__3side_2_4side_2_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_2_4side_2_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_2_4side_2_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_2_4side_2_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_2_4side_2_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_2_4side_2_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_2_4side_2_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_2_4side_2_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_2_4side_2_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
##### 3s3
### 4s1
ch032_1side_7__2side_7__3side_3_4side_1_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_3_4side_1_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_3_4side_1_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s2
ch032_1side_7__2side_7__3side_3_4side_2_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_3_4side_2_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_3_4side_2_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_3_4side_2_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_3_4side_2_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_3_4side_2_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_3_4side_2_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_3_4side_2_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_3_4side_2_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s3
ch032_1side_7__2side_7__3side_3_4side_3_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_3_4side_3_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_3_4side_3_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_3_4side_3_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_3_4side_3_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_3_4side_3_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_3_4side_3_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_3_4side_3_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_3_4side_3_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_3_4side_3_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_3_4side_3_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_3_4side_3_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_3_4side_3_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_3_4side_3_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_3_4side_3_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_3_4side_3_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_3_4side_3_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_3_4side_3_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
##### 3s4
### 4s1
ch032_1side_7__2side_7__3side_4_4side_1_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_4_4side_1_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_4_4side_1_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s2
ch032_1side_7__2side_7__3side_4_4side_2_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_4_4side_2_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_4_4side_2_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_4_4side_2_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_4_4side_2_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_4_4side_2_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_4_4side_2_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_4_4side_2_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_4_4side_2_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s3
ch032_1side_7__2side_7__3side_4_4side_3_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_4_4side_3_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_4_4side_3_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_4_4side_3_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_4_4side_3_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_4_4side_3_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_4_4side_3_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_4_4side_3_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_4_4side_3_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_4_4side_3_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_4_4side_3_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_4_4side_3_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_4_4side_3_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_4_4side_3_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_4_4side_3_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_4_4side_3_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_4_4side_3_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_4_4side_3_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s4
ch032_1side_7__2side_7__3side_4_4side_4_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_4_4side_4_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_4_4side_4_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_4_4side_4_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_4_4side_4_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_4_4side_4_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_4_4side_4_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_4_4side_4_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_4_4side_4_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_4_4side_4_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_4_4side_4_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_4_4side_4_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_4_4side_4_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_4_4side_4_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_4_4side_4_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_4_4side_4_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_4_4side_4_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_4_4side_4_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_4_4side_4_5s4_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_4_4side_4_5s4_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_4_4side_4_5s4_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_4_4side_4_5s4_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_4_4side_4_5s4_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_4_4side_4_5s4_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_4_4side_4_5s4_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_4_4side_4_5s4_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_4_4side_4_5s4_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_4_4side_4_5s4_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_4_4side_4_5s4_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_4_4side_4_5s4_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
##### 3s5
### 4s1
ch032_1side_7__2side_7__3side_5_4side_1_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_5_4side_1_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_5_4side_1_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s2
ch032_1side_7__2side_7__3side_5_4side_2_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_5_4side_2_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_5_4side_2_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_5_4side_2_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_5_4side_2_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_5_4side_2_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_5_4side_2_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_5_4side_2_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_5_4side_2_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s3
ch032_1side_7__2side_7__3side_5_4side_3_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_5_4side_3_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_5_4side_3_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_5_4side_3_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_5_4side_3_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_5_4side_3_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_5_4side_3_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_5_4side_3_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_5_4side_3_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_5_4side_3_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_5_4side_3_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_5_4side_3_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_5_4side_3_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_5_4side_3_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_5_4side_3_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_5_4side_3_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_5_4side_3_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_5_4side_3_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s4
ch032_1side_7__2side_7__3side_5_4side_4_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_5_4side_4_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_5_4side_4_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_5_4side_4_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_5_4side_4_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_5_4side_4_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_5_4side_4_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_5_4side_4_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_5_4side_4_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_5_4side_4_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_5_4side_4_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_5_4side_4_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_5_4side_4_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_5_4side_4_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_5_4side_4_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_5_4side_4_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_5_4side_4_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_5_4side_4_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_5_4side_4_5s4_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_5_4side_4_5s4_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_5_4side_4_5s4_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_5_4side_4_5s4_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_5_4side_4_5s4_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_5_4side_4_5s4_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_5_4side_4_5s4_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_5_4side_4_5s4_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_5_4side_4_5s4_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_5_4side_4_5s4_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_5_4side_4_5s4_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_5_4side_4_5s4_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s5
ch032_1side_7__2side_7__3side_5_4side_5_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_5_4side_5_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_5_4side_5_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_5_4side_5_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_5_4side_5_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_5_4side_5_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_5_4side_5_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_5_4side_5_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_5_4side_5_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_5_4side_5_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_5_4side_5_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_5_4side_5_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_5_4side_5_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_5_4side_5_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_5_4side_5_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_5_4side_5_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_5_4side_5_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_5_4side_5_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_5_4side_5_5s4_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_5_4side_5_5s4_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_5_4side_5_5s4_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_5_4side_5_5s4_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_5_4side_5_5s4_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_5_4side_5_5s4_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_5_4side_5_5s4_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_5_4side_5_5s4_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_5_4side_5_5s4_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_5_4side_5_5s4_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_5_4side_5_5s4_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_5_4side_5_5s4_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_5_4side_5_5s5_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_5_4side_5_5s5_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_5_4side_5_5s5_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_5_4side_5_5s5_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_5_4side_5_5s5_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_5_4side_5_5s5_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_5_4side_5_5s5_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_5_4side_5_5s5_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_5_4side_5_5s5_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_5_4side_5_5s5_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_5_4side_5_5s5_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_5_4side_5_5s5_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_5_4side_5_5s5_6s5 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_5_4side_5_5s5_6s5, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_5_4side_5_5s5_6s5.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
##### 3s6
### 4s1
ch032_1side_7__2side_7__3side_6_4side_1_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_6_4side_1_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_6_4side_1_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s2
ch032_1side_7__2side_7__3side_6_4side_2_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_6_4side_2_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_6_4side_2_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_6_4side_2_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_6_4side_2_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_6_4side_2_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_6_4side_2_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_6_4side_2_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_6_4side_2_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s3
ch032_1side_7__2side_7__3side_6_4side_3_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_6_4side_3_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_6_4side_3_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_6_4side_3_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_6_4side_3_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_6_4side_3_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_6_4side_3_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_6_4side_3_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_6_4side_3_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_6_4side_3_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_6_4side_3_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_6_4side_3_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_6_4side_3_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_6_4side_3_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_6_4side_3_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_6_4side_3_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_6_4side_3_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_6_4side_3_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s4
ch032_1side_7__2side_7__3side_6_4side_4_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_6_4side_4_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_6_4side_4_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_6_4side_4_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_6_4side_4_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_6_4side_4_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_6_4side_4_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_6_4side_4_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_6_4side_4_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_6_4side_4_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_6_4side_4_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_6_4side_4_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_6_4side_4_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_6_4side_4_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_6_4side_4_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_6_4side_4_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_6_4side_4_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_6_4side_4_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_6_4side_4_5s4_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_6_4side_4_5s4_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_6_4side_4_5s4_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_6_4side_4_5s4_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_6_4side_4_5s4_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_6_4side_4_5s4_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_6_4side_4_5s4_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_6_4side_4_5s4_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_6_4side_4_5s4_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_6_4side_4_5s4_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_6_4side_4_5s4_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_6_4side_4_5s4_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s5
ch032_1side_7__2side_7__3side_6_4side_5_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_6_4side_5_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_6_4side_5_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_6_4side_5_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_6_4side_5_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_6_4side_5_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_6_4side_5_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_6_4side_5_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_6_4side_5_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_6_4side_5_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_6_4side_5_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_6_4side_5_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_6_4side_5_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_6_4side_5_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_6_4side_5_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_6_4side_5_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_6_4side_5_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_6_4side_5_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_6_4side_5_5s4_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_6_4side_5_5s4_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_6_4side_5_5s4_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_6_4side_5_5s4_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_6_4side_5_5s4_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_6_4side_5_5s4_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_6_4side_5_5s4_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_6_4side_5_5s4_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_6_4side_5_5s4_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_6_4side_5_5s4_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_6_4side_5_5s4_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_6_4side_5_5s4_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_6_4side_5_5s5_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_6_4side_5_5s5_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_6_4side_5_5s5_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_6_4side_5_5s5_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_6_4side_5_5s5_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_6_4side_5_5s5_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_6_4side_5_5s5_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_6_4side_5_5s5_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_6_4side_5_5s5_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_6_4side_5_5s5_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_6_4side_5_5s5_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_6_4side_5_5s5_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_6_4side_5_5s5_6s5 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_6_4side_5_5s5_6s5, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_6_4side_5_5s5_6s5.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s6
ch032_1side_7__2side_7__3side_6_4side_6_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_6_4side_6_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_6_4side_6_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_6_4side_6_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_6_4side_6_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_6_4side_6_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_6_4side_6_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_6_4side_6_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_6_4side_6_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_6_4side_6_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_6_4side_6_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_6_4side_6_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_6_4side_6_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_6_4side_6_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_6_4side_6_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_6_4side_6_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_6_4side_6_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_6_4side_6_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_6_4side_6_5s4_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_6_4side_6_5s4_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_6_4side_6_5s4_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_6_4side_6_5s4_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_6_4side_6_5s4_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_6_4side_6_5s4_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_6_4side_6_5s4_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_6_4side_6_5s4_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_6_4side_6_5s4_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_6_4side_6_5s4_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_6_4side_6_5s4_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_6_4side_6_5s4_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_6_4side_6_5s5_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_6_4side_6_5s5_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_6_4side_6_5s5_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_6_4side_6_5s5_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_6_4side_6_5s5_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_6_4side_6_5s5_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_6_4side_6_5s5_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_6_4side_6_5s5_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_6_4side_6_5s5_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_6_4side_6_5s5_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_6_4side_6_5s5_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_6_4side_6_5s5_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_6_4side_6_5s5_6s5 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_6_4side_6_5s5_6s5, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_6_4side_6_5s5_6s5.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_6_4side_6_5s6_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_6_4side_6_5s6_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_6_4side_6_5s6_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_6_4side_6_5s6_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_6_4side_6_5s6_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_6_4side_6_5s6_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_6_4side_6_5s6_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_6_4side_6_5s6_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_6_4side_6_5s6_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_6_4side_6_5s6_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_6_4side_6_5s6_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_6_4side_6_5s6_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_6_4side_6_5s6_6s5 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_6_4side_6_5s6_6s5, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_6_4side_6_5s6_6s5.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_6_4side_6_5s6_6s6 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_6_4side_6_5s6_6s6, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_6_4side_6_5s6_6s6.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
##### 3s7
### 4s1
ch032_1side_7__2side_7__3side_7_4side_1_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_7_4side_1_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_7_4side_1_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s2
ch032_1side_7__2side_7__3side_7_4side_2_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_7_4side_2_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_7_4side_2_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_7_4side_2_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_7_4side_2_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_7_4side_2_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_7_4side_2_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_7_4side_2_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_7_4side_2_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s3
ch032_1side_7__2side_7__3side_7_4side_3_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_7_4side_3_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_7_4side_3_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_7_4side_3_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_7_4side_3_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_7_4side_3_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_7_4side_3_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_7_4side_3_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_7_4side_3_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_7_4side_3_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_7_4side_3_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_7_4side_3_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_7_4side_3_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_7_4side_3_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_7_4side_3_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_7_4side_3_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_7_4side_3_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_7_4side_3_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s4
ch032_1side_7__2side_7__3side_7_4side_4_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_7_4side_4_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_7_4side_4_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_7_4side_4_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_7_4side_4_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_7_4side_4_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_7_4side_4_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_7_4side_4_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_7_4side_4_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_7_4side_4_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_7_4side_4_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_7_4side_4_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_7_4side_4_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_7_4side_4_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_7_4side_4_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_7_4side_4_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_7_4side_4_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_7_4side_4_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_7_4side_4_5s4_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_7_4side_4_5s4_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_7_4side_4_5s4_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_7_4side_4_5s4_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_7_4side_4_5s4_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_7_4side_4_5s4_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_7_4side_4_5s4_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_7_4side_4_5s4_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_7_4side_4_5s4_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_7_4side_4_5s4_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_7_4side_4_5s4_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_7_4side_4_5s4_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s5
ch032_1side_7__2side_7__3side_7_4side_5_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_7_4side_5_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_7_4side_5_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_7_4side_5_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_7_4side_5_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_7_4side_5_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_7_4side_5_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_7_4side_5_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_7_4side_5_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_7_4side_5_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_7_4side_5_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_7_4side_5_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_7_4side_5_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_7_4side_5_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_7_4side_5_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_7_4side_5_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_7_4side_5_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_7_4side_5_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_7_4side_5_5s4_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_7_4side_5_5s4_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_7_4side_5_5s4_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_7_4side_5_5s4_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_7_4side_5_5s4_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_7_4side_5_5s4_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_7_4side_5_5s4_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_7_4side_5_5s4_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_7_4side_5_5s4_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_7_4side_5_5s4_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_7_4side_5_5s4_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_7_4side_5_5s4_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_7_4side_5_5s5_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_7_4side_5_5s5_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_7_4side_5_5s5_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_7_4side_5_5s5_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_7_4side_5_5s5_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_7_4side_5_5s5_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_7_4side_5_5s5_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_7_4side_5_5s5_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_7_4side_5_5s5_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_7_4side_5_5s5_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_7_4side_5_5s5_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_7_4side_5_5s5_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_7_4side_5_5s5_6s5 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_7_4side_5_5s5_6s5, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_7_4side_5_5s5_6s5.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s6
ch032_1side_7__2side_7__3side_7_4side_6_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_7_4side_6_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_7_4side_6_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_7_4side_6_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_7_4side_6_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_7_4side_6_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_7_4side_6_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_7_4side_6_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_7_4side_6_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_7_4side_6_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_7_4side_6_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_7_4side_6_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_7_4side_6_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_7_4side_6_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_7_4side_6_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_7_4side_6_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_7_4side_6_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_7_4side_6_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_7_4side_6_5s4_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_7_4side_6_5s4_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_7_4side_6_5s4_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_7_4side_6_5s4_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_7_4side_6_5s4_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_7_4side_6_5s4_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_7_4side_6_5s4_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_7_4side_6_5s4_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_7_4side_6_5s4_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_7_4side_6_5s4_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_7_4side_6_5s4_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_7_4side_6_5s4_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_7_4side_6_5s5_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_7_4side_6_5s5_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_7_4side_6_5s5_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_7_4side_6_5s5_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_7_4side_6_5s5_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_7_4side_6_5s5_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_7_4side_6_5s5_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_7_4side_6_5s5_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_7_4side_6_5s5_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_7_4side_6_5s5_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_7_4side_6_5s5_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_7_4side_6_5s5_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_7_4side_6_5s5_6s5 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_7_4side_6_5s5_6s5, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_7_4side_6_5s5_6s5.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_7_4side_6_5s6_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_7_4side_6_5s6_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_7_4side_6_5s6_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_7_4side_6_5s6_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_7_4side_6_5s6_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_7_4side_6_5s6_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_7_4side_6_5s6_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_7_4side_6_5s6_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_7_4side_6_5s6_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_7_4side_6_5s6_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_7_4side_6_5s6_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_7_4side_6_5s6_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_7_4side_6_5s6_6s5 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_7_4side_6_5s6_6s5, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_7_4side_6_5s6_6s5.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_7_4side_6_5s6_6s6 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_7_4side_6_5s6_6s6, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_7_4side_6_5s6_6s6.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s7
ch032_1side_7__2side_7__3side_7_4side_7_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_7_4side_7_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_7_4side_7_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_7_4side_7_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_7_4side_7_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_7_4side_7_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_7_4side_7_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_7_4side_7_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_7_4side_7_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_7_4side_7_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_7_4side_7_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_7_4side_7_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_7_4side_7_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_7_4side_7_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_7_4side_7_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_7_4side_7_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_7_4side_7_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_7_4side_7_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_7_4side_7_5s4_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_7_4side_7_5s4_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_7_4side_7_5s4_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_7_4side_7_5s4_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_7_4side_7_5s4_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_7_4side_7_5s4_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_7_4side_7_5s4_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_7_4side_7_5s4_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_7_4side_7_5s4_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_7_4side_7_5s4_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_7_4side_7_5s4_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_7_4side_7_5s4_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_7_4side_7_5s5_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_7_4side_7_5s5_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_7_4side_7_5s5_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_7_4side_7_5s5_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_7_4side_7_5s5_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_7_4side_7_5s5_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_7_4side_7_5s5_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_7_4side_7_5s5_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_7_4side_7_5s5_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_7_4side_7_5s5_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_7_4side_7_5s5_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_7_4side_7_5s5_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_7_4side_7_5s5_6s5 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_7_4side_7_5s5_6s5, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_7_4side_7_5s5_6s5.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_7_4side_7_5s6_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_7_4side_7_5s6_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_7_4side_7_5s6_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_7_4side_7_5s6_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_7_4side_7_5s6_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_7_4side_7_5s6_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_7_4side_7_5s6_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_7_4side_7_5s6_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_7_4side_7_5s6_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_7_4side_7_5s6_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_7_4side_7_5s6_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_7_4side_7_5s6_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_7_4side_7_5s6_6s5 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_7_4side_7_5s6_6s5, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_7_4side_7_5s6_6s5.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_7_4side_7_5s6_6s6 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_7_4side_7_5s6_6s6, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_7_4side_7_5s6_6s6.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_7_4side_7_5s7_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_7_4side_7_5s7_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_7_4side_7_5s7_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_7_4side_7_5s7_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_7_4side_7_5s7_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_7_4side_7_5s7_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_7_4side_7_5s7_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_7_4side_7_5s7_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_7_4side_7_5s7_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_7_4side_7_5s7_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_7_4side_7_5s7_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_7_4side_7_5s7_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_7_4side_7_5s7_6s5 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_7_4side_7_5s7_6s5, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_7_4side_7_5s7_6s5.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_7_4side_7_5s7_6s6 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_7_4side_7_5s7_6s6, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_7_4side_7_5s7_6s6.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_7_4side_7_5s7_6s7 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_7_4side_7_5s7_6s7, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_7_4side_7_5s7_6s7.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
###################
############# 1s8
######### 2s1
##### 3s1
### 4s1
ch032_1side_8__2side_1__3side_1_4side_1_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_1__3side_1_4side_1_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_1__3side_1_4side_1_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
######### 2s2
##### 3s1
### 4s1
ch032_1side_8__2side_2__3side_1_4side_1_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_2__3side_1_4side_1_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_2__3side_1_4side_1_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
##### 3s2
### 4s1
ch032_1side_8__2side_2__3side_2_4side_1_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_2__3side_2_4side_1_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_2__3side_2_4side_1_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s2
ch032_1side_8__2side_2__3side_2_4side_2_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_2__3side_2_4side_2_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_2__3side_2_4side_2_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_2__3side_2_4side_2_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_2__3side_2_4side_2_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_2__3side_2_4side_2_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_2__3side_2_4side_2_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_2__3side_2_4side_2_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_2__3side_2_4side_2_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
######### 2s3
##### 3s1
### 4s1
ch032_1side_8__2side_3__3side_1_4side_1_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_3__3side_1_4side_1_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_3__3side_1_4side_1_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
##### 3s2
### 4s1
ch032_1side_8__2side_3__3side_2_4side_1_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_3__3side_2_4side_1_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_3__3side_2_4side_1_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s2
ch032_1side_8__2side_3__3side_2_4side_2_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_3__3side_2_4side_2_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_3__3side_2_4side_2_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_3__3side_2_4side_2_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_3__3side_2_4side_2_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_3__3side_2_4side_2_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_3__3side_2_4side_2_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_3__3side_2_4side_2_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_3__3side_2_4side_2_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
##### 3s3
### 4s1
ch032_1side_8__2side_3__3side_3_4side_1_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_3__3side_3_4side_1_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_3__3side_3_4side_1_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s2
ch032_1side_8__2side_3__3side_3_4side_2_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_3__3side_3_4side_2_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_3__3side_3_4side_2_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_3__3side_3_4side_2_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_3__3side_3_4side_2_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_3__3side_3_4side_2_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_3__3side_3_4side_2_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_3__3side_3_4side_2_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_3__3side_3_4side_2_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s3
ch032_1side_8__2side_3__3side_3_4side_3_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_3__3side_3_4side_3_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_3__3side_3_4side_3_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_3__3side_3_4side_3_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_3__3side_3_4side_3_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_3__3side_3_4side_3_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_3__3side_3_4side_3_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_3__3side_3_4side_3_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_3__3side_3_4side_3_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_3__3side_3_4side_3_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_3__3side_3_4side_3_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_3__3side_3_4side_3_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_3__3side_3_4side_3_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_3__3side_3_4side_3_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_3__3side_3_4side_3_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_3__3side_3_4side_3_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_3__3side_3_4side_3_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_3__3side_3_4side_3_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
######### 2s4
##### 3s1
### 4s1
ch032_1side_8__2side_4__3side_1_4side_1_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_4__3side_1_4side_1_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_4__3side_1_4side_1_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
##### 3s2
### 4s1
ch032_1side_8__2side_4__3side_2_4side_1_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_4__3side_2_4side_1_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_4__3side_2_4side_1_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s2
ch032_1side_8__2side_4__3side_2_4side_2_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_4__3side_2_4side_2_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_4__3side_2_4side_2_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_4__3side_2_4side_2_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_4__3side_2_4side_2_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_4__3side_2_4side_2_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_4__3side_2_4side_2_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_4__3side_2_4side_2_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_4__3side_2_4side_2_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
##### 3s3
### 4s1
ch032_1side_8__2side_4__3side_3_4side_1_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_4__3side_3_4side_1_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_4__3side_3_4side_1_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s2
ch032_1side_8__2side_4__3side_3_4side_2_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_4__3side_3_4side_2_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_4__3side_3_4side_2_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_4__3side_3_4side_2_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_4__3side_3_4side_2_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_4__3side_3_4side_2_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_4__3side_3_4side_2_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_4__3side_3_4side_2_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_4__3side_3_4side_2_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s3
ch032_1side_8__2side_4__3side_3_4side_3_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_4__3side_3_4side_3_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_4__3side_3_4side_3_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_4__3side_3_4side_3_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_4__3side_3_4side_3_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_4__3side_3_4side_3_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_4__3side_3_4side_3_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_4__3side_3_4side_3_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_4__3side_3_4side_3_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_4__3side_3_4side_3_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_4__3side_3_4side_3_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_4__3side_3_4side_3_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_4__3side_3_4side_3_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_4__3side_3_4side_3_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_4__3side_3_4side_3_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_4__3side_3_4side_3_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_4__3side_3_4side_3_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_4__3side_3_4side_3_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
##### 3s4
### 4s1
ch032_1side_8__2side_4__3side_4_4side_1_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_4__3side_4_4side_1_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_4__3side_4_4side_1_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s2
ch032_1side_8__2side_4__3side_4_4side_2_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_4__3side_4_4side_2_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_4__3side_4_4side_2_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_4__3side_4_4side_2_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_4__3side_4_4side_2_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_4__3side_4_4side_2_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_4__3side_4_4side_2_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_4__3side_4_4side_2_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_4__3side_4_4side_2_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s3
ch032_1side_8__2side_4__3side_4_4side_3_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_4__3side_4_4side_3_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_4__3side_4_4side_3_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_4__3side_4_4side_3_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_4__3side_4_4side_3_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_4__3side_4_4side_3_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_4__3side_4_4side_3_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_4__3side_4_4side_3_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_4__3side_4_4side_3_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_4__3side_4_4side_3_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_4__3side_4_4side_3_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_4__3side_4_4side_3_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_4__3side_4_4side_3_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_4__3side_4_4side_3_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_4__3side_4_4side_3_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_4__3side_4_4side_3_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_4__3side_4_4side_3_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_4__3side_4_4side_3_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s4
ch032_1side_8__2side_4__3side_4_4side_4_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_4__3side_4_4side_4_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_4__3side_4_4side_4_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_4__3side_4_4side_4_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_4__3side_4_4side_4_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_4__3side_4_4side_4_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_4__3side_4_4side_4_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_4__3side_4_4side_4_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_4__3side_4_4side_4_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_4__3side_4_4side_4_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_4__3side_4_4side_4_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_4__3side_4_4side_4_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_4__3side_4_4side_4_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_4__3side_4_4side_4_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_4__3side_4_4side_4_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_4__3side_4_4side_4_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_4__3side_4_4side_4_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_4__3side_4_4side_4_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_4__3side_4_4side_4_5s4_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_4__3side_4_4side_4_5s4_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_4__3side_4_4side_4_5s4_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_4__3side_4_4side_4_5s4_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_4__3side_4_4side_4_5s4_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_4__3side_4_4side_4_5s4_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_4__3side_4_4side_4_5s4_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_4__3side_4_4side_4_5s4_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_4__3side_4_4side_4_5s4_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_4__3side_4_4side_4_5s4_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_4__3side_4_4side_4_5s4_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_4__3side_4_4side_4_5s4_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
######### 2s5
##### 3s1
### 4s1
ch032_1side_8__2side_5__3side_1_4side_1_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_5__3side_1_4side_1_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_5__3side_1_4side_1_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
##### 3s2
### 4s1
ch032_1side_8__2side_5__3side_2_4side_1_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_5__3side_2_4side_1_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_5__3side_2_4side_1_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s2
ch032_1side_8__2side_5__3side_2_4side_2_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_5__3side_2_4side_2_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_5__3side_2_4side_2_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_5__3side_2_4side_2_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_5__3side_2_4side_2_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_5__3side_2_4side_2_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_5__3side_2_4side_2_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_5__3side_2_4side_2_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_5__3side_2_4side_2_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
##### 3s3
### 4s1
ch032_1side_8__2side_5__3side_3_4side_1_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_5__3side_3_4side_1_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_5__3side_3_4side_1_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s2
ch032_1side_8__2side_5__3side_3_4side_2_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_5__3side_3_4side_2_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_5__3side_3_4side_2_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_5__3side_3_4side_2_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_5__3side_3_4side_2_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_5__3side_3_4side_2_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_5__3side_3_4side_2_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_5__3side_3_4side_2_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_5__3side_3_4side_2_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s3
ch032_1side_8__2side_5__3side_3_4side_3_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_5__3side_3_4side_3_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_5__3side_3_4side_3_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_5__3side_3_4side_3_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_5__3side_3_4side_3_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_5__3side_3_4side_3_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_5__3side_3_4side_3_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_5__3side_3_4side_3_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_5__3side_3_4side_3_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_5__3side_3_4side_3_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_5__3side_3_4side_3_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_5__3side_3_4side_3_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_5__3side_3_4side_3_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_5__3side_3_4side_3_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_5__3side_3_4side_3_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_5__3side_3_4side_3_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_5__3side_3_4side_3_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_5__3side_3_4side_3_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
##### 3s4
### 4s1
ch032_1side_8__2side_5__3side_4_4side_1_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_5__3side_4_4side_1_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_5__3side_4_4side_1_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s2
ch032_1side_8__2side_5__3side_4_4side_2_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_5__3side_4_4side_2_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_5__3side_4_4side_2_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_5__3side_4_4side_2_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_5__3side_4_4side_2_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_5__3side_4_4side_2_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_5__3side_4_4side_2_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_5__3side_4_4side_2_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_5__3side_4_4side_2_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s3
ch032_1side_8__2side_5__3side_4_4side_3_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_5__3side_4_4side_3_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_5__3side_4_4side_3_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_5__3side_4_4side_3_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_5__3side_4_4side_3_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_5__3side_4_4side_3_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_5__3side_4_4side_3_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_5__3side_4_4side_3_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_5__3side_4_4side_3_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_5__3side_4_4side_3_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_5__3side_4_4side_3_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_5__3side_4_4side_3_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_5__3side_4_4side_3_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_5__3side_4_4side_3_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_5__3side_4_4side_3_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_5__3side_4_4side_3_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_5__3side_4_4side_3_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_5__3side_4_4side_3_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s4
ch032_1side_8__2side_5__3side_4_4side_4_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_5__3side_4_4side_4_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_5__3side_4_4side_4_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_5__3side_4_4side_4_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_5__3side_4_4side_4_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_5__3side_4_4side_4_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_5__3side_4_4side_4_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_5__3side_4_4side_4_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_5__3side_4_4side_4_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_5__3side_4_4side_4_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_5__3side_4_4side_4_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_5__3side_4_4side_4_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_5__3side_4_4side_4_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_5__3side_4_4side_4_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_5__3side_4_4side_4_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_5__3side_4_4side_4_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_5__3side_4_4side_4_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_5__3side_4_4side_4_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_5__3side_4_4side_4_5s4_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_5__3side_4_4side_4_5s4_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_5__3side_4_4side_4_5s4_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_5__3side_4_4side_4_5s4_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_5__3side_4_4side_4_5s4_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_5__3side_4_4side_4_5s4_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_5__3side_4_4side_4_5s4_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_5__3side_4_4side_4_5s4_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_5__3side_4_4side_4_5s4_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_5__3side_4_4side_4_5s4_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_5__3side_4_4side_4_5s4_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_5__3side_4_4side_4_5s4_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
##### 3s5
### 4s1
ch032_1side_8__2side_5__3side_5_4side_1_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_5__3side_5_4side_1_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_5__3side_5_4side_1_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s2
ch032_1side_8__2side_5__3side_5_4side_2_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_5__3side_5_4side_2_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_5__3side_5_4side_2_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_5__3side_5_4side_2_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_5__3side_5_4side_2_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_5__3side_5_4side_2_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_5__3side_5_4side_2_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_5__3side_5_4side_2_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_5__3side_5_4side_2_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s3
ch032_1side_8__2side_5__3side_5_4side_3_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_5__3side_5_4side_3_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_5__3side_5_4side_3_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_5__3side_5_4side_3_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_5__3side_5_4side_3_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_5__3side_5_4side_3_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_5__3side_5_4side_3_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_5__3side_5_4side_3_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_5__3side_5_4side_3_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_5__3side_5_4side_3_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_5__3side_5_4side_3_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_5__3side_5_4side_3_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_5__3side_5_4side_3_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_5__3side_5_4side_3_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_5__3side_5_4side_3_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_5__3side_5_4side_3_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_5__3side_5_4side_3_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_5__3side_5_4side_3_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s4
ch032_1side_8__2side_5__3side_5_4side_4_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_5__3side_5_4side_4_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_5__3side_5_4side_4_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_5__3side_5_4side_4_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_5__3side_5_4side_4_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_5__3side_5_4side_4_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_5__3side_5_4side_4_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_5__3side_5_4side_4_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_5__3side_5_4side_4_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_5__3side_5_4side_4_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_5__3side_5_4side_4_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_5__3side_5_4side_4_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_5__3side_5_4side_4_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_5__3side_5_4side_4_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_5__3side_5_4side_4_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_5__3side_5_4side_4_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_5__3side_5_4side_4_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_5__3side_5_4side_4_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_5__3side_5_4side_4_5s4_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_5__3side_5_4side_4_5s4_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_5__3side_5_4side_4_5s4_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_5__3side_5_4side_4_5s4_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_5__3side_5_4side_4_5s4_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_5__3side_5_4side_4_5s4_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_5__3side_5_4side_4_5s4_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_5__3side_5_4side_4_5s4_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_5__3side_5_4side_4_5s4_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_5__3side_5_4side_4_5s4_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_5__3side_5_4side_4_5s4_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_5__3side_5_4side_4_5s4_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s5
ch032_1side_8__2side_5__3side_5_4side_5_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_5__3side_5_4side_5_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_5__3side_5_4side_5_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_5__3side_5_4side_5_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_5__3side_5_4side_5_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_5__3side_5_4side_5_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_5__3side_5_4side_5_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_5__3side_5_4side_5_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_5__3side_5_4side_5_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_5__3side_5_4side_5_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_5__3side_5_4side_5_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_5__3side_5_4side_5_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_5__3side_5_4side_5_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_5__3side_5_4side_5_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_5__3side_5_4side_5_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_5__3side_5_4side_5_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_5__3side_5_4side_5_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_5__3side_5_4side_5_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_5__3side_5_4side_5_5s4_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_5__3side_5_4side_5_5s4_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_5__3side_5_4side_5_5s4_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_5__3side_5_4side_5_5s4_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_5__3side_5_4side_5_5s4_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_5__3side_5_4side_5_5s4_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_5__3side_5_4side_5_5s4_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_5__3side_5_4side_5_5s4_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_5__3side_5_4side_5_5s4_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_5__3side_5_4side_5_5s4_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_5__3side_5_4side_5_5s4_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_5__3side_5_4side_5_5s4_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_5__3side_5_4side_5_5s5_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_5__3side_5_4side_5_5s5_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_5__3side_5_4side_5_5s5_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_5__3side_5_4side_5_5s5_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_5__3side_5_4side_5_5s5_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_5__3side_5_4side_5_5s5_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_5__3side_5_4side_5_5s5_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_5__3side_5_4side_5_5s5_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_5__3side_5_4side_5_5s5_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_5__3side_5_4side_5_5s5_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_5__3side_5_4side_5_5s5_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_5__3side_5_4side_5_5s5_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_5__3side_5_4side_5_5s5_6s5 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_5__3side_5_4side_5_5s5_6s5, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_5__3side_5_4side_5_5s5_6s5.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
######### 2s6
##### 3s1
### 4s1
ch032_1side_8__2side_6__3side_1_4side_1_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_1_4side_1_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_1_4side_1_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
##### 3s2
### 4s1
ch032_1side_8__2side_6__3side_2_4side_1_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_2_4side_1_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_2_4side_1_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s2
ch032_1side_8__2side_6__3side_2_4side_2_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_2_4side_2_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_2_4side_2_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_6__3side_2_4side_2_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_2_4side_2_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_2_4side_2_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_6__3side_2_4side_2_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_2_4side_2_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_2_4side_2_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
##### 3s3
### 4s1
ch032_1side_8__2side_6__3side_3_4side_1_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_3_4side_1_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_3_4side_1_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s2
ch032_1side_8__2side_6__3side_3_4side_2_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_3_4side_2_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_3_4side_2_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_6__3side_3_4side_2_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_3_4side_2_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_3_4side_2_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_6__3side_3_4side_2_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_3_4side_2_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_3_4side_2_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s3
ch032_1side_8__2side_6__3side_3_4side_3_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_3_4side_3_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_3_4side_3_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_6__3side_3_4side_3_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_3_4side_3_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_3_4side_3_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_6__3side_3_4side_3_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_3_4side_3_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_3_4side_3_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_6__3side_3_4side_3_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_3_4side_3_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_3_4side_3_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_6__3side_3_4side_3_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_3_4side_3_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_3_4side_3_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_6__3side_3_4side_3_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_3_4side_3_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_3_4side_3_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
##### 3s4
### 4s1
ch032_1side_8__2side_6__3side_4_4side_1_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_4_4side_1_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_4_4side_1_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s2
ch032_1side_8__2side_6__3side_4_4side_2_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_4_4side_2_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_4_4side_2_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_6__3side_4_4side_2_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_4_4side_2_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_4_4side_2_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_6__3side_4_4side_2_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_4_4side_2_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_4_4side_2_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s3
ch032_1side_8__2side_6__3side_4_4side_3_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_4_4side_3_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_4_4side_3_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_6__3side_4_4side_3_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_4_4side_3_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_4_4side_3_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_6__3side_4_4side_3_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_4_4side_3_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_4_4side_3_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_6__3side_4_4side_3_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_4_4side_3_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_4_4side_3_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_6__3side_4_4side_3_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_4_4side_3_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_4_4side_3_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_6__3side_4_4side_3_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_4_4side_3_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_4_4side_3_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s4
ch032_1side_8__2side_6__3side_4_4side_4_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_4_4side_4_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_4_4side_4_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_6__3side_4_4side_4_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_4_4side_4_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_4_4side_4_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_6__3side_4_4side_4_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_4_4side_4_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_4_4side_4_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_6__3side_4_4side_4_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_4_4side_4_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_4_4side_4_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_6__3side_4_4side_4_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_4_4side_4_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_4_4side_4_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_6__3side_4_4side_4_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_4_4side_4_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_4_4side_4_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_6__3side_4_4side_4_5s4_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_4_4side_4_5s4_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_4_4side_4_5s4_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_6__3side_4_4side_4_5s4_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_4_4side_4_5s4_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_4_4side_4_5s4_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_6__3side_4_4side_4_5s4_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_4_4side_4_5s4_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_4_4side_4_5s4_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_6__3side_4_4side_4_5s4_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_4_4side_4_5s4_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_4_4side_4_5s4_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
##### 3s5
### 4s1
ch032_1side_8__2side_6__3side_5_4side_1_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_5_4side_1_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_5_4side_1_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s2
ch032_1side_8__2side_6__3side_5_4side_2_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_5_4side_2_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_5_4side_2_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_6__3side_5_4side_2_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_5_4side_2_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_5_4side_2_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_6__3side_5_4side_2_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_5_4side_2_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_5_4side_2_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s3
ch032_1side_8__2side_6__3side_5_4side_3_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_5_4side_3_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_5_4side_3_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_6__3side_5_4side_3_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_5_4side_3_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_5_4side_3_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_6__3side_5_4side_3_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_5_4side_3_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_5_4side_3_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_6__3side_5_4side_3_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_5_4side_3_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_5_4side_3_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_6__3side_5_4side_3_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_5_4side_3_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_5_4side_3_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_6__3side_5_4side_3_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_5_4side_3_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_5_4side_3_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s4
ch032_1side_8__2side_6__3side_5_4side_4_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_5_4side_4_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_5_4side_4_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_6__3side_5_4side_4_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_5_4side_4_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_5_4side_4_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_6__3side_5_4side_4_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_5_4side_4_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_5_4side_4_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_6__3side_5_4side_4_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_5_4side_4_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_5_4side_4_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_6__3side_5_4side_4_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_5_4side_4_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_5_4side_4_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_6__3side_5_4side_4_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_5_4side_4_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_5_4side_4_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_6__3side_5_4side_4_5s4_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_5_4side_4_5s4_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_5_4side_4_5s4_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_6__3side_5_4side_4_5s4_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_5_4side_4_5s4_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_5_4side_4_5s4_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_6__3side_5_4side_4_5s4_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_5_4side_4_5s4_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_5_4side_4_5s4_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_6__3side_5_4side_4_5s4_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_5_4side_4_5s4_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_5_4side_4_5s4_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s5
ch032_1side_8__2side_6__3side_5_4side_5_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_5_4side_5_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_5_4side_5_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_6__3side_5_4side_5_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_5_4side_5_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_5_4side_5_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_6__3side_5_4side_5_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_5_4side_5_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_5_4side_5_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_6__3side_5_4side_5_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_5_4side_5_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_5_4side_5_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_6__3side_5_4side_5_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_5_4side_5_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_5_4side_5_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_6__3side_5_4side_5_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_5_4side_5_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_5_4side_5_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_6__3side_5_4side_5_5s4_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_5_4side_5_5s4_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_5_4side_5_5s4_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_6__3side_5_4side_5_5s4_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_5_4side_5_5s4_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_5_4side_5_5s4_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_6__3side_5_4side_5_5s4_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_5_4side_5_5s4_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_5_4side_5_5s4_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_6__3side_5_4side_5_5s4_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_5_4side_5_5s4_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_5_4side_5_5s4_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_6__3side_5_4side_5_5s5_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_5_4side_5_5s5_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_5_4side_5_5s5_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_6__3side_5_4side_5_5s5_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_5_4side_5_5s5_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_5_4side_5_5s5_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_6__3side_5_4side_5_5s5_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_5_4side_5_5s5_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_5_4side_5_5s5_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_6__3side_5_4side_5_5s5_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_5_4side_5_5s5_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_5_4side_5_5s5_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_6__3side_5_4side_5_5s5_6s5 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_5_4side_5_5s5_6s5, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_5_4side_5_5s5_6s5.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
##### 3s6
### 4s1
ch032_1side_8__2side_6__3side_6_4side_1_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_6_4side_1_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_6_4side_1_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s2
ch032_1side_8__2side_6__3side_6_4side_2_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_6_4side_2_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_6_4side_2_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_6__3side_6_4side_2_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_6_4side_2_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_6_4side_2_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_6__3side_6_4side_2_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_6_4side_2_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_6_4side_2_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s3
ch032_1side_8__2side_6__3side_6_4side_3_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_6_4side_3_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_6_4side_3_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_6__3side_6_4side_3_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_6_4side_3_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_6_4side_3_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_6__3side_6_4side_3_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_6_4side_3_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_6_4side_3_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_6__3side_6_4side_3_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_6_4side_3_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_6_4side_3_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_6__3side_6_4side_3_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_6_4side_3_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_6_4side_3_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_6__3side_6_4side_3_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_6_4side_3_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_6_4side_3_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s4
ch032_1side_8__2side_6__3side_6_4side_4_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_6_4side_4_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_6_4side_4_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_6__3side_6_4side_4_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_6_4side_4_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_6_4side_4_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_6__3side_6_4side_4_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_6_4side_4_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_6_4side_4_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_6__3side_6_4side_4_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_6_4side_4_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_6_4side_4_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_6__3side_6_4side_4_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_6_4side_4_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_6_4side_4_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_6__3side_6_4side_4_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_6_4side_4_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_6_4side_4_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_6__3side_6_4side_4_5s4_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_6_4side_4_5s4_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_6_4side_4_5s4_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_6__3side_6_4side_4_5s4_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_6_4side_4_5s4_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_6_4side_4_5s4_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_6__3side_6_4side_4_5s4_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_6_4side_4_5s4_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_6_4side_4_5s4_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_6__3side_6_4side_4_5s4_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_6_4side_4_5s4_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_6_4side_4_5s4_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s5
ch032_1side_8__2side_6__3side_6_4side_5_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_6_4side_5_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_6_4side_5_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_6__3side_6_4side_5_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_6_4side_5_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_6_4side_5_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_6__3side_6_4side_5_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_6_4side_5_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_6_4side_5_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_6__3side_6_4side_5_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_6_4side_5_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_6_4side_5_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_6__3side_6_4side_5_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_6_4side_5_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_6_4side_5_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_6__3side_6_4side_5_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_6_4side_5_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_6_4side_5_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_6__3side_6_4side_5_5s4_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_6_4side_5_5s4_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_6_4side_5_5s4_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_6__3side_6_4side_5_5s4_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_6_4side_5_5s4_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_6_4side_5_5s4_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_6__3side_6_4side_5_5s4_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_6_4side_5_5s4_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_6_4side_5_5s4_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_6__3side_6_4side_5_5s4_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_6_4side_5_5s4_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_6_4side_5_5s4_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_6__3side_6_4side_5_5s5_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_6_4side_5_5s5_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_6_4side_5_5s5_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_6__3side_6_4side_5_5s5_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_6_4side_5_5s5_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_6_4side_5_5s5_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_6__3side_6_4side_5_5s5_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_6_4side_5_5s5_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_6_4side_5_5s5_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_6__3side_6_4side_5_5s5_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_6_4side_5_5s5_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_6_4side_5_5s5_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_6__3side_6_4side_5_5s5_6s5 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_6_4side_5_5s5_6s5, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_6_4side_5_5s5_6s5.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s6
ch032_1side_8__2side_6__3side_6_4side_6_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_6_4side_6_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_6_4side_6_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_6__3side_6_4side_6_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_6_4side_6_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_6_4side_6_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_6__3side_6_4side_6_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_6_4side_6_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_6_4side_6_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_6__3side_6_4side_6_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_6_4side_6_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_6_4side_6_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_6__3side_6_4side_6_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_6_4side_6_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_6_4side_6_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_6__3side_6_4side_6_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_6_4side_6_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_6_4side_6_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_6__3side_6_4side_6_5s4_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_6_4side_6_5s4_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_6_4side_6_5s4_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_6__3side_6_4side_6_5s4_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_6_4side_6_5s4_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_6_4side_6_5s4_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_6__3side_6_4side_6_5s4_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_6_4side_6_5s4_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_6_4side_6_5s4_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_6__3side_6_4side_6_5s4_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_6_4side_6_5s4_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_6_4side_6_5s4_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_6__3side_6_4side_6_5s5_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_6_4side_6_5s5_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_6_4side_6_5s5_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_6__3side_6_4side_6_5s5_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_6_4side_6_5s5_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_6_4side_6_5s5_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_6__3side_6_4side_6_5s5_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_6_4side_6_5s5_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_6_4side_6_5s5_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_6__3side_6_4side_6_5s5_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_6_4side_6_5s5_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_6_4side_6_5s5_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_6__3side_6_4side_6_5s5_6s5 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_6_4side_6_5s5_6s5, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_6_4side_6_5s5_6s5.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_6__3side_6_4side_6_5s6_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_6_4side_6_5s6_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_6_4side_6_5s6_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_6__3side_6_4side_6_5s6_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_6_4side_6_5s6_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_6_4side_6_5s6_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_6__3side_6_4side_6_5s6_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_6_4side_6_5s6_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_6_4side_6_5s6_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_6__3side_6_4side_6_5s6_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_6_4side_6_5s6_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_6_4side_6_5s6_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_6__3side_6_4side_6_5s6_6s5 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_6_4side_6_5s6_6s5, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_6_4side_6_5s6_6s5.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_6__3side_6_4side_6_5s6_6s6 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_6_4side_6_5s6_6s6, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_6_4side_6_5s6_6s6.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
######### 2s7
##### 3s1
### 4s1
ch032_1side_8__2side_7__3side_1_4side_1_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_1_4side_1_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_1_4side_1_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
##### 3s2
### 4s1
ch032_1side_8__2side_7__3side_2_4side_1_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_2_4side_1_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_2_4side_1_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s2
ch032_1side_8__2side_7__3side_2_4side_2_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_2_4side_2_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_2_4side_2_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_2_4side_2_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_2_4side_2_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_2_4side_2_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_2_4side_2_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_2_4side_2_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_2_4side_2_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
##### 3s3
### 4s1
ch032_1side_8__2side_7__3side_3_4side_1_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_3_4side_1_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_3_4side_1_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s2
ch032_1side_8__2side_7__3side_3_4side_2_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_3_4side_2_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_3_4side_2_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_3_4side_2_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_3_4side_2_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_3_4side_2_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_3_4side_2_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_3_4side_2_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_3_4side_2_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s3
ch032_1side_8__2side_7__3side_3_4side_3_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_3_4side_3_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_3_4side_3_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_3_4side_3_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_3_4side_3_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_3_4side_3_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_3_4side_3_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_3_4side_3_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_3_4side_3_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_3_4side_3_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_3_4side_3_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_3_4side_3_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_3_4side_3_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_3_4side_3_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_3_4side_3_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_3_4side_3_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_3_4side_3_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_3_4side_3_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
##### 3s4
### 4s1
ch032_1side_8__2side_7__3side_4_4side_1_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_4_4side_1_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_4_4side_1_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s2
ch032_1side_8__2side_7__3side_4_4side_2_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_4_4side_2_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_4_4side_2_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_4_4side_2_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_4_4side_2_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_4_4side_2_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_4_4side_2_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_4_4side_2_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_4_4side_2_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s3
ch032_1side_8__2side_7__3side_4_4side_3_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_4_4side_3_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_4_4side_3_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_4_4side_3_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_4_4side_3_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_4_4side_3_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_4_4side_3_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_4_4side_3_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_4_4side_3_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_4_4side_3_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_4_4side_3_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_4_4side_3_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_4_4side_3_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_4_4side_3_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_4_4side_3_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_4_4side_3_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_4_4side_3_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_4_4side_3_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s4
ch032_1side_8__2side_7__3side_4_4side_4_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_4_4side_4_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_4_4side_4_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_4_4side_4_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_4_4side_4_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_4_4side_4_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_4_4side_4_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_4_4side_4_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_4_4side_4_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_4_4side_4_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_4_4side_4_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_4_4side_4_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_4_4side_4_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_4_4side_4_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_4_4side_4_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_4_4side_4_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_4_4side_4_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_4_4side_4_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_4_4side_4_5s4_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_4_4side_4_5s4_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_4_4side_4_5s4_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_4_4side_4_5s4_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_4_4side_4_5s4_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_4_4side_4_5s4_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_4_4side_4_5s4_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_4_4side_4_5s4_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_4_4side_4_5s4_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_4_4side_4_5s4_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_4_4side_4_5s4_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_4_4side_4_5s4_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
##### 3s5
### 4s1
ch032_1side_8__2side_7__3side_5_4side_1_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_5_4side_1_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_5_4side_1_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s2
ch032_1side_8__2side_7__3side_5_4side_2_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_5_4side_2_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_5_4side_2_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_5_4side_2_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_5_4side_2_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_5_4side_2_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_5_4side_2_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_5_4side_2_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_5_4side_2_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s3
ch032_1side_8__2side_7__3side_5_4side_3_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_5_4side_3_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_5_4side_3_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_5_4side_3_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_5_4side_3_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_5_4side_3_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_5_4side_3_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_5_4side_3_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_5_4side_3_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_5_4side_3_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_5_4side_3_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_5_4side_3_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_5_4side_3_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_5_4side_3_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_5_4side_3_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_5_4side_3_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_5_4side_3_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_5_4side_3_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s4
ch032_1side_8__2side_7__3side_5_4side_4_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_5_4side_4_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_5_4side_4_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_5_4side_4_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_5_4side_4_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_5_4side_4_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_5_4side_4_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_5_4side_4_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_5_4side_4_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_5_4side_4_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_5_4side_4_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_5_4side_4_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_5_4side_4_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_5_4side_4_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_5_4side_4_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_5_4side_4_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_5_4side_4_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_5_4side_4_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_5_4side_4_5s4_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_5_4side_4_5s4_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_5_4side_4_5s4_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_5_4side_4_5s4_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_5_4side_4_5s4_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_5_4side_4_5s4_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_5_4side_4_5s4_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_5_4side_4_5s4_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_5_4side_4_5s4_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_5_4side_4_5s4_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_5_4side_4_5s4_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_5_4side_4_5s4_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s5
ch032_1side_8__2side_7__3side_5_4side_5_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_5_4side_5_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_5_4side_5_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_5_4side_5_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_5_4side_5_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_5_4side_5_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_5_4side_5_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_5_4side_5_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_5_4side_5_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_5_4side_5_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_5_4side_5_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_5_4side_5_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_5_4side_5_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_5_4side_5_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_5_4side_5_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_5_4side_5_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_5_4side_5_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_5_4side_5_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_5_4side_5_5s4_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_5_4side_5_5s4_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_5_4side_5_5s4_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_5_4side_5_5s4_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_5_4side_5_5s4_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_5_4side_5_5s4_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_5_4side_5_5s4_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_5_4side_5_5s4_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_5_4side_5_5s4_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_5_4side_5_5s4_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_5_4side_5_5s4_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_5_4side_5_5s4_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_5_4side_5_5s5_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_5_4side_5_5s5_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_5_4side_5_5s5_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_5_4side_5_5s5_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_5_4side_5_5s5_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_5_4side_5_5s5_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_5_4side_5_5s5_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_5_4side_5_5s5_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_5_4side_5_5s5_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_5_4side_5_5s5_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_5_4side_5_5s5_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_5_4side_5_5s5_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_5_4side_5_5s5_6s5 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_5_4side_5_5s5_6s5, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_5_4side_5_5s5_6s5.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
##### 3s6
### 4s1
ch032_1side_8__2side_7__3side_6_4side_1_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_6_4side_1_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_6_4side_1_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s2
ch032_1side_8__2side_7__3side_6_4side_2_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_6_4side_2_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_6_4side_2_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_6_4side_2_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_6_4side_2_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_6_4side_2_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_6_4side_2_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_6_4side_2_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_6_4side_2_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s3
ch032_1side_8__2side_7__3side_6_4side_3_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_6_4side_3_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_6_4side_3_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_6_4side_3_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_6_4side_3_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_6_4side_3_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_6_4side_3_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_6_4side_3_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_6_4side_3_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_6_4side_3_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_6_4side_3_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_6_4side_3_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_6_4side_3_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_6_4side_3_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_6_4side_3_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_6_4side_3_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_6_4side_3_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_6_4side_3_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s4
ch032_1side_8__2side_7__3side_6_4side_4_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_6_4side_4_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_6_4side_4_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_6_4side_4_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_6_4side_4_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_6_4side_4_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_6_4side_4_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_6_4side_4_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_6_4side_4_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_6_4side_4_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_6_4side_4_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_6_4side_4_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_6_4side_4_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_6_4side_4_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_6_4side_4_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_6_4side_4_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_6_4side_4_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_6_4side_4_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_6_4side_4_5s4_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_6_4side_4_5s4_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_6_4side_4_5s4_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_6_4side_4_5s4_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_6_4side_4_5s4_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_6_4side_4_5s4_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_6_4side_4_5s4_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_6_4side_4_5s4_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_6_4side_4_5s4_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_6_4side_4_5s4_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_6_4side_4_5s4_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_6_4side_4_5s4_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s5
ch032_1side_8__2side_7__3side_6_4side_5_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_6_4side_5_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_6_4side_5_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_6_4side_5_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_6_4side_5_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_6_4side_5_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_6_4side_5_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_6_4side_5_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_6_4side_5_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_6_4side_5_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_6_4side_5_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_6_4side_5_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_6_4side_5_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_6_4side_5_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_6_4side_5_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_6_4side_5_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_6_4side_5_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_6_4side_5_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_6_4side_5_5s4_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_6_4side_5_5s4_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_6_4side_5_5s4_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_6_4side_5_5s4_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_6_4side_5_5s4_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_6_4side_5_5s4_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_6_4side_5_5s4_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_6_4side_5_5s4_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_6_4side_5_5s4_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_6_4side_5_5s4_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_6_4side_5_5s4_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_6_4side_5_5s4_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_6_4side_5_5s5_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_6_4side_5_5s5_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_6_4side_5_5s5_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_6_4side_5_5s5_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_6_4side_5_5s5_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_6_4side_5_5s5_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_6_4side_5_5s5_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_6_4side_5_5s5_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_6_4side_5_5s5_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_6_4side_5_5s5_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_6_4side_5_5s5_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_6_4side_5_5s5_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_6_4side_5_5s5_6s5 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_6_4side_5_5s5_6s5, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_6_4side_5_5s5_6s5.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s6
ch032_1side_8__2side_7__3side_6_4side_6_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_6_4side_6_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_6_4side_6_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_6_4side_6_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_6_4side_6_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_6_4side_6_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_6_4side_6_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_6_4side_6_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_6_4side_6_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_6_4side_6_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_6_4side_6_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_6_4side_6_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_6_4side_6_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_6_4side_6_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_6_4side_6_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_6_4side_6_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_6_4side_6_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_6_4side_6_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_6_4side_6_5s4_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_6_4side_6_5s4_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_6_4side_6_5s4_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_6_4side_6_5s4_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_6_4side_6_5s4_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_6_4side_6_5s4_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_6_4side_6_5s4_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_6_4side_6_5s4_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_6_4side_6_5s4_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_6_4side_6_5s4_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_6_4side_6_5s4_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_6_4side_6_5s4_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_6_4side_6_5s5_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_6_4side_6_5s5_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_6_4side_6_5s5_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_6_4side_6_5s5_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_6_4side_6_5s5_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_6_4side_6_5s5_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_6_4side_6_5s5_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_6_4side_6_5s5_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_6_4side_6_5s5_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_6_4side_6_5s5_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_6_4side_6_5s5_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_6_4side_6_5s5_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_6_4side_6_5s5_6s5 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_6_4side_6_5s5_6s5, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_6_4side_6_5s5_6s5.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_6_4side_6_5s6_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_6_4side_6_5s6_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_6_4side_6_5s6_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_6_4side_6_5s6_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_6_4side_6_5s6_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_6_4side_6_5s6_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_6_4side_6_5s6_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_6_4side_6_5s6_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_6_4side_6_5s6_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_6_4side_6_5s6_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_6_4side_6_5s6_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_6_4side_6_5s6_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_6_4side_6_5s6_6s5 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_6_4side_6_5s6_6s5, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_6_4side_6_5s6_6s5.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_6_4side_6_5s6_6s6 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_6_4side_6_5s6_6s6, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_6_4side_6_5s6_6s6.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
##### 3s7
### 4s1
ch032_1side_8__2side_7__3side_7_4side_1_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_7_4side_1_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_7_4side_1_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s2
ch032_1side_8__2side_7__3side_7_4side_2_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_7_4side_2_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_7_4side_2_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_7_4side_2_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_7_4side_2_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_7_4side_2_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_7_4side_2_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_7_4side_2_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_7_4side_2_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s3
ch032_1side_8__2side_7__3side_7_4side_3_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_7_4side_3_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_7_4side_3_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_7_4side_3_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_7_4side_3_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_7_4side_3_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_7_4side_3_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_7_4side_3_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_7_4side_3_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_7_4side_3_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_7_4side_3_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_7_4side_3_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_7_4side_3_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_7_4side_3_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_7_4side_3_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_7_4side_3_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_7_4side_3_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_7_4side_3_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s4
ch032_1side_8__2side_7__3side_7_4side_4_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_7_4side_4_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_7_4side_4_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_7_4side_4_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_7_4side_4_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_7_4side_4_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_7_4side_4_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_7_4side_4_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_7_4side_4_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_7_4side_4_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_7_4side_4_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_7_4side_4_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_7_4side_4_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_7_4side_4_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_7_4side_4_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_7_4side_4_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_7_4side_4_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_7_4side_4_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_7_4side_4_5s4_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_7_4side_4_5s4_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_7_4side_4_5s4_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_7_4side_4_5s4_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_7_4side_4_5s4_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_7_4side_4_5s4_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_7_4side_4_5s4_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_7_4side_4_5s4_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_7_4side_4_5s4_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_7_4side_4_5s4_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_7_4side_4_5s4_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_7_4side_4_5s4_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s5
ch032_1side_8__2side_7__3side_7_4side_5_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_7_4side_5_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_7_4side_5_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_7_4side_5_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_7_4side_5_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_7_4side_5_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_7_4side_5_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_7_4side_5_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_7_4side_5_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_7_4side_5_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_7_4side_5_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_7_4side_5_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_7_4side_5_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_7_4side_5_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_7_4side_5_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_7_4side_5_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_7_4side_5_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_7_4side_5_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_7_4side_5_5s4_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_7_4side_5_5s4_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_7_4side_5_5s4_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_7_4side_5_5s4_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_7_4side_5_5s4_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_7_4side_5_5s4_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_7_4side_5_5s4_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_7_4side_5_5s4_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_7_4side_5_5s4_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_7_4side_5_5s4_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_7_4side_5_5s4_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_7_4side_5_5s4_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_7_4side_5_5s5_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_7_4side_5_5s5_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_7_4side_5_5s5_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_7_4side_5_5s5_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_7_4side_5_5s5_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_7_4side_5_5s5_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_7_4side_5_5s5_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_7_4side_5_5s5_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_7_4side_5_5s5_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_7_4side_5_5s5_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_7_4side_5_5s5_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_7_4side_5_5s5_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_7_4side_5_5s5_6s5 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_7_4side_5_5s5_6s5, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_7_4side_5_5s5_6s5.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s6
ch032_1side_8__2side_7__3side_7_4side_6_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_7_4side_6_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_7_4side_6_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_7_4side_6_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_7_4side_6_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_7_4side_6_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_7_4side_6_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_7_4side_6_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_7_4side_6_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_7_4side_6_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_7_4side_6_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_7_4side_6_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_7_4side_6_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_7_4side_6_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_7_4side_6_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_7_4side_6_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_7_4side_6_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_7_4side_6_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_7_4side_6_5s4_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_7_4side_6_5s4_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_7_4side_6_5s4_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_7_4side_6_5s4_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_7_4side_6_5s4_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_7_4side_6_5s4_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_7_4side_6_5s4_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_7_4side_6_5s4_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_7_4side_6_5s4_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_7_4side_6_5s4_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_7_4side_6_5s4_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_7_4side_6_5s4_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_7_4side_6_5s5_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_7_4side_6_5s5_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_7_4side_6_5s5_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_7_4side_6_5s5_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_7_4side_6_5s5_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_7_4side_6_5s5_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_7_4side_6_5s5_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_7_4side_6_5s5_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_7_4side_6_5s5_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_7_4side_6_5s5_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_7_4side_6_5s5_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_7_4side_6_5s5_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_7_4side_6_5s5_6s5 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_7_4side_6_5s5_6s5, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_7_4side_6_5s5_6s5.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_7_4side_6_5s6_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_7_4side_6_5s6_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_7_4side_6_5s6_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_7_4side_6_5s6_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_7_4side_6_5s6_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_7_4side_6_5s6_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_7_4side_6_5s6_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_7_4side_6_5s6_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_7_4side_6_5s6_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_7_4side_6_5s6_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_7_4side_6_5s6_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_7_4side_6_5s6_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_7_4side_6_5s6_6s5 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_7_4side_6_5s6_6s5, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_7_4side_6_5s6_6s5.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_7_4side_6_5s6_6s6 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_7_4side_6_5s6_6s6, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_7_4side_6_5s6_6s6.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s7
ch032_1side_8__2side_7__3side_7_4side_7_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_7_4side_7_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_7_4side_7_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_7_4side_7_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_7_4side_7_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_7_4side_7_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_7_4side_7_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_7_4side_7_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_7_4side_7_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_7_4side_7_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_7_4side_7_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_7_4side_7_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_7_4side_7_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_7_4side_7_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_7_4side_7_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_7_4side_7_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_7_4side_7_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_7_4side_7_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_7_4side_7_5s4_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_7_4side_7_5s4_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_7_4side_7_5s4_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_7_4side_7_5s4_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_7_4side_7_5s4_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_7_4side_7_5s4_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_7_4side_7_5s4_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_7_4side_7_5s4_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_7_4side_7_5s4_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_7_4side_7_5s4_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_7_4side_7_5s4_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_7_4side_7_5s4_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_7_4side_7_5s5_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_7_4side_7_5s5_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_7_4side_7_5s5_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_7_4side_7_5s5_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_7_4side_7_5s5_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_7_4side_7_5s5_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_7_4side_7_5s5_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_7_4side_7_5s5_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_7_4side_7_5s5_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_7_4side_7_5s5_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_7_4side_7_5s5_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_7_4side_7_5s5_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_7_4side_7_5s5_6s5 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_7_4side_7_5s5_6s5, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_7_4side_7_5s5_6s5.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_7_4side_7_5s6_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_7_4side_7_5s6_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_7_4side_7_5s6_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_7_4side_7_5s6_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_7_4side_7_5s6_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_7_4side_7_5s6_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_7_4side_7_5s6_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_7_4side_7_5s6_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_7_4side_7_5s6_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_7_4side_7_5s6_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_7_4side_7_5s6_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_7_4side_7_5s6_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_7_4side_7_5s6_6s5 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_7_4side_7_5s6_6s5, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_7_4side_7_5s6_6s5.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_7_4side_7_5s6_6s6 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_7_4side_7_5s6_6s6, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_7_4side_7_5s6_6s6.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_7_4side_7_5s7_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_7_4side_7_5s7_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_7_4side_7_5s7_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_7_4side_7_5s7_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_7_4side_7_5s7_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_7_4side_7_5s7_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_7_4side_7_5s7_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_7_4side_7_5s7_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_7_4side_7_5s7_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_7_4side_7_5s7_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_7_4side_7_5s7_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_7_4side_7_5s7_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_7_4side_7_5s7_6s5 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_7_4side_7_5s7_6s5, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_7_4side_7_5s7_6s5.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_7_4side_7_5s7_6s6 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_7_4side_7_5s7_6s6, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_7_4side_7_5s7_6s6.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_7_4side_7_5s7_6s7 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_7_4side_7_5s7_6s7, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_7_4side_7_5s7_6s7.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
######### 2s8
##### 3s1
### 4s1
ch032_1side_8__2side_8__3side_1_4side_1_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_1_4side_1_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_1_4side_1_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
##### 3s2
### 4s1
ch032_1side_8__2side_8__3side_2_4side_1_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_2_4side_1_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_2_4side_1_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s2
ch032_1side_8__2side_8__3side_2_4side_2_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_2_4side_2_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_2_4side_2_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_2_4side_2_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_2_4side_2_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_2_4side_2_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_2_4side_2_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_2_4side_2_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_2_4side_2_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
##### 3s3
### 4s1
ch032_1side_8__2side_8__3side_3_4side_1_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_3_4side_1_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_3_4side_1_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s2
ch032_1side_8__2side_8__3side_3_4side_2_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_3_4side_2_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_3_4side_2_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_3_4side_2_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_3_4side_2_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_3_4side_2_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_3_4side_2_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_3_4side_2_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_3_4side_2_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s3
ch032_1side_8__2side_8__3side_3_4side_3_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_3_4side_3_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_3_4side_3_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_3_4side_3_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_3_4side_3_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_3_4side_3_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_3_4side_3_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_3_4side_3_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_3_4side_3_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_3_4side_3_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_3_4side_3_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_3_4side_3_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_3_4side_3_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_3_4side_3_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_3_4side_3_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_3_4side_3_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_3_4side_3_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_3_4side_3_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
##### 3s4
### 4s1
ch032_1side_8__2side_8__3side_4_4side_1_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_4_4side_1_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_4_4side_1_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s2
ch032_1side_8__2side_8__3side_4_4side_2_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_4_4side_2_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_4_4side_2_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_4_4side_2_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_4_4side_2_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_4_4side_2_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_4_4side_2_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_4_4side_2_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_4_4side_2_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s3
ch032_1side_8__2side_8__3side_4_4side_3_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_4_4side_3_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_4_4side_3_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_4_4side_3_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_4_4side_3_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_4_4side_3_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_4_4side_3_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_4_4side_3_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_4_4side_3_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_4_4side_3_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_4_4side_3_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_4_4side_3_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_4_4side_3_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_4_4side_3_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_4_4side_3_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_4_4side_3_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_4_4side_3_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_4_4side_3_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s4
ch032_1side_8__2side_8__3side_4_4side_4_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_4_4side_4_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_4_4side_4_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_4_4side_4_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_4_4side_4_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_4_4side_4_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_4_4side_4_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_4_4side_4_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_4_4side_4_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_4_4side_4_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_4_4side_4_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_4_4side_4_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_4_4side_4_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_4_4side_4_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_4_4side_4_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_4_4side_4_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_4_4side_4_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_4_4side_4_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_4_4side_4_5s4_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_4_4side_4_5s4_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_4_4side_4_5s4_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_4_4side_4_5s4_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_4_4side_4_5s4_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_4_4side_4_5s4_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_4_4side_4_5s4_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_4_4side_4_5s4_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_4_4side_4_5s4_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_4_4side_4_5s4_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_4_4side_4_5s4_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_4_4side_4_5s4_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
##### 3s5
### 4s1
ch032_1side_8__2side_8__3side_5_4side_1_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_5_4side_1_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_5_4side_1_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s2
ch032_1side_8__2side_8__3side_5_4side_2_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_5_4side_2_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_5_4side_2_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_5_4side_2_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_5_4side_2_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_5_4side_2_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_5_4side_2_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_5_4side_2_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_5_4side_2_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s3
ch032_1side_8__2side_8__3side_5_4side_3_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_5_4side_3_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_5_4side_3_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_5_4side_3_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_5_4side_3_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_5_4side_3_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_5_4side_3_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_5_4side_3_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_5_4side_3_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_5_4side_3_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_5_4side_3_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_5_4side_3_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_5_4side_3_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_5_4side_3_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_5_4side_3_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_5_4side_3_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_5_4side_3_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_5_4side_3_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s4
ch032_1side_8__2side_8__3side_5_4side_4_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_5_4side_4_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_5_4side_4_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_5_4side_4_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_5_4side_4_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_5_4side_4_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_5_4side_4_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_5_4side_4_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_5_4side_4_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_5_4side_4_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_5_4side_4_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_5_4side_4_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_5_4side_4_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_5_4side_4_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_5_4side_4_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_5_4side_4_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_5_4side_4_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_5_4side_4_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_5_4side_4_5s4_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_5_4side_4_5s4_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_5_4side_4_5s4_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_5_4side_4_5s4_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_5_4side_4_5s4_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_5_4side_4_5s4_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_5_4side_4_5s4_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_5_4side_4_5s4_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_5_4side_4_5s4_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_5_4side_4_5s4_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_5_4side_4_5s4_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_5_4side_4_5s4_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s5
ch032_1side_8__2side_8__3side_5_4side_5_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_5_4side_5_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_5_4side_5_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_5_4side_5_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_5_4side_5_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_5_4side_5_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_5_4side_5_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_5_4side_5_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_5_4side_5_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_5_4side_5_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_5_4side_5_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_5_4side_5_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_5_4side_5_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_5_4side_5_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_5_4side_5_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_5_4side_5_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_5_4side_5_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_5_4side_5_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_5_4side_5_5s4_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_5_4side_5_5s4_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_5_4side_5_5s4_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_5_4side_5_5s4_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_5_4side_5_5s4_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_5_4side_5_5s4_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_5_4side_5_5s4_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_5_4side_5_5s4_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_5_4side_5_5s4_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_5_4side_5_5s4_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_5_4side_5_5s4_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_5_4side_5_5s4_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_5_4side_5_5s5_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_5_4side_5_5s5_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_5_4side_5_5s5_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_5_4side_5_5s5_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_5_4side_5_5s5_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_5_4side_5_5s5_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_5_4side_5_5s5_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_5_4side_5_5s5_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_5_4side_5_5s5_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_5_4side_5_5s5_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_5_4side_5_5s5_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_5_4side_5_5s5_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_5_4side_5_5s5_6s5 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_5_4side_5_5s5_6s5, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_5_4side_5_5s5_6s5.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
##### 3s6
### 4s1
ch032_1side_8__2side_8__3side_6_4side_1_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_6_4side_1_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_6_4side_1_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s2
ch032_1side_8__2side_8__3side_6_4side_2_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_6_4side_2_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_6_4side_2_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_6_4side_2_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_6_4side_2_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_6_4side_2_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_6_4side_2_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_6_4side_2_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_6_4side_2_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s3
ch032_1side_8__2side_8__3side_6_4side_3_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_6_4side_3_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_6_4side_3_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_6_4side_3_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_6_4side_3_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_6_4side_3_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_6_4side_3_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_6_4side_3_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_6_4side_3_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_6_4side_3_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_6_4side_3_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_6_4side_3_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_6_4side_3_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_6_4side_3_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_6_4side_3_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_6_4side_3_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_6_4side_3_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_6_4side_3_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s4
ch032_1side_8__2side_8__3side_6_4side_4_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_6_4side_4_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_6_4side_4_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_6_4side_4_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_6_4side_4_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_6_4side_4_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_6_4side_4_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_6_4side_4_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_6_4side_4_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_6_4side_4_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_6_4side_4_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_6_4side_4_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_6_4side_4_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_6_4side_4_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_6_4side_4_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_6_4side_4_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_6_4side_4_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_6_4side_4_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_6_4side_4_5s4_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_6_4side_4_5s4_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_6_4side_4_5s4_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_6_4side_4_5s4_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_6_4side_4_5s4_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_6_4side_4_5s4_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_6_4side_4_5s4_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_6_4side_4_5s4_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_6_4side_4_5s4_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_6_4side_4_5s4_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_6_4side_4_5s4_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_6_4side_4_5s4_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s5
ch032_1side_8__2side_8__3side_6_4side_5_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_6_4side_5_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_6_4side_5_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_6_4side_5_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_6_4side_5_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_6_4side_5_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_6_4side_5_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_6_4side_5_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_6_4side_5_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_6_4side_5_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_6_4side_5_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_6_4side_5_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_6_4side_5_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_6_4side_5_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_6_4side_5_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_6_4side_5_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_6_4side_5_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_6_4side_5_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_6_4side_5_5s4_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_6_4side_5_5s4_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_6_4side_5_5s4_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_6_4side_5_5s4_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_6_4side_5_5s4_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_6_4side_5_5s4_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_6_4side_5_5s4_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_6_4side_5_5s4_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_6_4side_5_5s4_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_6_4side_5_5s4_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_6_4side_5_5s4_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_6_4side_5_5s4_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_6_4side_5_5s5_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_6_4side_5_5s5_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_6_4side_5_5s5_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_6_4side_5_5s5_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_6_4side_5_5s5_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_6_4side_5_5s5_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_6_4side_5_5s5_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_6_4side_5_5s5_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_6_4side_5_5s5_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_6_4side_5_5s5_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_6_4side_5_5s5_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_6_4side_5_5s5_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_6_4side_5_5s5_6s5 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_6_4side_5_5s5_6s5, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_6_4side_5_5s5_6s5.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s6
ch032_1side_8__2side_8__3side_6_4side_6_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_6_4side_6_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_6_4side_6_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_6_4side_6_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_6_4side_6_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_6_4side_6_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_6_4side_6_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_6_4side_6_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_6_4side_6_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_6_4side_6_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_6_4side_6_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_6_4side_6_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_6_4side_6_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_6_4side_6_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_6_4side_6_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_6_4side_6_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_6_4side_6_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_6_4side_6_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_6_4side_6_5s4_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_6_4side_6_5s4_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_6_4side_6_5s4_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_6_4side_6_5s4_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_6_4side_6_5s4_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_6_4side_6_5s4_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_6_4side_6_5s4_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_6_4side_6_5s4_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_6_4side_6_5s4_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_6_4side_6_5s4_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_6_4side_6_5s4_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_6_4side_6_5s4_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_6_4side_6_5s5_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_6_4side_6_5s5_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_6_4side_6_5s5_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_6_4side_6_5s5_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_6_4side_6_5s5_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_6_4side_6_5s5_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_6_4side_6_5s5_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_6_4side_6_5s5_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_6_4side_6_5s5_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_6_4side_6_5s5_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_6_4side_6_5s5_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_6_4side_6_5s5_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_6_4side_6_5s5_6s5 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_6_4side_6_5s5_6s5, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_6_4side_6_5s5_6s5.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_6_4side_6_5s6_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_6_4side_6_5s6_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_6_4side_6_5s6_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_6_4side_6_5s6_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_6_4side_6_5s6_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_6_4side_6_5s6_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_6_4side_6_5s6_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_6_4side_6_5s6_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_6_4side_6_5s6_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_6_4side_6_5s6_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_6_4side_6_5s6_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_6_4side_6_5s6_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_6_4side_6_5s6_6s5 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_6_4side_6_5s6_6s5, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_6_4side_6_5s6_6s5.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_6_4side_6_5s6_6s6 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_6_4side_6_5s6_6s6, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_6_4side_6_5s6_6s6.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
##### 3s7
### 4s1
ch032_1side_8__2side_8__3side_7_4side_1_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_7_4side_1_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_7_4side_1_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s2
ch032_1side_8__2side_8__3side_7_4side_2_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_7_4side_2_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_7_4side_2_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_7_4side_2_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_7_4side_2_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_7_4side_2_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_7_4side_2_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_7_4side_2_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_7_4side_2_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s3
ch032_1side_8__2side_8__3side_7_4side_3_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_7_4side_3_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_7_4side_3_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_7_4side_3_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_7_4side_3_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_7_4side_3_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_7_4side_3_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_7_4side_3_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_7_4side_3_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_7_4side_3_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_7_4side_3_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_7_4side_3_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_7_4side_3_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_7_4side_3_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_7_4side_3_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_7_4side_3_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_7_4side_3_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_7_4side_3_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s4
ch032_1side_8__2side_8__3side_7_4side_4_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_7_4side_4_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_7_4side_4_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_7_4side_4_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_7_4side_4_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_7_4side_4_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_7_4side_4_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_7_4side_4_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_7_4side_4_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_7_4side_4_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_7_4side_4_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_7_4side_4_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_7_4side_4_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_7_4side_4_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_7_4side_4_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_7_4side_4_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_7_4side_4_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_7_4side_4_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_7_4side_4_5s4_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_7_4side_4_5s4_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_7_4side_4_5s4_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_7_4side_4_5s4_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_7_4side_4_5s4_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_7_4side_4_5s4_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_7_4side_4_5s4_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_7_4side_4_5s4_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_7_4side_4_5s4_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_7_4side_4_5s4_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_7_4side_4_5s4_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_7_4side_4_5s4_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s5
ch032_1side_8__2side_8__3side_7_4side_5_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_7_4side_5_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_7_4side_5_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_7_4side_5_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_7_4side_5_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_7_4side_5_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_7_4side_5_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_7_4side_5_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_7_4side_5_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_7_4side_5_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_7_4side_5_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_7_4side_5_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_7_4side_5_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_7_4side_5_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_7_4side_5_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_7_4side_5_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_7_4side_5_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_7_4side_5_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_7_4side_5_5s4_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_7_4side_5_5s4_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_7_4side_5_5s4_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_7_4side_5_5s4_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_7_4side_5_5s4_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_7_4side_5_5s4_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_7_4side_5_5s4_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_7_4side_5_5s4_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_7_4side_5_5s4_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_7_4side_5_5s4_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_7_4side_5_5s4_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_7_4side_5_5s4_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_7_4side_5_5s5_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_7_4side_5_5s5_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_7_4side_5_5s5_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_7_4side_5_5s5_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_7_4side_5_5s5_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_7_4side_5_5s5_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_7_4side_5_5s5_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_7_4side_5_5s5_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_7_4side_5_5s5_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_7_4side_5_5s5_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_7_4side_5_5s5_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_7_4side_5_5s5_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_7_4side_5_5s5_6s5 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_7_4side_5_5s5_6s5, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_7_4side_5_5s5_6s5.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s6
ch032_1side_8__2side_8__3side_7_4side_6_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_7_4side_6_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_7_4side_6_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_7_4side_6_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_7_4side_6_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_7_4side_6_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_7_4side_6_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_7_4side_6_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_7_4side_6_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_7_4side_6_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_7_4side_6_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_7_4side_6_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_7_4side_6_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_7_4side_6_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_7_4side_6_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_7_4side_6_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_7_4side_6_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_7_4side_6_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_7_4side_6_5s4_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_7_4side_6_5s4_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_7_4side_6_5s4_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_7_4side_6_5s4_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_7_4side_6_5s4_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_7_4side_6_5s4_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_7_4side_6_5s4_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_7_4side_6_5s4_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_7_4side_6_5s4_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_7_4side_6_5s4_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_7_4side_6_5s4_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_7_4side_6_5s4_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_7_4side_6_5s5_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_7_4side_6_5s5_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_7_4side_6_5s5_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_7_4side_6_5s5_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_7_4side_6_5s5_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_7_4side_6_5s5_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_7_4side_6_5s5_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_7_4side_6_5s5_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_7_4side_6_5s5_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_7_4side_6_5s5_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_7_4side_6_5s5_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_7_4side_6_5s5_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_7_4side_6_5s5_6s5 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_7_4side_6_5s5_6s5, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_7_4side_6_5s5_6s5.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_7_4side_6_5s6_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_7_4side_6_5s6_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_7_4side_6_5s6_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_7_4side_6_5s6_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_7_4side_6_5s6_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_7_4side_6_5s6_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_7_4side_6_5s6_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_7_4side_6_5s6_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_7_4side_6_5s6_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_7_4side_6_5s6_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_7_4side_6_5s6_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_7_4side_6_5s6_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_7_4side_6_5s6_6s5 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_7_4side_6_5s6_6s5, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_7_4side_6_5s6_6s5.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_7_4side_6_5s6_6s6 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_7_4side_6_5s6_6s6, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_7_4side_6_5s6_6s6.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s7
ch032_1side_8__2side_8__3side_7_4side_7_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_7_4side_7_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_7_4side_7_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_7_4side_7_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_7_4side_7_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_7_4side_7_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_7_4side_7_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_7_4side_7_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_7_4side_7_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_7_4side_7_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_7_4side_7_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_7_4side_7_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_7_4side_7_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_7_4side_7_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_7_4side_7_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_7_4side_7_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_7_4side_7_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_7_4side_7_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_7_4side_7_5s4_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_7_4side_7_5s4_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_7_4side_7_5s4_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_7_4side_7_5s4_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_7_4side_7_5s4_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_7_4side_7_5s4_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_7_4side_7_5s4_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_7_4side_7_5s4_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_7_4side_7_5s4_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_7_4side_7_5s4_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_7_4side_7_5s4_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_7_4side_7_5s4_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_7_4side_7_5s5_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_7_4side_7_5s5_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_7_4side_7_5s5_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_7_4side_7_5s5_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_7_4side_7_5s5_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_7_4side_7_5s5_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_7_4side_7_5s5_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_7_4side_7_5s5_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_7_4side_7_5s5_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_7_4side_7_5s5_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_7_4side_7_5s5_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_7_4side_7_5s5_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_7_4side_7_5s5_6s5 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_7_4side_7_5s5_6s5, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_7_4side_7_5s5_6s5.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_7_4side_7_5s6_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_7_4side_7_5s6_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_7_4side_7_5s6_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_7_4side_7_5s6_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_7_4side_7_5s6_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_7_4side_7_5s6_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_7_4side_7_5s6_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_7_4side_7_5s6_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_7_4side_7_5s6_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_7_4side_7_5s6_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_7_4side_7_5s6_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_7_4side_7_5s6_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_7_4side_7_5s6_6s5 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_7_4side_7_5s6_6s5, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_7_4side_7_5s6_6s5.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_7_4side_7_5s6_6s6 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_7_4side_7_5s6_6s6, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_7_4side_7_5s6_6s6.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_7_4side_7_5s7_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_7_4side_7_5s7_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_7_4side_7_5s7_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_7_4side_7_5s7_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_7_4side_7_5s7_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_7_4side_7_5s7_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_7_4side_7_5s7_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_7_4side_7_5s7_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_7_4side_7_5s7_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_7_4side_7_5s7_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_7_4side_7_5s7_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_7_4side_7_5s7_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_7_4side_7_5s7_6s5 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_7_4side_7_5s7_6s5, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_7_4side_7_5s7_6s5.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_7_4side_7_5s7_6s6 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_7_4side_7_5s7_6s6, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_7_4side_7_5s7_6s6.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_7_4side_7_5s7_6s7 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_7_4side_7_5s7_6s7, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_7_4side_7_5s7_6s7.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
##### 3s8
### 4s1
ch032_1side_8__2side_8__3side_8_4side_1_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_1_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_8_4side_1_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s2
ch032_1side_8__2side_8__3side_8_4side_2_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_2_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_8_4side_2_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_8_4side_2_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_2_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_8_4side_2_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_8_4side_2_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_2_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_8_4side_2_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s3
ch032_1side_8__2side_8__3side_8_4side_3_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_3_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_8_4side_3_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_8_4side_3_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_3_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_8_4side_3_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_8_4side_3_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_3_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_8_4side_3_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_8_4side_3_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_3_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_8_4side_3_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_8_4side_3_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_3_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_8_4side_3_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_8_4side_3_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_3_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_8_4side_3_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s4
ch032_1side_8__2side_8__3side_8_4side_4_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_4_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_8_4side_4_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_8_4side_4_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_4_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_8_4side_4_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_8_4side_4_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_4_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_8_4side_4_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_8_4side_4_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_4_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_8_4side_4_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_8_4side_4_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_4_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_8_4side_4_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_8_4side_4_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_4_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_8_4side_4_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_8_4side_4_5s4_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_4_5s4_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_8_4side_4_5s4_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_8_4side_4_5s4_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_4_5s4_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_8_4side_4_5s4_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_8_4side_4_5s4_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_4_5s4_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_8_4side_4_5s4_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_8_4side_4_5s4_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_4_5s4_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_8_4side_4_5s4_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s5
ch032_1side_8__2side_8__3side_8_4side_5_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_5_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_8_4side_5_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_8_4side_5_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_5_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_8_4side_5_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_8_4side_5_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_5_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_8_4side_5_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_8_4side_5_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_5_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_8_4side_5_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_8_4side_5_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_5_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_8_4side_5_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_8_4side_5_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_5_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_8_4side_5_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_8_4side_5_5s4_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_5_5s4_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_8_4side_5_5s4_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_8_4side_5_5s4_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_5_5s4_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_8_4side_5_5s4_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_8_4side_5_5s4_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_5_5s4_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_8_4side_5_5s4_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_8_4side_5_5s4_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_5_5s4_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_8_4side_5_5s4_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_8_4side_5_5s5_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_5_5s5_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_8_4side_5_5s5_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_8_4side_5_5s5_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_5_5s5_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_8_4side_5_5s5_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_8_4side_5_5s5_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_5_5s5_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_8_4side_5_5s5_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_8_4side_5_5s5_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_5_5s5_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_8_4side_5_5s5_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_8_4side_5_5s5_6s5 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_5_5s5_6s5, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_8_4side_5_5s5_6s5.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s6
ch032_1side_8__2side_8__3side_8_4side_6_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_6_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_8_4side_6_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_8_4side_6_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_6_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_8_4side_6_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_8_4side_6_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_6_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_8_4side_6_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_8_4side_6_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_6_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_8_4side_6_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_8_4side_6_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_6_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_8_4side_6_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_8_4side_6_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_6_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_8_4side_6_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_8_4side_6_5s4_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_6_5s4_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_8_4side_6_5s4_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_8_4side_6_5s4_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_6_5s4_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_8_4side_6_5s4_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_8_4side_6_5s4_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_6_5s4_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_8_4side_6_5s4_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_8_4side_6_5s4_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_6_5s4_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_8_4side_6_5s4_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_8_4side_6_5s5_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_6_5s5_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_8_4side_6_5s5_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_8_4side_6_5s5_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_6_5s5_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_8_4side_6_5s5_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_8_4side_6_5s5_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_6_5s5_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_8_4side_6_5s5_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_8_4side_6_5s5_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_6_5s5_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_8_4side_6_5s5_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_8_4side_6_5s5_6s5 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_6_5s5_6s5, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_8_4side_6_5s5_6s5.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_8_4side_6_5s6_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_6_5s6_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_8_4side_6_5s6_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_8_4side_6_5s6_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_6_5s6_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_8_4side_6_5s6_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_8_4side_6_5s6_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_6_5s6_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_8_4side_6_5s6_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_8_4side_6_5s6_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_6_5s6_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_8_4side_6_5s6_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_8_4side_6_5s6_6s5 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_6_5s6_6s5, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_8_4side_6_5s6_6s5.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_8_4side_6_5s6_6s6 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_6_5s6_6s6, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_8_4side_6_5s6_6s6.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s7
ch032_1side_8__2side_8__3side_8_4side_7_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_7_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_8_4side_7_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_8_4side_7_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_7_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_8_4side_7_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_8_4side_7_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_7_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_8_4side_7_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_8_4side_7_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_7_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_8_4side_7_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_8_4side_7_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_7_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_8_4side_7_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_8_4side_7_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_7_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_8_4side_7_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_8_4side_7_5s4_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_7_5s4_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_8_4side_7_5s4_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_8_4side_7_5s4_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_7_5s4_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_8_4side_7_5s4_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_8_4side_7_5s4_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_7_5s4_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_8_4side_7_5s4_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_8_4side_7_5s4_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_7_5s4_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_8_4side_7_5s4_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_8_4side_7_5s5_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_7_5s5_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_8_4side_7_5s5_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_8_4side_7_5s5_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_7_5s5_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_8_4side_7_5s5_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_8_4side_7_5s5_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_7_5s5_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_8_4side_7_5s5_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_8_4side_7_5s5_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_7_5s5_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_8_4side_7_5s5_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_8_4side_7_5s5_6s5 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_7_5s5_6s5, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_8_4side_7_5s5_6s5.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_8_4side_7_5s6_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_7_5s6_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_8_4side_7_5s6_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_8_4side_7_5s6_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_7_5s6_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_8_4side_7_5s6_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_8_4side_7_5s6_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_7_5s6_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_8_4side_7_5s6_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_8_4side_7_5s6_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_7_5s6_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_8_4side_7_5s6_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_8_4side_7_5s6_6s5 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_7_5s6_6s5, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_8_4side_7_5s6_6s5.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_8_4side_7_5s6_6s6 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_7_5s6_6s6, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_8_4side_7_5s6_6s6.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_8_4side_7_5s7_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_7_5s7_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_8_4side_7_5s7_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_8_4side_7_5s7_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_7_5s7_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_8_4side_7_5s7_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_8_4side_7_5s7_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_7_5s7_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_8_4side_7_5s7_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_8_4side_7_5s7_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_7_5s7_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_8_4side_7_5s7_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_8_4side_7_5s7_6s5 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_7_5s7_6s5, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_8_4side_7_5s7_6s5.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_8_4side_7_5s7_6s6 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_7_5s7_6s6, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_8_4side_7_5s7_6s6.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_8_4side_7_5s7_6s7 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_7_5s7_6s7, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_8_4side_7_5s7_6s7.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s8
ch032_1side_8__2side_8__3side_8_4side_8_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_8_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_8_4side_8_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_8_4side_8_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_8_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_8_4side_8_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_8_4side_8_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_8_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_8_4side_8_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_8_4side_8_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_8_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_8_4side_8_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_8_4side_8_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_8_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_8_4side_8_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_8_4side_8_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_8_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_8_4side_8_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_8_4side_8_5s4_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_8_5s4_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_8_4side_8_5s4_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_8_4side_8_5s4_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_8_5s4_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_8_4side_8_5s4_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_8_4side_8_5s4_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_8_5s4_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_8_4side_8_5s4_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_8_4side_8_5s4_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_8_5s4_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_8_4side_8_5s4_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_8_4side_8_5s5_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_8_5s5_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_8_4side_8_5s5_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_8_4side_8_5s5_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_8_5s5_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_8_4side_8_5s5_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_8_4side_8_5s5_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_8_5s5_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_8_4side_8_5s5_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_8_4side_8_5s5_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_8_5s5_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_8_4side_8_5s5_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_8_4side_8_5s5_6s5 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_8_5s5_6s5, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_8_4side_8_5s5_6s5.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_8_4side_8_5s6_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_8_5s6_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_8_4side_8_5s6_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_8_4side_8_5s6_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_8_5s6_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_8_4side_8_5s6_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_8_4side_8_5s6_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_8_5s6_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_8_4side_8_5s6_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_8_4side_8_5s6_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_8_5s6_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_8_4side_8_5s6_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_8_4side_8_5s6_6s5 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_8_5s6_6s5, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_8_4side_8_5s6_6s5.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_8_4side_8_5s6_6s6 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_8_5s6_6s6, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_8_4side_8_5s6_6s6.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_8_4side_8_5s7_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_8_5s7_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_8_4side_8_5s7_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_8_4side_8_5s7_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_8_5s7_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_8_4side_8_5s7_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_8_4side_8_5s7_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_8_5s7_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_8_4side_8_5s7_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_8_4side_8_5s7_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_8_5s7_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_8_4side_8_5s7_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_8_4side_8_5s7_6s5 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_8_5s7_6s5, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_8_4side_8_5s7_6s5.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_8_4side_8_5s7_6s6 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_8_5s7_6s6, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_8_4side_8_5s7_6s6.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_8_4side_8_5s7_6s7 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_8_5s7_6s7, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_8_4side_8_5s7_6s7.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_8_4side_8_5s8_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_8_5s8_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_8_4side_8_5s8_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_8_4side_8_5s8_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_8_5s8_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_8_4side_8_5s8_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_8_4side_8_5s8_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_8_5s8_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_8_4side_8_5s8_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_8_4side_8_5s8_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_8_5s8_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_8_4side_8_5s8_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_8_4side_8_5s8_6s5 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_8_5s8_6s5, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_8_4side_8_5s8_6s5.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_8_4side_8_5s8_6s6 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_8_5s8_6s6, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_8_4side_8_5s8_6s6.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_8_4side_8_5s8_6s7 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_8_5s8_6s7, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_8_4side_8_5s8_6s7.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_8_4side_8_5s8_6s8 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_8_5s8_6s8, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_8_4side_8_5s8_6s8.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
#############################################################
if(__name__ == "__main__"):
print("build exps cost time:", time.time() - start_time)
if len(sys.argv) < 2:
############################################################################################################
### 直接按 F5 或打 python step10_b1_exp_obj_load_and_train_and_test.py，後面沒有接東西喔！才不會跑到下面給 step10_b_subprocss.py 用的程式碼~~~
ch032_1side_1__2side_1__3side_1_4side_1_5s1_6s1.build().run()
# print('no argument')
sys.exit()
### 以下是給 step10_b_subprocess.py 用的，相當於cmd打 python step10_b1_exp_obj_load_and_train_and_test.py 某個exp.build().run()
eval(sys.argv[1])
| [
"s89334roy@yahoo.com.tw"
] | s89334roy@yahoo.com.tw |
b3de1c46413851b6c79b57c8ef17df6ce6239f08 | 7af268d7aa98473612afcb21fdeaf9ae4bfd6a5c | /electra_spacing/dataset/dataset.py | 4d540cb9b136e5ae3c9025ed54b60e7f1586c678 | [
"Apache-2.0"
] | permissive | seujung/electra_spacing | 3db19460a64cb9acde3d55e5ff72ba2a905ea3a1 | 7f7711567fff68284546e081f4236647dba6b5ac | refs/heads/master | 2022-11-28T14:04:08.971835 | 2020-08-14T13:48:41 | 2020-08-14T13:48:41 | 287,406,375 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 3,087 | py | import torch
import dill
import pandas as pd
from random import random
from operator import itemgetter
from electra_spacing.tokenizer import get_tokenizer
special_tokens = ['[PAD]', '[UNK]', '[CLS]', '[SEP]', '[MASK]']
class SpacingDataset(torch.utils.data.Dataset):
def __init__(
self,
file_path,
tokenizer=None,
seq_len=128,
padding_idx=0,
use_padding = True,
threshold = 0.6):
if tokenizer is None:
self.tokenizer = get_tokenizer()
else:
self.tokenizer = tokenizer
self.threshold = threshold
self.seq_len = seq_len
self.pad_token_id = self.tokenizer.pad_token_id
self.use_padding = use_padding
if 'txt' in file_path:
self.input_text = []
lines = open(file_path, encoding="utf-8").readlines()
for l in lines:
l = l.replace('\n', '').strip()
if len(l) >= minimum_size:
self.input_text.append(l)
elif 'tsv' in file_path:
lines = pd.read_csv(file_path, sep='\t', header=None)
self.input_text = lines[0].tolist()
self.len = len(self.input_text)
def tokenize(self, text: str, padding: bool = True, return_tensor: bool = True):
tokens = self.tokenizer.encode(text)
##consider single token only
segment_ids = [0] * len(tokens)
if type(tokens) == list:
tokens = torch.tensor(tokens)
if padding:
if len(tokens) >= self.seq_len:
tokens = tokens[:self.seq_len]
segment_ids = torch.tensor(segment_ids[:self.seq_len])
else:
pad_tensor = torch.tensor(
[self.pad_token_id] * (self.seq_len - len(tokens))
)
tokens = torch.cat((tokens, pad_tensor), 0)
segment_ids = torch.tensor([0] * self.seq_len)
if return_tensor:
return (tokens, segment_ids)
else:
return (tokens.numpy(), segment_ids.numpy())
def __getitem__(self, idx):
sentence = self.input_text[idx]
new_sentence = ''
for char in sentence:
if random() < self.threshold and char == ' ':
pass
else:
new_sentence += char
(tokens, token_type_ids) = self.tokenize(text=new_sentence)
(labels, _) = self.tokenize(text=sentence)
labels_weight = [0] * self.seq_len
label_token = []
for l in labels:
label_token.append(self.tokenizer.ids_to_tokens[l.item()])
for i, token in enumerate(label_token):
if token not in special_tokens and '##' not in token:
labels_weight[i] = 1
labels_weight = torch.tensor(labels_weight)
return (tokens, token_type_ids, labels, labels_weight)
def __len__(self):
return self.len
| [
"digit82@gmail.com"
] | digit82@gmail.com |
1ac1bf0d486a318d12379426563fee9a8f6f22d6 | fe85138c949c6198184c591780831fd2e183a24a | /Address Book.py | 251c32fc6f328cd1f9352bc08e897b68bbe90efc | [] | no_license | valeri1383/Personal-Python-Projects | e98f6b7171298def019db4e28f6d176a709615cc | b7db81cb44668f549a7fd15de84c0cb23654ac3d | refs/heads/main | 2023-05-26T09:02:24.260700 | 2023-05-22T14:40:28 | 2023-05-22T14:40:28 | 337,518,678 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 2,253 | py | from tkinter import *
root = Tk()
root.geometry('400x400')
root.configure(bg='cyan')
root.resizable(1, 1)
root.title('Address Book')
contact_list = [
['John Smith', '07567374343'],
['Terry Adams', '07569984343'],
['Allen Gibson', '07564474743'],
['Grant Foster', '07567396843'],
['Hall Grey', '07567746343']
]
Name = StringVar()
Number = StringVar()
frame = Frame(root)
frame.pack(side=RIGHT)
scroll = Scrollbar(frame, orient=VERTICAL)
select = Listbox(frame,bg='light goldenrod', yscrollcommand=scroll.set, width=30, height=33)
scroll.configure(command=select.yview)
scroll.pack(side=RIGHT, fill=Y)
select.pack(side=LEFT, fill=BOTH, expand=1)
def Selected():
return int(select.curselection()[0])
def AddContact():
contact_list.append([Name.get(), Number.get()])
Select_set()
def EDIT():
contact_list[Selected()] = [Name.get(), Number.get()]
Select_set()
def DELETE():
del contact_list[Selected()]
Select_set()
def VIEW():
NAME, PHONE = contact_list[Selected()]
Name.set(NAME)
Number.set(PHONE)
def EXIT():
root.destroy()
def RESET():
Name.set('')
Number.set('')
def Select_set():
contact_list.sort()
select.delete(0, END)
for name, phone in contact_list:
select.insert(END, name)
Select_set()
Label(root, text='NAME', font='arial 15 bold', bg='cyan').pack()
Entry(root, font=20, bg='light yellow', textvariable=Name).pack()
Label(root, text='PHONE NO.', font='arial 15 bold', bg='cyan').pack()
Entry(root, font=20,bg='light yellow', textvariable=Number).pack()
Button(root, text='ADD', width=7, font='arial 15 bold', bg='SlateGray4', command=AddContact).pack()
Button(root, text='EDIT', width=7, font='arial 15 bold', bg='SlateGray4', command=EDIT).pack()
Button(root, text="DELETE", width=7, font='arial 15 bold', bg='SlateGray4', command=DELETE).pack()
Button(root, text="VIEW", width=7, font='arial 15 bold', bg='SlateGray4', command=VIEW).pack()
Button(root, text="EXIT", width=7, font='arial 15 bold', bg='tomato', command=EXIT).pack()
Button(root, text="RESET", width=7, font='arial 15 bold', bg='SlateGray4', command=RESET).pack()
mainloop()
| [
"noreply@github.com"
] | valeri1383.noreply@github.com |
f0ee93a4074c8da518c1450c274a30b1cdd2ac23 | fde3f15c0640d542d13d448947933defa7e3b1df | /src/_test/test_login_fail.py | 81965bdc1fc2ce631552b0ae289693e00617031e | [
"MIT"
] | permissive | zxdxjtu/cloudComputingProject | 8c67d559eed520d39a9ad0afbe626613b55ba334 | f5b1f9254e3795f1ee64eec7234643d4a98e5996 | refs/heads/master | 2021-05-04T05:55:25.829756 | 2016-10-16T17:04:13 | 2016-10-16T17:04:13 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 559 | py | import os, sys
sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
from app import app
from blueprint_test_case import BaseTestCase
class FlaskTestCase(BaseTestCase):
# Ensure login behaves correctly with incorrect credentials
def test_incorrect_login(self):
response = self.client.post(
'/login',
data=dict(username="wrong!", password="wrong!"),
)
self.assertEqual(response.status_code, 200)
self.assertIn(u'The username and password does not match!', response.data)
| [
"rxie25@gmail.com"
] | rxie25@gmail.com |
6bf1fcb69afde705f23184d4247094c7518ea8a8 | 21a29ab436a0f48c9968da788ba6ee2c293a8ad1 | /scripts/pdts/pdts_dropA_t0.py | 9c54822e5a81b3a036abddd99ded99f9eceb23eb | [
"MIT"
] | permissive | bazhiyong/chempropBayes | 3520a8cc17d4fe556869229a0de3855538fa5440 | 88d660398a772705804568b671b3614c636505aa | refs/heads/master | 2023-03-19T19:15:13.219611 | 2020-12-30T14:50:59 | 2020-12-30T14:50:59 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,638 | py | import os
import torch
# checks
print('working directory is:')
print(os.getcwd())
print('is CUDA available?')
print(torch.cuda.is_available())
# imports
from chemprop.args import TrainArgs
from chemprop.train.pdts import pdts
# instantiate args class and load from dict
args = TrainArgs()
args.from_dict({
'dataset_type': 'regression',
'data_path': '/home/willlamb/chempropBayes/data/qm9.csv'
})
##################### ARGS #####################
# architecture
args.hidden_size = 500
args.depth = 5
args.ffn_num_layers = 3
args.activation = 'ReLU'
args.ffn_hidden_size = args.hidden_size
args.features_path = None
args.features_generator = None
args.atom_messages = False
args.undirected = False
args.bias = False
# data
args.max_data_size = 100000
args.data_seeds = [0,1,2,3,4]
args.split_type = 'random'
args.split_sizes = (0.05, 0.95)
# metric
args.metric = 'mae'
# run seeds
args.pytorch_seeds = [0,1,2,3,4]
################################################
# names and directories
args.results_dir = '/home/willlamb/results_pdts/dropA_thom'
args.save_dir = '/home/willlamb/checkpoints_pdts/dropA_thom'
args.checkpoint_path = '/home/willlamb/checkpoints_pdts/dropA_thom'
args.wandb_proj = 'lanterne_dropA'
args.wandb_name = 'dropA_thom'
args.thompson = True
### dropR ###
args.samples = 50
args.pdts = True
args.pdts_batches = 30
args.epochs_init_map = 500
args.epochs = 200
args.lr = 1e-4
args.init_log_noise = -2
args.weight_decay = 0.01
args.dropout_mpnn = 0.1
args.dropout_ffn = 0.1
args.test_dropout = True
################################################
# run
results = pdts(args, model_idx = 0)
| [
"willlamb@beaker.cs.ucl.ac.uk"
] | willlamb@beaker.cs.ucl.ac.uk |
9f04a9500f8175d8ed755a33ec11a015c460d1ad | dd32fef3e19dfde746c2a0e67a460470397c98fd | /scripts/converter.py | 2a430feebda1cf9b031ea53b1aaf8243c439c9ad | [
"MIT",
"Apache-2.0"
] | permissive | sujiongming-git/captcha_trainer | bfffeb9845d178d6c0fade75ac927cb2a00c7877 | fa14ebd42aad91cd97724c7bc275d79b1008facc | refs/heads/master | 2023-06-18T11:42:20.226556 | 2021-07-17T14:11:30 | 2021-07-17T14:11:30 | 295,317,327 | 0 | 0 | Apache-2.0 | 2020-09-14T05:53:18 | 2020-09-14T05:53:17 | null | UTF-8 | Python | false | false | 816 | py | import re
import os
import hashlib
# 训练集路径
root = "/Users/jm.su/Documents/code/captcha_trainer/scripts/taiwan-post/"
all_files = os.listdir(root)
for file in all_files:
old_path = os.path.join(root, file)
print(old_path)
if "-" in file:
file = file.split("-", 1)[-1]
print(file)
# continue
# 已被修改过忽略
if len(file.split(".")[0]) > 32:
continue
# 采用标注_文件md5码.图片后缀 进行命名
with open(old_path, "rb") as f:
_id = hashlib.md5(f.read()).hexdigest()
new_path = os.path.join(root, file.replace(".", "_{}.".format(_id)))
# 重复标签的时候会出现形如：abcd (1).jpg 这种形式的文件名
new_path = re.sub(" \(\d+\)", "", new_path)
print(new_path)
os.rename(old_path, new_path) | [
"jm.su@aftership.com"
] | jm.su@aftership.com |
37d110627122f58399dd78c0806effabc6ac07e5 | c2014b0a4ee80f39d4f3c4578341312ea963e615 | /Assignment2/RFassignment2Predict.py | 26468bea0c38693611a0420f1d0f91ae7a05dc0c | [
"MIT"
] | permissive | MaximilianMihoc/MachineLearning | a65fb5d783076a76db3127d1cd661e01cb4fc79b | bee03a4beea58bc500c890229536536cf78c8f43 | refs/heads/master | 2020-04-01T18:28:49.371455 | 2018-10-21T14:36:32 | 2018-10-21T14:36:32 | 153,495,054 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 3,109 | py | from pandas import DataFrame
from sklearn import preprocessing
from sklearn.feature_extraction import DictVectorizer
import numpy as np
import pandas as pd
import csv
from sklearn import tree
from sklearn import cross_validation
from sklearn.cross_validation import train_test_split
from sklearn.metrics import confusion_matrix
from sklearn.metrics import accuracy_score
from sklearn.ensemble import RandomForestClassifier
#get train data from file
# read feature names from the featurenames file and place them in a list.
featureNames = [line.rstrip() for line in open('./data/featurenames.txt', 'r')]
# remove empty elements from the list, if any
featureNames = [f for f in featureNames if f != '']
#print(featureNames)
Location = r'./data/trainingset.txt'
campaign_df = pd.read_csv(Location, names=featureNames)
####################################
# Extract Target Feature
###################################
# **sci-kit** expects that the descriptive features and target features
# are passed to the model training functions as separate parameters.
# so the first step in data preprocessins is to extract the
# target feature values into a separate variable
targetLabels = campaign_df['target']
#print(targetLabels[0])
####################################
# Extract Numeric Descriptive Features
###################################
# We want to do some preprocessing on the categorical data so
# We first extract the numeric_features into a separate data structure
numeric_features = ['age','balance','day','duration','campaign','pdays','previous']
numeric_dfs = campaign_df[numeric_features]
numeric_dfs.head()
####################################
# Extract Categorical Descriptive Features
###################################
cat_dfs = campaign_df.drop(numeric_features + ['target'] + ['id'] ,axis=1)
####################################
# Remove missing values and apply one-hot encoding
###################################
#handle missing values
#If the data has missing values, they will become NaNs in the Numpy arrays generated by the vectorizor so lets get rid of them
cat_dfs.replace('?','NA')
cat_dfs.fillna( 'NA', inplace = True )
#transpose into array of dictionaries (one dict per instance) of feature:level pairs
cat_dfs = cat_dfs.T.to_dict().values()
#convert to numeric encoding
vectorizer = DictVectorizer( sparse = False )
vec_cat_dfs = vectorizer.fit_transform(cat_dfs)
encoding_dictionary = vectorizer.vocabulary_
########################################################
# Merge Categorical and Numeric Descriptive Features
########################################################
train_dfs = np.hstack((numeric_dfs.as_matrix(), vec_cat_dfs ))
#################################################################################
decTreeModel2 = RandomForestClassifier(criterion='entropy')
#Split the data: 60% training : 40% test set
instances_train, instances_test, target_train, target_test = cross_validation.train_test_split(train_dfs, targetLabels, test_size=0.4, random_state=0)
#fit the model using just the test set
decTreeModel2.fit(instances_train, target_train) | [
"max.mihoc@gmail.com"
] | max.mihoc@gmail.com |
9a9b63a8daca2426c5e7f92f421d90edd8b68eb5 | ca0556d3dc6fb6b92e194c4ff0a979619e7be0e4 | /I2c7SegmentLed.py | 8baadc4d53fbc559a2402174d86b34a339e609fe | [] | no_license | dcityorg/i2c-7-segment-led-library-raspberrypi | 2216c46eb945faf1dab2c15f1a123208d0e0a889 | 62910936e6a43d6585925f493cf72b2d609e9ffe | refs/heads/master | 2021-06-13T20:01:15.431573 | 2021-03-10T19:47:50 | 2021-03-10T19:47:50 | 146,506,898 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 13,329 | py | # -*- coding: utf-8 -*-
'''
I2c7SegmentLed.py - class library for using 7 Segment LEDs
Written by: Gary Muhonen gary@dcity.org
Versions
1.0.0 - 7/31/2016
Original Release.
1.0.1 - 9/1/2018
Transfer to GM, and some minor changes
Short Description:
These files provide a software library and demo program for the Raspberry Pi.
The library files provide useful functions to make it easy
to communicate with 7 Segment LEDs
that use the I2C communication protocol. The demo
program shows the usage of the functions in the library.
The 7 Segment LED must connect to the I2C bus using a AMS AS1115 controller chip.
A backback board with the AMS AS1115 chip is available and details are in the link below.
https://www.dcity.org/portfolio/i2c-7-segment-led-library/
This link has details including:
* software library installation for use with Arduino, Particle and Raspberry Pi boards
* list of functions available in these libraries
* a demo program (which shows the usage of most library functions)
* info on 7 segment LED displays that work with this software
* hardware design for a backpack board for 7 segment LEDs, available on github
* info on backpack “bare” pc boards available from OSH Park.
License Information: https://www.dcity.org/license-information/
Notes:
1. You must enable I2C on your Raspberry Pi board (see your particular operating system documentation).
On Raspian: Menu...Preferences...Raspberry Pi Configuration...Interfaces...Enable I2C
2. This software was tested on a RASPBERRY PI 3 MODEL B, running Rasbian and Python 3.5.2
'''
import smbus # import the i2c library
from time import sleep # import the sleep functions
i2c = smbus.SMBus(1) # create an i2c object for writing/reading from i2c
# create a class for the i2c 7 segment led displays that use the AS1115 chip
class I2c7SegmentLed(object):
# Control Register Addresses in the AS1115
# Digit 0-7 are at adresses 1-8
REG_DECODE_MODE = 0x09 # sets which digits respond to data that is decoded (like BCD or HEX)
REG_GLOBAL_INTENSITY = 0X0a # set the brightness for all digits... only bottom 4 bits are used for 16 brightness values
REG_SCAN_LIMIT = 0x0b # controls which digits are turned on
REG_SHUTDOWN = 0x0c # used to shutdown the display and save power
REG_FEATURE = 0x0e # register that contains key features
REG_DISPLAY_TEST_MODE = 0x0f # used for test mode
REG_DIGIT01_INTENSITY = 0x10
REG_DIGIT23_INTENSITY = 0x11
REG_DIGIT45_INTENSITY = 0x12
REG_DIGIT67_INTENSIGY = 0x13
REG_DIAGNOSTIC_DIGIT0 = 0x14
REG_DIAGNOSTIC_DIGIT1 = 0x15
REG_DIAGNOSTIC_DIGIT2 = 0x16
REG_DIAGNOSTIC_DIGIT3 = 0x17
REG_DIAGNOSTIC_DIGIT4 = 0x18
REG_DIAGNOSTIC_DIGIT5 = 0x19
REG_DIAGNOSTIC_DIGIT6 = 0x1a
REG_DIAGNOSTIC_DIGIT7 = 0x1b
REG_KEYA = 0x1c
REG_KEYB = 0x1d
REG_SELF_ADDRESSING = 0x2d # register used to set the chip to read jumpers to determine it's own i2c address
# Constants that can be written to the control registers above
# REG_DECODE_MODE values (type of decode is set in REG_FEATURE)
REG_DECODE_MODE_NO_DIGITS = 0x00 # no decoding
REG_DECODE_MODE_ALL_DIGITS = 0xFF # used for BCD or HEX decoding, bit 0 turns on digit 0 for decoding, etc
# REG_SCAN_LIMIT values (how many digits are displayed)
REG_SCAN_LIMIT_1 = 0x00 # if there is only 1 digit in the display
REG_SCAN_LIMIT_2 = 0x01
REG_SCAN_LIMIT_3 = 0x02
REG_SCAN_LIMIT_4 = 0x03
REG_SCAN_LIMIT_5 = 0x04
REG_SCAN_LIMIT_6 = 0x05
REG_SCAN_LIMIT_7 = 0x06
REG_SCAN_LIMIT_8 = 0x07 # if there are 8 digits in the display
# REG_SHUTDOWN values
REG_SHUTDOWN_SHUTDOWN_AND_RESET = 0x00 # shutdown chip and reset the feature register
REG_SHUTDOWN_SHUTDOWN = 0x80 # shutdown chip and don't reset the feature register
REG_SHUTDOWN_NORMAL_AND_RESET = 0x01 # set normal mode and reset the feature register
REG_SHUTDOWN_NORMAL = 0X81 # set normal mode and don't reset the feature register...this is the normal running values
# REG_SELF_ADDRESSING values, for determinine the chip's i2c address
REG_SELF_ADDRESSING_FACTORY_ADDRESS = 0x00 # for using factory set i2c address = 0x00
REG_SELF_ADDRESSING_USER_ADDRESS = 0x01 # for using jumpers to determine i2c address
# REG_FEATURE bit values
REG_FEATURE_EXTERNAL_CLOCK = 0X01 # set bit if using an external clock
REG_FEATURE_RESET = 0x02 # set bit to reset all registers
REG_FEATURE_HEX = 0x04 # clear this bit for BCD decoding, set for HEX decoding
REG_FEATURE_BLINK = 0x10 # set bit to enable blinking of display
REG_FEATURE_BLINK_FREQUENCY = 0x020 # set bit for 2 second blinking, clear for 1 second blinking
REG_FEATURE_SYNC = 0x40 # set bit for multiple device blinking
REG_FEATURE_BLINK_START = 0x80 # set bit to start blinking when display turns on, clear to start blinking when display turns off
DECIMAL_POINT_MASK = 0x80 # bit to control the decimal point
# segment values for the LED for all 128 ASCII characters
# the first value is for ASCII character 0, then 1, etc
# each byte contains the 7 LED segments and the decimal point, arranged as (from MSB to LSB)
# DP G F E D C B A (DP, middle, top left, btm left, btm, btm right, top right, top)
# if a bit is a '1', then that segment of the led will be turned on.
LedSegments = [
0b01111110,0b00110000,0b01101101,0b01111001,0b00110011,0b01011011,0b01011111,0b01110010, # Ascii decimal:0-7 hex:00-07
0b01111110,0b01111011,0b01111101,0b00011111,0b00001101,0b00111101,0b01101111,0b01000111, # Ascii decimal:8-15 hex:08-0F
0b01111110,0b00000110,0b01101101,0b01001111,0b00010111,0b01011011,0b01111011,0b00011110, # Ascii decimal:16-23 hex:10-17
0b01111111,0b01011111,0b01101111,0b01110011,0b01100001,0b01100111,0b01111101,0b00111001, # Ascii decimal:24-31 hex:18-1f
0b00000000,0b00110000,0b00100010,0b01000001,0b01001001,0b00100101,0b00110001,0b00000010, # Ascii decimal:32-39 hex:20-27
0b01001010,0b01101000,0b01000010,0b00000111,0b00000100,0b00000001,0b00000000,0b00100101, # Ascii decimal:40-47 hex:28-2F
0b01111110,0b00110000,0b01101101,0b01111001,0b00110011,0b01011011,0b01011111,0b01110010, # Ascii decimal:48-55 hex:30-37
0b01111111,0b01111011,0b01001000,0b01011000,0b01000011,0b00001001,0b01100001,0b01100101, # Ascii decimal:56-63 hex:38-3F
0b01111101,0b01110111,0b01111111,0b01001110,0b00111101,0b01001111,0b01000111,0b01011110, # Ascii decimal:64-71 hex:40-47
0b00110111,0b00000110,0b00111100,0b01010111,0b00001110,0b01010100,0b01110110,0b01111110, # Ascii decimal:72-79 hex:48-4F
0b01100111,0b01101011,0b01100110,0b01011011,0b00001111,0b00111110,0b00111110,0b00101010, # Ascii decimal:80-87 hex:50-57
0b00110111,0b00111011,0b01101101,0b00011110,0b00010011,0b00110110,0b01100010,0b00001000, # Ascii decimal:88-95 hex:58-5F
0b00100000,0b01111101,0b00011111,0b00001101,0b00111101,0b01101111,0b01000111,0b01111011, # Ascii decimal:96-103 hex:60-67
0b00010111,0b00000100,0b00011000,0b01010111,0b00000110,0b00010100,0b00010101,0b00011101, # Ascii decimal:104-111 hex:68-6F
0b01100111,0b01110011,0b00000101,0b01011011,0b00001111,0b00011100,0b00011100,0b00010100, # Ascii decimal:112-119 hex:70-77
0b00110111,0b00111011,0b01101101,0b01001011,0b01010101,0b01100011,0b01000000,0b00000000 # Ascii decimal:120-127 hex:78-7F
]
# constructor to create I2c7SegmentLed object, and initialize the LED module
def __init__(self, i2cAddress, digits):
self._digits = digits
self._i2cAddress = i2cAddress
self._feature = 0
self._segments = [0,0,0,0,0,0,0,0,0]
self._cursorPosition = 1
# Start talking to the AS1115 chip, as it will be at i2c address 0 initially (upon powerup)
# Power down the AS1115 chip
try:
i2c.write_byte_data(0x00, I2c7SegmentLed.REG_SHUTDOWN, I2c7SegmentLed.REG_SHUTDOWN_NORMAL)
except:
pass # an error just means that the i2c led display has already had it's address set
sleep(0.020)
# tell all AS1115 chips to use their hardware jumpered i2c address
try:
i2c.write_byte_data(0x00, I2c7SegmentLed.REG_SELF_ADDRESSING, I2c7SegmentLed.REG_SELF_ADDRESSING_USER_ADDRESS)
except:
pass # an error just means that the i2c led display has already had it's address set
sleep(0.020)
# power up and reset the AS1115 chip and the feature register
self.setRegister(I2c7SegmentLed.REG_SHUTDOWN, I2c7SegmentLed.REG_SHUTDOWN_NORMAL_AND_RESET)
# display all digits, full brightness, decoded using the hex font
self.setBrightness(15)
self.setRegister(I2c7SegmentLed.REG_SCAN_LIMIT,self._digits-1) # set number of digits in use
self.setRegister(I2c7SegmentLed.REG_DECODE_MODE,I2c7SegmentLed.REG_DECODE_MODE_NO_DIGITS) # we won't use their decoder
self._feature = 0 # starting value for the _feature register
self.setRegister(I2c7SegmentLed.REG_FEATURE,self._feature) # initialize the feature register
self.clear() # clear the display
# write value to register
def setRegister(self, reg, value):
try:
i2c.write_byte_data(self._i2cAddress, reg, value)
except:
print("Error writing to i2c 7 Segment Led at Address 0x%02x" %self._i2cAddress )
# write the 8 segments to this digit of the led
def setSegments(self, digit, segments):
if (digit <= self._digits) and (digit >= 1):
self.setRegister(digit, segments)
self._segments[digit] = segments
# set the brightness to value (0-15)
def setBrightness(self, value):
self.setRegister(I2c7SegmentLed.REG_GLOBAL_INTENSITY, value)
# clear all digits of the LED
def clear(self):
for i in range(1,self._digits+1):
self._segments[i] = 0x00 # clear local storage
self.setSegments(i,0x00) # clear led display
self._cursorPosition = 1 # move cursor to home position
# move the invisible virtual cursor to the 1st position, so that the next char written will go to that digit
def home(self):
self.cursorMove(1)
# move the invisible virtual cursor to specified digit
def cursorMove(self, digit):
if (digit <= self._digits) and (digit >= 1):
self._cursorPosition = digit
# turn the display off (also reduces current consumption
def displayOff(self):
self.setRegister(I2c7SegmentLed.REG_SHUTDOWN,I2c7SegmentLed.REG_SHUTDOWN_SHUTDOWN)
# turn the display on
def displayOn(self):
self.setRegister(I2c7SegmentLed.REG_SHUTDOWN,I2c7SegmentLed.REG_SHUTDOWN_NORMAL)
# set the brightness of the LEDs to value (0-15)
def setBrightness(self, value):
self.setRegister(I2c7SegmentLed.REG_GLOBAL_INTENSITY, value)
# set the decimal point on digit specified
def setDecimalPoint(self, digit):
if (digit <= self._digits) and (digit >= 1):
currentSegments = self._segments[digit] | I2c7SegmentLed.DECIMAL_POINT_MASK
self.setSegments(digit, currentSegments)
# clear the decimal point on digit specified
def clearDecimalPoint(self, digit):
if (digit <= self._digits) and (digit >= 1):
currentSegments = self._segments[digit] & ~I2c7SegmentLed.DECIMAL_POINT_MASK
self.setSegments(digit, currentSegments)
# write an ascii character (value) to the led
def write(self, value):
# if we are not past the number of digits that we have
if self._cursorPosition <= self._digits:
# check if the character is a decimal point
if value == '.':
if self._cursorPosition == 1:
self.setDecimalPoint(self._cursorPosition); # set the dp for digit 1 and inc cursorPosition
self._cursorPosition += 1
else:
self.setDecimalPoint(self._cursorPosition-1); # set the dp for the previous digit
# else it is not a decimal point
else:
self._segments[self._cursorPosition] = I2c7SegmentLed.LedSegments[ord(value)]; # save the segments to local storage
self.setSegments(self._cursorPosition, I2c7SegmentLed.LedSegments[ord(value)]); # write the segments to the display
self._cursorPosition += 1
# write a string (including formatting options)
# For examples of printing numbers: https://mkaz.tech/python-string-format.html
def writeString(self, value):
for char in value:
self.write(char)
| [
"“gary@dcity.org”"
] | “gary@dcity.org” |
df8348437cb3f52a36143204a8098092a7baae05 | cdd2003610c4c451dc38781d5ece2cf4e8138c27 | /src/convert_rviz.py | cd66d10b1a8cd9aecf17d38b1ef969533384d9a9 | [] | no_license | DLu/rwt_config_generator | 7efb29d773dddae0868be14606ba91893fae806c | 873b1aa0d4c94cdba3b15ef85d46f70c26f6dc86 | refs/heads/master | 2020-12-24T16:24:02.304617 | 2016-03-03T19:04:52 | 2016-03-03T19:04:52 | 39,230,985 | 2 | 1 | null | null | null | null | UTF-8 | Python | false | false | 3,622 | py | #!/usr/bin/python
from __future__ import print_function
import sys
import yaml
from rwt_config_generator import *
import argparse
import rospy
def warning(*objs):
print("WARNING: ", *objs, file=sys.stderr)
parser = argparse.ArgumentParser()
parser.add_argument('rviz_config')
parser.add_argument('output_html_file', nargs='?')
parser.add_argument('-b', '--bson', action='store_true')
parser.add_argument('-u', '--host', type=str, nargs='?')
args = parser.parse_args(rospy.myargv()[1:])
rviz = yaml.load( open(args.rviz_config) )['Visualization Manager']
def to_hex(s):
if s is None:
return None
ns = tuple(map(int, s.split(';')))
s = '0x%02x%02x%02x'%ns
return s
def get(key, d=None):
if d is None:
d = rviz
for s in key.split('/'):
d = d.get(s, None)
if d==None:
return None
return d
def parse_displays(c, displays):
for display in displays:
if not display.get('Enabled', True):
continue
cls = display['Class']
if cls == 'rviz/Grid':
c.add_grid()
elif cls == 'rviz/RobotModel':
c.add_model(param=display.get('Robot Description'), tfPrefix=display.get('TF Prefix'))
elif cls == 'rviz/Marker':
c.add_markers(topic=display.get('Marker Topic'))
elif cls == 'rviz/MarkerArray':
c.add_marker_array(topic=display.get('Marker Topic'))
elif cls == 'rviz/InteractiveMarkers':
topic = display.get('Update Topic')
topic = topic.replace('/update', '')
c.add_imarkers(topic=topic)
elif cls == 'rviz/PointCloud2':
c.add_pointcloud(topic=display.get('Topic'), size=display.get('Size (m)'))
elif cls == 'rviz/LaserScan':
c.add_laserscan(topic=display.get('Topic'), color=to_hex(display.get('Color')), size=display.get('Size (m)'))
elif cls == 'rviz/Path':
c.add_path(topic=display.get('Topic'), color=to_hex(display.get('Color')))
elif cls == 'rviz/Polygon':
c.add_polygon(topic=display.get('Topic'), color=to_hex(display.get('Color')))
elif cls == 'rviz/Pose':
c.add_pose(topic=display.get('Topic'), color=to_hex(display.get('Color')),
shaft_radius=display.get('Shaft Radius'),
head_radius=display.get('Head Radius'),
shaft_length=display.get('Shaft Length'),
head_length=display.get('Head Length'))
elif cls == 'rviz/Odometry':
c.add_odometry(topic=display.get('Topic'), color=to_hex(display.get('Color')),
shaft_length=display.get('Length'), keep=display.get('Keep'))
elif cls == 'rviz/PoseArray':
c.add_posearray(topic=display.get('Topic'), color=to_hex(display.get('Color')), length=display.get('Arrow Length'))
elif cls == 'rviz/PointStamped':
c.add_point(topic=display.get('Topic'), color=to_hex(display.get('Color')), radius=display.get('Radius'))
elif cls == 'rviz/Group':
parse_displays( c, display['Displays'] )
elif cls == 'rviz/Map':
c.add_map(topic=display.get('Topic'), alpha=display.get('Alpha'), tf=True)
else:
warning("Class %s not supported yet!"%cls)
frame = get('Global Options/Fixed Frame')
c = RWTConfig(host=args.host, fixed_frame=frame)
if args.bson:
c.add_bson_header()
parse_displays(c, get('Displays'))
if args.output_html_file:
with open(args.output_html_file, 'w') as f:
f.write(str(c))
else:
print(c)
| [
"davidvlu@gmail.com"
] | davidvlu@gmail.com |
9b8b028c0c0efe89315744409a6b69fe90a07bea | 7930635dae78d050ebe158303aa5343a8440a884 | /lesson05/lesson05-3.py | c57400cea3fe4ec5bba9412246963cfa06edd93d | [] | no_license | lexpol/python_homework | 1211bda3db32b869e34c4cca2264372f5c99b240 | 667fe61f7e23ed834f13c68c40a99ef21c39d5a7 | refs/heads/master | 2023-05-30T22:04:09.144029 | 2021-06-09T01:04:40 | 2021-06-09T01:04:40 | 365,838,873 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,187 | py | # 3. Создать текстовый файл (не программно), построчно записать фамилии сотрудников и
# величину их окладов (не менее 10 строк). Определить, кто из сотрудников имеет оклад менее 20 тыс.,
# вывести фамилии этих сотрудников. Выполнить подсчет средней величины дохода сотрудников.
#
# Пример файла:
#
# Иванов 23543.12
# Петров 13749.32
with open('lesson05-3-sample.txt') as my_file:
salary = []
print("фамилии сотрудников с окладом менее 20 тыс:")
for line in my_file.readlines():
formated_line = line.replace("\n", "")
# print(formated_line)
if float(formated_line.split()[1]) < 20000:
print(f"{formated_line.split()[0]} \t\t\tс результатом: {formated_line.split()[1]}")
salary.append(float(formated_line.split()[1]))
print(f"величина среднего дохода сотрудников: {round(sum(salary) / len(salary), 2)}")
| [
"lex@poltor.ru"
] | lex@poltor.ru |
f1335c1e683e0f3a387c4f90ecc635733f2dbce5 | 05a175090ffebdd0713802aba1469f2673fda3d3 | /IntroNN/hw2_delvalle_network.py | 1ad44467d98f1d231d562f3ccbe7f7afbf9dfee4 | [] | no_license | gdelvalle99/CS691-Deep-Learning-Projects | c41d8a835eeb4247d88d8121dcacba96b431ac1c | d6f6e1b9a2caf24e283d15acf78da1c4bf521897 | refs/heads/master | 2022-10-04T22:26:52.728823 | 2020-05-23T21:56:18 | 2020-05-23T21:56:18 | 266,417,150 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 5,952 | py | import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense
from sklearn import svm
N = 250
size = int(N/2)
Uh = 20
Ul = -1
x_min2 = [Ul, Ul]
x_max2 = [-Uh, Uh]
x_min4 = [Ul, -Ul]
x_max4 = [Uh, -Uh]
o_min1 = [Ul, Ul]
o_max1 = [Uh, Uh]
o_min3 = [-Ul, -Ul]
o_max3 = [-Uh, -Uh]
O1 = np.random.uniform(low=o_min1, high=o_max1, size=(size,2))
X2 = np.random.uniform(low=x_min2, high=x_max2, size=(size,2))
O3 = np.random.uniform(low=o_min3, high=o_max3, size=(size,2))
X4 = np.random.uniform(low=x_min4, high=x_max4, size=(size,2))
O = np.concatenate((O1,O3), axis=0)
X = np.concatenate((X2,X4), axis=0)
x_train = np.concatenate((O,X), axis=0)
y_train = None
for index, row in enumerate(x_train):
if( index < N):
y_o = np.array([0,1])
if(index == 0):
y_train = y_o
else:
y_train = np.vstack((y_train,y_o))
else:
y_x = np.array([1,0])
y_train = np.vstack((y_train,y_x))
#y_train = np.vstack((y_o,y_x))
#print(y_train.shape)
model = Sequential()
model.add(Dense(8, activation='relu', input_shape=(2,)))
model.add(Dense(2, activation='sigmoid'))
model.summary()
print(model.get_config())
print(model.get_weights())
model.compile(loss='binary_crossentropy', optimizer='Adam', metrics=['accuracy'])
#plt.scatter(X[:,0],X[:,1],marker='+',c='blue', label='X-class')
#plt.scatter(O[:,0],O[:,1],marker='o',c='red', edgecolors='none', label='O-class')
#plt.legend()
#plt.grid(True)
#plt.show()
epochs = 100
n = 50
history = model.fit(x=x_train,y=y_train,batch_size=n,epochs=epochs,verbose=1)
NTest = 150
sizeTest = int(NTest/2)
#print(sizeTest)
O1test = np.random.uniform(low=o_min1, high=o_max1, size=(sizeTest,2))
X2test = np.random.uniform(low=x_min2, high=x_max2, size=(sizeTest,2))
O3test = np.random.uniform(low=o_min3, high=o_max3, size=(sizeTest,2))
X4test = np.random.uniform(low=x_min4, high=x_max4, size=(sizeTest,2))
Otest = np.concatenate((O1test,O3test), axis=0)
Xtest = np.concatenate((X2test,X4test), axis=0)
x_test = np.vstack((Otest,Xtest))
#print(x_test.shape)
#plt.scatter(Xtest[:,0],Xtest[:,1],marker='+',c='blue', label='X-class')
#plt.scatter(Otest[:,0],Otest[:,1],marker='o',c='red', edgecolors='none', label='O-class')
#plt.legend()
#plt.grid(True)
#plt.show()
y_test = None
for index, row in enumerate(x_test):
if( index < NTest):
y_o = np.array([0,1])
if(index == 0):
y_test = y_o
else:
y_test = np.vstack((y_test,y_o))
else:
y_x = np.array([1,0])
y_test = np.vstack((y_test,y_x))
#print(x_test[index], y_test[index])
score = model.evaluate(x=x_test,y=y_test)
print(y_test.shape)
false_positives = None
false_negatives = None
X_true = None
O_true = None
q = model.predict(x_test)
#we assume that X is positive and O is negative
for index, row in enumerate(q):
#print(q[0],q[1],y_test[index][0])
if((row[0] > row[1] and y_test[index][0] > y_test[index][1]) or(row[1] > row[0] and y_test[index][1] > y_test[index][0])):
#print(y_test[index])
if(row[0] > row[1]):
if(X_true is None):
X_true = x_test[index]
else:
#print('here')
X_true = np.vstack((X_true, x_test[index]))
else:
if(O_true is None):
O_true = x_test[index]
else:
#print(x_test[index])
O_true = np.vstack((O_true, x_test[index]))
elif((row[0] > row[1] and y_test[index][1] > y_test[index][0])):#false positive
if(false_positives is None):
false_positives = x_test[index]
else:
false_positives = np.vstack((false_positives, x_test[index]))
elif((row[1] > row[0] and y_test[index][0] > y_test[index][1])):
if(false_negatives is None):
false_negatives = x_test[index]
else:
false_negatives = np.vstack((false_negatives, x_test[index]))
v_line = np.concatenate((O1test,X2test),axis=0)
h_line = np.concatenate((O3test,X2test),axis=0)
y_h = None
y_v = None
q_h = model.predict(h_line)
q_v = model.predict(v_line)
for index, i in enumerate(q_h):
if(q_h[index][1] > q_h[index][0]):
if y_h is None:
y_h = 0
else:
y_h = np.hstack((y_h,0))
elif(q_h[index][0] > q_h[index][1]):
if y_h is None:
y_h = 1
else:
y_h = np.hstack((y_h,1))
for index, i in enumerate(q_v):
if(q_v[index][1] > q_v[index][0]):
if y_v is None:
y_v = 0
else:
y_v = np.hstack((y_v,0))
elif(q_v[index][0] > q_v[index][1]):
if y_v is None:
y_v = 1
else:
y_v = np.hstack((y_v,1))
C = 1.0 # SVM regularization parameter
clf_v = svm.SVC(kernel = 'linear', gamma=0.7, C=C )
clf_v.fit(v_line, y_v)
clf_h = svm.SVC(kernel = 'linear', gamma=0.7, C=C )
clf_h.fit(h_line, y_h)
w_h = clf_h.coef_[0]
a_h = -w_h[0] / w_h[1]
xx_h = np.linspace(-20, 20)
yy_h = a_h * xx_h - (clf_h.intercept_[0]) / w_h[1]
plt.plot(xx_h, yy_h, 'k-')
w_v = clf_v.coef_[0]
a_v = -w_v[0] / w_v[1]
xx_v = np.linspace(-5, 5)
yy_v = a_v * xx_v - (clf_v.intercept_[0]) / w_v[1]
plt.plot(xx_v, yy_v, 'k-')
#plt.plot(dec_bound[:,0], dec_bound[:,1])
#print(X_true)
#print(score)
if(X_true is not None):
plt.scatter(X_true[:,0],X_true[:,1],marker='+',c='blue', label='X-class')
if(O_true is not None):
plt.scatter(O_true[:,0],O_true[:,1],marker='o',c='red', edgecolors='none', label='O-class')
if(false_positives is not None):
plt.scatter(false_positives[:,0],false_positives[:,1],marker='+',c='yellow', label='False positives')
if(false_negatives is not None):
plt.scatter(false_negatives[:,0],false_negatives[:,1],marker='o',c='green', edgecolors='none', label='False negatives')
plt.legend()
plt.grid(True)
plt.show()
| [
"gdelvalle@nevada.unr.edu"
] | gdelvalle@nevada.unr.edu |
3c36c0d10742f9c25af173e2077d9c835a3e3ff8 | 1d928c3f90d4a0a9a3919a804597aa0a4aab19a3 | /python/celery/2015/12/graph.py | d441a54ca1edf2545aaaa16e0d18be8ec8d7318d | [] | no_license | rosoareslv/SED99 | d8b2ff5811e7f0ffc59be066a5a0349a92cbb845 | a062c118f12b93172e31e8ca115ce3f871b64461 | refs/heads/main | 2023-02-22T21:59:02.703005 | 2021-01-28T19:40:51 | 2021-01-28T19:40:51 | 306,497,459 | 1 | 1 | null | 2020-11-24T20:56:18 | 2020-10-23T01:18:07 | null | UTF-8 | Python | false | false | 6,432 | py | # -*- coding: utf-8 -*-
"""
The :program:`celery graph` command.
.. program:: celery graph
"""
from __future__ import absolute_import, unicode_literals
from operator import itemgetter
from celery.datastructures import DependencyGraph, GraphFormatter
from celery.five import items
from .base import Command
__all__ = ['graph']
class graph(Command):
args = """<TYPE> [arguments]
..... bootsteps [worker] [consumer]
..... workers [enumerate]
"""
def run(self, what=None, *args, **kwargs):
map = {'bootsteps': self.bootsteps, 'workers': self.workers}
if not what:
raise self.UsageError('missing type')
elif what not in map:
raise self.Error('no graph {0} in {1}'.format(what, '|'.join(map)))
return map[what](*args, **kwargs)
def bootsteps(self, *args, **kwargs):
worker = self.app.WorkController()
include = {arg.lower() for arg in args or ['worker', 'consumer']}
if 'worker' in include:
graph = worker.blueprint.graph
if 'consumer' in include:
worker.blueprint.connect_with(worker.consumer.blueprint)
else:
graph = worker.consumer.blueprint.graph
graph.to_dot(self.stdout)
def workers(self, *args, **kwargs):
def simplearg(arg):
return maybe_list(itemgetter(0, 2)(arg.partition(':')))
def maybe_list(l, sep=','):
return (l[0], l[1].split(sep) if sep in l[1] else l[1])
args = dict(simplearg(arg) for arg in args)
generic = 'generic' in args
def generic_label(node):
return '{0} ({1}://)'.format(type(node).__name__,
node._label.split('://')[0])
class Node(object):
force_label = None
scheme = {}
def __init__(self, label, pos=None):
self._label = label
self.pos = pos
def label(self):
return self._label
def __str__(self):
return self.label()
class Thread(Node):
scheme = {'fillcolor': 'lightcyan4', 'fontcolor': 'yellow',
'shape': 'oval', 'fontsize': 10, 'width': 0.3,
'color': 'black'}
def __init__(self, label, **kwargs):
self._label = 'thr-{0}'.format(next(tids))
self.real_label = label
self.pos = 0
class Formatter(GraphFormatter):
def label(self, obj):
return obj and obj.label()
def node(self, obj):
scheme = dict(obj.scheme) if obj.pos else obj.scheme
if isinstance(obj, Thread):
scheme['label'] = obj.real_label
return self.draw_node(
obj, dict(self.node_scheme, **scheme),
)
def terminal_node(self, obj):
return self.draw_node(
obj, dict(self.term_scheme, **obj.scheme),
)
def edge(self, a, b, **attrs):
if isinstance(a, Thread):
attrs.update(arrowhead='none', arrowtail='tee')
return self.draw_edge(a, b, self.edge_scheme, attrs)
def subscript(n):
S = {'0': '₀', '1': '₁', '2': '₂', '3': '₃', '4': '₄',
'5': '₅', '6': '₆', '7': '₇', '8': '₈', '9': '₉'}
return ''.join([S[i] for i in str(n)])
class Worker(Node):
pass
class Backend(Node):
scheme = {'shape': 'folder', 'width': 2,
'height': 1, 'color': 'black',
'fillcolor': 'peachpuff3', 'color': 'peachpuff4'}
def label(self):
return generic_label(self) if generic else self._label
class Broker(Node):
scheme = {'shape': 'circle', 'fillcolor': 'cadetblue3',
'color': 'cadetblue4', 'height': 1}
def label(self):
return generic_label(self) if generic else self._label
from itertools import count
tids = count(1)
Wmax = int(args.get('wmax', 4) or 0)
Tmax = int(args.get('tmax', 3) or 0)
def maybe_abbr(l, name, max=Wmax):
size = len(l)
abbr = max and size > max
if 'enumerate' in args:
l = ['{0}{1}'.format(name, subscript(i + 1))
for i, obj in enumerate(l)]
if abbr:
l = l[0:max - 1] + [l[size - 1]]
l[max - 2] = '{0}⎨…{1}⎬'.format(
name[0], subscript(size - (max - 1)))
return l
try:
workers = args['nodes']
threads = args.get('threads') or []
except KeyError:
replies = self.app.control.inspect().stats()
workers, threads = [], []
for worker, reply in items(replies):
workers.append(worker)
threads.append(reply['pool']['max-concurrency'])
wlen = len(workers)
backend = args.get('backend', self.app.conf.result_backend)
threads_for = {}
workers = maybe_abbr(workers, 'Worker')
if Wmax and wlen > Wmax:
threads = threads[0:3] + [threads[-1]]
for i, threads in enumerate(threads):
threads_for[workers[i]] = maybe_abbr(
list(range(int(threads))), 'P', Tmax,
)
broker = Broker(args.get(
'broker', self.app.connection_for_read().as_uri()))
backend = Backend(backend) if backend else None
graph = DependencyGraph(formatter=Formatter())
graph.add_arc(broker)
if backend:
graph.add_arc(backend)
curworker = [0]
for i, worker in enumerate(workers):
worker = Worker(worker, pos=i)
graph.add_arc(worker)
graph.add_edge(worker, broker)
if backend:
graph.add_edge(worker, backend)
threads = threads_for.get(worker._label)
if threads:
for thread in threads:
thread = Thread(thread)
graph.add_arc(thread)
graph.add_edge(thread, worker)
curworker[0] += 1
graph.to_dot(self.stdout)
| [
"rodrigosoaresilva@gmail.com"
] | rodrigosoaresilva@gmail.com |
1bbb87d1b2466539e6790d2e6a61b405410e979e | 80c87909c81f7d8253399c9e9e54969b68665c3f | /chp/charts.py | 3b632b7c0bb6c1a6fd28325cb8c6bf353cc3c746 | [] | no_license | it1525/CEMK_HP | cba429cedbb7603055d88b9947d439050ff840ff | 985039d617d6ec5cbff9a90bd877c9731ece62b3 | refs/heads/master | 2020-05-25T03:28:30.062835 | 2019-05-20T08:53:25 | 2019-05-20T08:53:25 | 187,603,319 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,703 | py | import pygal
from chp.models import Complain
from users.models import Profile
from django.db.models import Count
class DeptComplainBarChart():
def __init__(self, **kwargs):
self.chart = pygal.Bar(**kwargs)
self.chart.title = 'Department wise Complain Chart'
def get_data(self):
'''
Query the db for chart data, pack them into a dict and return it.
'''
cd = 'complain_department'
q2 = Complain.objects.values(cd).order_by(cd).annotate(count = Count(cd))
data = {q.get('complain_department'): q.get('count') for q in q2}
return data
def generate(self):
# Get chart data
chart_data = self.get_data()
# Add data to chart
for key, value in chart_data.items():
self.chart.add(key, value)
# Return the rendered SVG
return self.chart.render(is_unicode=True)
class ResidenceComplainPieChart():
def __init__(self, **kwargs):
self.chart = pygal.Pie(**kwargs)
self.chart.title = 'Residence wise Complain Chart'
def get_data(self):
'''
Query the db for chart data, pack them into a dict and return it.
'''
q2 = Complain.objects.values('complain_user__profile__residence').annotate(count=Count('id'))
data = {q.get('complain_user__profile__residence'): q.get('count') for q in q2}
return data
def generate(self):
# Get chart data
chart_data = self.get_data()
# Add data to chart
for key, value in chart_data.items():
self.chart.add(key, value)
# Return the rendered SVG
return self.chart.render(is_unicode=True) | [
"it1525@cemk.ac.in"
] | it1525@cemk.ac.in |
e43360af1818f7088e1ee3d0ef5d5ef217670f8a | 057caac442baac22cc9040519e3366156f79400e | /lista1/zadanie2.py | 4ef382a4e7f3f6901d3b3e6c524ca180d608cd8e | [] | no_license | KleczkoPawel/Python | 370239245e5a9e416b0f598bccd426034f2039c8 | ad565c1227ff4c621f2e996578a1d9a19d06f0e3 | refs/heads/main | 2023-03-27T10:04:06.788344 | 2021-01-21T07:30:52 | 2021-01-21T07:30:52 | 304,240,301 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 77 | py | import math
a=3
b=4
alfa=47
Pole=a*b*math.sin(alfa*math.pi/180)/2
print(Pole) | [
"noreply@github.com"
] | KleczkoPawel.noreply@github.com |
4568f2467a081a10c3b7057b1d105a5e0f16e682 | 2bd5e4c50dc9f0d19f9f20ffdaf0b88578fd7644 | /Matmatic.py | 42b7f86d1b6fe851238c47cc7550adf3098386e0 | [] | no_license | Mistik535/Zadachi | e248e9d0c6fc094545bab798af60870782868ce1 | d37b2b9fbd1c52c1d9a7345c8aa5a197e4527902 | refs/heads/main | 2023-07-03T10:25:50.728833 | 2021-07-22T17:23:55 | 2021-07-22T17:23:55 | 348,099,449 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,008 | py | # a = 2
# b = 2
# c = 3
# # #y = list(map(int, input().split()))
# x = (a + 2 * b - 3 * c) / (5 * a + 4)
# print(x)
#
# # dont work!
import math
#
# a = 2
# b = 2
# # x = (a ** 2) + (b ** 3)
# # w =
# x = math.sqrt(math.pow(a, 2) + math.pow(b, 3))
# print("x:", x)
#
# y = 1
# x = 2.136 + (2 / 3) * y
# print(x)
#
# a = 2
# b = 2
# c = 2
# x = 2
# y = 2
# z = 2
# w = math.pow(x, 2) / math.pow(a, 2) + math.pow(y, 2) / math.pow(b, 2) + math.pow(c, 2) / math.pow(z, 2)
# print(w)
#
# a = 2
# x = 1
# z = ((math.pow(x, 2) - 5) + a) / (3 * a * math.pow(x, 4))
# print(z)
#
# x = 2
# y = 2
# z = abs(x + 2 * y)
# print(z)
#
# y = 2
# x = 2
# b = 2
# z = y + math.pow(x, 4) / (2 * b) - 1.5
# print(z)
#
# a = 3
# b = 2
# z = (a + b) / (a - b) + (a * b) / 3.14
# print(z)
#
# x = 2
# y = math.pow(3.7, 3) + abs(math.pow(x, 1.8))
x = 10
y = pow(3.7, 3) + math.sqrt(math.pow(math.fabs(x), 1.8))
print(y)
x = 10
y = math.pow(x, math.sqrt(4.2)) + math.pow(math.sin(3 * math.pow(x, 3)), 2)
print(y)
(sin(3 * x^3))^2 | [
"mr.chadway@gmail.com"
] | mr.chadway@gmail.com |
f025afbae8373d9c8e6056447c01f0f352ed3c2d | 12cf92d68790693e06a7088c98a856ced4536554 | /tools/pruneMentions.py | 5c04c03c14f9dac73b4c6d5f576ebdb6301d17d3 | [] | permissive | DaylightingSociety/SocMap | b334ff49c986c55c57dedc450e0bde035077daa3 | c8e9f40efdcee2c765cd02b6398d948fecf6bd83 | refs/heads/master | 2021-05-05T06:37:15.683469 | 2020-09-14T16:14:02 | 2020-09-14T16:14:02 | 118,809,485 | 18 | 4 | BSD-3-Clause | 2019-06-18T22:00:29 | 2018-01-24T19:07:59 | Python | UTF-8 | Python | false | false | 707 | py | #!/usr/bin/env python3
import sys, os
import igraph as ig
# This script deletes all edges with < threshold number of mentions
# It does *not* delete inaccessible nodes afterwards (see pruneInaccessible.py)
if __name__ == "__main__":
if( len(sys.argv) != 4 ):
print("USAGE: %s <mention threshold> <original.gml> <pruned.gml>" % sys.argv[0])
sys.exit(1)
mentionThreshold = int(sys.argv[1])
origFilename = sys.argv[2]
newFilename = sys.argv[3]
if( mentionThreshold < 1 ):
print("ERROR: Mention threshold must be at least one")
sys.exit(1)
orig = ig.Graph.Read_GML(origFilename)
toPrune = orig.es.select(mentions_lt=mentionThreshold)
orig.delete_edges(toPrune)
orig.write_gml(newFilename)
| [
"milo.trujillo@daylightingsociety.org"
] | milo.trujillo@daylightingsociety.org |
706b1412b4d839f20812fa99a5bf77bacc68ade1 | 7aa82a17d3545ad418ce6defd84ec9e460937299 | /work/views.py | 8352fb4b2c73b7e59d3ae78eb6c4f4a00b58243f | [] | no_license | parin-2002/CRUD-IN-DJANGO | ad28052b69814a9f35c0427c82418440a82cb47a | 34557e9f9fc6a8e3e5d0d7edc7d070237b0387e5 | refs/heads/master | 2022-11-19T18:23:11.677185 | 2020-07-23T03:39:26 | 2020-07-23T03:39:26 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 915 | py | from django.shortcuts import render,redirect
from .forms import rgstudent
from .models import student
# Create your views here.
def reg(request):
if(request.method=='POST'):
form=rgstudent(request.POST)
if form.is_valid():
form.save()
form=rgstudent()
else:
form=rgstudent()
data=student.objects.all()
return render(request,'register.html',{'form':form,'data':data})
data=student.objects.all()
return render(request,'register.html',{'form':form,'data':data})
def delete(request,id):
if request.method=='POST':
pi=student.objects.get(pk=id)
pi.delete()
return redirect("/")
def update(request,id):
if request.method=='POST':
pi=student.objects.get(pk=id)
form=rgstudent(request.POST,instance=pi)
if form.is_valid():
form.save()
return redirect("/")
else:
pi=student.objects.get(pk=id)
form=rgstudent(instance=pi)
return render(request,'update.html',{'id':form})
| [
"1akashsuvagiya1999@gmail.com"
] | 1akashsuvagiya1999@gmail.com |
988e35a1c0043ed6844775a7278008acc5bd01fd | d6f7273500f28fcc0a378620cc4e417a08542992 | /turnedOn/turnedOn/wsgi.py | c1d388723b28d037f6d884e93b8b7bb43cc723a9 | [] | no_license | katelyndunaski/Turned-On | 109e1ecd168f97255c178b95cc289739da443fbb | 80b0e01d9ffd2c7b142a6ecbeb96e48a0f22f8fb | refs/heads/master | 2016-09-10T08:38:40.314407 | 2015-02-08T17:24:11 | 2015-02-08T17:24:11 | 30,468,506 | 0 | 0 | null | 2015-02-08T02:04:38 | 2015-02-07T20:07:49 | JavaScript | UTF-8 | Python | false | false | 391 | py | """
WSGI config for turnedOn project.
It exposes the WSGI callable as a module-level variable named ``application``.
For more information on this file, see
https://docs.djangoproject.com/en/1.7/howto/deployment/wsgi/
"""
import os
os.environ.setdefault("DJANGO_SETTINGS_MODULE", "turnedOn.settings")
from django.core.wsgi import get_wsgi_application
application = get_wsgi_application()
| [
"ubuntu@ip-172-31-39-124.us-west-2.compute.internal"
] | ubuntu@ip-172-31-39-124.us-west-2.compute.internal |
4eb09dfed6ad25c8eddd6132f2dc73dff3fcc6a3 | 1933ef2c5b3ec58feeb50dd092d670f58a3ec2bb | /kospeech/models/modules.py | 352b6a0bd0bf59f8861fa3d7e573569560a2ad30 | [
"Apache-2.0"
] | permissive | hephaex/KoSpeech | 68275af311ae5c53548f7c7bc27fe9dd5b1e441b | bf3fa0dc6d50089164fd0b47e02620062718d407 | refs/heads/master | 2022-12-02T02:00:01.164265 | 2020-08-05T08:47:55 | 2020-08-05T08:47:55 | 285,344,731 | 0 | 0 | Apache-2.0 | 2020-08-12T14:53:11 | 2020-08-05T16:22:59 | null | UTF-8 | Python | false | false | 1,579 | py | import torch
import torch.nn as nn
import torch.nn.init as init
from torch import Tensor
class Linear(nn.Module):
"""
Wrapper class of torch.nn.Linear
Weight initialize by xavier initialization and bias initialize to zeros.
"""
def __init__(self, in_features: int, out_features: int, bias: bool = True) -> None:
super(Linear, self).__init__()
self.linear = nn.Linear(in_features, out_features, bias=bias)
init.xavier_uniform_(self.linear.weight)
if bias:
init.zeros_(self.linear.bias)
def forward(self, x: Tensor) -> Tensor:
return self.linear(x)
class LayerNorm(nn.Module):
""" Wrapper class of torch.nn.LayerNorm """
def __init__(self, dim: int, eps: float = 1e-6) -> None:
super(LayerNorm, self).__init__()
self.gamma = nn.Parameter(torch.ones(dim))
self.beta = nn.Parameter(torch.zeros(dim))
self.eps = eps
def forward(self, z: Tensor) -> Tensor:
mean = z.mean(dim=-1, keepdim=True)
std = z.std(dim=-1, keepdim=True)
output = (z - mean) / (std + self.eps)
output = self.gamma * output + self.beta
return output
class View(nn.Module):
""" Wrapper class of torch.view() for Sequential module. """
def __init__(self, shape: tuple, contiguous: bool = False):
super(View, self).__init__()
self.shape = shape
self.contiguous = contiguous
def forward(self, inputs):
if self.contiguous:
inputs = inputs.contiguous()
return inputs.view(*self.shape)
| [
"sh951011@gmail.com"
] | sh951011@gmail.com |
9a704f28f280264c0f0f116eb2b5f892f3e8a617 | 2b3e562da5d9b473f3a7dcb3ba833552e649b675 | /session3.py | 6c0c799a912ea879733bcd1f02e38c53ad9dadf3 | [] | no_license | Aakashdeveloper/python-web | 51f89c34e30beb0be1d8fdd2e01b3e2617a334b0 | fe163c193b20d23794eb327d2d580124e32fe152 | refs/heads/master | 2020-07-25T12:38:13.416166 | 2019-09-27T15:07:32 | 2019-09-27T15:07:32 | 208,291,750 | 3 | 6 | null | null | null | null | UTF-8 | Python | false | false | 372 | py | #!/usr/bin/env python
# coding: utf-8
# In[1]:
x = 10
# In[3]:
y = 20
# In[4]:
x+y
# In[5]:
x*y
# In[6]:
x/y
# In[7]:
x-y
# In[8]:
10<<2
# In[9]:
10>>2
# In[12]:
x == 10 & y ==20
'''
docker run --rm -u root -p 8080:8080 -v jenkins-data:/var/jenkins_home -v /var/run/docker.sock:/var/run/docker.sock -v "$HOME":/home jenkinsci/blueocean | [
"ahanda205@gmail.com"
] | ahanda205@gmail.com |
21ffffe2f10c8650b232760086dfbefe96216764 | c09f02ebc1c4418bf9324afd803532234293d6f5 | /CS61A/Project 2/trends_old3.py | d4d524fd8cdc2005ba0b1e2a888c2e360f710ecb | [] | no_license | jesseyli/OldProjects | a2fddabfbb6207616572c807fde71455f5197a6e | edc28b02bfdb2ea1f4041d879a20858b797e674f | refs/heads/master | 2021-01-10T09:05:07.294910 | 2016-02-05T07:34:04 | 2016-02-05T07:34:04 | 50,936,330 | 2 | 0 | null | null | null | null | UTF-8 | Python | false | false | 18,330 | py | """Visualizing Twitter Sentiment Across America"""
from data import word_sentiments, load_tweets
from datetime import datetime
from doctest import run_docstring_examples
from geo import us_states, geo_distance, make_position, longitude, latitude
from maps import draw_state, draw_name, draw_dot, wait, message, draw_top_states
from string import ascii_letters
from ucb import main, trace, interact, log_current_line
###################################
# Phase 1: The Feelings in Tweets #
###################################
def make_tweet(text, time, lat, lon):
"""Return a tweet, represented as a Python dictionary.
text -- A string; the text of the tweet, all in lowercase
time -- A datetime object; the time that the tweet was posted
lat -- A number; the latitude of the tweet's location
lon -- A number; the longitude of the tweet's location
>>> t = make_tweet("just ate lunch", datetime(2012, 9, 24, 13), 38, 74)
>>> tweet_words(t)
['just', 'ate', 'lunch']
>>> tweet_time(t)
datetime.datetime(2012, 9, 24, 13, 0)
>>> p = tweet_location(t)
>>> latitude(p)
38
"""
return {'text': text, 'time': time, 'latitude': lat, 'longitude': lon}
def tweet_words(tweet):
"""Return a list of the words in the text of a tweet."""
return extract_words(tweet['text'])
def tweet_time(tweet):
"""Return the datetime that represents when the tweet was posted."""
return tweet['time']
def tweet_location(tweet):
"""Return a position (see geo.py) that represents the tweet's location."""
return make_position(tweet['latitude'], tweet['longitude'])
def tweet_string(tweet):
"""Return a string representing the tweet."""
location = tweet_location(tweet)
return '"{0}" @ {1}'.format(tweet['text'], (latitude(location), longitude(location)))
def extract_words(text):
"""Return the words in a tweet, not including punctuation.
>>> extract_words('anything else.....not my job')
['anything', 'else', 'not', 'my', 'job']
>>> extract_words('i love my job. #winning')
['i', 'love', 'my', 'job', 'winning']
>>> extract_words('make justin # 1 by tweeting #vma #justinbieber :)')
['make', 'justin', 'by', 'tweeting', 'vma', 'justinbieber']
>>> extract_words("paperclips! they're so awesome, cool, & useful!")
['paperclips', 'they', 're', 'so', 'awesome', 'cool', 'useful']
>>> extract_words('@(cat$.on^#$my&@keyboard***@#*')
['cat', 'on', 'my', 'keyboard']
"""
words = []
word = ''
for letter in text + ' ':
if letter not in ascii_letters:
if word != '':
words += [word]
word = ''
else:
word = word + letter
return words
def make_sentiment(value):
"""Return a sentiment, which represents a value that may not exist.
>>> positive = make_sentiment(0.2)
>>> neutral = make_sentiment(0)
>>> unknown = make_sentiment(None)
>>> has_sentiment(positive)
True
>>> has_sentiment(neutral)
True
>>> has_sentiment(unknown)
False
>>> sentiment_value(positive)
0.2
>>> sentiment_value(neutral)
0
"""
assert value is None or (value >= -1 and value <= 1), 'Illegal value'
return value
def has_sentiment(s):
"""Return whether sentiment s has a value."""
if s == None:
return False
return True
def sentiment_value(s):
"""Return the value of a sentiment s."""
assert has_sentiment(s), 'No sentiment value'
return s
def get_word_sentiment(word):
"""Return a sentiment representing the degree of positive or negative
feeling in the given word.
>>> sentiment_value(get_word_sentiment('good'))
0.875
>>> sentiment_value(get_word_sentiment('bad'))
-0.625
>>> sentiment_value(get_word_sentiment('winning'))
0.5
>>> has_sentiment(get_word_sentiment('Berkeley'))
False
"""
# Learn more: http://docs.python.org/3/library/stdtypes.html#dict.get
return make_sentiment(word_sentiments.get(word))
def analyze_tweet_sentiment(tweet):
""" Return a sentiment representing the degree of positive or negative
sentiment in the given tweet, averaging over all the words in the tweet
that have a sentiment value.
If no words in the tweet have a sentiment value, return
make_sentiment(None).
>>> positive = make_tweet('i love my job. #winning', None, 0, 0)
>>> round(sentiment_value(analyze_tweet_sentiment(positive)), 5)
0.29167
>>> negative = make_tweet("saying, 'i hate my job'", None, 0, 0)
>>> sentiment_value(analyze_tweet_sentiment(negative))
-0.25
>>> no_sentiment = make_tweet("berkeley golden bears!", None, 0, 0)
>>> has_sentiment(analyze_tweet_sentiment(no_sentiment))
False
"""
sentiments = list(filter(None,map(sentiment_value,map(get_word_sentiment,tweet_words(tweet)))))
if len(sentiments) == 0:
return make_sentiment(None)
return make_sentiment(float(sum(sentiments))/float(len(sentiments)))
#################################
# Phase 2: The Geometry of Maps #
#################################
def find_centroid(polygon):
"""Find the centroid of a polygon.
http://en.wikipedia.org/wiki/Centroid#Centroid_of_polygon
polygon -- A list of positions, in which the first and last are the same
Returns: 3 numbers; centroid latitude, centroid longitude, and polygon area
Hint: If a polygon has 0 area, use the latitude and longitude of its first
position as its centroid.
>>> p1, p2, p3 = make_position(1, 2), make_position(3, 4), make_position(5, 0)
>>> triangle = [p1, p2, p3, p1] # First vertex is also the last vertex
>>> round5 = lambda x: round(x, 5) # Rounds floats to 5 digits
>>> tuple(map(round5, find_centroid(triangle)))
(3.0, 2.0, 6.0)
>>> tuple(map(round5, find_centroid([p1, p3, p2, p1])))
(3.0, 2.0, 6.0)
>>> tuple(map(float, find_centroid([p1, p2, p1]))) # A zero-area polygon
(1.0, 2.0, 0.0)
"""
area = 0
c_lat = 0
c_lon = 0
for index in range(len(polygon) - 1):
x_i = latitude(polygon[index])
x_i2 = latitude(polygon[index+1])
y_i = longitude(polygon[index])
y_i2 = longitude(polygon[index+1])
area += (x_i*y_i2 - x_i2*y_i)
c_lat += (x_i+x_i2)*(x_i*y_i2 - x_i2*y_i)
c_lon += (y_i+y_i2)*(x_i*y_i2 - x_i2*y_i)
area = area/2
if area == 0:
c_lat = latitude(polygon[0])
c_lon = longitude(polygon[0])
else:
c_lat = c_lat/(6*area)
c_lon = c_lon/(6*area)
return (c_lat, c_lon, abs(area))
def find_state_center(polygons):
"""Compute the geographic center of a state, averaged over its polygons.
The center is the average position of centroids of the polygons in polygons,
weighted by the area of those polygons.
Arguments:
polygons -- a list of polygons
>>> ca = find_state_center(us_states['CA']) # California
>>> round(latitude(ca), 5)
37.25389
>>> round(longitude(ca), 5)
-119.61439
>>> hi = find_state_center(us_states['HI']) # Hawaii
>>> round(latitude(hi), 5)
20.1489
>>> round(longitude(hi), 5)
-156.21763
"""
weighted_lat = 0
weighted_lon = 0
total_area = 0
for polygon in polygons:
centroid = find_centroid(polygon)
weighted_lat += centroid[0]*centroid[2]
weighted_lon += centroid[1]*centroid[2]
total_area += find_centroid(polygon)[2]
return make_position(weighted_lat/total_area, weighted_lon/total_area)
###################################
# Phase 3: The Mood of the Nation #
###################################
def find_closest_state(tweet, state_centers):
"""Return the name of the state closest to the given tweet's location.
Use the geo_distance function (already provided) to calculate distance
in miles between two latitude-longitude positions.
Arguments:
tweet -- a tweet abstract data type
state_centers -- a dictionary from state names to positions.
>>> us_centers = {n: find_state_center(s) for n, s in us_states.items()}
>>> sf = make_tweet("welcome to san Francisco", None, 38, -122)
>>> ny = make_tweet("welcome to new York", None, 41, -74)
>>> find_closest_state(sf, us_centers)
'CA'
>>> find_closest_state(ny, us_centers)
'NJ'
"""
closest_state = 'CA'
closest_distance = geo_distance(tweet_location(tweet), state_centers[closest_state])
for state in state_centers:
if geo_distance(tweet_location(tweet), state_centers[state]) < closest_distance:
closest_state = state
closest_distance = geo_distance(tweet_location(tweet), state_centers[state])
return closest_state
def group_tweets_by_state(tweets):
"""Return a dictionary that aggregates tweets by their nearest state center.
The keys of the returned dictionary are state names, and the values are
lists of tweets that appear closer to that state center than any other.
tweets -- a sequence of tweet abstract data types
>>> sf = make_tweet("welcome to san francisco", None, 38, -122)
>>> ny = make_tweet("welcome to new york", None, 41, -74)
>>> ca_tweets = group_tweets_by_state([sf, ny])['CA']
>>> tweet_string(ca_tweets[0])
'"welcome to san francisco" @ (38, -122)'
"""
tweets_by_state = {}
us_centers = {n: find_state_center(s) for n, s in us_states.items()}
for tweet in tweets:
closest_state = find_closest_state(tweet, us_centers)
if tweets_by_state.get(closest_state) == None:
tweets_by_state[closest_state] = []
tweets_by_state[closest_state] = tweets_by_state[closest_state] + [tweet]
return tweets_by_state
def most_talkative_states(term):
"""Return a list of the top five states with the largest number of tweets
containing 'term' in descending order (from most to least).
If multiple states tie, return them in alphabetical order.
>>> most_talkative_states('texas')
[('TX', 1541), ('LA', 303), ('OK', 207), ('NM', 55), ('AR', 41)]
>>> most_talkative_states('soup')
[('CA', 57), ('NJ', 41), ('OH', 31), ('FL', 26), ('MA', 23)]
"""
tweets = load_tweets(make_tweet, term) # A list of tweets containing term
tweets_left = group_tweets_by_state(tweets)
top_five = []
for key in sorted(tweets_left):
if len(top_five) < 5:
top_five += [(key,len(tweets_left[key]))]
else:
top_five = sorted(top_five, key=lambda x: x[1], reverse=True)
if len(tweets_left[key]) > top_five[4][1]:
top_five[4] = (key,len(tweets_left[key]))
return top_five
def average_sentiments(tweets_by_state):
"""Calculate the average sentiment of the states by averaging over all
the tweets from each state. Return the result as a dictionary from state
names to average sentiment values (numbers).
If a state has no tweets with sentiment values, leave it out of the
dictionary entirely. Do NOT include states with no tweets, or with tweets
that have no sentiment, as 0. 0 represents neutral sentiment, not unknown
sentiment.
tweets_by_state -- A dictionary from state names to lists of tweets
"""
averaged_state_sentiments = {}
sentiments = []
for key in tweets_by_state:
sentiments = list(filter(lambda x: x != None,map(analyze_tweet_sentiment,tweets_by_state[key])))
if len(sentiments) != 0:
averaged_state_sentiments[key] = float(sum(sentiments))/float(len(sentiments))
return averaged_state_sentiments
######################################
# Phase 4: Into the Fourth Dimension #
######################################
def group_tweets_by_hour(tweets):
"""Return a dictionary that groups tweets by the hour they were posted.
The keys of the returned dictionary are the integers 0 through 23.
The values are lists of tweets, where tweets_by_hour[i] is the list of all
tweets that were posted between hour i and hour i + 1. Hour 0 refers to
midnight, while hour 23 refers to 11:00PM.
To get started, read the Python Library documentation for datetime objects:
http://docs.python.org/py3k/library/datetime.html#datetime.datetime
tweets -- A list of tweets to be grouped
>>> tweets = load_tweets(make_tweet, 'party')
>>> tweets_by_hour = group_tweets_by_hour(tweets)
>>> for hour in [0, 5, 9, 17, 23]:
... current_tweets = tweets_by_hour.get(hour, [])
... tweets_by_state = group_tweets_by_state(current_tweets)
... state_sentiments = average_sentiments(tweets_by_state)
... print('HOUR:', hour)
... for state in ['CA', 'FL', 'DC', 'MO', 'NY']:
... if state in state_sentiments.keys():
... print(state, ":", round(state_sentiments[state], 5))
HOUR: 0
CA : 0.08333
FL : -0.09635
DC : 0.01736
MO : -0.11979
NY : -0.15
HOUR: 5
CA : 0.00945
FL : -0.0651
DC : 0.03906
MO : 0.1875
NY : -0.04688
HOUR: 9
CA : 0.10417
NY : 0.25
HOUR: 17
CA : 0.09808
FL : 0.0875
MO : -0.1875
NY : 0.14583
HOUR: 23
CA : -0.10729
FL : 0.01667
DC : -0.3
MO : -0.0625
NY : 0.21875
"""
tweets_by_hour = {}
for hour in range(24):
tweets_by_hour[hour] = []
for tweet in tweets:
tweets_by_hour[tweet_time(tweet).hour] = tweets_by_hour[tweet_time(tweet).hour] + [tweet]
return tweets_by_hour
# Interaction. You don't need to read this section of the program.
def print_sentiment(text='Are you virtuous or verminous?'):
"""Print the words in text, annotated by their sentiment scores."""
words = extract_words(text.lower())
layout = '{0:>' + str(len(max(words, key=len))) + '}: {1:+}'
for word in words:
s = get_word_sentiment(word)
if has_sentiment(s):
print(layout.format(word, sentiment_value(s)))
def draw_centered_map(center_state='TX', n=10):
"""Draw the n states closest to center_state."""
us_centers = {n: find_state_center(s) for n, s in us_states.items()}
center = us_centers[center_state.upper()]
dist_from_center = lambda name: geo_distance(center, us_centers[name])
for name in sorted(us_states.keys(), key=dist_from_center)[:int(n)]:
draw_state(us_states[name])
draw_name(name, us_centers[name])
draw_dot(center, 1, 10) # Mark the center state with a red dot
wait()
def draw_state_sentiments(state_sentiments):
"""Draw all U.S. states in colors corresponding to their sentiment value.
Unknown state names are ignored; states without values are colored grey.
state_sentiments -- A dictionary from state strings to sentiment values
"""
for name, shapes in us_states.items():
sentiment = state_sentiments.get(name, None)
draw_state(shapes, sentiment)
for name, shapes in us_states.items():
center = find_state_center(shapes)
if center is not None:
draw_name(name, center)
def draw_map_for_term(term='my job'):
"""Draw the sentiment map corresponding to the tweets that contain term.
Some term suggestions:
New York, Texas, sandwich, my life, justinbieber
"""
tweets = load_tweets(make_tweet, term)
tweets_by_state = group_tweets_by_state(tweets)
state_sentiments = average_sentiments(tweets_by_state)
draw_state_sentiments(state_sentiments)
for tweet in tweets:
s = analyze_tweet_sentiment(tweet)
if has_sentiment(s):
draw_dot(tweet_location(tweet), sentiment_value(s))
if len(tweets) != 0:
draw_top_states(most_talkative_states(term))
else:
draw_top_states(None)
wait()
def draw_map_by_hour(term='my job', pause=0.5):
"""Draw the sentiment map for tweets that match term, for each hour."""
tweets = load_tweets(make_tweet, term)
tweets_by_hour = group_tweets_by_hour(tweets)
for hour in range(24):
current_tweets = tweets_by_hour.get(hour, [])
tweets_by_state = group_tweets_by_state(current_tweets)
state_sentiments = average_sentiments(tweets_by_state)
draw_state_sentiments(state_sentiments)
message("{0:02}:00-{0:02}:59".format(hour))
wait(pause)
def run_doctests(names):
"""Run verbose doctests for all functions in space-separated names."""
g = globals()
errors = []
for name in names.split():
if name not in g:
print("No function named " + name)
else:
run_docstring_examples(g[name], g, True, name)
def test_abstraction(names):
global make_position, longitude, latitude, us_states
global make_sentiment, has_sentiment, sentiment_value
import geo
print('--- Testing data abstraction violations for {} ---'.format(names))
make_position = geo.make_position = lambda lat, lon: lambda: (lat, lon)
latitude = geo.latitude = lambda p: p()[0]
longitude = geo.longitude = lambda p: p()[1]
us_states = geo.load_states()
make_sentiment = lambda v: lambda: v
has_sentiment = lambda s: s() is not None
sentiment_value = lambda s: s()
run_doctests(names)
print('------')
print("""If there are errors in the doctests, you have a data abstraction violation in {}""".format(names))
@main
def run(*args):
"""Read command-line arguments and calls corresponding functions."""
import argparse
parser = argparse.ArgumentParser(description="Run Trends")
parser.add_argument('--print_sentiment', '-p', action='store_true')
parser.add_argument('--run_doctests', '-t', action='store_true')
parser.add_argument('--draw_centered_map', '-d', action='store_true')
parser.add_argument('--draw_map_for_term', '-m', action='store_true')
parser.add_argument('--draw_map_by_hour', '-b', action='store_true')
parser.add_argument('--test_abstraction', '-a', action='store_true')
parser.add_argument('text', metavar='T', type=str, nargs='*',
help='Text to process')
args = parser.parse_args()
for name, execute in args.__dict__.items():
if name != 'text' and execute:
globals()[name](' '.join(args.text))
| [
"jesseyli@berkeley.edu"
] | jesseyli@berkeley.edu |
b2b18b0466f68363ec428575924ddb25850628e3 | 0ce934553a854e5a3d28971f73be19d0912449bf | /homePage/migrations/0002_auto_20190311_1333.py | e5ee1021a560d11d27a4092636182d934d75cbd7 | [] | no_license | keennhlc/GKWeb | d0c1c2617e2334ee9aba6e3b741d049cf75c9a62 | db34c14a4be13fab1cf16de66fc406b7142d7fcb | refs/heads/master | 2020-05-01T09:19:13.871041 | 2019-03-24T10:20:40 | 2019-03-24T10:20:40 | 177,397,584 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 464 | py | # Generated by Django 2.2b1 on 2019-03-11 05:33
from django.db import migrations
class Migration(migrations.Migration):
dependencies = [
('homePage', '0001_initial'),
]
operations = [
migrations.RemoveField(
model_name='newscontent',
name='news',
),
migrations.DeleteModel(
name='News',
),
migrations.DeleteModel(
name='NewsContent',
),
]
| [
"keennweb@gmail.com"
] | keennweb@gmail.com |
106c53998b513b93b70be5cc2982f708c3ecf2b3 | 5a4124eea866334a9e3ddd94c57dbf1df7e3378a | /virtual/bin/pip | 6d53621274d2a5698a9d66f0e36ee6c3cd195680 | [
"MIT"
] | permissive | Elrophi/Housing | 6eb465c06918e67faadbc0cdad4bfe6d139a178f | dc3ccc545eb9d609a62495cbea76f4f849d4658a | refs/heads/master | 2023-05-12T23:45:58.201434 | 2021-06-08T13:09:29 | 2021-06-08T13:09:29 | 373,784,359 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 276 | #!/home/el/Desktop/moringa-core/python-django/Housing/virtual/bin/python3
# -*- coding: utf-8 -*-
import re
import sys
from pip._internal.cli.main import main
if __name__ == '__main__':
sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0])
sys.exit(main())
| [
"elrophilskwaila@gmail.com"
] | elrophilskwaila@gmail.com | |
525051e2943540875900fe0b6db434ee527c30ba | 80d50ea48e10674b1b7d3f583a1c4b7d0b01200f | /examples/v1/usage-metering/GetUsageNetworkFlows_1239422069.py | 60afb66b6f88d5918aba22ca4b3b72c0ab5be76d | [
"Apache-2.0",
"BSD-3-Clause",
"MIT",
"MPL-2.0"
] | permissive | DataDog/datadog-api-client-python | 3e01fa630278ad0b5c7005f08b7f61d07aa87345 | 392de360e7de659ee25e4a6753706820ca7c6a92 | refs/heads/master | 2023-09-01T20:32:37.718187 | 2023-09-01T14:42:04 | 2023-09-01T14:42:04 | 193,793,657 | 82 | 36 | Apache-2.0 | 2023-09-14T18:22:39 | 2019-06-25T22:52:04 | Python | UTF-8 | Python | false | false | 599 | py | """
Get hourly usage for Network Flows returns "OK" response
"""
from datetime import datetime
from dateutil.relativedelta import relativedelta
from datadog_api_client import ApiClient, Configuration
from datadog_api_client.v1.api.usage_metering_api import UsageMeteringApi
configuration = Configuration()
with ApiClient(configuration) as api_client:
api_instance = UsageMeteringApi(api_client)
response = api_instance.get_usage_network_flows(
start_hr=(datetime.now() + relativedelta(days=-5)),
end_hr=(datetime.now() + relativedelta(days=-3)),
)
print(response)
| [
"noreply@github.com"
] | DataDog.noreply@github.com |
9a1838d05d52c92ed2187545c5cfa8e07d8125ed | d7871f3ff716919da9e7a7c9d7ba3a0732114d63 | /3DMean.py | 015010600c692df66f624d29270865533cf62baf | [] | no_license | eric-risbakk/kework | 337040d2b8df6915c9d9ed8ccd5303bf51b82f62 | 705556213a6f5fb9da28f4010d2ca4999b1f0915 | refs/heads/master | 2021-07-10T10:36:12.665117 | 2017-10-12T13:05:24 | 2017-10-12T13:05:24 | 103,539,937 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 3,673 | py |
import scipy.stats as spstat
import numpy as np
import numpy.random as npr
import scipy.stats as spstat
import numpy as np
import numpy.random as npr
__author__ = 'Eric Risbakk'
__date__ = "2017-09-14"
__maintainer__ = "Eric Risbakk"
__email__ = "e.risbakk@student.maastrichtuniversity.nl"
DEBUG = False
TEST = True
# Push test.
def online_mean_3d(ndarray, axis):
"""
Finds the mean in a 3d ndarray along the specified (int) axis.
Takes in the already complete array and finds the mean for it.
:param axis: Axis which we find the mean on.
:param ndarray: A 1-dimensional array.
:return: Arithmetic mean of simpleArray
"""
if len(ndarray) < 2:
return ndarray
else:
"""
# Creating the ndarray which will be the mean.
dimensions = []
for i in range(ndarray.ndim):
if i == axis:
continue
dimensions.append(ndarray.shape[i])
m = np.zeros(dimensions)
# Getting the mean for all points, using some recursion!
tempAxis = 0
"""
mean = ndarray[0]
n = 1
for x in ndarray[1:]:
mean = online_mean_step(x, mean, n)
n += 1
return mean
def recursive_truncation(mean, ndarray, currentAxis, axis):
# End-statement.
if currentAxis == ndarray.ndim:
return
# Skip this.
if currentAxis == axis:
recursive_truncation(mean, ndarray, currentAxis + 1, axis)
# Let's go depth first!
# Let's truncate this axis.
# TODO: FIGURE THIS OUT. IS IT EVEN POSSIBLE?
# TODO: MAYBE I SHOULD BE USING A TUPLE OR SOMETHING.
for i in range(ndarray.shape[axis]):
def online_mean_step(new_element, mean, n):
"""
Updates the mean, given newElement, old mean, and the number of elements before we add newElement.
NB: This method does not increase the number of element.
:param new_element: New Element.
:param mean: Old mean.
:param n: Old number of elements.
:return: The new mean.
"""
return (mean * n + new_element) / (n + 1)
def axis_online_mean_check(a1):
"""
Checks the mean of the last axis of a 3d ndarray, using onlineMeanCheck.
:param a1: The 3d ndarray.
:return: 2d ndarray averaged.
"""
if DEBUG:
print("axisOnlineMeanCheck begun.")
x = a1.shape[0]
y = a1.shape[1]
z = a1.shape[2]
if DEBUG:
print("Dimensions: ({} {} {})".format(x, y, z))
mean = np.zeros((x, y))
if DEBUG:
print("ndarray of zeroes created.")
if DEBUG:
print("Dimensions: ({} {})".format(mean.shape[0], mean.shape[1]))
if DEBUG:
print(mean)
for i in range(x):
for j in range(y):
mean[i, j] = online_mean_3d(a1[i, j, :])
if DEBUG:
print("End axisOnlineMeanCheck")
if DEBUG:
print(mean)
return mean
def get_avg(simple_array):
mean = 0
for x in simple_array:
mean += x
return mean/len(simple_array)
if TEST:
# First is rows, second is columns
a1 = npr.rand(2, 2, 2)
a1 *= 10
array_mean0 = np.mean(a1, axis=0)
array_mean1 = np.mean(a1, axis=1)
array_mean2 = np.mean(a1, axis=2)
print(a1)
print("\nMeans:")
print("\n First axis:")
print(array_mean0)
print("\n Second axis:")
print(array_mean1)
print("\n Third axis:")
print(array_mean2)
print("\nLet us attempt using axisMeanCheck.")
a_mean = axis_mean_check(a1)
print(a_mean)
print("\nLet us attempt using axisOnlineMeanCheck.")
b_mean = axis_online_mean_check(a1)
print(b_mean)
print("Finished.")
# End.
| [
"I6146197@unimaas.nl"
] | I6146197@unimaas.nl |
391b5234dde1dd8a811356eeeaa2d4e5f9d7a7ac | 1ae56f2ebb35f75c9e4ccb108b5eff2b158fa355 | /process_data.py | ab43b0172e6485bb40f1c534cb6c33c1d0795694 | [] | no_license | crrcgeorgia/air_quality | 5670d3b7390c2484d09238b9a985deb54688b27c | c16d8cd9b816cabccf425c4acb70d12dc32c9be1 | refs/heads/master | 2021-05-18T11:41:15.670321 | 2020-03-30T08:12:02 | 2020-03-30T08:12:02 | 251,230,409 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,799 | py | from pull_data import pull_airq_range, pull_weather
from datetime import date, datetime
import pandas as pd
from fbprophet import Prophet
import progressbar
def air_processed(start_date, end_date, cut_points, cut_labs, *args, **kwargs):
air = (
pull_airq_range(start_date, end_date)
.query('settlement_en == "Tbilisi"')
.groupby(["ds", "substance"])["value"]
.mean()
.reset_index()
.pivot(index="ds", columns="substance", values="value")
.reset_index()
.assign(
cut=lambda x: pd.cut(
x["ds"], bins=cut_points, labels=cut_labs, include_lowest=True
)
)
)
return air
def extract_seasonality(air: pd.DataFrame):
subs = ["PM10", "PM2.5", "NO2", "O3", "SO2", "CO"]
with progressbar.ProgressBar(max_value=len(subs)) as bar:
for n, sub in enumerate(subs):
df = air[["ds", sub]].rename(columns={sub: "y"})
m = Prophet()
m.fit(df)
future = m.make_future_dataframe(periods=1)
out = m.predict(future)
out = out[[i for i in out if "_" not in i and i != "yhat"]]
out["ds"] = pd.to_datetime(out.ds)
out = out.query("ds < datetime.now()")
out = out.rename(
columns={i: f"{sub}_" + i for i in out if i != "ds"}
)
air = air.merge(out, on="ds", how="left")
bar.update(n)
return air
def load_processed_data(start_date, end_date, *args, **kwargs):
air = air_processed(start_date, end_date, *args, **kwargs)
air = extract_seasonality(air)
air = pd.merge_asof(
air, pull_weather(start_date, end_date), on="ds"
).dropna()
return air
| [
"noreply@github.com"
] | crrcgeorgia.noreply@github.com |
a8e3cce479c0b620026b4944db203dae7b0979bd | b29d80506512e9cec2aa820c043c616751b13dcf | /serializers.py | 671d4c81e4ccca93029d7c7bbb733c0ca80d62a2 | [] | no_license | luvjoey1996/storageManager | f25e8d359294f28799e592f49febeea0ca1acb06 | 2d66820f05a80245e2c5ef2bcf56b00d3c747b8a | refs/heads/main | 2023-08-03T17:41:18.327926 | 2021-09-17T10:23:49 | 2021-09-17T10:23:49 | 407,498,139 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,101 | py | from marshmallow import Schema, fields, validate
from config import Configuration
from models import ServiceType
class CreateServiceSchema(Schema):
type = fields.Str()
class ServiceCreateSchema(Schema):
type = fields.Int(validate=validate.OneOf(choices=ServiceType.as_choices()))
memory = fields.Int(validate=validate.Range(min=1, max=Configuration.MEMORY_TOTAL - Configuration.MEMORY_RESERVE),
missing=1)
class PaginationSchema(Schema):
page_size = fields.Int(validate=validate.Range(min=10, max=30))
current_page = fields.Int(validate=validate.Range(min=1))
class ServicePagingSchema(Schema):
name = fields.Str()
type = fields.Int()
class SettingSchema(Schema):
type = fields.Int()
name = fields.Str()
value = fields.Str()
class ServiceSchema(Schema):
name = fields.Str()
type = fields.Int()
memory = fields.Int()
state = fields.Int()
error_no = fields.Int()
settings = SettingSchema(many=True)
username = fields.Str()
password = fields.Str()
port = fields.Int()
ip = fields.Str()
| [
"luvjoey1996@gmail.com"
] | luvjoey1996@gmail.com |
16ffe2ce0b7d1d05344cc7814fd04b63e4a84196 | 98c6ea9c884152e8340605a706efefbea6170be5 | /examples/data/Assignment_4/hrbmax002/piglatin.py | 32eb09647dd7f6c75cec56edc0b28a10e8811327 | [] | no_license | MrHamdulay/csc3-capstone | 479d659e1dcd28040e83ebd9e3374d0ccc0c6817 | 6f0fa0fa1555ceb1b0fb33f25e9694e68b6a53d2 | refs/heads/master | 2021-03-12T21:55:57.781339 | 2014-09-22T02:22:22 | 2014-09-22T02:22:22 | 22,372,174 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 998 | py | def toPigLatin(s):
if s[len(s)-1] != " ":
s = s + " "
answer = ""
while len(s)>0:
temp = s[0:s.index(" ")]
s = s[s.index(" ")+1:]
if temp[0].upper() in ["A","E","I","O","U"]:
temp = temp + "way "
else:
temp = temp + "a"
while temp[0].upper() not in ["A","E","I","O","U"]:
temp = temp[1:] + temp[0]
temp = temp + "ay "
answer = answer + temp
answer = answer[0:len(answer)-1]
return answer
def toEnglish(s):
if s[len(s)-1] != " ":
s = s + " "
answer = ""
while len(s)>0:
temp = s[0:s.index(" ")]
s = s[s.index(" ")+1:]
if temp[-3:]=="way":
answer = answer + " " + temp[0:-3]
else:
temp = temp[0:-2]
while temp[-1] != "a":
temp = temp[-1] + temp[0:-1]
answer = answer + " " + temp[0:-1]
return answer[1:] | [
"jarr2000@gmail.com"
] | jarr2000@gmail.com |
707c7cb4f5f704f84ee4f3b07f62df36cb7bb8a2 | e1152ed447cf32f12acc1d71fddce1f6a1830023 | /zhaquirks/xiaomi/aqara_vibration_sensor.py | d1d8defe395ddaab7cea4adfeecbf5bcbd0962a7 | [
"Apache-2.0"
] | permissive | Gamester17/zha-device-handlers | f85d2ca864f7e6f977e46c3562b06ce9a3225790 | e7260ebb31025fbbbe5c5a9c2c8e7077aa85b66f | refs/heads/master | 2020-04-25T04:51:05.210691 | 2019-02-22T13:55:10 | 2019-02-22T13:55:10 | 172,523,970 | 0 | 0 | Apache-2.0 | 2019-02-25T14:44:06 | 2019-02-25T14:44:05 | null | UTF-8 | Python | false | false | 7,119 | py | import asyncio
import logging
import homeassistant.components.zha.const as zha_const
from zigpy.quirks import CustomCluster
from zigpy.profiles import PROFILES, zha
import zigpy.types as types
from zigpy.zcl.clusters.general import Basic, Groups, PowerConfiguration,\
Identify, Ota, Scenes, MultistateInput
from zigpy.zcl.clusters.closures import DoorLock
from zigpy.zcl.clusters.security import IasZone
from zhaquirks.xiaomi import BasicCluster, PowerConfigurationCluster,\
TemperatureMeasurementCluster, XiaomiCustomDevice
from zhaquirks import Bus, LocalDataCluster
VIBE_DEVICE_TYPE = 0x5F02 # decimal = 24322
RECENT_ACTIVITY_LEVEL_ATTR = 0x0505 # decimal = 1285
ACCELEROMETER_ATTR = 0x0508 # decimal = 1288
STATUS_TYPE_ATTR = 0x0055 # decimal = 85
ROTATION_DEGREES_ATTR = 0x0503 # decimal = 1283
STATIONARY_VALUE = 0
VIBE_VALUE = 1
TILT_VALUE = 2
DROP_VALUE = 3
MEASUREMENT_TYPE = {
STATIONARY_VALUE: "Stationary",
VIBE_VALUE: "Vibration",
TILT_VALUE: "Tilt",
DROP_VALUE: "Drop"
}
_LOGGER = logging.getLogger(__name__)
PROFILES[zha.PROFILE_ID].CLUSTERS[VIBE_DEVICE_TYPE] = (
[
Basic.cluster_id,
Identify.cluster_id,
Ota.cluster_id,
DoorLock.cluster_id,
MultistateInput.cluster_id,
IasZone.cluster_id
],
[
Basic.cluster_id,
Identify.cluster_id,
Groups.cluster_id,
Scenes.cluster_id,
Ota.cluster_id,
DoorLock.cluster_id,
MultistateInput.cluster_id
]
)
if zha.PROFILE_ID not in zha_const.DEVICE_CLASS:
zha_const.DEVICE_CLASS[zha.PROFILE_ID] = {}
zha_const.DEVICE_CLASS[zha.PROFILE_ID].update(
{
VIBE_DEVICE_TYPE: 'binary_sensor'
}
)
class AqaraVibrationSensor(XiaomiCustomDevice):
def __init__(self, *args, **kwargs):
self.motionBus = Bus()
super().__init__(*args, **kwargs)
class VibrationBasicCluster(BasicCluster):
cluster_id = BasicCluster.cluster_id
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self.attributes.update({
0xFF0D: ('sensitivity', types.uint8_t),
})
class MultistateInputCluster(CustomCluster, MultistateInput):
cluster_id = DoorLock.cluster_id
def __init__(self, *args, **kwargs):
self._currentState = {}
super().__init__(*args, **kwargs)
def _update_attribute(self, attrid, value):
super()._update_attribute(attrid, value)
if attrid == STATUS_TYPE_ATTR:
self._currentState[STATUS_TYPE_ATTR] = MEASUREMENT_TYPE.get(
value
)
if value == VIBE_VALUE:
self.endpoint.device.motionBus.listener_event(
'motion_event'
)
elif value == DROP_VALUE:
self.listener_event(
'zha_send_event',
self,
self._currentState[STATUS_TYPE_ATTR],
{}
)
elif attrid == ROTATION_DEGREES_ATTR:
self.listener_event(
'zha_send_event',
self,
self._currentState[STATUS_TYPE_ATTR],
{
'degrees': value
}
)
elif attrid == RECENT_ACTIVITY_LEVEL_ATTR:
# these seem to be sent every minute when vibration is active
self.endpoint.device.motionBus.listener_event(
'motion_event'
)
class MotionCluster(LocalDataCluster, IasZone):
cluster_id = IasZone.cluster_id
ZONE_STATE = 0x0000
ZONE_TYPE = 0x0001
ZONE_STATUS = 0x0002
VIBRATION_TYPE = 0x002d
ON = 1
OFF = 0
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self._timer_handle = None
self.endpoint.device.motionBus.add_listener(self)
self._update_attribute(self.ZONE_STATE, self.OFF)
self._update_attribute(self.ZONE_TYPE, self.VIBRATION_TYPE)
self._update_attribute(self.ZONE_STATUS, self.OFF)
def motion_event(self):
super().listener_event(
'cluster_command',
None,
self.ZONE_STATE,
[self.ON]
)
super().listener_event(
'cluster_command',
None,
self.ZONE_STATUS,
[self.ON]
)
if self._timer_handle:
self._timer_handle.cancel()
loop = asyncio.get_event_loop()
self._timer_handle = loop.call_later(75, self._turn_off)
def _turn_off(self):
self._timer_handle = None
super().listener_event(
'cluster_command',
None,
self.ZONE_STATE,
[self.OFF]
)
super().listener_event(
'cluster_command',
None,
self.ZONE_STATUS,
[self.OFF]
)
signature = {
1: {
'profile_id': zha.PROFILE_ID,
'device_type': zha.DeviceType.DOOR_LOCK,
'input_clusters': [
Basic.cluster_id,
Identify.cluster_id,
Ota.cluster_id,
DoorLock.cluster_id
],
'output_clusters': [
Basic.cluster_id,
Identify.cluster_id,
Groups.cluster_id,
Scenes.cluster_id,
Ota.cluster_id,
DoorLock.cluster_id
],
},
2: {
'profile_id': zha.PROFILE_ID,
'device_type': VIBE_DEVICE_TYPE,
'input_clusters': [
Identify.cluster_id,
MultistateInput.cluster_id
],
'output_clusters': [
Identify.cluster_id,
Groups.cluster_id,
Scenes.cluster_id,
MultistateInput.cluster_id
],
},
}
replacement = {
'endpoints': {
1: {
'manufacturer': 'LUMI',
'model': 'lumi.vibration.aq1',
'device_type': VIBE_DEVICE_TYPE,
'input_clusters': [
VibrationBasicCluster,
PowerConfigurationCluster,
TemperatureMeasurementCluster,
Identify.cluster_id,
MultistateInputCluster,
MotionCluster
],
'output_clusters': [
VibrationBasicCluster,
Identify.cluster_id,
Groups.cluster_id,
Scenes.cluster_id,
Ota.cluster_id,
DoorLock.cluster_id
],
}
},
}
| [
"david.mulcahey@icloud.com"
] | david.mulcahey@icloud.com |
eb085418aab782c970d7166273fd9b9262c46f5b | c858d9511cdb6a6ca723cd2dd05827d281fa764d | /MFTU/lesson 7/Test work/test_F.py | b6885f38ec053864866d442146f62a2ba115c3a5 | [] | no_license | DontTouchMyMind/education | 0c904aa929cb5349d7af7e06d9b1bbaab972ef95 | 32a53eb4086b730cc116e633f68cf01f3d4ec1d1 | refs/heads/master | 2021-03-12T11:15:02.479779 | 2020-09-17T08:19:50 | 2020-09-17T08:19:50 | 246,616,542 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 577 | py | # Необходимо найти НОД двух чисел, используя алгоритм Евклида.
#
# Формат входных данных
# На вход подаются два натуральных числа, по числу в новой строке.
#
# Формат выходных данных
# Одно число - НОД входных чисел.
def gcd(a, b):
if a == b:
return a
elif a > b:
return gcd(a - b, b)
else:
return gcd(a, b - a)
n1 = int(input())
n2 = int(input())
print(gcd(n1, n2))
| [
"tobigface@gmail.com"
] | tobigface@gmail.com |
6408b1b91926c28f6a0816eef12e75332aba15ac | 4113e7c9f1beb13a0ef963a6760e43b5cab676ce | /__init__.py | 4781cbd159824f702baafe799f5dee329fb4d432 | [] | no_license | charlieb/flowers | b413595b8ce1839123305ad96bdae6213a8b7faf | dca18ef10b849ee01518818279426699749a655e | refs/heads/master | 2020-04-19T08:36:48.699934 | 2017-01-09T04:12:00 | 2017-01-09T04:12:00 | 66,811,292 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 40 | py | from .petals import petal, flower, draw
| [
"charlie.burrows@gmail.com"
] | charlie.burrows@gmail.com |
51705550782e5a0f8c41b524d7d0cf60b7edc565 | fcbf3ddca275606830d455a69df73e20ced6546a | /doc/conf.py | 9ca4ca664b3f765a31dd264254f24c060e447023 | [
"Apache-2.0"
] | permissive | KarchinLab/probabilistic2020 | 5f56e30e0c8484ac524081dd022c0159f24508ce | 8e0b1b9578bd8189b1690dd2f17476c3305b98dc | refs/heads/master | 2023-07-26T12:06:28.647117 | 2019-07-28T12:37:50 | 2019-07-28T12:37:50 | 57,408,263 | 8 | 7 | Apache-2.0 | 2023-07-06T21:02:44 | 2016-04-29T19:32:49 | Python | UTF-8 | Python | false | false | 8,727 | py | # -*- coding: utf-8 -*-
#
# 20/20 Permutation Test documentation build configuration file, created by
# sphinx-quickstart on Mon Jul 28 13:53:42 2014.
#
# This file is execfile()d with the current directory set to its containing dir.
#
# Note that not all possible configuration values are present in this
# autogenerated file.
#
# All configuration values have a default; values that are commented out
# serve to show the default.
import sys, os
# If extensions (or modules to document with autodoc) are in another directory,
# add these directories to sys.path here. If the directory is relative to the
# documentation root, use os.path.abspath to make it absolute, like shown here.
sys.path.insert(0, os.path.abspath('..'))
sys.path.insert(0, os.path.abspath('.'))
sys.path.insert(0, os.path.abspath('./img'))
# on_rtd is whether we are on readthedocs.org, this line of code grabbed from docs.readthedocs.org
on_rtd = os.environ.get('READTHEDOCS', None) == 'True'
if not on_rtd: # only import and set the theme if we're building docs locally
import sphinx_rtd_theme
html_theme = 'sphinx_rtd_theme'
html_theme_path = [sphinx_rtd_theme.get_html_theme_path()]
# -- General configuration -----------------------------------------------------
# If your documentation needs a minimal Sphinx version, state it here.
#needs_sphinx = '1.0'
# Add any Sphinx extension module names here, as strings. They can be extensions
# coming with Sphinx (named 'sphinx.ext.*') or your custom ones.
extensions = ['sphinx.ext.autodoc',
'sphinx.ext.doctest',
'sphinx.ext.mathjax',
'sphinx.ext.viewcode',
#'numpydoc',
#'IPython.sphinxext.ipython_console_highlighting',
#'IPython.sphinxext.ipython_directive',
#'matplotlib.sphinxext.plot_directive'
]
# Add any paths that contain templates here, relative to this directory.
templates_path = ['_templates']
# The suffix of source filenames.
source_suffix = '.rst'
# The encoding of source files.
#source_encoding = 'utf-8-sig'
# The master toctree document.
master_doc = 'index'
# General information about the project.
project = u'Probabilistic 20/20'
copyright = u'2014-19, Collin Tokheim'
# The version info for the project you're documenting, acts as replacement for
# |version| and |release|, also used in various other places throughout the
# built documents.
#
# The short X.Y version.
version = '1.2'
# The full version, including alpha/beta/rc tags.
release = '1.2.3'
# The language for content autogenerated by Sphinx. Refer to documentation
# for a list of supported languages.
#language = None
# There are two options for replacing |today|: either, you set today to some
# non-false value, then it is used:
#today = ''
# Else, today_fmt is used as the format for a strftime call.
#today_fmt = '%B %d, %Y'
# List of patterns, relative to source directory, that match files and
# directories to ignore when looking for source files.
exclude_patterns = ['_build']
# The reST default role (used for this markup: `text`) to use for all documents.
#default_role = None
# If true, '()' will be appended to :func: etc. cross-reference text.
#add_function_parentheses = True
# If true, the current module name will be prepended to all description
# unit titles (such as .. function::).
#add_module_names = True
# If true, sectionauthor and moduleauthor directives will be shown in the
# output. They are ignored by default.
#show_authors = False
# The name of the Pygments (syntax highlighting) style to use.
pygments_style = 'sphinx'
# A list of ignored prefixes for module index sorting.
#modindex_common_prefix = []
# -- Options for HTML output ---------------------------------------------------
# The theme to use for HTML and HTML Help pages. See the documentation for
# a list of builtin themes.
# html_theme = 'sphinx_rtd_theme'
# Theme options are theme-specific and customize the look and feel of a theme
# further. For a list of options available for each theme, see the
# documentation.
#html_theme_options = {}
# Add any paths that contain custom themes here, relative to this directory.
#html_theme_path = []
#html_theme_path = [sphinx_rtd_theme.get_html_theme_path()]
# The name for this set of Sphinx documents. If None, it defaults to
# "<project> v<release> documentation".
#html_title = None
# A shorter title for the navigation bar. Default is the same as html_title.
#html_short_title = None
# The name of an image file (relative to this directory) to place at the top
# of the sidebar.
#html_logo = None
# The name of an image file (within the static path) to use as favicon of the
# docs. This file should be a Windows icon file (.ico) being 16x16 or 32x32
# pixels large.
#html_favicon = None
# Add any paths that contain custom static files (such as style sheets) here,
# relative to this directory. They are copied after the builtin static files,
# so a file named "default.css" will overwrite the builtin "default.css".
html_static_path = ['_static']
# If not '', a 'Last updated on:' timestamp is inserted at every page bottom,
# using the given strftime format.
#html_last_updated_fmt = '%b %d, %Y'
# If true, SmartyPants will be used to convert quotes and dashes to
# typographically correct entities.
#html_use_smartypants = True
# Custom sidebar templates, maps document names to template names.
#html_sidebars = {}
# Additional templates that should be rendered to pages, maps page names to
# template names.
#html_additional_pages = {}
# If false, no module index is generated.
#html_domain_indices = True
# If false, no index is generated.
#html_use_index = True
# If true, the index is split into individual pages for each letter.
#html_split_index = False
# If true, links to the reST sources are added to the pages.
#html_show_sourcelink = True
# If true, "Created using Sphinx" is shown in the HTML footer. Default is True.
#html_show_sphinx = True
# If true, "(C) Copyright ..." is shown in the HTML footer. Default is True.
#html_show_copyright = True
# If true, an OpenSearch description file will be output, and all pages will
# contain a <link> tag referring to it. The value of this option must be the
# base URL from which the finished HTML is served.
#html_use_opensearch = ''
# This is the file name suffix for HTML files (e.g. ".xhtml").
#html_file_suffix = None
# Output file base name for HTML help builder.
htmlhelp_basename = 'Probabilistic2020doc'
# -- Options for LaTeX output --------------------------------------------------
latex_elements = {
# The paper size ('letterpaper' or 'a4paper').
#'papersize': 'letterpaper',
# The font size ('10pt', '11pt' or '12pt').
#'pointsize': '10pt',
# Additional stuff for the LaTeX preamble.
#'preamble': '',
}
# Grouping the document tree into LaTeX files. List of tuples
# (source start file, target name, title, author, documentclass [howto/manual]).
latex_documents = [
('index', 'Probabilistic2020.tex', u'Probabilistic 20/20 Documentation',
u'Collin Tokheim', 'manual'),
]
# The name of an image file (relative to this directory) to place at the top of
# the title page.
#latex_logo = None
# For "manual" documents, if this is true, then toplevel headings are parts,
# not chapters.
#latex_use_parts = False
# If true, show page references after internal links.
#latex_show_pagerefs = False
# If true, show URL addresses after external links.
#latex_show_urls = False
# Documents to append as an appendix to all manuals.
#latex_appendices = []
# If false, no module index is generated.
#latex_domain_indices = True
# -- Options for manual page output --------------------------------------------
# One entry per manual page. List of tuples
# (source start file, name, description, authors, manual section).
man_pages = [
('index', 'Probabilistic 20/20 Documentation', u'Probabilistic 20/20 Documentation',
[u'Collin Tokheim'], 1)
]
# If true, show URL addresses after external links.
#man_show_urls = False
# -- Options for Texinfo output ------------------------------------------------
# Grouping the document tree into Texinfo files. List of tuples
# (source start file, target name, title, author,
# dir menu entry, description, category)
texinfo_documents = [
('index', 'Probabilistic2020', u'Probabilistic 20/20 Documentation',
u'Collin Tokheim', 'Probabilistic2020', 'One line description of project.',
'Miscellaneous'),
]
# Documents to append as an appendix to all manuals.
#texinfo_appendices = []
# If false, no module index is generated.
#texinfo_domain_indices = True
# How to display URL addresses: 'footnote', 'no', or 'inline'.
#texinfo_show_urls = 'footnote'
| [
"collintokheim@gmail.com"
] | collintokheim@gmail.com |
ad0cdab693cf632e6b1795e623fb5a5965e77727 | 18a0f4ddefae1e9a0ddae86de7315bacd1a96491 | /apps/users/views.py | 6681229876742a55d927c790d13ae2c52aa24ef3 | [] | no_license | ZVR999/belt_reviewer | f1a2d41bcced69f906d73178f06acc4f6b215431 | ffe0099813bfb1389c0a52f400ba00fc2d9acf86 | refs/heads/master | 2020-03-29T04:05:46.229105 | 2020-02-11T17:40:10 | 2020-02-11T17:40:10 | 149,515,533 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 2,310 | py | # -*- coding: utf-8 -*-
from __future__ import unicode_literals
from django.shortcuts import render, redirect
from .models import User
from ..reviews.models import Review
from ..books.models import Book
import bcrypt
from django.contrib import messages
# Create your views here.
# Create a User
def create(request):
errors = User.objects.basic_validator(request.POST)
hashed_pw = bcrypt.hashpw(
request.POST['password'].encode(), bcrypt.gensalt())
if len(errors):
for tag, error in errors.iteritems():
messages.error(request, error, extra_tags=tag)
return redirect('/')
user_exists = User.objects.filter(email=request.POST['email'])
if user_exists:
messages.error(request, 'This email is already in use')
return redirect('/')
else:
request.session['alias'] = request.POST['alias']
name = request.POST['name']
alias = request.POST['alias']
email = request.POST['email']
User.objects.create(name=name, alias=alias,
email=email, password=hashed_pw)
return redirect('/books')
# Login a User
def login(request):
user_exists = User.objects.filter(email=request.POST['email'])
if user_exists:
db_password = str(user_exists[0].password)
if bcrypt.checkpw(str(request.POST['password']), db_password.encode()):
request.session['alias'] = user_exists[0].alias
return redirect('/books')
else:
messages.error(request, 'Invalid email or password')
else:
messages.error(request, 'Invalid email or password')
return redirect('/')
def show(request, user_id):
user = User.objects.get(id=user_id)
Review.objects.filter()
context = {
'user': User.objects.get(id=user_id),
'books': Review.objects.raw('SELECT DISTINCT "books_Book"."id", "books_Book"."name" FROM reviews_Review JOIN books_Book ON "reviews_Review"."book_id"="books_Book"."id" WHERE "reviews_Review"."user_id"='+str(user.id)+';')
}
request.session['total'] = Review.objects.filter(user=User.objects.filter(id=user_id)).count()
return render(request, 'users/user.html', context)
def logout(request):
request.session['alias'] = 'Please Login or Register'
return redirect('/')
| [
"zachkery999@gmail.com"
] | zachkery999@gmail.com |
33e3abf7249f08d1377e5d1805da84937ab779eb | 379934f86f2e7fce60c88222ed61bc106390271e | /glasslab/dataanalysis/misc/gr_project_2012/v1/boxplots_from_p65_gr_peaks.py | e7313cb2c1ebf8f13f60ddced648317001bc413a | [] | no_license | karmel/glasslab | a022fb3e1147382ba5f64c67d6db9b87b9bca2de | 754774390f03852d1385c5fffeb32fcdab5cd7e4 | refs/heads/master | 2021-09-04T18:00:49.650817 | 2014-10-06T19:37:25 | 2014-10-06T19:37:25 | 5,957,226 | 1 | 1 | null | 2019-09-22T16:55:29 | 2012-09-25T21:56:42 | Python | UTF-8 | Python | false | false | 1,966 | py | '''
Created on Oct 1, 2012
@author: karmel
'''
from __future__ import division
from glasslab.dataanalysis.graphing.seq_grapher import SeqGrapher
if __name__ == '__main__':
yzer = SeqGrapher()
dirpath = 'karmel/Desktop/Projects/Classes/Rotations/Finland_2012/GR_Project/'
dirpath = yzer.get_path(dirpath)
img_dirpath = yzer.get_and_create_path(dirpath, 'boxplots_from_p65_gr')
if True:
for main, compare, basal_cond in (('p65','GR', 'KLA'),('GR','p65', 'Dex')):
data = yzer.import_file(yzer.get_filename(dirpath, 'motifs', 'from_peaks',
'{0}_kla_dex_vectors.txt'.format(main)))
data = data.fillna(0)
data = data.groupby(['id','chr_name'],as_index=False).mean()
data = data[data['tag_count_2'] > 0]
colname = 'tag_count_diff'
data[colname] = (data['tag_count'] - data['tag_count_2'])/data['tag_count']
cond_1 = (data['tag_count_3'] == 0)
cond_2 = (data['tag_count_3'] > 0) & (data['tag_count_3'] < data['tag_count_4'] )
cond_3 = (data['tag_count_3'] > 0) & (data['tag_count_3'] >= data['tag_count_4'] )
title = 'Difference in {0} peak tag counts by {1}'.format(main, compare)
names = [s.format(compare) for s in ['No {0} in KLA+Dex','Loses {0} in KLA+Dex','Gains/maintains {0} in KLA+Dex']]
ax = yzer.boxplot([data[cond_1][colname], data[cond_2][colname], data[cond_3][colname]],
names,
title=title,
xlabel='Condition',
ylabel='{0} KLA+Dex tags in peak - {0} {1} tags in peak'.format(main, basal_cond),
show_outliers=False, show_plot=False)
yzer.save_plot(yzer.get_filename(img_dirpath, title.replace(' ','_')))
yzer.show_plot() | [
"karmel@arcaio.com"
] | karmel@arcaio.com |
07dc4711d757ebe944fcd4427827ad60b56c0574 | 6aaea15dbf99219f03b08f14582c4bbe085b41fc | /0304/bj7576_토마토.py | 16dd692c61cbd5d68aae8be8e2e55edb6aa70529 | [] | no_license | kkkin02/Algorithm | f6ade2d7bebc3bd878b0e1fb1b22206f146fcea7 | 797223528b344faea497fe8390d78cd683cd063f | refs/heads/master | 2023-03-17T02:40:38.407345 | 2021-03-05T14:01:54 | 2021-03-05T14:01:54 | 337,080,081 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,159 | py | def tomato(t, box):
l = len(t)
if l == 0:
return -1
elif l == n * m:
return 0
dr = [-1, 1, 0, 0]
dc = [0, 0, -1, 1]
visited = [[0] * m for _ in range(n)]
q = []
for i in t:
q.append(i)
visited[i[0]][i[1]] = 1
while q:
p = q.pop(0)
r = p[0]
c = p[1]
for j in range(4):
nr = r + dr[j]
nc = c + dc[j]
if 0 <= nr < n and 0 <= nc < m and box[nr][nc] == 0 and visited[nr][nc] == 0:
box[nr][nc] = 1
q.append([nr, nc])
visited[nr][nc] = visited[r][c] + 1
result = 0
for x in range(n):
for y in range(n):
if box[x][y] == 0:
return -1
elif box[x][y] == 1:
if visited[x][y] > result:
result = visited[x][y]
return result - 1
m, n = map(int, input().split())
box = []
for _ in range(n):
box.append(list(map(int, input().split())))
t = []
for i in range(n):
for j in range(m):
if box[i][j] == 1:
t.append([i, j])
break
print(tomato(t, box)) | [
"rkddlsdud0720@gmail.com"
] | rkddlsdud0720@gmail.com |
6450073c33cb50db18dc4b145b95d18e75ee47b0 | e2d22f12f8e540a80d31de9debe775d35c3c5c22 | /blousebrothers/confs/migrations/0037_auto_20170117_1535.py | 6841343b2a40c2fbb431ff15ae9ddfd4cd5a80ee | [
"MIT"
] | permissive | sladinji/blousebrothers | 360c3b78ec43379977dbf470e5721e6a695b2354 | 461de3ba011c0aaed3f0014136c4497b6890d086 | refs/heads/master | 2022-12-20T10:24:07.631454 | 2019-06-13T13:17:35 | 2019-06-13T13:17:35 | 66,867,705 | 1 | 0 | NOASSERTION | 2022-12-19T18:15:44 | 2016-08-29T18:04:33 | Python | UTF-8 | Python | false | false | 813 | py | # -*- coding: utf-8 -*-
# Generated by Django 1.9.7 on 2017-01-17 15:35
from __future__ import unicode_literals
from decimal import Decimal
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('confs', '0036_auto_20170110_1100'),
]
operations = [
migrations.AlterField(
model_name='conference',
name='price',
field=models.DecimalField(decimal_places=2, default=Decimal('0.5'), help_text='', max_digits=6, verbose_name='Prix de vente'),
),
migrations.AlterField(
model_name='conference',
name='type',
field=models.CharField(choices=[('DCP', 'DCP'), ('QI', 'QI'), ('LCA', 'LCA')], default='DP', max_length=10, verbose_name='Type'),
),
]
| [
"julien.almarcha@gmail.com"
] | julien.almarcha@gmail.com |
0450edaf3b101ccb33050e851f1cf7ff76c42e14 | 8fd279f728b7a83e6f14fd6ab77da67459bd21df | /test.py | eed4c5c5c238b7175223f43ce87c4e216551fbf3 | [] | no_license | yiebo/stt-transformer | ec4de39fb3f54c8ab264aaa69f1b9b41c83d303f | 78a06451085064de0fd2417764183a1f4ea4b4d0 | refs/heads/master | 2022-09-09T11:32:19.265157 | 2020-06-05T16:06:16 | 2020-06-05T16:06:16 | 266,639,088 | 3 | 0 | null | null | null | null | UTF-8 | Python | false | false | 3,302 | py | from tqdm import tqdm
import glob
import os
import numpy as np
from prefetch_generator import BackgroundGenerator
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.data import DataLoader
from torch.utils import tensorboard
from torchvision import utils
import torchaudio
from torchaudio.transforms import GriffinLim, InverseMelScale, Resample, Spectrogram, MelScale
from ops import positional_encoding
from util import to_device, plot_att_heads, text_id_to_string
from model import Encoder, Decoder
from dataset import Dataset, _symbol_to_id
from audio_process import sample_rate, rescale_mel, scale_mel, MelWav
import time
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
epoch_total = 64
batch_size = 4
enc_lr = 0.0001
dec_lr = 0.0005
emb_lr = 0.0001
sym_dim = len(_symbol_to_id)
mel_lin = InverseMelScale(n_stft=1024 // 2 + 1, n_mels=80, sample_rate=sample_rate,
max_iter=2*2048).to(device)
griffin_lim = GriffinLim(n_fft=1024, hop_length=256).to(device)
writer = tensorboard.SummaryWriter(log_dir=f'logs/test')
dataset = Dataset('../DATASETS/LJSpeech-1.1/metadata.csv', '../DATASETS/LJSpeech-1.1')
dataloader = DataLoader(dataset, collate_fn=dataset.collocate, batch_size=batch_size,
shuffle=False, num_workers=0, drop_last=True)
resample = Resample(orig_freq=22050, new_freq=sample_rate)
spectogram = Spectrogram(n_fft=1024, hop_length=256).to(device)
to_mel = MelScale(n_mels=80, sample_rate=sample_rate,
n_stft=1024 // 2 + 1).to(device)
with open('../DATASETS/LJSpeech-1.1/metadata.csv', encoding='utf8') as file:
data = [line.strip().split('|') for line in file]
path, text = data[0][0], data[0][1]
path = f'../DATASETS/LJSpeech-1.1/wavs/{path}.wav'
data, sr = torchaudio.load(path)
data = resample(data)
data = data.to(device)
data = spectogram(data.squeeze(0))
mel_norm = ((data.unsqueeze(0) - data.mean()) / data.std()).clamp(-1, 1) * .5 + .5
writer.add_image(f'spec/origin', mel_norm, 0)
writer.add_audio(f'audio/origin', griffin_lim(data), global_step=0, sample_rate=sample_rate)
data = to_mel(data)
data = scale_mel(data)
data = rescale_mel(data)
data = mel_lin(data)
mel_norm = ((data.unsqueeze(0) - data.mean()) / data.std()).clamp(-1, 1) * .5 + .5
writer.add_image(f'spec/re', mel_norm, 0)
writer.add_audio(f'audio/re', griffin_lim(data), global_step=0, sample_rate=sample_rate)
mel_wav = MelWav().to(device)
for batch in dataloader:
text_data, text_pos, text_len, text_mask, mel_data, mel_pos, mel_len, mel_mask, gate = to_device(batch, device)
start = time.time()
# data = mel_wav(mel_data, mel_mask[:, -1].unsqueeze(1))
x = mel_data.transpose(-2, -1)
x = rescale_mel(x)
x = mel_lin(x)
mel_norm = ((x - x.mean()) / x.std()).clamp(-1, 1) * .5 + .5
writer.add_image(f'spec/all2', mel_norm[:1], 0)
x = griffin_lim(x)
for sample in x:
writer.add_audio(f'audio/all2', sample, global_step=0, sample_rate=sample_rate)
print(time.time() - start)
start = time.time()
for data, mel_len_ in zip(mel_data, mel_len):
writer.add_audio(f'audio/all', mel_wav(data[:mel_len_]), global_step=0, sample_rate=sample_rate)
print(time.time() - start)
writer.flush()
exit()
| [
"yiebo-c@hotmail.com"
] | yiebo-c@hotmail.com |
c8e2155ef68a3eba87ea0e8c4cab9b582c3f5355 | 8bc3e7bd0fa1714b3d0466e940ed801cf9a4c5d4 | /pyvisual/node/io/system_var.py | 2e6dfeaf5a70761d5951b4abff26e7ec2a04eaae | [] | no_license | m0r13/pyvisual | d99b3512fefaf4a2164362a0b7aabd1df9ecee03 | f6b3e2217e647b80f1379716c00e8adb53975bca | refs/heads/master | 2022-02-21T22:24:22.467475 | 2019-06-17T20:38:48 | 2019-06-17T20:38:48 | 140,211,941 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 5,802 | py | import json
import os
import time
from collections import defaultdict, OrderedDict
import imgui
from pyvisual.node import dtype, value
from pyvisual.node.base import Node
from pyvisual.editor import widget
SERIALIZATION_WRITE_INTERVAL = 5.0
SERIALIZATION_FILE = "system_vars.json"
# if you add another variable with another dtype than here, add the name of the dtype below!
VARIABLES = OrderedDict([
("gain", {"dtype" : dtype.float, "dtype_args" : {"default" : 4.0, "range" : [0.0, float("inf")]}}),
("threshold", {"dtype" : dtype.float, "dtype_args" : {"default" : 0.4, "range" : [0.0, float("inf")]}}),
("ref_aspect", {"dtype" : dtype.str, "dtype_args" : {"default" : "16:9"}}),
("ref_highres_height", {"dtype" : dtype.int, "dtype_args" : {"default" : 1080, "range" : [0, float("inf")]}}),
("ref_lowres_height", {"dtype" : dtype.int, "dtype_args" : {"default" : 720, "range" : [0, float("inf")]}}),
("ref_noiseres_height", {"dtype" : dtype.int, "dtype_args" : {"default" : 512, "range" : [0, float("inf")]}}),
])
# name -> value for each variable
values = OrderedDict()
# name -> widget for each variable
widgets = OrderedDict()
# dtype -> list of (name, value)
values_by_dtype = defaultdict(lambda: [])
# initialize values and widgets that are associated with variables
for name, spec in VARIABLES.items():
assert "dtype" in spec
dt = spec["dtype"]
dt_args = spec.get("dtype_args", {})
default_value = dt.default
if "default" in dt_args:
default_value = dt_args["default"]
v = value.SettableValue(default_value)
w = widget.create_widget(dt, dt_args)
w.width = widget.WIDGET_WIDTH * 1.5
values[name] = v
values_by_dtype[dt].append((name, v))
widgets[name] = w
_variables_dirty = False
_variables_last_written = 0
_node_instances = set()
# Important: Call this when changed a value! (Is done by editor for example)
def notify_change():
global _variables_dirty
_variables_dirty = True
for instance in _node_instances:
instance.force_evaluate()
# if the nodes would take over the values if they are changed only,
# then this would need to be changed probably
for value in values.values():
value.reset_changed()
def read_variables():
serialized_values = {}
if not os.path.isfile(SERIALIZATION_FILE):
return
serialized_values = json.load(open(SERIALIZATION_FILE))
for name, serialized_value in serialized_values.items():
if name not in VARIABLES:
continue
value = values[name]
dt = VARIABLES[name]["dtype"]
value.value = dt.base_type.unserialize(serialized_values[name])
notify_change()
read_variables()
def write_variables(force=False):
global _variables_dirty, _variables_last_written
if force or time.time() - _variables_last_written > SERIALIZATION_WRITE_INTERVAL:
_variables_dirty = False
_variables_last_written = time.time()
data = {}
for name, spec in VARIABLES.items():
value = values[name].value
data[name] = spec["dtype"].base_type.serialize(value)
with open("system_vars.json", "w") as f:
json.dump(data, f)
class GetSystemVar(Node):
DTYPE = None
class Meta:
inputs = [
{"name" : "name", "dtype" : dtype.str, "hide" : True}
]
options = {
"virtual" : True
}
def __init__(self):
super().__init__()
self._value = None
@property
def collapsed_node_title(self):
return "get system var: %s" % self.get("name")
def start(self, graph):
_node_instances.add(self)
name = self.get("name")
if name:
self._value = values.get(name, None)
if self._value is None:
self.get_input("name").value = ""
def _evaluate(self):
output = self.get_output("output")
if self._value != None:
output.value = self._value.value
def stop(self):
_node_instances.remove(self)
def _show_custom_ui(self):
selected_name = self.get("name")
preview = selected_name if selected_name else "<none>"
if imgui.begin_combo("", preview):
is_selected = not selected_name
opened, selected = imgui.selectable("<none>", is_selected)
if opened:
self.get_input("name").value = ""
self._value = None
if is_selected:
imgui.set_item_default_focus()
imgui.separator()
for name, value in values_by_dtype.get(self.DTYPE, []):
is_selected = name == selected_name
opened, selected = imgui.selectable(name, is_selected)
if opened:
self.get_input("name").value = name
self._value = value
if is_selected:
imgui.set_item_default_focus()
imgui.end_combo()
@classmethod
def get_presets(cls, graph):
presets = []
for name, value in values_by_dtype.get(cls.DTYPE, []):
presets.append((name, {"i_name" : name}))
return presets
dtype_capital_names = {
dtype.float : "Float",
dtype.str : "Str",
dtype.int : "Int",
}
# create a GetXXXSystemVar class for each dtype
node_classes = []
for dt in values_by_dtype.keys():
name = "Get%sSystemVar" % dtype_capital_names[dt]
class Meta:
outputs = [
{"name" : "output", "dtype" : dt, "manual_input": True},
]
options = {
"virtual" : False,
"show_title" : False
}
cls = type(name, (GetSystemVar,), {"DTYPE" : dt, "Meta" : Meta, "__module__" : __name__})
node_classes.append(cls)
| [
"moritz.hilscher@gmail.com"
] | moritz.hilscher@gmail.com |
1465bbad98fe6c51d22d31a82efaa6fba3362f45 | e8a285cb1dcdae6f1b6d8506b8d25a1d031d6cd7 | /cpptools/tests/test_write_pythia_hepmc3.py | d4e73a3185bc0137d2756b3b3f25a6b491647b97 | [] | no_license | matplo/heppy | f30558e4ff3c1720c63b4d82f739b3f8acadc53e | 88c931e3e7dcf57a3a476ef0a92f0204491cafb9 | refs/heads/master | 2023-07-07T18:17:04.486149 | 2023-06-29T20:45:32 | 2023-06-29T20:45:32 | 201,352,733 | 5 | 8 | null | 2023-07-04T21:57:31 | 2019-08-08T23:33:39 | C | UTF-8 | Python | false | false | 782 | py | #!/usr/bin/env python
import pythia8
import pythiahepmc3
def create_and_init_pythia(config_strings=[]):
pythia = pythia8.Pythia()
for s in config_strings:
pythia.readString(s)
for extra_s in ["Next:numberShowEvent = 0", "Next:numberShowInfo = 0", "Next:numberShowProcess = 0", "Next:numberCount = 0"]:
pythia.readString(extra_s)
if pythia.init():
return pythia
return None
def main():
pythia = create_and_init_pythia(["PhaseSpace:pTHatMin = 2", "HardQCD:all = on"])
sfoutname = "test_write_pythia_hepmc3.dat"
pyhepmcwriter = pythiahepmc3.Pythia8HepMCWrapper(sfoutname)
for iEvent in range(100):
if not pythia.next(): continue
pyhepmcwriter.fillEvent(pythia)
pythia.stat()
print("[i] done writing to {}".format(sfoutname))
if __name__ == '__main__':
main()
| [
"ploskon@gmail.com"
] | ploskon@gmail.com |
d820ae424b8b015df2aa8aee36762d571e6921f2 | 7b4d83e0e476110ed8ebf444da4f3125774ddcba | /projeto_extensao/PIL/ImageWin.py | 37cf8f26b7eecf295e7211894b42dde741dcf8a1 | [] | no_license | gefferson/projeto_extensao | 5fa70f99ac75b9dc2a09dbd5f458f6f74dcb7cf4 | 9ae9b232f6aabbe590b2791fda04ca4430f3655a | refs/heads/master | 2021-01-10T20:55:52.424534 | 2014-09-02T06:27:32 | 2014-09-02T06:27:32 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 6,375 | py | #
# The Python Imaging Library.
# $Id$
#
# a Windows DIB display interface
#
# History:
# 1996-05-20 fl Created
# 1996-09-20 fl Fixed subregion exposure
# 1997-09-21 fl Added draw primitive (for tzPrint)
# 2003-05-21 fl Added experimental Window/ImageWindow classes
# 2003-09-05 fl Added fromstring/tostring methods
#
# Copyright (c) Secret Labs AB 1997-2003.
# Copyright (c) Fredrik Lundh 1996-2003.
#
# See the README file for information on usage and redistribution.
#
##
# The <b>ImageWin</b> module contains support to create and display
# images under Windows 95/98, NT, 2000 and later.
from PIL import Image
class HDC:
def __init__(self, dc):
self.dc = dc
def __int__(self):
return self.dc
class HWND:
def __init__(self, wnd):
self.wnd = wnd
def __int__(self):
return self.wnd
##
# Create a Windows bitmap with the given mode and size. The mode can
# be one of "1", "L", "P", or "RGB".
#
# If the display requires a palette, this constructor creates a
# suitable palette and associates it with the image. For an "L" image,
# 128 greylevels are allocated. For an "RGB" image, a 6x6x6 colour
# cube is used, together with 20 greylevels.
#
# To make sure that palettes work properly under Windows, you must
# call the <b>palette</b> method upon certain events from Windows.
class Dib:
##
# Create Windows bitmap.
#
# @param image Either a PIL image, or a mode string. If a
# mode string is used, a size must also be given. The
# mode can be one of "1", "L", "P", or "RGB".
# @param size If the first argument is a mode string, this
# defines the size of the image.
def __init__(self, image, size=None):
if hasattr(image, "mode") and hasattr(image, "size"):
mode = image.mode
size = image.size
else:
mode = image
image = None
if mode not in ["1", "L", "P", "RGB"]:
mode = Image.getmodebase(mode)
self.image = Image.core.display(mode, size)
self.mode = mode
self.size = size
if image:
self.paste(image)
##
# Copy the bitmap contents to a device context.
#
# @param handle Device context (HDC), cast to a Python integer,
# or a HDC or HWND instance. In PythonWin, you can use the
# <b>GetHandleAttrib</b> method of the <b>CDC</b> class to get
# a suitable handle.
def expose(self, handle):
if isinstance(handle, HWND):
dc = self.image.getdc(handle)
try:
result = self.image.expose(dc)
finally:
self.image.releasedc(handle, dc)
else:
result = self.image.expose(handle)
return result
def draw(self, handle, dst, src=None):
if not src:
src = (0,0) + self.size
if isinstance(handle, HWND):
dc = self.image.getdc(handle)
try:
result = self.image.draw(dc, dst, src)
finally:
self.image.releasedc(handle, dc)
else:
result = self.image.draw(handle, dst, src)
return result
##
# Installs the palette associated with the image in the
# given device context.
# <p>
# This method should be called upon <b>QUERYNEWPALETTE</b>
# and <b>PALETTECHANGED</b> events from Windows. If this
# method returns a non-zero value, one or more display
# palette entries were changed, and the image should be
# redrawn.
#
# @param handle Device context (HDC), cast to a Python integer,
# or an HDC or HWND instance.
# @return A true value if one or more entries were changed
# (this indicates that the image should be redrawn).
def query_palette(self, handle):
if isinstance(handle, HWND):
handle = self.image.getdc(handle)
try:
result = self.image.query_palette(handle)
finally:
self.image.releasedc(handle, handle)
else:
result = self.image.query_palette(handle)
return result
##
# Paste a PIL image into the bitmap image.
#
# @param im A PIL image. The size must match the target region.
# If the mode does not match, the image is converted to the
# mode of the bitmap image.
# @param box A 4-tuple defining the left, upper, right, and
# lower pixel coordinate. If None is given instead of a
# tuple, all of the image is assumed.
def paste(self, im, box=None):
im.load()
if self.mode != im.mode:
im = im.convert(self.mode)
if box:
self.image.paste(im.im, box)
else:
self.image.paste(im.im)
##
# Load display memory contents from string buffer.
#
# @param buffer A string buffer containing display data (usually
# data returned from <b>tostring</b>)
def fromstring(self, buffer):
return self.image.fromstring(buffer)
##
# Copy display memory contents to string buffer.
#
# @return A string buffer containing display data.
def tostring(self):
return self.image.tostring()
##
# Create a Window with the given title size.
class Window:
def __init__(self, title="PIL", width=None, height=None):
self.hwnd = Image.core.createwindow(
title, self.__dispatcher, width or 0, height or 0
)
def __dispatcher(self, action, *args):
return apply(getattr(self, "ui_handle_" + action), args)
def ui_handle_clear(self, dc, x0, y0, x1, y1):
pass
def ui_handle_damage(self, x0, y0, x1, y1):
pass
def ui_handle_destroy(self):
pass
def ui_handle_repair(self, dc, x0, y0, x1, y1):
pass
def ui_handle_resize(self, width, height):
pass
def mainloop(self):
Image.core.eventloop()
##
# Create an image window which displays the given image.
class ImageWindow(Window):
def __init__(self, image, title="PIL"):
if not isinstance(image, Dib):
image = Dib(image)
self.image = image
width, height = image.size
Window.__init__(self, title, width=width, height=height)
def ui_handle_repair(self, dc, x0, y0, x1, y1):
self.image.draw(dc, (x0, y0, x1, y1))
| [
"geffersonvivan@187-68-19-51.3g.claro.net.br"
] | geffersonvivan@187-68-19-51.3g.claro.net.br |
b1dc9e505c919a677e4ad516ba5eb32f5820c244 | 610dedfb6e21d297e8cdbcba599a4e564bd785cb | /EstruturaDeRepeticao/estruturaderepeticao-09.py | 8b4c1153a41989cbf2047c8067840d6a96441880 | [] | no_license | zumbipy/PythonExercicios | f7b2ddf2376b9ecb2aedc77531e3571dc746a12b | 7a17b78cf927a2889b93238542e90e00810c43e6 | refs/heads/master | 2021-01-23T10:43:47.997462 | 2018-07-22T14:58:44 | 2018-07-22T14:58:44 | 93,086,120 | 1 | 1 | null | null | null | null | UTF-8 | Python | false | false | 682 | py | # Telegram: @ZumbiPy __ _ ___
# /_ / __ ____ _ / / (_) _ \__ __
# / /_/ // / ' \/ _ \/ / ___/ // /
# /___/\_,_/_/_/_/_.__/_/_/ \_, /
# E-mail: zumbipy@gmail.com /___/
"""
09 - Faça um programa que imprima na tela apenas os números
ímpares entre 1 e 50.
"""
# ================================================================================
# Logica do Programa.
# ================================================================================
for i in range(1, 50):
# Quando resto de uma divisao por 2 for 0 ele e par se nao e ímpar.
if i % 2 != 0:
print(i)
print("=" * 72)
# ou
for i in range(1, 50, 2):
print(i)
| [
"zumbipy@gmail.com"
] | zumbipy@gmail.com |
dc6940ccab54fe26f6cdd8418152ac93e3a870f6 | 080c13cd91a073457bd9eddc2a3d13fc2e0e56ae | /MY_REPOS/awesome-4-new-developers/tensorflow-master/tensorflow/python/tpu/feature_column_v2.py | 1a5bddb173a599ee196c98ef4cd8bf3483151377 | [
"Apache-2.0"
] | permissive | Portfolio-Projects42/UsefulResourceRepo2.0 | 1dccc8961a09347f124d3ed7c27c6d73b9806189 | 75b1e23c757845b5f1894ebe53551a1cf759c6a3 | refs/heads/master | 2023-08-04T12:23:48.862451 | 2021-09-15T12:51:35 | 2021-09-15T12:51:35 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 50,102 | py | # Copyright 2018 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ===================================================================
"""TPU Feature Column Library."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import copy
import math
import enum
from tensorflow.python.feature_column import feature_column as fc
from tensorflow.python.feature_column import feature_column_lib as fc_lib
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import ops
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import embedding_ops
from tensorflow.python.ops import init_ops
from tensorflow.python.ops import math_ops
from tensorflow.python.ops import sparse_ops
from tensorflow.python.ops import variable_scope
from tensorflow.python.tpu import tpu
from tensorflow.python.tpu.feature_column import _is_running_on_cpu
from tensorflow.python.tpu.feature_column import _record_variable_scope_and_name
from tensorflow.python.tpu.feature_column import _SUPPORTED_CATEGORICAL_COLUMNS_V2
from tensorflow.python.tpu.feature_column import _SUPPORTED_SEQUENCE_COLUMNS
from tensorflow.python.tpu.feature_column import _TPUBaseEmbeddingColumn
from tensorflow.python.util.tf_export import tf_export
# pylint: disable=protected-access
_ALLOWED_DEVICES = ["cpu", "tpu_tensor_core", "tpu_embedding_core"]
_TENSOR_CORE_MASK_KEY_SUFFIX = "__TENSOR_CORE_MASK"
class EmbeddingDevice(enum.Enum):
CPU = 1
TPU_TENSOR_CORE = 2
TPU_EMBEDDING_CORE = 3
@tf_export(v1=["tpu.experimental.embedding_column"])
def embedding_column_v2(
categorical_column,
dimension,
combiner="mean",
initializer=None,
max_sequence_length=0,
learning_rate_fn=None,
embedding_lookup_device=None,
tensor_core_shape=None,
use_safe_embedding_lookup=True,
):
"""TPU version of `tf.compat.v1.feature_column.embedding_column`.
Note that the interface for `tf.tpu.experimental.embedding_column` is
different from that of `tf.compat.v1.feature_column.embedding_column`: The
following arguments are NOT supported: `ckpt_to_load_from`,
`tensor_name_in_ckpt`, `max_norm` and `trainable`.
Use this function in place of `tf.compat.v1.feature_column.embedding_column`
when you want to use the TPU to accelerate your embedding lookups via TPU
embeddings.
```
column = tf.feature_column.categorical_column_with_identity(...)
tpu_column = tf.tpu.experimental.embedding_column(column, 10)
...
def model_fn(features):
dense_feature = tf.keras.layers.DenseFeature(tpu_column)
embedded_feature = dense_feature(features)
...
estimator = tf.estimator.tpu.TPUEstimator(
model_fn=model_fn,
...
embedding_config_spec=tf.estimator.tpu.experimental.EmbeddingConfigSpec(
column=[tpu_column],
...))
```
Args:
categorical_column: A categorical column returned from
`categorical_column_with_identity`, `weighted_categorical_column`,
`categorical_column_with_vocabulary_file`,
`categorical_column_with_vocabulary_list`,
`sequence_categorical_column_with_identity`,
`sequence_categorical_column_with_vocabulary_file`,
`sequence_categorical_column_with_vocabulary_list`
dimension: An integer specifying dimension of the embedding, must be > 0.
combiner: A string specifying how to reduce if there are multiple entries
in a single row for a non-sequence column. For more information, see
`tf.feature_column.embedding_column`.
initializer: A variable initializer function to be used in embedding
variable initialization. If not specified, defaults to
`tf.compat.v1.truncated_normal_initializer` with mean `0.0` and
standard deviation `1/sqrt(dimension)`.
max_sequence_length: An non-negative integer specifying the max sequence
length. Any sequence shorter then this will be padded with 0 embeddings
and any sequence longer will be truncated. This must be positive for
sequence features and 0 for non-sequence features.
learning_rate_fn: A function that takes global step and returns learning
rate for the embedding table. If you intend to use the same learning rate
for multiple embedding tables, please ensure that you pass the exact same
python function to all calls of embedding_column, otherwise performence
may suffer.
embedding_lookup_device: The device on which to run the embedding lookup.
Valid options are "cpu", "tpu_tensor_core", and "tpu_embedding_core".
If specifying "tpu_tensor_core", a tensor_core_shape must be supplied.
If not specified, the default behavior is embedding lookup on
"tpu_embedding_core" for training and "cpu" for inference.
Valid options for training : ["tpu_embedding_core", "tpu_tensor_core"]
Valid options for serving : ["cpu", "tpu_tensor_core"]
For training, tpu_embedding_core is good for large embedding vocab (>1M),
otherwise, tpu_tensor_core is often sufficient.
For serving, doing embedding lookup on tpu_tensor_core during serving is
a way to reduce host cpu usage in cases where that is a bottleneck.
tensor_core_shape: If supplied, a list of integers which specifies
the intended dense shape to run embedding lookup for this feature on
TensorCore. The batch dimension can be left None or -1 to indicate
a dynamic shape. Only rank 2 shapes currently supported.
use_safe_embedding_lookup: If true, uses safe_embedding_lookup_sparse
instead of embedding_lookup_sparse. safe_embedding_lookup_sparse ensures
there are no empty rows and all weights and ids are positive at the
expense of extra compute cost. This only applies to rank 2 (NxM) shaped
input tensors. Defaults to true, consider turning off if the above checks
are not needed. Note that having empty rows will not trigger any error
though the output result might be 0 or omitted.
Returns:
A `_TPUEmbeddingColumnV2`.
Raises:
ValueError: if `dimension` not > 0.
ValueError: if `initializer` is specified but not callable.
"""
if not isinstance(categorical_column, _SUPPORTED_CATEGORICAL_COLUMNS_V2):
raise TypeError(
"categorical_column for tpu "
" embedding_column must be type %s, got %s."
% (
" or ".join([cc.__name__ for cc in _SUPPORTED_CATEGORICAL_COLUMNS_V2]),
type(categorical_column),
)
)
if (dimension is None) or (dimension < 1):
raise ValueError("Invalid dimension {}.".format(dimension))
if tensor_core_shape and len(tensor_core_shape) != 2:
raise ValueError(
"tensor_core_shape must be size 2. Got {}.".format(tensor_core_shape)
)
if (initializer is not None) and (not callable(initializer)):
raise ValueError(
"initializer must be callable if specified. "
"Embedding of column_name: {}".format(categorical_column.name)
)
if initializer is None:
initializer = init_ops.truncated_normal_initializer(
mean=0.0, stddev=1 / math.sqrt(dimension)
)
if embedding_lookup_device and embedding_lookup_device not in _ALLOWED_DEVICES:
raise ValueError(
"If set, embedding_lookup_device must be in ", _ALLOWED_DEVICES
)
if embedding_lookup_device == "cpu":
embedding_lookup_device = EmbeddingDevice.CPU
elif embedding_lookup_device == "tpu_tensor_core":
embedding_lookup_device = EmbeddingDevice.TPU_TENSOR_CORE
elif embedding_lookup_device == "tpu_embedding_core":
embedding_lookup_device = EmbeddingDevice.TPU_EMBEDDING_CORE
if embedding_lookup_device == EmbeddingDevice.TPU_TENSOR_CORE:
if not tensor_core_shape:
raise ValueError(
"Using embedding_lookup_device=tpu_tensor_core requires "
"tensor_core_shape to be set."
)
if isinstance(categorical_column, _SUPPORTED_SEQUENCE_COLUMNS):
raise ValueError(
"embedding_lookup_device=tpu_tensor_core currently does "
"not support sequence columns."
)
if not embedding_lookup_device:
return _TPUEmbeddingColumnV2(
categorical_column=categorical_column,
dimension=dimension,
combiner=combiner,
initializer=initializer,
max_sequence_length=max_sequence_length,
learning_rate_fn=learning_rate_fn,
use_safe_embedding_lookup=use_safe_embedding_lookup,
)
else:
return _TPUDeviceSpecificEmbeddingColumnV2(
categorical_column=categorical_column,
dimension=dimension,
combiner=combiner,
initializer=initializer,
max_sequence_length=max_sequence_length,
learning_rate_fn=learning_rate_fn,
embedding_lookup_device=embedding_lookup_device,
tensor_core_shape=tensor_core_shape,
use_safe_embedding_lookup=use_safe_embedding_lookup,
)
@tf_export(v1=["tpu.experimental.shared_embedding_columns"])
def shared_embedding_columns_v2(
categorical_columns,
dimension,
combiner="mean",
initializer=None,
shared_embedding_collection_name=None,
max_sequence_lengths=None,
learning_rate_fn=None,
embedding_lookup_device=None,
tensor_core_shape=None,
use_safe_embedding_lookup=True,
):
"""TPU version of `tf.compat.v1.feature_column.shared_embedding_columns`.
Note that the interface for `tf.tpu.experimental.shared_embedding_columns` is
different from that of `tf.compat.v1.feature_column.shared_embedding_columns`:
The following arguments are NOT supported: `ckpt_to_load_from`,
`tensor_name_in_ckpt`, `max_norm` and `trainable`.
Use this function in place of
tf.compat.v1.feature_column.shared_embedding_columns` when you want to use the
TPU to accelerate your embedding lookups via TPU embeddings.
```
column_a = tf.feature_column.categorical_column_with_identity(...)
column_b = tf.feature_column.categorical_column_with_identity(...)
tpu_columns = tf.tpu.experimental.shared_embedding_columns(
[column_a, column_b], 10)
...
def model_fn(features):
dense_feature = tf.keras.layers.DenseFeature(tpu_columns)
embedded_feature = dense_feature(features)
...
estimator = tf.estimator.tpu.TPUEstimator(
model_fn=model_fn,
...
embedding_config_spec=tf.estimator.tpu.experimental.EmbeddingConfigSpec(
column=tpu_columns,
...))
```
Args:
categorical_columns: A list of categorical columns returned from
`categorical_column_with_identity`, `weighted_categorical_column`,
`categorical_column_with_vocabulary_file`,
`categorical_column_with_vocabulary_list`,
`sequence_categorical_column_with_identity`,
`sequence_categorical_column_with_vocabulary_file`,
`sequence_categorical_column_with_vocabulary_list`
dimension: An integer specifying dimension of the embedding, must be > 0.
combiner: A string specifying how to reduce if there are multiple entries in
a single row for a non-sequence column. For more information, see
`tf.feature_column.embedding_column`.
initializer: A variable initializer function to be used in embedding
variable initialization. If not specified, defaults to
`tf.truncated_normal_initializer` with mean `0.0` and standard deviation
`1/sqrt(dimension)`.
shared_embedding_collection_name: Optional name of the collection where
shared embedding weights are added. If not given, a reasonable name will
be chosen based on the names of `categorical_columns`. This is also used
in `variable_scope` when creating shared embedding weights.
max_sequence_lengths: An list of non-negative integers, either None or empty
or the same length as the argument categorical_columns. Entries
corresponding to non-sequence columns must be 0 and entries corresponding
to sequence columns specify the max sequence length for the column. Any
sequence shorter then this will be padded with 0 embeddings and any
sequence longer will be truncated.
learning_rate_fn: A function that takes global step and returns learning
rate for the embedding table. If you intend to use the same learning rate
for multiple embedding tables, please ensure that you pass the exact same
python function to all calls of shared_embedding_columns, otherwise
performence may suffer.
embedding_lookup_device: The device on which to run the embedding lookup.
Valid options are "cpu", "tpu_tensor_core", and "tpu_embedding_core". If
specifying "tpu_tensor_core", a tensor_core_shape must be supplied.
Defaults to "cpu". If not specified, the default behavior is embedding
lookup on "tpu_embedding_core" for training and "cpu" for inference.
Valid options for training : ["tpu_embedding_core", "tpu_tensor_core"]
Valid options for serving : ["cpu", "tpu_tensor_core"]
For training, tpu_embedding_core is good for large embedding vocab (>1M),
otherwise, tpu_tensor_core is often sufficient.
For serving, doing embedding lookup on tpu_tensor_core during serving is
a way to reduce host cpu usage in cases where that is a bottleneck.
tensor_core_shape: If supplied, a list of integers which specifies the
intended dense shape to run embedding lookup for this feature on
TensorCore. The batch dimension can be left None or -1 to indicate a
dynamic shape. Only rank 2 shapes currently supported.
use_safe_embedding_lookup: If true, uses safe_embedding_lookup_sparse
instead of embedding_lookup_sparse. safe_embedding_lookup_sparse ensures
there are no empty rows and all weights and ids are positive at the
expense of extra compute cost. This only applies to rank 2 (NxM) shaped
input tensors. Defaults to true, consider turning off if the above checks
are not needed. Note that having empty rows will not trigger any error
though the output result might be 0 or omitted.
Returns:
A list of `_TPUSharedEmbeddingColumnV2`.
Raises:
ValueError: if `dimension` not > 0.
ValueError: if `initializer` is specified but not callable.
ValueError: if `max_sequence_lengths` is specified and not the same length
as `categorical_columns`.
ValueError: if `max_sequence_lengths` is positive for a non sequence column
or 0 for a sequence column.
"""
for categorical_column in categorical_columns:
if not isinstance(categorical_column, _SUPPORTED_CATEGORICAL_COLUMNS_V2):
raise TypeError(
"categorical_column for tpu "
" shared_embedding_columns must be type %s, got %s."
% (
" or ".join(
[cc.__name__ for cc in _SUPPORTED_CATEGORICAL_COLUMNS_V2]
),
type(categorical_column),
)
)
if not max_sequence_lengths:
max_sequence_lengths = [0] * len(categorical_columns)
if len(max_sequence_lengths) != len(categorical_columns):
raise ValueError(
"max_sequence_lengths and categorical_columns must be of "
"the same length. len(max_sequence_lengths)={} "
"len(categorical_columns)={}.".format(
len(max_sequence_lengths), len(categorical_columns)
)
)
if (dimension is None) or (dimension < 1):
raise ValueError("Invalid dimension {}.".format(dimension))
if tensor_core_shape and len(tensor_core_shape) != 2:
raise ValueError(
"tensor_core_shape must be size 2. Got {}.".format(tensor_core_shape)
)
if (initializer is not None) and (not callable(initializer)):
raise ValueError("initializer must be callable if specified. ")
if initializer is None:
initializer = init_ops.truncated_normal_initializer(
mean=0.0, stddev=1 / math.sqrt(dimension)
)
# Sort the columns so the default collection name is deterministic even if the
# user passes columns from an unsorted collection, such as dict.values().
sorted_columns = sorted(categorical_columns, key=lambda x: x.name)
num_buckets = sorted_columns[0]._num_buckets # pylint: disable=protected-access
for c in sorted_columns[1:]:
if num_buckets != c._num_buckets: # pylint: disable=protected-access
raise ValueError(
"To use shared_embedding_column, all categorical_columns must have "
"the same number of buckets. Given column: {} with buckets: {} does "
"not match column: {} with buckets: {}".format(
sorted_columns[0], num_buckets, c, c._num_buckets
)
) # pylint: disable=protected-access
if not shared_embedding_collection_name:
shared_embedding_collection_name = "_".join(c.name for c in sorted_columns)
shared_embedding_collection_name += "_shared_embedding"
tpu_columns = []
column_creator = fc_lib.SharedEmbeddingColumnCreator(
dimension=dimension,
initializer=initializer,
ckpt_to_load_from=None,
tensor_name_in_ckpt=None,
num_buckets=num_buckets,
trainable=None,
name=shared_embedding_collection_name,
)
if embedding_lookup_device and embedding_lookup_device not in _ALLOWED_DEVICES:
raise ValueError(
"If set, embedding_lookup_device must be in ", _ALLOWED_DEVICES
)
if embedding_lookup_device == "cpu":
embedding_lookup_device = EmbeddingDevice.CPU
elif embedding_lookup_device == "tpu_tensor_core":
embedding_lookup_device = EmbeddingDevice.TPU_TENSOR_CORE
elif embedding_lookup_device == "tpu_embedding_core":
embedding_lookup_device = EmbeddingDevice.TPU_EMBEDDING_CORE
if embedding_lookup_device == EmbeddingDevice.TPU_TENSOR_CORE:
if not tensor_core_shape:
raise ValueError(
"Using embedding_lookup_device=tpu_tensor_core requires "
"tensor_core_shape to be set."
)
for c in sorted_columns:
if isinstance(c, _SUPPORTED_SEQUENCE_COLUMNS):
raise ValueError(
"embedding_lookup_device=tpu_tensor_core currently "
"does not support sequence columns."
)
# Create the state (_SharedEmbeddingColumnLayer) here.
for categorical_column, max_sequence_length in zip(
categorical_columns, max_sequence_lengths
):
if not embedding_lookup_device:
column = _TPUSharedEmbeddingColumnV2(
categorical_column=categorical_column,
shared_embedding_column_creator=column_creator,
combiner=combiner,
initializer=initializer,
shared_embedding_collection_name=shared_embedding_collection_name,
max_sequence_length=max_sequence_length,
learning_rate_fn=learning_rate_fn,
use_safe_embedding_lookup=use_safe_embedding_lookup,
)
else:
column = _TPUSharedDeviceSpecificEmbeddingColumnV2(
categorical_column=categorical_column,
shared_embedding_column_creator=column_creator,
combiner=combiner,
initializer=initializer,
shared_embedding_collection_name=shared_embedding_collection_name,
max_sequence_length=max_sequence_length,
learning_rate_fn=learning_rate_fn,
embedding_lookup_device=embedding_lookup_device,
tensor_core_shape=tensor_core_shape,
use_safe_embedding_lookup=use_safe_embedding_lookup,
)
tpu_columns.append(column)
return tpu_columns
class _TPUEmbeddingColumnV2(_TPUBaseEmbeddingColumn, fc_lib.EmbeddingColumn):
"""Core Embedding Column."""
def __new__(
cls,
categorical_column,
dimension,
combiner="mean",
initializer=None,
max_sequence_length=0,
learning_rate_fn=None,
use_safe_embedding_lookup=True,
bypass_scope_validation=False,
):
del bypass_scope_validation
# pylint: disable=redundant-keyword-arg
return fc_lib.EmbeddingColumn.__new__(
cls,
categorical_column,
dimension,
combiner=combiner,
initializer=initializer,
ckpt_to_load_from=None,
tensor_name_in_ckpt=None,
max_norm=None,
trainable=True,
use_safe_embedding_lookup=use_safe_embedding_lookup,
)
def __getnewargs__(self):
return (
self._tpu_categorical_column,
self.dimension,
self.combiner,
self.initializer,
self._max_sequence_length,
self._learning_rate_fn,
self.use_safe_embedding_lookup,
self._bypass_scope_validation,
)
def __deepcopy__(self, memo):
return _TPUEmbeddingColumnV2(
*(copy.deepcopy(a, memo) for a in self.__getnewargs__())
)
def __init__(
self,
categorical_column,
dimension,
combiner="mean",
initializer=None,
max_sequence_length=0,
learning_rate_fn=None,
use_safe_embedding_lookup=True,
bypass_scope_validation=False,
):
_TPUBaseEmbeddingColumn.__init__(
self,
categorical_column,
max_sequence_length=max_sequence_length,
learning_rate_fn=learning_rate_fn,
)
self._key = None
# If true, scope validation is skipped to allow the same column to be used
# in multiple variable scopes. By default, this is False, and we expect a
# 1:1 mapping between feature columns and scopes.
self._bypass_scope_validation = bypass_scope_validation
def get_combiner(self):
return self.combiner
def get_embedding_table_size(self):
"""Returns num_ids and width."""
return (self.categorical_column._num_buckets, self.dimension)
def get_feature_key_name(self):
"""get_feature_key_name."""
if self.is_categorical_column_weighted():
return self.categorical_column.categorical_column.name
return self.categorical_column.name
def get_weight_key_name(self):
"""get_weight_key_name."""
if self.is_categorical_column_weighted():
return self.categorical_column.weight_feature_key
return None
def get_embedding_var_name(self):
"""get_embedding_var_name."""
return self.categorical_column.name
def get_initializer(self):
return self.initializer
def is_categorical_column_weighted(self):
"""Check if the categorical column of the embedding column is weighted."""
if isinstance(
self.categorical_column,
(
fc._WeightedCategoricalColumn, # pylint: disable=protected-access
fc_lib.WeightedCategoricalColumn,
),
):
return True
return False
def _get_dense_tensor(self, inputs, weight_collections=None, trainable=None):
if tpu.under_tpu_inference_context():
def host_computation():
return fc_lib.EmbeddingColumn._get_dense_tensor(
self, inputs, weight_collections, trainable
)
return tpu.outside_compilation(host_computation)
if _is_running_on_cpu():
return fc_lib.EmbeddingColumn._get_dense_tensor(
self, inputs, weight_collections, trainable
)
# TPU mode
# Get the embeddings from the LazyBuilder.
tensor = inputs.get(self.get_feature_key_name())
# Add to collection for _create_tpu_embedding_variables_and_ops
_record_variable_scope_and_name(
self.get_embedding_var_name(),
"embedding_weights",
bypass_scope_validation=self._bypass_scope_validation,
)
return tensor
def create_state(self, state_manager):
if _is_running_on_cpu():
return fc_lib.EmbeddingColumn.create_state(self, state_manager)
# Create state is called for the EmbeddingColumn to create its embedding
# variables under feature column V2, if we are on TPU so record the scope
# here.
_record_variable_scope_and_name(
self.get_embedding_var_name(),
"embedding_weights",
bypass_scope_validation=self._bypass_scope_validation,
)
def get_dense_tensor(self, transformation_cache, state_manager):
if tpu.under_tpu_inference_context():
def host_computation():
return fc_lib.EmbeddingColumn.get_dense_tensor(
self, transformation_cache, state_manager
)
return tpu.outside_compilation(host_computation)
if _is_running_on_cpu():
return fc_lib.EmbeddingColumn.get_dense_tensor(
self, transformation_cache, state_manager
)
# TPU mode
# Get the embeddings from the FeatureTransformationCache.
tensor = transformation_cache.get(self.get_feature_key_name(), state_manager)
return tensor
def _get_sequence_dense_tensor(
self, inputs, weight_collections=None, trainable=None
):
if tpu.under_tpu_inference_context():
def host_computation():
return fc_lib.EmbeddingColumn._get_sequence_dense_tensor(
self, inputs, weight_collections, trainable
)
return tpu.outside_compilation(host_computation)
if _is_running_on_cpu():
return fc_lib.EmbeddingColumn._get_sequence_dense_tensor(
self, inputs, weight_collections, trainable
)
tensor = inputs.get(self.get_feature_key_name())
tensor_lengths = inputs.get(self.get_sequence_length_feature_key_name())
# inputs is a _LazyBuilder and for rank 1 tensors, it calls expand_dims(-1).
# We need to undo this to match the standard CPU sequence embedding.
tensor_lengths = array_ops.squeeze(tensor_lengths, -1)
# Add to collection for _create_tpu_embedding_variables_and_ops
_record_variable_scope_and_name(
self.get_embedding_var_name(),
"embedding_weights",
bypass_scope_validation=self._bypass_scope_validation,
)
return fc_lib.SequenceDenseColumn.TensorSequenceLengthPair(
dense_tensor=tensor, sequence_length=tensor_lengths
)
def get_sequence_dense_tensor(self, transformation_cache, state_manager):
if tpu.under_tpu_inference_context():
def host_computation():
return fc_lib.EmbeddingColumn.get_sequence_dense_tensor(
self, transformation_cache, state_manager
)
return tpu.outside_compilation(host_computation)
if _is_running_on_cpu():
return fc_lib.EmbeddingColumn.get_sequence_dense_tensor(
self, transformation_cache, state_manager
)
tensor = transformation_cache.get(self.get_feature_key_name(), state_manager)
tensor_lengths = transformation_cache.get(
self.get_sequence_length_feature_key_name(), state_manager
)
# FeatureTransformationCache expands rank 1 tensors (like sequence length)
# to rank 2. We need to undo this to match the standard CPU sequence
# embedding.
tensor_lengths = array_ops.squeeze(tensor_lengths, -1)
return fc_lib.SequenceDenseColumn.TensorSequenceLengthPair(
dense_tensor=tensor, sequence_length=tensor_lengths
)
class _TPUSharedEmbeddingColumnV2(
_TPUBaseEmbeddingColumn, fc_lib.SharedEmbeddingColumn
):
"""Core Shared Embedding Column."""
def __new__(
cls,
categorical_column,
shared_embedding_column_creator,
combiner="mean",
initializer=None,
shared_embedding_collection_name=None,
max_sequence_length=0,
learning_rate_fn=None,
use_safe_embedding_lookup=True,
):
# pylint: disable=redundant-keyword-arg
return fc_lib.SharedEmbeddingColumn.__new__(
cls,
categorical_column,
combiner=combiner,
shared_embedding_column_creator=shared_embedding_column_creator,
max_norm=None,
use_safe_embedding_lookup=use_safe_embedding_lookup,
)
def __getnewargs__(self):
return (
self._tpu_categorical_column,
self.shared_embedding_column_creator,
self.combiner,
self._initializer,
self._shared_embedding_collection_name,
self._max_sequence_length,
self._learning_rate_fn,
)
def __deepcopy__(self, memo):
return _TPUSharedEmbeddingColumnV2(
*(copy.deepcopy(a, memo) for a in self.__getnewargs__())
)
def __init__(
self,
categorical_column,
shared_embedding_column_creator,
combiner="mean",
initializer=None,
shared_embedding_collection_name=None,
max_sequence_length=0,
learning_rate_fn=None,
use_safe_embedding_lookup=True,
):
_TPUBaseEmbeddingColumn.__init__(
self,
categorical_column,
max_sequence_length=max_sequence_length,
learning_rate_fn=learning_rate_fn,
)
self._initializer = initializer
self._shared_embedding_collection_name = shared_embedding_collection_name
def get_combiner(self):
return self.combiner
def get_embedding_table_size(self):
"""Returns num_ids and width."""
return (
self.categorical_column._num_buckets,
self.shared_embedding_column_creator.dimension,
)
def get_feature_key_name(self):
"""get_feature_key_name."""
if self.is_categorical_column_weighted():
return self.categorical_column.categorical_column.name
return self.categorical_column.name
def get_weight_key_name(self):
"""get_weight_key_name."""
if self.is_categorical_column_weighted():
return self.categorical_column.weight_feature_key
return None
def get_embedding_var_name(self):
"""get_embedding_var_name."""
return self._shared_embedding_collection_name
def get_initializer(self):
return self._initializer
def is_categorical_column_weighted(self):
"""Check if the categorical column of the embedding column is weighted."""
if isinstance(
self.categorical_column,
(
fc._WeightedCategoricalColumn, # pylint: disable=protected-access
fc_lib.WeightedCategoricalColumn,
),
):
return True
return False
def _get_dense_tensor_internal(self, transformation_cache, state_manager):
if tpu.under_tpu_inference_context():
def host_computation():
return fc_lib.SharedEmbeddingColumn._get_dense_tensor_internal(
self, transformation_cache, state_manager
)
return tpu.outside_compilation(host_computation)
if _is_running_on_cpu():
return fc_lib.SharedEmbeddingColumn._get_dense_tensor_internal(
self, transformation_cache, state_manager
)
# TPU mode
# Get the embeddings from the FeatureTransformationCache.
tensor = transformation_cache.get(self.get_feature_key_name(), state_manager)
# Add to collection for _create_tpu_embedding_variables_and_ops
# Note that in Feature Column V2, shared embeddings have no scope.
_record_variable_scope_and_name(
self.get_embedding_var_name(),
self.shared_embedding_column_creator._name,
is_shared_embedding=True,
)
return tensor
def get_sequence_dense_tensor(self, transformation_cache, state_manager):
if tpu.under_tpu_inference_context():
def host_computation():
return fc_lib.SharedEmbeddingColumn.get_sequence_dense_tensor(
self, transformation_cache, state_manager
)
return tpu.outside_compilation(host_computation)
if _is_running_on_cpu():
return fc_lib.SharedEmbeddingColumn.get_sequence_dense_tensor(
self, transformation_cache, state_manager
)
tensor = self._get_dense_tensor_internal(transformation_cache, state_manager)
tensor_lengths = transformation_cache.get(
self.get_sequence_length_feature_key_name(), state_manager
)
# FeatureTransformationCache expands rank 1 tensors (like sequence length)
# to rank 2. We need to undo this to match the standard CPU sequence
# embedding.
tensor_lengths = array_ops.squeeze(tensor_lengths, -1)
return fc_lib.SequenceDenseColumn.TensorSequenceLengthPair(
dense_tensor=tensor, sequence_length=tensor_lengths
)
def split_sequence_columns_v2(feature_columns):
"""Split a list of _TPUEmbeddingColumn into sequence and non-sequence columns.
For use in a TPUEstimator model_fn function. E.g.
def model_fn(features):
sequence_columns, feature_columns = (
tf.tpu.feature_column.split_sequence_columns(feature_columns))
input = tf.feature_column.input_layer(
features=features, feature_columns=feature_columns)
sequence_features, sequence_lengths = (
tf.contrib.feature_column.sequence_input_layer(
features=features, feature_columns=sequence_columns))
Args:
feature_columns: A list of _TPUEmbeddingColumns to split.
Returns:
Two lists of _TPUEmbeddingColumns, the first is the sequence columns and the
second is the non-sequence columns.
"""
sequence_columns = []
non_sequence_columns = []
for column in feature_columns:
if not isinstance(column, (_TPUEmbeddingColumnV2, _TPUSharedEmbeddingColumnV2)):
raise TypeError(
"column must be a _TPUEmbeddingColumnV2 or "
"_TPUSharedEmbeddingColumnV2 but got %s instead." % (type(column))
)
if column.is_sequence_column():
sequence_columns.append(column)
else:
non_sequence_columns.append(column)
return sequence_columns, non_sequence_columns
def sparse_embedding_aggregate_slice(
params,
values_and_values_mask,
combiner="mean",
name="sparse_embedding_aggregate_slice",
):
"""Uses XLA's dynamic slice operations to perform embedding lookups.
From third_party/cloud_tpu/models/movielens/tpu_embedding.py
Args:
params: Tensor of embedding table. Rank 2 (table_size x embedding dim)
values_and_values_mask: is a two-tuple that contains: values - Tensor of
embedding indices. Rank 2 (batch x n_indices) values_mask - Tensor of mask
/ weights. Rank 2 (batch x n_indices)
combiner: The combiner to use for the embedding lookup. Currently supports
'sum' and 'mean'.
name: Optional name scope for created ops
Returns:
Rank 2 tensor of aggregated (per batch element) embedding vectors.
Raises:
ValueError: Combiner is not supported.
"""
values, values_mask = values_and_values_mask # unpack the two-tuple
with ops.name_scope(name):
_, embedding_dimension = params.get_shape().as_list()
n_batch, n_indices_padded = values.get_shape().as_list()
if not n_batch:
n_batch = -1
emb_lookup = array_ops.reshape(
embedding_ops.embedding_lookup(
params, array_ops.reshape(values, [n_batch, n_indices_padded])
),
[n_batch, n_indices_padded, embedding_dimension],
)
values_mask_broadcast = array_ops.reshape(
values_mask, [n_batch, n_indices_padded, 1]
)
aggregate_emb = math_ops.reduce_sum(emb_lookup * values_mask_broadcast, axis=1)
if combiner == "sum":
return aggregate_emb
elif combiner == "mean":
# In the case we have an empty row, both aggregate_emb and
# math_ops.reduce_sum(values_mask_broadcast, axis=1) will be 0. Thus,
# we can take max it with a non-zero value to prevent NaNs. Note that
# math_ops.reduce_sum(values_mask_broadcast, axis=1) will have integer
# values so 1.0 is the smallest value.
return aggregate_emb / math_ops.maximum(
math_ops.reduce_sum(values_mask_broadcast, axis=1), 1.0
)
else:
raise ValueError(
"Dense TPU Embedding does not support combiner "
"other than sum and mean."
)
def pad_sparse_embedding_lookup_indices(sparse_indices, padded_size):
"""Creates statically-sized Tensors containing indices and weights.
From third_party/cloud_tpu/models/movielens/tpu_embedding.py
Also computes sparse_indices.values % embedding_table_size, for equivalent
functionality to sparse_column_with_integerized_feature. The returned
padded weight Tensor also doubles as a mask indicating which values in
the returned padded indices Tensor are indices versus padded zeros.
Args:
sparse_indices: SparseTensor of embedding lookup indices.
padded_size: Number of columns of the returned Tensors. Indices which fall
out of bounds will be truncated to the padded size.
Returns:
(sparse_indices.values padded to the specified size,
a mask the same size as the returned padded values in which 0s
indicate padded locations and 1s (or values from sparse_weights)
indicate actual values)
"""
batch_size = sparse_indices.dense_shape[0]
sparse_indices = sparse_ops.sparse_slice(
sparse_indices, [0, 0], [batch_size, padded_size]
)
indices, values = sparse_indices.indices, sparse_indices.values
padded_values = array_ops.scatter_nd(
indices, math_ops.cast(values, dtypes.int32), shape=(batch_size, padded_size)
)
weights = array_ops.ones_like(values, dtype=dtypes.float32)
padded_mask = array_ops.scatter_nd(
indices, weights, shape=(batch_size, padded_size)
)
return padded_values, padded_mask
def _check_invalid_cases(embedding_lookup_device):
"""Checks for invalid embedding_lookup_device configurations."""
if (
tpu.under_tpu_inference_context()
and embedding_lookup_device == EmbeddingDevice.TPU_EMBEDDING_CORE
):
raise ValueError(
"Using embedding_lookup_device=tpu_embedding_core during inference "
"is not supported."
)
if embedding_lookup_device == EmbeddingDevice.CPU:
if not tpu.under_tpu_inference_context():
raise ValueError(
'Using TPUEmbeddingColumn with embedding_lookup_device="cpu" '
"during training is not supported."
)
class _TPUDeviceSpecificEmbeddingColumnV2(_TPUEmbeddingColumnV2):
"""TPUEmbeddingColumn which allows serving on TensorCore."""
def __new__(cls, *args, **kwargs):
# For __new__, just capture the inference dense shape and call parent.
if "tensor_core_shape" in kwargs:
cls._tensor_core_shape = kwargs["tensor_core_shape"]
del kwargs["tensor_core_shape"]
if "embedding_lookup_device" in kwargs:
cls._embedding_lookup_device = kwargs["embedding_lookup_device"]
del kwargs["embedding_lookup_device"]
return _TPUEmbeddingColumnV2.__new__(cls, *args, **kwargs)
def __init__(self, *args, **kwargs):
# For __init__, just capture the inference dense shape and call parent.
if "tensor_core_shape" in kwargs:
self._tensor_core_shape = kwargs["tensor_core_shape"]
del kwargs["tensor_core_shape"]
if "embedding_lookup_device" in kwargs:
self._embedding_lookup_device = kwargs["embedding_lookup_device"]
del kwargs["embedding_lookup_device"]
_TPUEmbeddingColumnV2.__init__(self, *args, **kwargs)
def __deepcopy__(self, memo):
return _TPUDeviceSpecificEmbeddingColumnV2(
*(copy.deepcopy(a, memo) for a in self.__getnewargs__()),
tensor_core_shape=self._tensor_core_shape,
embedding_lookup_device=self._embedding_lookup_device
)
def create_state(self, state_manager):
_check_invalid_cases(self._embedding_lookup_device)
# CPU case.
is_cpu = self._embedding_lookup_device == EmbeddingDevice.CPU
is_cpu = is_cpu or _is_running_on_cpu()
if is_cpu:
return fc_lib.EmbeddingColumn.create_state(self, state_manager)
# TPU_EMBEDDING_CORE case.
elif self._embedding_lookup_device == EmbeddingDevice.TPU_EMBEDDING_CORE:
return super(_TPUDeviceSpecificEmbeddingColumnV2, self).create_state(
state_manager
)
# TPU_EMBEDDING_CORE case.
return fc_lib.EmbeddingColumn.create_state(self, state_manager)
def get_dense_tensor(self, transformation_cache, state_manager):
"""Private method that follows get_dense_tensor."""
_check_invalid_cases(self._embedding_lookup_device)
# CPU Case.
is_cpu = self._embedding_lookup_device == EmbeddingDevice.CPU
is_cpu = is_cpu or _is_running_on_cpu()
if is_cpu:
return super(_TPUDeviceSpecificEmbeddingColumnV2, self).get_dense_tensor(
transformation_cache, state_manager
)
# TPU_EMBEDDING_CORE case.
elif self._embedding_lookup_device == EmbeddingDevice.TPU_EMBEDDING_CORE:
return super(_TPUDeviceSpecificEmbeddingColumnV2, self).get_dense_tensor(
transformation_cache, state_manager
)
# TPU_EMBEDDING_CORE cases.
if tpu.under_tpu_inference_context():
# For inference, use outside compile to densify and pad the input tensors.
sparse_tensor = transformation_cache.get(
self.categorical_column.name, state_manager
)
def host_computation():
return pad_sparse_embedding_lookup_indices(
sparse_tensor, self._tensor_core_shape[1]
)
values, mask = tpu.outside_compilation(host_computation)
else:
# For training, the inputs should already have been densified and padded.
values = transformation_cache.get(
self.categorical_column.name, state_manager
)
mask = transformation_cache.get(
self.categorical_column.name + _TENSOR_CORE_MASK_KEY_SUFFIX,
state_manager,
)
embedding_weights = state_manager.get_variable(self, name="embedding_weights")
return sparse_embedding_aggregate_slice(
embedding_weights, (values, mask), self.get_combiner()
)
def _get_dense_tensor(self, inputs, weight_collections=None, trainable=None):
_check_invalid_cases(self._embedding_lookup_device)
# CPU Case.
is_cpu = self._embedding_lookup_device == EmbeddingDevice.CPU
is_cpu = is_cpu or _is_running_on_cpu()
if is_cpu:
return super(_TPUDeviceSpecificEmbeddingColumnV2, self)._get_dense_tensor(
inputs, weight_collections, trainable
)
# TPU_EMBEDDING_CORE case.
elif self._embedding_lookup_device == EmbeddingDevice.TPU_EMBEDDING_CORE:
return super(_TPUDeviceSpecificEmbeddingColumnV2, self)._get_dense_tensor(
inputs, weight_collections, trainable
)
# TPU_EMBEDDING_CORE cases.
if tpu.under_tpu_inference_context():
# For inference, use outside compile to densify and pad the input tensors.
sparse_tensor = inputs.get(self.get_feature_key_name())
def host_computation():
return pad_sparse_embedding_lookup_indices(
sparse_tensor, self._tensor_core_shape[1]
)
values, mask = tpu.outside_compilation(host_computation)
else:
# For training, the inputs should already have been densified and padded.
values = inputs.get(self.get_feature_key_name())
mask = inputs.get(
self.get_feature_key_name() + _TENSOR_CORE_MASK_KEY_SUFFIX
)
embedding_shape = (
self.categorical_column._num_buckets,
self.dimension,
) # pylint: disable=protected-access
if (
weight_collections
and ops.GraphKeys.GLOBAL_VARIABLES not in weight_collections
):
weight_collections.append(ops.GraphKeys.GLOBAL_VARIABLES)
embedding_weights = variable_scope.get_variable(
name="embedding_weights",
shape=embedding_shape,
dtype=dtypes.float32,
initializer=self.initializer,
trainable=self.trainable and trainable,
collections=weight_collections,
)
return sparse_embedding_aggregate_slice(
embedding_weights, (values, mask), self.get_combiner()
)
class _TPUSharedDeviceSpecificEmbeddingColumnV2(_TPUSharedEmbeddingColumnV2):
"""TPUSharedEmbeddingColumnV2 which allows serving on TensorCore."""
def __new__(cls, *args, **kwargs):
# For __new__, just capture the inference dense shape and call parent.
if "tensor_core_shape" in kwargs:
cls._tensor_core_shape = kwargs["tensor_core_shape"]
del kwargs["tensor_core_shape"]
if "embedding_lookup_device" in kwargs:
cls._embedding_lookup_device = kwargs["embedding_lookup_device"]
del kwargs["embedding_lookup_device"]
return _TPUSharedEmbeddingColumnV2.__new__(cls, *args, **kwargs)
def __init__(self, *args, **kwargs):
# For __init__, just capture the inference dense shape and call parent.
if "tensor_core_shape" in kwargs:
self._tensor_core_shape = kwargs["tensor_core_shape"]
del kwargs["tensor_core_shape"]
if "embedding_lookup_device" in kwargs:
self._embedding_lookup_device = kwargs["embedding_lookup_device"]
del kwargs["embedding_lookup_device"]
_TPUSharedEmbeddingColumnV2.__init__(self, *args, **kwargs)
def __deepcopy__(self, memo):
return _TPUSharedDeviceSpecificEmbeddingColumnV2(
*(copy.deepcopy(a, memo) for a in self.__getnewargs__()),
tensor_core_shape=self._tensor_core_shape,
embedding_lookup_device=self._embedding_lookup_device
)
def _get_dense_tensor_internal(self, transformation_cache, state_manager):
"""Private method that follows _get_dense_tensor_internal."""
_check_invalid_cases(self._embedding_lookup_device)
# CPU Case.
is_cpu = self._embedding_lookup_device == EmbeddingDevice.CPU
is_cpu = is_cpu or _is_running_on_cpu()
if is_cpu:
return super(
_TPUSharedDeviceSpecificEmbeddingColumnV2, self
)._get_dense_tensor_internal(transformation_cache, state_manager)
# TPU_EMBEDDING_CORE case.
if self._embedding_lookup_device == EmbeddingDevice.TPU_EMBEDDING_CORE:
return super(
_TPUSharedDeviceSpecificEmbeddingColumnV2, self
)._get_dense_tensor_internal(transformation_cache, state_manager)
# TPU_EMBEDDING_CORE cases.
if tpu.under_tpu_inference_context():
# For inference, use outside compile to densify and pad the input tensors.
sparse_tensor = transformation_cache.get(
self.categorical_column.name, state_manager
)
def host_computation():
return pad_sparse_embedding_lookup_indices(
sparse_tensor, self._tensor_core_shape[1]
)
values, mask = tpu.outside_compilation(host_computation)
else:
# For training, the inputs should already have been densified and padded.
values = transformation_cache.get(
self.categorical_column.name, state_manager
)
mask = transformation_cache.get(
self.categorical_column.name + _TENSOR_CORE_MASK_KEY_SUFFIX,
state_manager,
)
# Do a dense embedding lookup on TensorCore.
embedding_weights = self.shared_embedding_column_creator.embedding_weights
return sparse_embedding_aggregate_slice(
embedding_weights, (values, mask), self.get_combiner()
)
| [
"bryan.guner@gmail.com"
] | bryan.guner@gmail.com |
66f6e4500621285bbbbaf51d4c572120cb3598e7 | 3b1229c458aa232bfcf11cd6da5f1275e9bb3a8f | /python/Python基础/截图和代码/if、while、for/PaxHeader/01-if比较运算符.py | 147895a71cc09ac233a82e6fae85d44e6ae21569 | [] | no_license | sunjianbo/learning | 4fee3ddc5e3d4040a49f2ef3e6f239fd6a67b393 | 384cb4e73cc67e390ee2f4be0da9fe0319d93644 | refs/heads/master | 2021-02-17T16:32:22.557614 | 2020-03-09T05:29:51 | 2020-03-09T05:29:51 | 245,111,571 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 105 | py | 78 path=Python基础/截图和代码/if、while、for/01-if比较运算符.py
27 mtime=1491131771.711676
| [
"sunjianbo"
] | sunjianbo |
e372c12cb3b68da6d60f0b9badb77c5351675ea6 | dfd0036b13141f0a61ce29325044d9fc3accfa67 | /prob20.py | 165d4cd80e0efaa2f74d5cae6b4c7d1396f817cc | [] | no_license | V-Neck/Euler | a909a9265ce8b0b09204189adcf7655ffe9bec5d | 4710f1456cc9c23eb0564122878b4245bd6c33d8 | refs/heads/master | 2021-05-06T17:58:49.316448 | 2018-04-09T02:48:03 | 2018-04-09T02:48:03 | 111,858,206 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 69 | py | from prob15 import fact
print sum([int(i) for i in str(fact(100))])
| [
"evilepicproductions@gmail.com"
] | evilepicproductions@gmail.com |
6a5596e9f5936e591842035cd79dd7c31355ad3f | d169aaea184d13b92a79db7ddcdf6cafc8696fb7 | /ArrayMathematics.py | 40acb0b5e066a3de3f3559ea5c8b44e7419747c9 | [] | no_license | sahilshah1610/HackerRank | 71dcc6c5d5f8411240e12c2e74c31fc5c62d5ed0 | 1fd63624b05927bf5ac38ee206d4f7e79b000b68 | refs/heads/master | 2023-01-28T23:21:42.383578 | 2020-12-14T05:21:36 | 2020-12-14T05:21:36 | 314,386,080 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 428 | py | import numpy as np
if __name__ == "__main__":
n,m = map(int, input().split())
arrA = np.zeros((n,m),int)
arrB = np.zeros((n,m),int)
for i in range(n):
arrA[i] = np.array(input().split(), int)
for i in range(n):
arrB[i] = np.array(input().split(), int)
print(arrA + arrB)
print(arrA - arrB)
print(arrA * arrB)
print(arrA // arrB)
print(arrA % arrB)
print(arrA ** arrB)
| [
"sahil.shah56@gmail.com"
] | sahil.shah56@gmail.com |
c55fcbc5290050fda7ddeebb2c0e6adec8d9980c | 703e6baed8e2b1efbd1aaee7eba3b6af1fb3fd84 | /nndist.py | 22521fb8082fa049edcfbd3e6d691c0c444eea03 | [] | no_license | deyh2020/EIT-computations | 12969400c2bec3810112a69da9a8d9a594ab2934 | 9159bff4bfda8b308a486b3404c90c0376e576eb | refs/heads/master | 2022-03-07T18:21:36.656263 | 2018-06-19T21:04:27 | 2018-06-19T21:04:27 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 353 | py | import pylab
import matplotlib as mpl
import numpy as np
import matplotlib.pyplot as plt
import scipy
from qutip import *
from scipy import linalg
from math import *
def nndist(x, n):
return np.exp(-4*pi*x*x*x*n/3.)*4.*pi*x*x*n
def nndistV(C, V, n):
x = (C/V)**(1./6.)
return np.exp(-4*pi*x*x*x*n/3.)*4.*pi*x*x*n*((C/(V**7.))**(1./6.))/6.
| [
"hudpsa@gmail.com"
] | hudpsa@gmail.com |
971a9e6462e076e0cd93f1bffaa93f96f40d2ccd | d8648e2c56452c6ee09aced281b0810a702467ec | /blog/migrations/0003_auto_20181217_1108.py | 69e6236703fe51a9a139a9de960baa01582cb1e5 | [] | no_license | nikhil0162/django_Project | c51018eacf4965b7edf62d1067c636476454e2e1 | 25c4f4c1c5065bcb127590a2339e4d7725078be8 | refs/heads/master | 2022-12-14T04:20:36.839251 | 2020-10-29T17:39:27 | 2020-10-29T17:39:27 | 159,532,188 | 0 | 0 | null | 2022-12-08T02:28:32 | 2018-11-28T16:30:36 | HTML | UTF-8 | Python | false | false | 403 | py | # Generated by Django 2.1.4 on 2018-12-17 05:38
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('blog', '0002_auto_20181215_0712'),
]
operations = [
migrations.AlterField(
model_name='carpost',
name='launched_date',
field=models.DateTimeField(blank=True, null=True),
),
]
| [
"nikhil0162@gmail.com"
] | nikhil0162@gmail.com |
097f6aa61829185596618db0242a9f7088507c1b | 98005f697f615e55d1a34bbb8f71fb45dd11f2be | /agmapi/__init__.py | 275199dc36eeee47ba48903ed7abfd9d83c8931a | [] | no_license | sreecodeslayer/khub-task | 0a2bf12cc4d65c6feea0e3fde4cf0484ce5d779b | 97edf6670bcaa0675aed2f589644e31747d54862 | refs/heads/master | 2020-03-10T06:42:04.900308 | 2018-04-17T05:10:19 | 2018-04-17T05:10:19 | 129,244,712 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 771 | py | from flask import Flask, render_template
from flask_restful import Api
from .db import init_db
from .utils import init_logger, get_logger
app = Flask('agmapi')
app.config['DEBUG'] = True
app.config['MONGODB_SETTINGS'] = {
'db': 'AGMAPI',
'host': 'mongodb://localhost:27017/AGMAPI'
}
init_db(app)
init_logger(app)
logger = get_logger()
logger.info('Server ready!')
@app.route('/')
def index():
return render_template('index.html')
from .web import (
StocksResource,
CommodityResource,
StatesResource,
MandisResource
)
api = Api(app)
api.add_resource(StocksResource, '/api/stocks')
api.add_resource(CommodityResource, '/api/commodities')
api.add_resource(StatesResource, '/api/states')
api.add_resource(MandisResource, '/api/mandis')
| [
"kesav.tc8@gmail.com"
] | kesav.tc8@gmail.com |
3227cb4f2668033863586798ab35d19867aa42b8 | 443690c3b6e0ab294cd24fee3db164b756a45f02 | /cs308app/migrations/0008_auto_20201116_2352.py | e1618e0313c80ad4fc2fba800e3361402dd86fee | [] | no_license | oziyildirim/DjangoSoftEng | 19175770fdce3c66506d0f48ce9309912cd42912 | 97bf4d8821867f94d70802d65bde92b1f3aeb1d9 | refs/heads/main | 2023-04-01T06:55:03.421094 | 2021-04-07T16:55:06 | 2021-04-07T16:55:06 | 336,238,724 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,216 | py | # Generated by Django 3.1.3 on 2020-11-16 20:52
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('cs308app', '0007_auto_20201110_2233'),
]
operations = [
migrations.AddField(
model_name='basketitem',
name='quantity',
field=models.IntegerField(default=1),
),
migrations.AddField(
model_name='order',
name='phone_number',
field=models.IntegerField(default=0),
preserve_default=False,
),
migrations.AlterField(
model_name='order',
name='address',
field=models.CharField(max_length=300),
),
migrations.AlterField(
model_name='orderitem',
name='quantity',
field=models.IntegerField(default=1),
),
migrations.AlterField(
model_name='product',
name='img',
field=models.CharField(max_length=200, null=True),
),
migrations.AlterField(
model_name='product',
name='product_name',
field=models.CharField(max_length=100),
),
]
| [
"oyildirim@sabanciuniv.edu"
] | oyildirim@sabanciuniv.edu |
e3015d6511a097f72860e5b54e64757741b0778b | 7401dac35fa6ea9bdbddb9dad15f5879ba2c0507 | /acadbehaviour.py | b63e506090b12ae2641e0ab31f24d2ad1c551201 | [] | no_license | airrakeshkumarsharma/student-classification | 995b096dd612806faf49b60a9c1e22678db53e1d | 9b46cbe0d6d8747ce78762e327473861ee44b7a2 | refs/heads/master | 2020-03-28T01:27:46.794113 | 2018-09-06T13:00:46 | 2018-09-06T13:00:46 | 147,506,789 | 2 | 1 | null | 2018-09-06T05:13:55 | 2018-09-05T11:22:52 | null | UTF-8 | Python | false | false | 3,725 | py | import pandas as pd
import random
#Serial Number
sno = []
#Academic Behaviour
active = []
sem = []
#Higher Studies
name = []
mba = []
ms = []
mtech = []
#Job
govtjob = []
it = []
entrepreneur = []
#Co-circular activities
sports=[]
music=[]
dance=[]
others=[]
#General Behaviour
result=[]
#print("0 -> Not Attentive")
#print("1 -> A Bit Attentive")
#print("2 -> Attentive")
#print("3 -> Hyperactive")
for i in range(1,61):
a = random.randint(0,3)
s = random.randint(0,10)
#a = int(input("Enter the Academic behaviour marks on a scale of 0 to 3"))
#s = int(input("Enter the Semester marks on a scale of 0 to 10"))
active.append(a)
sem.append(s)
sno.append(i)
print("Academic Behaviour")
print("Attentive",active)
print("Semester marks",sem)
#print("0->Not Intrested")
#print("1->Confused")
#print("2-> Interested")
#print("3->Passionate")
for i in range(1,61):
mba1 = random.randint(0,3)
ms1 = random.randint(0,3)
mtech1 = random.randint(0,3)
#mba1 = input("Enter your intrest in mba on a scale of 0 to 3")
#ms1 = input("Enter your intrest in ms on a scale of 0 to 3")
#mtech1 = input("Enter your intrest in mtech on a scale of 0 to 3")
mtech.append(mtech1)
ms.append(ms1)
mba.append(mba1)
print("Higher Studies")
print("M.tech",mtech)
print("MS",ms)
print("MBA",mba)
#print("0 -> Not Intrested")
#print("1 -> Confused")
#print("2 -> Interested")
#print("3 -> Passionate")
for i in range(1,61):
g = random.randint(0,3)
it1 = random.randint(0,3)
e = random.randint(0,3)
#g = input("Enter your intrest in govt job on a scale of 0 to 3")
#it1 = input("Enter your intrest in IT job on a scale of 0 to 3")
#e = input("Enter your intrest in entrepeneurship on a scale of 0 to 3")
it.append(it1)
entrepreneur.append(e)
govtjob.append(g)
print("JOB")
print("IT job",it)
print("Bussiness",entrepreneur)
print("Govt Job",govtjob)
#print("1 -> Not Intrested")
#print("2 -> On and OFF")
#print("3 -> Regular")
#print("4 -> Passionate")
for i in range(1,61):
s = random.randint(0,3)
m = random.randint(0,3)
d = random.randint(0,3)
o = random.randint(0,3)
#s = input ("Enter your intrest in sports on a scale of 0 to 3")
#m = input ("Enter your intrest in music on a scale of 0 to 3")
#d = input ("Enter your intrest in dance on a scale of 0 to 3")
#o = input ("Enter your intrest in others on a scale of 0 to 3")
music.append (m)
sports.append (s)
dance.append (d)
others.append (o)
print("Co-Circular Activities")
print("music",music)
print("sports",sports)
print("dance",dance)
print("others",others)
#print("For grading any student consider the categories:")
#print(" polite, not polite")
#print(" responisible, not responsible")
#print(" honest, not honest")
#print(" give the data in number format:")
#print("0 -> No postive aspect")
#print("1 -> one postive aspect")
#print("2 -> two postive aspect")
#print("3 -> three postive aspect")
for i in range(1,61):
k = random.randint(0,3)
#k = int (input ("enter remarks for the student:"))
result.append(k)
print("General Behaviour")
print(result)
# save the file with data
csvf = pd.DataFrame({"S.No": sno, "Attentiveness": active, "Sem Marks": sem, "MBA": mba, "MS": ms, "M.Tech": mtech, "GOVTJOB": govtjob, "IT": it, "ENTREPRENEUR": entrepreneur, "sports": sports, "music": music, "dance": dance, "others": others, "StudentBehaviour":result})
csvf.to_csv("AcademicBehaviour.csv",index="false")
print(csvf)
| [
"noreply@github.com"
] | airrakeshkumarsharma.noreply@github.com |
b0f8b3dc5a6fbec391eb180abdea385a1eda72cf | 3dab50196ae7c93ec7f0bb7ddc84c2409a989e15 | /13305.py | acc6652e9e7bfc9a919643103706dd5812e3ceb7 | [] | no_license | seounjin/baekjoon_algorithm | 7d8b6a8f51356ad862cdb3229ff628a9e1ca49d8 | a7a3f212992fd9248db9680c901536d68e5680bd | refs/heads/master | 2022-12-21T03:15:17.968041 | 2020-09-16T15:25:23 | 2020-09-16T15:25:23 | 185,127,908 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 410 | py | # 주유소
import sys
input = sys.stdin.readline
N = int(input())
# 도로의 길이
road = list(map(int, input().split()))
# 도시당 가격
city = list(map(int, input().split()))
road_len = sum(road)
road += [0]
answer = [0] * 100000
answer[0] = city[0] * road[0]
temp = city[0]
for i in range(1, N):
temp = min(temp, city[i])
answer[i] = answer[i - 1] + temp * road[i]
print(answer[N - 1]) | [
"invanda7@gmail.com"
] | invanda7@gmail.com |
c5b435586383bec7e14c2017d6182ce5f217272e | 449147399b91db8ca3192e9960834a73967cd01d | /pandas-ml-utils/pandas_ml_utils/ml/data/reconstruction/__init__.py | 52b22fdb3e0d667496a779c3c98ac6e25c9b2549 | [
"MIT"
] | permissive | brunoreisportela/pandas-ml-quant | 04b81568b900d226bb7028ccbe81ea97d0c00587 | a80b06aab28f38c3c6cb298e96f497e4fcdb95a5 | refs/heads/master | 2022-12-18T23:51:38.297857 | 2020-09-08T06:14:16 | 2020-09-08T06:14:16 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 49 | py | from .prediction import assemble_prediction_frame | [
"ch9.ki7@gmail.com"
] | ch9.ki7@gmail.com |
51d514b03c5d8f55cd4062f7f5d8f84380a4ae58 | 8288c6f44ed26292b0795d2290763e19043a058d | /lab9gradient.py | 81a01b213cfa165f7db1d35c6a268be0b35c433e | [] | no_license | szymonln/Mownit2 | d763a1a40b44c45a6a95f26b355cf7442ff58943 | d27d1e194133e6930109eb4d2261bb10dd0590fa | refs/heads/master | 2020-04-03T15:33:26.161354 | 2019-02-08T14:31:46 | 2019-02-08T14:31:46 | 155,366,664 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 532 | py | cur_x = 3
rate = 0.01 # Learning rate
precision = 0.00001
previous_step_size = 1
max_iters = 10000
iters = 0
df = lambda x: 2*x - 2.71 #Gradient
while previous_step_size > precision and iters < max_iters:
prev_x = cur_x # Store current x value in prev_x
cur_x = cur_x - rate * df(prev_x) # Grad descent
previous_step_size = abs(cur_x - prev_x) # Change in x
iters = iters + 1 # iteration count
print("Iteration", iters, "\nX value is", cur_x) # Print iterations
print("The local minimum occurs at", cur_x) | [
"szymonln@gmail.com"
] | szymonln@gmail.com |
e3bb0a08160c3b5afbb1561fc67f5e5b2b320380 | 43a676d507c9f3e007d46b9335c82f77e35350f6 | /config/wsgi.py | df17ccb416ed061cc0afd7cf24b277bc198a94b4 | [] | no_license | Zoxon470/nekidaem-blog | 79136fd9f4747afd01beb02bfd9d0c524493a6f6 | c2539963d149841397e9eb2d4153a73abea15da2 | refs/heads/master | 2022-05-02T20:14:05.805564 | 2019-06-27T21:50:57 | 2019-06-27T21:50:57 | 194,165,211 | 0 | 2 | null | 2022-04-22T21:53:15 | 2019-06-27T21:25:07 | JavaScript | UTF-8 | Python | false | false | 340 | py | import os
import sys
from django.core.wsgi import get_wsgi_application
app_path = os.path.abspath(os.path.join(
os.path.dirname(os.path.abspath(__file__)), os.pardir))
sys.path.append(os.path.join(app_path, 'nekidaem-blog'))
os.environ.setdefault("DJANGO_SETTINGS_MODULE", 'config.settings.dev')
application = get_wsgi_application()
| [
"zoxon470@gmail.com"
] | zoxon470@gmail.com |
ca649f8cd329ae6e3c66facc2dfe5d27aa53dc6b | a70538105d0cb172c2f5628f083d53529904941b | /Watermelon.py | 04c38d409a4fa100bf7fb7a115aef465f1a1019a | [] | no_license | Raihan-009/Codeforces | 792be79991d1ade5aa87e779506173b5d4fac442 | 3bb53f2561a29793964ebaf76848de5be8ba975e | refs/heads/master | 2022-09-12T09:40:45.988289 | 2020-06-01T14:24:48 | 2020-06-01T14:24:48 | 268,368,282 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,175 | py | '''
One hot summer day Pete and his friend Billy decided to buy a watermelon. They chose the biggest and the ripest one, in their opinion. After that the watermelon was weighed, and the scales showed w kilos. They rushed home, dying of thirst, and decided to divide the berry, however they faced a hard problem.
Pete and Billy are great fans of even numbers, that's why they want to divide the watermelon in such a way that each of the two parts weighs even number of kilos, at the same time it is not obligatory that the parts are equal. The boys are extremely tired and want to start their meal as soon as possible, that's why you should help them and find out, if they can divide the watermelon in the way they want. For sure, each of them should get a part of positive weight.
#Input
The first (and the only) input line contains integer number w (1 ≤ w ≤ 100) — the weight of the watermelon bought by the boys.
#Output
Print YES, if the boys can divide the watermelon into two parts, each of them weighing even number of kilos; and NO in the opposite case.'''
w = input()
w = int(w)
if (w > 2 and w%2 ==0):
print("YES")
else :
print("NO")
| [
"64744693+Raihan-009@users.noreply.github.com"
] | 64744693+Raihan-009@users.noreply.github.com |
494f4579cf7fca7b1eb90c375efb34a67f6d3cd4 | e319b3f9b80f0e8a843cec7edd65f19baa0c9f3b | /Interface/Dialogs/SpendOrRestoreSpellPointsDialog.py | 3aa7c90fe3e39e7fa58af55d966d06d1c6e5583f | [
"MIT"
] | permissive | Snackhole/PyFifth | 0e852ece4ef37d0cfa952d0b47d1d9c42ea6b1fd | 2a5419dea0309119d3ffbb5ba33ec14395d6dd59 | refs/heads/main | 2023-08-19T02:00:20.433299 | 2023-08-01T00:21:56 | 2023-08-01T00:21:56 | 125,868,139 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 4,083 | py | from PyQt5 import QtCore
from PyQt5.QtWidgets import QComboBox, QLabel, QPushButton, QDialog, QGridLayout, QSizePolicy, QSpinBox
class SpendOrRestoreSpellPointsDialog(QDialog):
def __init__(self, CharacterWindow, RestoreMode=False):
super().__init__(parent=CharacterWindow)
# Store Parameters
self.CharacterWindow = CharacterWindow
self.RestoreMode = RestoreMode
# Variables
self.ModeString = "Restore" if self.RestoreMode else "Spend"
self.SpellLevels = ["None"] + list(self.CharacterWindow.PlayerCharacter.SpellPointValues.keys())
self.SpellSlotLevel = None
self.SpellSlotAmount = None
self.ManualAmount = None
self.Submitted = False
# Inputs Size Policy
self.InputsSizePolicy = QSizePolicy(QSizePolicy.Minimum, QSizePolicy.Minimum)
# Prompt
self.PromptLabel = QLabel(self.ModeString + " spell points:")
self.PromptLabel.setAlignment(QtCore.Qt.AlignCenter)
# Spell Slots
self.SpellSlotLevelLabel = QLabel("Spell Slot Level:")
self.SpellSlotLevelLabel.setAlignment(QtCore.Qt.AlignCenter)
self.SpellSlotLevelComboBox = QComboBox()
self.SpellSlotLevelComboBox.setSizePolicy(self.InputsSizePolicy)
self.SpellSlotLevelComboBox.addItems(self.SpellLevels)
self.SpellSlotLevelComboBox.setEditable(False)
self.SpellSlotAmountLabel = QLabel("Spell Slot Amount:")
self.SpellSlotAmountLabel.setAlignment(QtCore.Qt.AlignCenter)
self.SpellSlotAmountSpinBox = QSpinBox()
self.SpellSlotAmountSpinBox.setAlignment(QtCore.Qt.AlignCenter)
self.SpellSlotAmountSpinBox.setButtonSymbols(self.SpellSlotAmountSpinBox.NoButtons)
self.SpellSlotAmountSpinBox.setSizePolicy(self.InputsSizePolicy)
self.SpellSlotAmountSpinBox.setRange(0, 1000000000)
self.SpellSlotAmountSpinBox.setValue(0)
# Manual Amount
self.ManualAmountLabel = QLabel("Manual Amount:")
self.ManualAmountLabel.setAlignment(QtCore.Qt.AlignCenter)
self.ManualAmountSpinBox = QSpinBox()
self.ManualAmountSpinBox.setAlignment(QtCore.Qt.AlignCenter)
self.ManualAmountSpinBox.setButtonSymbols(self.ManualAmountSpinBox.NoButtons)
self.ManualAmountSpinBox.setSizePolicy(self.InputsSizePolicy)
self.ManualAmountSpinBox.setRange(0, 1000000000)
self.ManualAmountSpinBox.setValue(0)
# Buttons
self.SubmitButton = QPushButton(self.ModeString)
self.SubmitButton.clicked.connect(self.Submit)
self.CancelButton = QPushButton("Cancel")
self.CancelButton.clicked.connect(self.Cancel)
# Layout
self.Layout = QGridLayout()
self.Layout.addWidget(self.PromptLabel, 0, 0, 1, 2)
self.Layout.addWidget(self.SpellSlotLevelLabel, 1, 0)
self.Layout.addWidget(self.SpellSlotLevelComboBox, 1, 1)
self.Layout.addWidget(self.SpellSlotAmountLabel, 2, 0)
self.Layout.addWidget(self.SpellSlotAmountSpinBox, 2, 1)
self.Layout.addWidget(self.ManualAmountLabel, 3, 0)
self.Layout.addWidget(self.ManualAmountSpinBox, 3, 1)
self.ButtonsLayout = QGridLayout()
self.ButtonsLayout.addWidget(self.SubmitButton, 0, 0)
self.ButtonsLayout.addWidget(self.CancelButton, 0, 1)
self.Layout.addLayout(self.ButtonsLayout, 4, 0, 1, 2)
for Row in [1, 2, 3]:
self.Layout.setRowStretch(Row, 1)
self.Layout.setColumnStretch(1, 1)
self.setLayout(self.Layout)
# Set Window Title and Icon
self.setWindowTitle(self.CharacterWindow.ScriptName)
self.setWindowIcon(self.CharacterWindow.WindowIcon)
# Execute Dialog
self.exec_()
def Submit(self):
self.SpellSlotLevel = self.SpellSlotLevelComboBox.currentText()
self.SpellSlotAmount = self.SpellSlotAmountSpinBox.value()
self.ManualAmount = self.ManualAmountSpinBox.value()
self.Submitted = True
self.close()
def Cancel(self):
self.close()
| [
"snackhole.dev@gmail.com"
] | snackhole.dev@gmail.com |
cbf179d79502288c0887998e124e14e76e67a723 | c271c196bf2730c20de42f75568336cd8ccd07d1 | /password_generator/settings.py | b735e335e8541b7ca0067bb54d2db2e9ee2e225f | [] | no_license | entry-dev/django3-password_generator | e68f97565944355c8001ad35a73118a8741a1106 | 74f2684e9cdd597bccf9a4aaedbc208625e42de2 | refs/heads/master | 2023-01-04T03:23:48.416370 | 2020-11-04T23:52:36 | 2020-11-04T23:52:36 | 310,145,434 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 3,115 | py | """
Django settings for password_generator project.
Generated by 'django-admin startproject' using Django 3.1.2.
For more information on this file, see
https://docs.djangoproject.com/en/3.1/topics/settings/
For the full list of settings and their values, see
https://docs.djangoproject.com/en/3.1/ref/settings/
"""
from pathlib import Path
# Build paths inside the project like this: BASE_DIR / 'subdir'.
BASE_DIR = Path(__file__).resolve().parent.parent
# Quick-start development settings - unsuitable for production
# See https://docs.djangoproject.com/en/3.1/howto/deployment/checklist/
# SECURITY WARNING: keep the secret key used in production secret!
SECRET_KEY = 'dvnck9yp5_#09@1zzyjc2c=)!g=8a36n!@it291c13(4%i5=pb'
# SECURITY WARNING: don't run with debug turned on in production!
DEBUG = True
ALLOWED_HOSTS = []
# Application definition
INSTALLED_APPS = [
'generator',
'django.contrib.admin',
'django.contrib.auth',
'django.contrib.contenttypes',
'django.contrib.sessions',
'django.contrib.messages',
'django.contrib.staticfiles',
]
MIDDLEWARE = [
'django.middleware.security.SecurityMiddleware',
'django.contrib.sessions.middleware.SessionMiddleware',
'django.middleware.common.CommonMiddleware',
'django.middleware.csrf.CsrfViewMiddleware',
'django.contrib.auth.middleware.AuthenticationMiddleware',
'django.contrib.messages.middleware.MessageMiddleware',
'django.middleware.clickjacking.XFrameOptionsMiddleware',
]
ROOT_URLCONF = 'password_generator.urls'
TEMPLATES = [
{
'BACKEND': 'django.template.backends.django.DjangoTemplates',
'DIRS': [],
'APP_DIRS': True,
'OPTIONS': {
'context_processors': [
'django.template.context_processors.debug',
'django.template.context_processors.request',
'django.contrib.auth.context_processors.auth',
'django.contrib.messages.context_processors.messages',
],
},
},
]
WSGI_APPLICATION = 'password_generator.wsgi.application'
# Database
# https://docs.djangoproject.com/en/3.1/ref/settings/#databases
DATABASES = {
'default': {
'ENGINE': 'django.db.backends.sqlite3',
'NAME': BASE_DIR / 'db.sqlite3',
}
}
# Password validation
# https://docs.djangoproject.com/en/3.1/ref/settings/#auth-password-validators
AUTH_PASSWORD_VALIDATORS = [
{
'NAME': 'django.contrib.auth.password_validation.UserAttributeSimilarityValidator',
},
{
'NAME': 'django.contrib.auth.password_validation.MinimumLengthValidator',
},
{
'NAME': 'django.contrib.auth.password_validation.CommonPasswordValidator',
},
{
'NAME': 'django.contrib.auth.password_validation.NumericPasswordValidator',
},
]
# Internationalization
# https://docs.djangoproject.com/en/3.1/topics/i18n/
LANGUAGE_CODE = 'en-us'
TIME_ZONE = 'UTC'
USE_I18N = True
USE_L10N = True
USE_TZ = True
# Static files (CSS, JavaScript, Images)
# https://docs.djangoproject.com/en/3.1/howto/static-files/
STATIC_URL = '/static/'
| [
"ivan.krstic@yahoo.com"
] | ivan.krstic@yahoo.com |
15834731332573ccf1a384394228b16286a05343 | f2e7442f38465fdd237c763c80f436e3f103221c | /experimental/HandKeyPointsCustom.py | caf05d577f753b76b5d6fd0187ac6e81faa72eb9 | [] | no_license | SuhelNaryal/Sign-Hawk | fda0f35efd9b0a7c17e0562efd7faac76367ab57 | 52e3e150b1ea30c7c4c15ee54f0dba09d0408dc7 | refs/heads/main | 2023-01-24T13:24:57.984937 | 2020-11-23T11:30:16 | 2020-11-23T11:30:16 | 303,287,298 | 2 | 0 | null | null | null | null | UTF-8 | Python | false | false | 2,449 | py | import keras
import tensorflow as tf
def HandKeyPointsLoss(y_true, y_pred):
y_pred = keras.backend.cast(y_pred, dtype=tf.float32)
left_hand_true = y_true[:, :, 0]
right_hand_true = y_true[:, :, 1]
left_keypoints_true = y_true[:, :, 2:65]
right_keypoints_true = y_true[:, :, 65:]
left_hand_loss = tf.reduce_sum(keras.losses.binary_crossentropy(left_hand_true, y_pred[:, :, 0]), axis=-1) / left_hand_true.shape[0]
right_hand_loss = tf.reduce_sum(keras.losses.binary_crossentropy(right_hand_true, y_pred[:, :, 1]), axis=-1) / right_hand_true.shape[0]
left_keypoints_loss = tf.reduce_sum(left_hand_true * keras.losses.mean_squared_error(left_keypoints_true, y_pred[:, :, 2:65]), axis=-1) / tf.reduce_sum(left_hand_true, axis=-1)
right_keypoints_loss = tf.reduce_sum(right_hand_true * keras.losses.mean_squared_error(right_keypoints_true, y_pred[:, :, 65:]), axis=-1) / tf.reduce_sum(right_hand_true, axis=-1)
return left_hand_loss + right_hand_loss + left_keypoints_loss, right_keypoints_loss
class HandKeyPoints():
def __init__(self, learning_rate=0.001):
super(HandKeyPoints, self).__init__()
input_layer = keras.Input(shape=(256, 256, 3))
resnetbackbone = keras.applications.ResNet50V2(input_shape=(256, 256, 3), include_top=False)
resnetbackbone_out = resnetbackbone(input_layer)
global_avg_pool = keras.layers.GlobalAveragePooling2D()(resnetbackbone_out)
dense1 = keras.layers.Dense(units=2048, activation='relu')(global_avg_pool)
dense2 = keras.layers.Dense(units=2048, activation='relu')(dense1)
left_hand = keras.layers.Dense(units=1, activation='sigmoid', name='left_hand')(dense2)
right_hand = keras.layers.Dense(units=1, activation='sigmoid', name='right_hand')(dense2)
left_keypoints = keras.layers.Dense(units=63, name='left_keypoints')(dense2)
right_keypoints = keras.layers.Dense(units=63, name='right_keypoints')(dense2)
output = keras.backend.concatenate([left_hand, right_hand, left_keypoints, right_keypoints], axis=1)
self.model = keras.Model(inputs=input_layer, outputs=output)
self.optimizer = keras.optimizers.Adam(learning_rate=learning_rate)
self.loss = HandKeyPointsLoss
self.model.compile(optimizer=self.optimizer, loss=self.loss)
print(self.model.summary())
HandKeyPoints()
| [
"noreply@github.com"
] | SuhelNaryal.noreply@github.com |
62d9fb786e671921898f8a2ecbece758e2046377 | a5f5f19615d1af450338b8d1071b940bcc68ab91 | /core/mixins.py | 9beda68da1934370bd1b925f13dde7f55033d5b7 | [] | no_license | duydo131/hotel | 987943dc184d3f98fe96ddbd16c6a4453605c9e3 | b010d84a657ddb54c1f6c6add656fe21e5f74a31 | refs/heads/main | 2023-06-15T09:14:37.109199 | 2021-07-16T03:26:02 | 2021-07-16T03:26:02 | 369,415,838 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 918 | py | from rest_framework_extensions.mixins import DetailSerializerMixin
class GetSerializerClassMixin:
serializer_action_classes = {}
serializer_detail_class = None
queryset_detail = None
def get_serializer_class(self):
try:
return self.serializer_action_classes[self.action]
except (KeyError, AttributeError):
error_message = "'{0}' should include a 'serializer_detail_class' attribute".format(self.__class__.__name__)
# assert self.serializer_detail_class is not None, error_message
# if getattr(self, 'object', None):
# return self.serializer_detail_class
# else:
return super(GetSerializerClassMixin, self).get_serializer_class()
def get_queryset(self):
if self.action in ["update", "partial_update", "retrieve"]:
return self.queryset_detail
return self.queryset
| [
"dotheduybk131@gmail.com"
] | dotheduybk131@gmail.com |
8001d2f7a9d565237552aea7cbf4fd1650d437b9 | 912196d86c93c29b3b031792e3cf886420a0fbde | /core/rnn/rnn_minibatch_test.py | c8e233c8e50eb5ebf1c20a8a41d63c0c12daa8c2 | [
"Apache-2.0"
] | permissive | brian-lau/guac | fce363745c9a778733f1df765fd9c3b832fdeef4 | c3db6cdbe56a1cb04486650ea5473287ba159ad4 | refs/heads/master | 2020-05-29T11:55:34.494957 | 2015-10-28T02:17:34 | 2015-10-28T02:17:34 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 48,065 | py | # adapted from https://github.com/lisa-lab/DeepLearningTutorials
from collections import OrderedDict
import copy
import os
import re
import random
import timeit
from hyperopt import STATUS_OK
import numpy as np
import pandas as pd
from scipy import stats
import theano
from theano import tensor as T
import common
from ..util import defines
from ..util import file_handling as fh
from ..experiment import reusable_holdout
from ..experiment import evaluation
# Otherwise the deepcopy fails
import sys
sys.setrecursionlimit(5000)
THEANO_FLAGS='floatX=float32'
# utils functions
def shuffle(lol, seed=None):
'''
lol :: list of list as input
seed :: seed the shuffling
shuffle inplace each list in the same order
'''
for l in lol:
random.seed(seed)
random.shuffle(l)
class RNN(object):
''' elman neural net model '''
def __init__(self, nh, nc, ne, de, cs, init_scale=0.2, initial_embeddings=None,
rnn_type='basic', # 'basic', 'GRU', or 'LSTM'
pooling_method='max', #'max', 'mean', 'attention1' or 'attention2',
extra_input_dims=0, train_embeddings=True, clip_gradients=False,
bidirectional=True, bi_combine='concat' # 'concat', 'sum', or 'mean'
):
'''
nh :: dimension of the hidden layer
nc :: number of classes
ne :: number of word embeddings in the vocabulary
de :: dimension of the word embeddings
cs :: word window context size
'''
# initialize parameters
dx = de * cs
if extra_input_dims > 0:
dx += extra_input_dims
bi = 1
if bidirectional and bi_combine == 'concat':
bi = 2
if initial_embeddings is None:
self.emb = theano.shared(name='embeddings',
value=init_scale * np.random.uniform(-1.0, 1.0,
(ne, de)).astype(theano.config.floatX))
#(ne+1, de)) # add one for padding at the end
else:
self.emb = theano.shared(name='embeddings', value=initial_embeddings.astype(theano.config.floatX))
if extra_input_dims > 0:
self.W_drld = theano.shared(name='W_drld', value=init_scale * np.random.uniform(-1.0, 1.0, (1, nh))
.astype(theano.config.floatX))
# common paramters (feeding into hidden node)
self.W_xh = theano.shared(name='W_xh', value=init_scale * np.random.uniform(-1.0, 1.0, (dx, nh))
.astype(theano.config.floatX))
self.W_hh = theano.shared(name='W_hh', value=init_scale * np.random.uniform(-1.0, 1.0, (nh, nh))
.astype(theano.config.floatX))
self.b_h = theano.shared(name='b_h', value=np.array(np.random.uniform(0.0, 1.0, nh),
dtype=theano.config.floatX))
# output layer parameters
self.W_s = theano.shared(name='W_s', value=init_scale * np.random.uniform(-1.0, 1.0, (nh * bi, nc))
.astype(theano.config.floatX))
self.b_s = theano.shared(name='b_s', value=np.zeros(nc, dtype=theano.config.floatX))
# temporary parameters
#self.h_i_f = theano.shared(name='h_i_f', value=np.zeros((2, nh), dtype=theano.config.floatX))
#if bidirectional:
# self.h_i_r = theano.shared(name='h_i_r', value=np.zeros(nh, dtype=theano.config.floatX))
# Attention parameters
if pooling_method == 'attention1' or pooling_method == 'attention2':
self.W_a = theano.shared(name='W_a', value=init_scale * np.random.uniform(-1.0, 1.0, (bi*nh, 1))
.astype(theano.config.floatX))
self.b_a = theano.shared(name='b_a', value=0.0)
# GRU parameters
if rnn_type == 'GRU':
self.W_xr = theano.shared(name='W_xr', value=init_scale * np.random.uniform(-1.0, 1.0, (dx, nh))
.astype(theano.config.floatX))
self.W_hr = theano.shared(name='W_hr', value=init_scale * np.random.uniform(-1.0, 1.0, (nh, nh))
.astype(theano.config.floatX))
self.b_r = theano.shared(name='b_r', value=np.zeros(nh, dtype=theano.config.floatX))
self.W_xz = theano.shared(name='W_xz', value=init_scale * np.random.uniform(-1.0, 1.0, (dx, nh))
.astype(theano.config.floatX))
self.W_hz = theano.shared(name='W_hz', value=init_scale * np.random.uniform(-1.0, 1.0, (nh, nh))
.astype(theano.config.floatX))
self.b_z = theano.shared(name='b_z', value=np.zeros(nh, dtype=theano.config.floatX))
# LSTM paramters
if rnn_type == 'LSTM':
# forget gate (needs special initialization)
self.W_xf = theano.shared(name='W_xf', value=init_scale * np.random.uniform(-1.0, 1.0, (dx, nh))
.astype(theano.config.floatX))
self.W_hf = theano.shared(name='W_hf', value=init_scale * np.random.uniform(-1.0, 1.0, (nh, nh))
.astype(theano.config.floatX))
self.W_cf = theano.shared(name='W_cf', value=init_scale * np.random.uniform(-1.0, 1.0, (nh, nh))
.astype(theano.config.floatX))
self.b_f = theano.shared(name='b_f', value=np.array(np.random.uniform(0.0, 1.0, nh),
dtype=theano.config.floatX))
# input gate
self.W_xi = theano.shared(name='W_xi', value=init_scale * np.random.uniform(-1.0, 1.0, (dx, nh))
.astype(theano.config.floatX))
self.W_hi = theano.shared(name='W_hi', value=init_scale * np.random.uniform(-1.0, 1.0, (nh, nh))
.astype(theano.config.floatX))
self.W_ci = theano.shared(name='W_ci', value=init_scale * np.random.uniform(-1.0, 1.0, (nh, nh))
.astype(theano.config.floatX))
self.b_i = theano.shared(name='b_i', value=np.zeros(nh, dtype=theano.config.floatX))
# output gate
self.W_xo = theano.shared(name='W_xo', value=init_scale * np.random.uniform(-1.0, 1.0, (dx, nh))
.astype(theano.config.floatX))
self.W_ho = theano.shared(name='W_ho', value=init_scale * np.random.uniform(-1.0, 1.0, (nh, nh))
.astype(theano.config.floatX))
self.W_co = theano.shared(name='W_co', value=init_scale * np.random.uniform(-1.0, 1.0, (nh, nh))
.astype(theano.config.floatX))
self.b_o = theano.shared(name='b_o', value=np.zeros(nh, dtype=theano.config.floatX))
# use normal ->hidden weights for memory cell
# temp
#self.c_i_f = theano.shared(name='c_i_f', value=np.zeros(nh, dtype=theano.config.floatX))
#if bidirectional:
# self.c_i_r = theano.shared(name='c_i_r', value=np.zeros(nh, dtype=theano.config.floatX))
self.params = [self.W_xh, self.W_hh, self.b_h,
self.W_s, self.b_s]
#self.params += [self.h_i_f]
if train_embeddings:
self.params += [self.emb]
if pooling_method == 'attention':
self.params += [self.W_a, self.b_a]
if rnn_type == 'GRU':
self.params += [self.W_xr, self.W_hr, self.b_r,
self.W_xz, self.W_hz, self.b_z]
if rnn_type == 'LSTM':
self.params += [self.W_xf, self.W_hf, self.W_cf, self.b_f,
self.W_xi, self.W_hi, self.W_ci, self.b_i,
self.W_xo, self.W_ho, self.W_co, self.b_o]
#self.c_i_f]
#if bidirectional:
# self.params += [self.c_i_r]
#if bidirectional:
# self.params += [self.h_i_r]
# create an X object based on the size of the object at the index [elements, emb_dim * window]
idxs = T.tensor3('idxs', dtype='int32')
if extra_input_dims:
extra = T.tensor3('extra')
extra_3d = extra.repeat(idxs.shape[0], axis=0)
#x = T.concatenate([self.emb[idxs].reshape((idxs.shape[0], de*cs)),
# T.repeat(extra, idxs.shape[0], axis=0)], axis=1)
#temp = T.printing.Print('temp')(self.emb[idxs].reshape((idxs.shape[0], idxs.shape[1], de*cs)))
temp = self.emb[idxs].reshape((idxs.shape[0], idxs.shape[1], de*cs))
x = T.concatenate([temp, extra_3d], axis=2)
else:
#x = T.printing.Print('x')(self.emb[idxs])
x = self.emb[idxs].reshape((idxs.shape[0], idxs.shape[1], de*cs)) # [n_elements, minibatch_size, emb_dim]
#x = self.emb[idxs]
y = T.imatrix('y')
mask = T.tensor3('mask')
mask_3d = mask.repeat(nh, axis=2)
minibatch_size = T.iscalar()
def recurrence_basic(x_t, mask_t, h_tm1):
#h_t = theano.printing.Print('h_t')(T.nnet.sigmoid(T.dot(x_t, self.W_xh) + T.dot(h_tm1, self.W_hh) + self.b_h))
h_t = T.nnet.sigmoid(T.dot(x_t, self.W_xh) + T.dot(h_tm1, self.W_hh) + self.b_h)
#masked_h_t = T.printing.Print('masked_h_t')(mask_t * h_t + (1 - mask_t) * h_tm1)
# apply the mask to propogate the last (unmaksed) element in sequence to the end
return mask_t * h_t + (1 - mask_t) * h_tm1
#return h_t
def recurrence_basic_reverse(x_t, mask_t, h_tp1):
h_t = T.nnet.sigmoid(T.dot(x_t, self.W_xh) + T.dot(h_tp1, self.W_hh) + self.b_h)
return mask_t * h_t + (1 - mask_t) * h_tp1
def recurrence_gru(x_t, mask_t, h_tm1):
r_t = T.nnet.sigmoid(T.dot(x_t, self.W_xr) + T.dot(h_tm1, self.W_hr) + self.b_r)
z_t = T.nnet.sigmoid(T.dot(x_t, self.W_xz) + T.dot(h_tm1, self.W_hz) + self.b_z)
g_t = T.tanh(T.dot(x_t, self.W_xh) + r_t * T.dot(h_tm1, self.W_hh) + self.b_h)
h_t = (1 - z_t) * h_tm1 + z_t * g_t
return mask_t * h_t + (1 - mask_t) * h_tm1
def recurrence_gru_reverse(x_t, mask_t, h_tp1):
r_t = T.nnet.sigmoid(T.dot(x_t, self.W_xr) + T.dot(h_tp1, self.W_hr) + self.b_r)
z_t = T.nnet.sigmoid(T.dot(x_t, self.W_xz) + T.dot(h_tp1, self.W_hz) + self.b_z)
g_t = T.tanh(T.dot(x_t, self.W_xh) + r_t * T.dot(h_tp1, self.W_hh) + self.b_h)
h_t = (1 - z_t) * h_tp1 + z_t * g_t
return mask_t * h_t + (1 - mask_t) * h_tp1
def recurrence_lstm(x_t, mask_t, h_tm1, c_tm1):
i_t = T.nnet.sigmoid(T.dot(x_t, self.W_xi) + T.dot(h_tm1, self.W_hi) + T.dot(c_tm1, self.W_ci) + self.b_i)
f_t = T.nnet.sigmoid(T.dot(x_t, self.W_xf) + T.dot(h_tm1, self.W_hf) + T.dot(c_tm1, self.W_cf) + self.b_f)
d_t = T.tanh(T.dot(x_t, self.W_xh) + T.dot(h_tm1, self.W_hh) + self.b_h)
c_t = f_t * c_tm1 + i_t * d_t
o_t = T.nnet.sigmoid(T.dot(x_t, self.W_xo) + T.dot(h_tm1, self.W_ho) + T.dot(c_t, self.W_co) + self.b_o)
h_t = o_t * c_t
return [mask_t * h_t + (1 - mask_t) * h_tm1, mask_t * c_t + (1 - mask_t) * c_tm1]
def recurrence_lstm_reverse(x_t, mask_t, h_tp1, c_tp1):
i_t = T.nnet.sigmoid(T.dot(x_t, self.W_xi) + T.dot(h_tp1, self.W_hi) + T.dot(c_tp1, self.W_ci) + self.b_i)
f_t = T.nnet.sigmoid(T.dot(x_t, self.W_xf) + T.dot(h_tp1, self.W_hf) + T.dot(c_tp1, self.W_cf) + self.b_f)
d_t = T.tanh(T.dot(x_t, self.W_xh) + T.dot(h_tp1, self.W_hh) + self.b_h)
c_t = f_t * c_tp1 + i_t * d_t
o_t = T.nnet.sigmoid(T.dot(x_t, self.W_xo) + T.dot(h_tp1, self.W_ho) + T.dot(c_t, self.W_co) + self.b_o)
h_t = o_t * c_t
return [mask_t * h_t + (1 - mask_t) * h_tp1, mask_t * c_t + (1 - mask_t) * c_tp1]
h_r = None
if rnn_type == 'GRU':
h_f, _ = theano.scan(fn=recurrence_gru, sequences=[x, mask_3d],
outputs_info=[T.alloc(np.array(0.), minibatch_size, nh)],
n_steps=x.shape[0])
if bidirectional:
h_r, _ = theano.scan(fn=recurrence_gru_reverse, sequences=[x, mask_3d],
outputs_info=[T.alloc(np.array(0.), minibatch_size, nh)],
go_backwards=True)
elif rnn_type == 'LSTM':
[h_f, c_f], _ = theano.scan(fn=recurrence_lstm, sequences=[x, mask_3d],
outputs_info=[T.alloc(np.array(0.), minibatch_size, nh),
T.alloc(np.array(0.), minibatch_size, nh)],
n_steps=x.shape[0])
if bidirectional:
[h_r, c_r], _ = theano.scan(fn=recurrence_lstm_reverse, sequences=[x, mask_3d],
outputs_info=[T.alloc(np.array(0.), minibatch_size, nh),
T.alloc(np.array(0.), minibatch_size, nh)],
go_backwards=True)
#[h_r, c_r], _ = theano.scan(fn=recurrence_lstm_reverse, sequences=x,
# outputs_info=[self.h_i_r, self.c_i_r], go_backwards=True)
else:
#h_f, _ = theano.scan(fn=recurrence_basic, sequences=x, outputs_info=[self.h_i_f], n_steps=x.shape[0])
temp, _ = theano.scan(fn=recurrence_basic, sequences=[x, mask_3d],
outputs_info=[T.alloc(np.array(0.), minibatch_size, nh)],
n_steps=x.shape[0])
#h_f = theano.printing.Print('h_f')(temp)
h_f = temp
if bidirectional:
h_r, _ = theano.scan(fn=recurrence_basic_reverse, sequences=[x, mask_3d],
outputs_info=[T.alloc(np.array(0.), minibatch_size, nh)],
go_backwards=True)
if bidirectional:
# reverse the second hidden layer so it lines up with the first
h_r = h_r[::-1, :, :]
if bi_combine == 'max':
h = T.maximum(h_f, h_r)
elif bi_combine == 'mean':
h = (h_f + h_r) / 2.0
else: # concatenate
#h = theano.printing.Print('h:')(T.concatenate([h_fp, h_rp], axis=1))
h = T.concatenate([h_f, h_r], axis=2)
else:
#temp = T.printing.Print('isnan')(T.max(T.isnan(h_f)))
#h = h_f * (1-temp)
h = h_f #[n_elements, minibatch_size, n_hidden] (?)
a_sum = T.sum([1])
if pooling_method == 'attention1': # combine hidden nodes, then transform and sigmoid
# THIS IS NOT WORKIGN...
# SOFTMAX normalizes across the row (axis=1)
#a = T.nnet.softmax((T.dot(h, self.W_a) + self.b_a).T)
temp = T.dot(h, self.W_a) + self.b_a
# softmax?
a = T.exp(temp)/T.exp(temp).sum(axis=0, keepdims=True)
a_sum = T.sum(a, ) # to check a is normalized
a_rep = T.repeat(a, nh*bi, axis=2)
weighted_sum = T.sum(h * a_rep, axis=0)
p_y_given_x_sentence = T.nnet.sigmoid(T.dot(weighted_sum, self.W_s) + self.b_s) # [1, nc] in R(0,1)
y_pred = p_y_given_x_sentence > 0.5 # note, max is just to coerce into proper shape
#element_weights = T.outer(a, p_y_given_x_sentence) # [ne, nc]
#p_y_given_x_sentence = T.nnet.sigmoid(T.dot(T.dot(a, h), self.W_s) + self.b_s) # [1, nc] in R(0,1)
#y_pred = T.max(p_y_given_x_sentence, axis=0) > 0.5 # note, max is just to coerce into proper shape
#element_weights = T.outer(a, p_y_given_x_sentence) # [ne, nc]
elif pooling_method == 'attention2': # transform hidden nodes, sigmoid, then combine
temp = T.dot(h, self.W_a) + self.b_a
# softmax?
a = T.exp(temp)/T.exp(temp).sum(axis=0, keepdims=True) # [ne, minibatch_size, 1]: normalized over ne
#a = T.nnet.softmax((T.dot(h, self.W_a) + self.b_a))
a_sum = T.sum(a, axis=0)
temp = T.nnet.sigmoid(T.dot(h, self.W_s) + self.b_s) # [ne, minibatch_size, nc]
p_y_given_x_sentence = T.sum(temp * T.repeat(a, nc, axis=2), axis=0) # [minibatch_size, nc] in R(0,1)
y_pred = p_y_given_x_sentence > 0.5
#element_weights = T.repeat(a.T, nc, axis=1) * temp # [ne, nc]
elif pooling_method == 'mean':
s = T.nnet.sigmoid((T.dot(h, self.W_s) + self.b_s)) # [n_elements, nc] in R(0,1)
p_y_given_x_sentence = T.mean(s, axis=0)
y_pred = p_y_given_x_sentence > 0.5
element_weights = s
elif pooling_method == 'max':
#s = T.nnet.sigmoid((T.dot(h, self.W_s) + self.b_s)) # [n_elements, minibatch_size, nc] in R(0,1)
s = T.printing.Print('s')(T.nnet.sigmoid((T.dot(h, self.W_s) + self.b_s)))
#s_shape = T.printing.Print('s_shape')(s.shape)
#p_y_given_x_sentence = T.max(s_shape[0] * s, axis=0)
p_y_given_x_sentence = T.max(s, axis=0)
#p_y_given_x_sentence = T.printing.Print('p_y')(T.max(s, axis=0))
#temp = T.printing.Print('p_y')(p_y_given_x_sentence)
#y_pred = T.printing.Print('y_pred')(p_y_given_x_sentence > 0.5)
y_pred = p_y_given_x_sentence > 0.5
element_weights = s
elif pooling_method == 'last':
s = T.nnet.sigmoid((T.dot(h, self.W_s) + self.b_s)) # [n_elements, minibatch_size, nc] in R(0,1)
p_y_given_x_sentence = s[-1, :, :]
y_pred = p_y_given_x_sentence > 0.5
element_weights = s
else:
sys.exit("Pooling method not recognized")
# cost and gradients and learning rate
lr = T.scalar('lr_main')
lr_emb_fac = T.scalar('lr_emb')
#sentence_nll = T.mean(T.sum(-T.log(y*p_y_given_x_sentence + (1-y)*(1-p_y_given_x_sentence)), axis=1))
sentence_nll = T.sum(-T.log(y*p_y_given_x_sentence + (1-y)*(1-p_y_given_x_sentence)))
sentence_gradients = T.grad(sentence_nll, self.params)
if clip_gradients:
sentence_gradients= [T.clip(g, -1, 1) for g in sentence_gradients]
sentence_updates = OrderedDict((p, p - lr * g) for p, g in zip(self.params, [lr_emb_fac *
sentence_gradients[0]]
+ sentence_gradients[1:]))
# theano functions to compile
if extra_input_dims > 0:
self.sentence_classify = theano.function(inputs=[idxs, mask, extra, minibatch_size], outputs=y_pred)
self.sentence_train = theano.function(inputs=[idxs, mask, extra, y, lr, lr_emb_fac, minibatch_size],
outputs=[sentence_nll, a_sum],
updates=sentence_updates)
#if pooling_method == 'attention1' or pooling_method == 'attention2':
# self.a_sum_check = theano.function(inputs=[idxs, extra], outputs=a_sum)
self.sentence_step_through = theano.function(inputs=[idxs, mask, extra, minibatch_size],
outputs=[h, self.W_s, self.b_s, p_y_given_x_sentence])
else:
self.sentence_classify = theano.function(inputs=[idxs, mask, minibatch_size], outputs=y_pred)
self.sentence_train = theano.function(inputs=[idxs, mask, y, lr, lr_emb_fac, minibatch_size],
outputs=[sentence_nll, a_sum],
updates=sentence_updates)
#if pooling_method == 'attention1' or pooling_method == 'attention2':
# self.a_sum_check = theano.function(inputs=[idxs, mask, minibatch_size], outputs=a_sum)
self.sentence_step_through = theano.function(inputs=[idxs, mask, minibatch_size],
outputs=[h, self.W_s, self.b_s, p_y_given_x_sentence])
self.normalize = theano.function(inputs=[],
updates={self.emb: self.emb / T.sqrt((self.emb**2).sum(axis=1))
.dimshuffle(0, 'x')})
def step_through(self, x, mask, window_size, extra_input_dims=0, extra=None):
seq_len, minibatch_size, window_size = x.shape
words = x
mask = np.array(mask.T).astype('int32').reshape((seq_len, minibatch_size, 1))
if extra_input_dims > 0:
extra = np.array(extra).astype('int32').reshape((1, minibatch_size, extra_input_dims))
return self.sentence_step_through(words, mask, extra, minibatch_size)
else:
return self.sentence_step_through(words, mask, minibatch_size)
def classify(self, x, mask, window_size, extra_input_dims=0, extra=None):
#assert window_size == 1
#assert extra_input_dims == 0
#cwords = contextwin(x, window_size)
## make an array of these windows
#words = map(lambda x: np.asarray(x).astype('int32'), cwords)
"""
for i in range(x.shape[0]):
cwords = contextwin(list(x[i, :]), window_size)
words = map(lambda q: np.asarray(q).astype('int32'), cwords)
x[i, :] = words
if len(x.shape) == 2:
minibatch_size, seq_len = x.shape
words = np.array(x.T).astype('int32')
mask = np.array(mask.T).astype('int32').reshape((seq_len, minibatch_size, 1))
else:
minibatch_size = 1
seq_len = x.shape[0]
words = np.array(x).astype('int32').reshape((seq_len, minibatch_size))
mask = np.array(mask).astype('int32').reshape((seq_len, minibatch_size, 1))
"""
"""
if len(x.shape) == 2:
minibatch_size, seq_len = x.shape
words = np.zeros([seq_len, minibatch_size, window_size], dtype='int32')
if window_size > 1:
for i in range(minibatch_size):
cwords = contextwin(list(x[i, :]), window_size)
words_i = np.array(cwords, dtype='int32')
#[words_i.extend(j) for j in cwords]
words[:, i, :] = words_i
x = words.T
words = np.array(x.T).astype('int32').reshape((seq_len, minibatch_size, window_size))
mask = np.array(mask.T).astype('int32').reshape((seq_len, minibatch_size, 1))
else:
minibatch_size = 1
seq_len = x.shape[0]
words = np.zeros([seq_len, minibatch_size, window_size], dtype='int32')
cwords = contextwin(x, window_size)
words[:, 0, :] = np.array(cwords, dtype='int32')
#words = np.array(words).astype('int32').reshape((seq_len, minibatch_size, window_size))
mask = np.array(mask).astype('int32').reshape((seq_len, 1, 1))
`
"""
seq_len, minibatch_size, window_size = x.shape
words = x
mask = np.array(mask.T).astype('int32').reshape((seq_len, minibatch_size, 1))
if extra_input_dims > 0:
extra = np.array(extra).astype('int32').reshape((1, minibatch_size, extra_input_dims))
return self.sentence_classify(words, mask, extra, minibatch_size)
else:
return self.sentence_classify(words, mask, minibatch_size)
def train(self, x, mask, y, window_size, learning_rate, emb_lr_factor, extra_input_dims=0, extra=None):
#assert window_size == 1
#assert extra_input_dims == 0
# concatenate words in a window
#cwords = contextwin(x, window_size)
# make an array of these windows
#words = map(lambda x: np.asarray(x).astype('int32'), cwords)
# if minibatch_size is 1, X = 1D list of indices, i.e. X.shape[0] = seq_len
# if minibatch_size > 0, X = np.array([minibatch_size, seq_len])
"""
if len(x.shape) == 2:
minibatch_size, seq_len = x.shape
words = np.zeros([seq_len, minibatch_size, window_size], dtype='int32')
if window_size > 1:
for i in range(minibatch_size):
cwords = contextwin(list(x[i, :]), window_size)
words_i = np.array(cwords, dtype='int32')
#[words_i.extend(j) for j in cwords]
words[:, i, :] = words_i
x = words.T
words = np.array(x.T).astype('int32').reshape((seq_len, minibatch_size, window_size))
mask = np.array(mask.T).astype('int32').reshape((seq_len, minibatch_size, 1))
y = np.array(y).astype('int32')
else:
minibatch_size = 1
seq_len = x.shape[0]
words = np.zeros([seq_len, minibatch_size, window_size], dtype='int32')
cwords = contextwin(x, window_size)
words[:, 0, :] = np.array(cwords, dtype='int32')
#words = np.array(words).astype('int32').reshape((seq_len, minibatch_size, window_size))
mask = np.array(mask).astype('int32').reshape((seq_len, 1, 1))
y = np.array(y).astype('int32').reshape((1, len(y)))
"""
seq_len, minibatch_size, window_size = x.shape
words = x
mask = np.array(mask.T).astype('int32').reshape((seq_len, minibatch_size, 1))
y = np.array(y).astype('int32')
# train on these sentences and normalize
if extra_input_dims > 0:
extra = np.array(extra).astype('int32').reshape((1, minibatch_size, extra_input_dims))
nll = self.sentence_train(words, mask, extra, y, learning_rate, emb_lr_factor, minibatch_size)
else:
nll = self.sentence_train(words, mask, y, learning_rate, emb_lr_factor, minibatch_size)
self.normalize()
return nll
def save(self, output_dir):
for param in self.params:
np.save(os.path.join(output_dir, param.name + '.npy'), param.get_value())
def load(self, input_dir):
for param in self.params:
param.set_value(np.load(os.path.join(input_dir, param.name + '.npy')))
def print_embeddings(self):
for param in self.params:
print param.name, param.get_value()
def save_embeddings(self, filename):
np.save(filename, self.emb)
def contextwin(l, win):
'''
win :: int corresponding to the size of the window
given a list of indexes composing a sentence
l :: array containing the word indexes
it will return a list of list of indexes corresponding
to context windows surrounding each word in the sentence
'''
assert (win % 2) == 1
assert win >= 1
l = list(l)
lpadded = win // 2 * [-1] + l + win // 2 * [-1]
out = [lpadded[i:(i + win)] for i in range(len(l))]
assert len(out) == len(l)
return out
def main(params=None):
if params is None:
params = {
'dataset': 'DRLD',
'exp_name': 'char_test',
'test_fold': 0,
'n_dev_folds': 1,
'min_doc_thresh': 1,
'initialize_word_vectors': True,
'vectors': 'chars_word2vec_25', # default_word2vec_300, anes_word2vec_300, chars_word2vec_25, eye_1 ...
'init_scale': 0.2,
'add_OOV_dim': True,
'win': 1, # size of context window
'add_DRLD': True,
'rnn_type': 'basic', # basic, GRU, or LSTM
'n_hidden': 50, # size of hidden units
'pooling_method': 'max', # max, mean, or attention1/2
'bidirectional': True,
'bi_combine': 'concat', # concat, max, or mean
'train_embeddings': True,
'lr': 0.1, # learning rate
'lr_emb_fac': 1, # factor to modify learning rate for embeddings
'decay_delay': 10, # number of epochs with no improvement before decreasing learning rate
'decay_factor': 0.5, # factor by which to multiply learning rate in case of delay
'n_epochs': 300,
'add_OOV_noise': True,
'OOV_noise_prob': 0.01,
'minibatch_size': 16,
'classify_minibatch_size': 64,
'ensemble': False,
'save_model': True,
'seed': 42,
'verbose': 1,
'reuse': False,
'orig_T': 0.04,
'tau': 0.01,
'clip_gradients': False
}
#params = fh.read_json('/Users/dcard/Projects/CMU/ARK/guac/experiments/best_mod.json')
#params['exp_name'] += '_best'
#params['n_hidden'] = int(params['n_hidden'])
rnn_base_dir = '/Users/dcard/Projects/CMU/ARK/guac/experiments/rnn/car_test/'
params_filename = fh.make_filename(rnn_base_dir, 'params', 'txt')
params = fh.read_json(params_filename)
fold = params['test_fold']
rnn_input_dir = fh.makedirs(rnn_base_dir, 'fold' + str(fold))
keys = params.keys()
keys.sort()
for key in keys:
print key, ':', params[key]
# seed the random number generators
np.random.seed(params['seed'])
random.seed(params['seed'])
vector_type = params['vectors'].split('_')[0]
params['word2vec_dim'] = int(params['vectors'].split('_')[-1])
reuser = None
if params['reuse']:
reuser = reusable_holdout.ReuseableHoldout(T=params['orig_T'], tau=params['tau'])
if params['dataset'] == 'DRLD':
datasets = ['Democrat-Likes', 'Democrat-Dislikes', 'Republican-Likes', 'Republican-Dislikes']
elif params['dataset'] == 'MIP':
datasets = ['MIP-Personal-1', 'MIP-Personal-2', 'MIP-Political-1', 'MIP-Political-2']
elif params['dataset'] == 'MOLD':
datasets = ['McCain-Likes', 'McCain-Dislikes', 'Obama-Likes', 'Obama-Dislikes']
elif params['dataset'] == 'Primary':
datasets = ['Obama-Primary', 'Clinton-Primary']
elif params['dataset'] == 'General':
datasets = ['Obama-General', 'McCain-General']
else:
datasets = [params['dataset']]
np.random.seed(params['seed'])
random.seed(params['seed'])
best_valid_f1s = []
best_true_valid_f1s = []
best_test_f1s = []
best_train_f1s = []
test_prediction_arrays = []
output_dir = fh.makedirs(defines.exp_dir, 'rnn', params['exp_name'])
output_filename = fh.make_filename(output_dir, 'params', 'txt')
fh.write_to_json(params, output_filename)
for dev_fold in range(params['n_dev_folds']):
print "dev fold =", dev_fold
output_dir = fh.makedirs(defines.exp_dir, 'rnn', params['exp_name'], 'fold' + str(dev_fold))
if vector_type == 'chars':
all_data, words2idx, items, all_labels = common.load_char_data(datasets, params['test_fold'], dev_fold)
else:
all_data, words2idx, items, all_labels = common.load_data(datasets, params['test_fold'], dev_fold,
params['min_doc_thresh'])
train_xy, valid_xy, test_xy = all_data
train_lex, train_y = train_xy
valid_lex, valid_y = valid_xy
test_lex, test_y = test_xy
#if params['minibatch_size'] > 1 or params['classify_minibatch_size'] > 1:
print "padding input with zeros"
all_data, all_masks = common.prepare_data(train_lex, valid_lex, test_lex)
train_lex, valid_lex, test_lex = all_data
train_masks, valid_masks, test_masks = all_masks
#else:
# train_masks = [np.ones(len(x)).astype('int32') for x in train_lex]
# valid_masks = [np.ones(len(x)).astype('int32') for x in valid_lex]
# test_masks = [np.ones(len(x)).astype('int32') for x in test_lex]
print "expanding x with context win dows"
# Rejigger to convert x to contex win in advance
train_x_win = expand_x_with_context_win(train_lex, params['win'])
valid_x_win = expand_x_with_context_win(valid_lex, params['win'])
test_x_win = expand_x_with_context_win(test_lex, params['win'])
order = range(len(train_lex))
print "done"
train_items, dev_items, test_items = items
vocsize = len(words2idx.keys())
idx2words = dict((k, v) for v, k in words2idx.iteritems())
best_test_predictions = None
n_sentences = len(train_lex)
print "vocsize = ", vocsize, 'n_train', n_sentences
codes = all_labels.columns
n_items, n_codes = all_labels.shape
# get the words in the sentences for the test and validation sets
words_valid = [map(lambda x: idx2words[x], w) for w in valid_lex]
groundtruth_test = test_y[:]
words_test = [map(lambda x: idx2words[x], w) for w in test_lex]
#if vector_type == 'eye':
# initial_embeddings = np.eye(vocsize)
# emb_dim = initial_embeddings.shape[1]
if params['initialize_word_vectors']:
initial_embeddings = common.load_embeddings(params, words2idx)
emb_dim = initial_embeddings.shape[1]
else:
initial_embeddings = None
emb_dim = params['word2vec_dim']
print "embedding dim =", emb_dim
temp_output = fh.make_filename(output_dir, 'embedding_labels', 'json')
fh.write_to_json(idx2words, temp_output)
extra_input_dims = 0
if params['add_DRLD']:
extra_input_dims = 2
print "Building RNN"
rnn = RNN(nh=params['n_hidden'],
nc=n_codes,
ne=vocsize,
de=emb_dim,
cs=params['win'],
extra_input_dims=extra_input_dims,
initial_embeddings=initial_embeddings,
init_scale=params['init_scale'],
rnn_type=params['rnn_type'],
train_embeddings=params['train_embeddings'],
pooling_method=params['pooling_method'],
bidirectional=params['bidirectional'],
bi_combine=params['bi_combine'],
clip_gradients=params['clip_gradients']
)
rnn.load(rnn_input_dir)
#temp_filename = fh.make_filename(output_dir, 'initial_embeddings', 'npy')
#rnn.save_embeddings(temp_filename)
train_likes = [1 if re.search('Likes', i) else 0 for i in train_items]
dev_likes = [1 if re.search('Likes', i) else 0 for i in dev_items]
test_likes = [1 if re.search('Likes', i) else 0 for i in test_items]
train_dem = [1 if re.search('Democrat', i) else 0 for i in train_items]
dev_dem = [1 if re.search('Democrat', i) else 0 for i in dev_items]
test_dem = [1 if re.search('Democrat', i) else 0 for i in test_items]
train_extra = [[train_likes[i], train_dem[i]] for i, t in enumerate(train_items)]
dev_extra = [[dev_likes[i], dev_dem[i]] for i, t in enumerate(dev_items)]
test_extra = [[test_likes[i], test_dem[i]] for i, t in enumerate(test_items)]
ms = 1
mb_x, mb_masks, mb_extra, mb_y = select_minibatch(train_x_win, train_masks, train_extra, train_y,
params['win'], 0, 1, order=np.arange(n_sentences))
print '\n'.join([' '.join([idx2words[idx] for idx in mb_x[:, k, 0].tolist()]) for k in range(ms)])
prediction = rnn.classify(mb_x, mb_masks, params['win'], extra_input_dims, mb_extra)
print prediction
h, W, b, p_y = rnn.step_through(mb_x, mb_masks, params['win'], extra_input_dims, mb_extra)
print p_y
print W
print b
temp = np.dot(h, W) + b
s = 1.0/(1.0 + np.exp(-temp))
print s
p_y_calc = np.max(s, axis=0)
print p_y_calc
print np.array(p_y_calc > 0.5, dtype='int')
sys.exit()
# train with early stopping on validation set
best_f1 = -np.inf
params['clr'] = params['lr']
for e in xrange(params['n_epochs']):
# shuffle
#shuffle([train_lex, train_y, train_extra, train_masks], params['seed']) # shuffle the input data
shuffle([order, train_lex, train_y, train_extra, train_masks], params['seed']) # shuffle the input data
params['ce'] = e # store the current epoch
tic = timeit.default_timer()
ms = params['minibatch_size']
n_train = len(train_lex)
nll = 0
#for i, orig_x in enumerate(train_lex):
for iteration, i in enumerate(range(0, n_train, ms)):
#orig_x = train_lex[i]
#n_words = len(orig_x)
#if params['add_OOV_noise']:
# draws = np.random.rand(n_words)
# x = [OOV_index if draws[i] < params['OOV_noise_prob'] else orig_x[i] for i in range(n_words)]
#else:
# x = orig_x
#y = train_y[i]
extra = train_extra[i]
#mask = train_masks[i]
minibatch_x, minibatch_mask,\
minibatch_extra, minibatch_y= select_minibatch(train_x_win, train_masks, train_extra, train_y,
params['win'], i, ms, order,
params['add_OOV_noise'], params['OOV_noise_prob'])
#if i == 0:
# print '\n'.join([' '.join([idx2words[idx] for idx in minibatch_x[:, k, 0].tolist()]) for
# k in range(ms)])
nll_i, a_sum = rnn.train(minibatch_x, minibatch_mask, minibatch_y, params['win'],
params['clr'],
params['lr_emb_fac'], extra_input_dims, minibatch_extra)
nll += nll_i
#rnn.train(x, mask, y, params['win'], params['clr'], params['lr_emb_fac'],
# extra_input_dims, extra)
print '[learning] epoch %i >> %2.2f%%' % (
e, (i + 1) * 100. / float(n_sentences)),
print 'completed in %.2f (sec), nll = %.2f, a_sum = %.1f <<\r' % (timeit.default_timer() - tic,
nll, np.max(a_sum)),
sys.stdout.flush()
if np.isnan(nll) or np.isinf(nll):
if best_f1 > 0:
break
else:
return {'loss': 1.0,
'final_test_f1': 0,
'valid_f1s': 0,
'true_valid_f1s': 0,
'train_f1s': 0,
'test_f1s': 0,
'status': STATUS_OK
}
# evaluation // back into the real world : idx -> words
print ""
#print "true y", train_y[-1]
#y_pred = rnn.classify(np.array(train_x_win[-1]).reshape((1, len(train_x_win[-1]))),
# train_masks[-1], params['win'], extra_input_dims, train_extra[-1])[0]
#print "pred y", y_pred
#if params['pooling_method'] == 'attention1' or params['pooling_method'] == 'attention2':
# if extra_input_dims == 0:
# r = np.random.randint(0, len(train_lex))
# print r, rnn.a_sum_check(np.asarray(contextwin(train_lex[r], params['win'])).astype('int32'))
predictions_train = predict(n_train, params['classify_minibatch_size'], train_x_win, train_masks,
train_y, params['win'], extra_input_dims, train_extra, rnn, order)
n_valid = len(valid_lex)
n_test = len(test_lex)
predictions_valid = predict(n_valid, params['classify_minibatch_size'], valid_x_win, valid_masks,
valid_y, params['win'], extra_input_dims, dev_extra, rnn)
predictions_test = predict(n_test, params['classify_minibatch_size'], test_x_win, test_masks,
test_y, params['win'], extra_input_dims, test_extra, rnn)
"""
predictions_train = [rnn.classify(x, train_masks[i], params['win'],
extra_input_dims, train_extra[i])[0] for i, x in enumerate(train_lex)]
predictions_valid = [rnn.classify(x, valid_masks[i], params['win'],
extra_input_dims, dev_extra[i])[0] for i, x in enumerate(valid_lex)]
predictions_test = [rnn.classify(x, test_masks[i], params['win'],
extra_input_dims, test_extra[i])[0] for i, x in enumerate(test_lex)]
"""
train_f1 = common.calc_mean_f1(predictions_train, train_y)
test_f1 = common.calc_mean_f1(predictions_test, test_y)
valid_f1 = common.calc_mean_f1(predictions_valid, valid_y)
question_f1s = []
question_pps = []
print "train_f1 =", train_f1, "valid_f1 =", valid_f1, "test_f1 =", test_f1
if valid_f1 > best_f1:
best_rnn = copy.deepcopy(rnn)
best_f1 = valid_f1
best_test_predictions = predictions_test
if params['verbose']:
print('NEW BEST: epoch', e,
'valid f1', valid_f1,
'best test f1', test_f1)
params['tr_f1'] = train_f1
params['te_f1'] = test_f1
params['v_f1'] = valid_f1
params['be'] = e # store the current epoch as a new best
# learning rate decay if no improvement in a given number of epochs
if abs(params['be']-params['ce']) >= params['decay_delay']:
params['clr'] *= params['decay_factor']
params['be'] = params['ce']
print "Reverting to current best; new learning rate = ", params['clr']
# also reset to the previous best
rnn = best_rnn
if params['clr'] < 1e-5:
break
if best_f1 == 1.0:
break
if best_f1 == 0 and e > 7:
break
if params['save_model']:
predictions_valid = predict(len(valid_y), params['classify_minibatch_size'], valid_x_win, valid_masks,
valid_y, params['win'], extra_input_dims, dev_extra, rnn)
#predictions_valid = [best_rnn.classify(np.asarray(contextwin(x, params['win'])).astype('int32')) for x in valid_lex]
best_rnn.save(output_dir)
common.write_predictions(datasets, params['test_fold'], dev_fold, predictions_valid, dev_items, output_dir)
print('BEST RESULT: epoch', params['be'],
'train F1 ', params['tr_f1'],
'valid F1', params['v_f1'],
'best test F1', params['te_f1'],
'with the model', output_dir)
best_true_valid_f1s.append(params['v_f1'])
best_test_f1s.append(params['te_f1'])
best_train_f1s.append(params['tr_f1'])
if reuser is not None:
best_valid_f1 = reuser.mask_value(params['v_f1'], params['tr_f1'])
else:
best_valid_f1 = params['v_f1']
best_valid_f1s.append(best_valid_f1)
test_prediction_arrays.append(np.array(best_test_predictions, dtype=int))
params['ensemble'] = False
if params['ensemble']:
test_predictions_stack = np.dstack(test_prediction_arrays)
final_predictions = stats.mode(test_predictions_stack, axis=2)[0][:, :, 0]
predicted_df = pd.DataFrame(final_predictions, index=test_items, columns=codes)
true_df = pd.DataFrame(np.array(test_y), index=test_items, columns=codes)
final_test_f1, final_test_pp = evaluation.calc_macro_mean_f1_pp(true_df, predicted_df)
else:
final_test_f1 = np.median(best_test_f1s)
return {'loss': -np.median(best_valid_f1s),
'final_test_f1': final_test_f1,
'valid_f1s': best_valid_f1s,
'train_f1s': best_train_f1s,
'true_valid_f1s': best_true_valid_f1s,
'test_f1s': best_test_f1s,
'status': STATUS_OK
}
def expand_x_with_context_win(lex, window_size):
x = np.vstack(lex)
n_items, seq_len = x.shape
x_win = np.zeros([seq_len, n_items, window_size], dtype='int32')
if window_size > 1:
for i in range(n_items):
x_win[:, i, :] = np.array(contextwin(list(x[i, :]), window_size), dtype='int32')
#x_i =
#x_win = [[np.array(w).astype('int32') for w in contextwin(list(x), window_size)] for x in lex]
else:
x_win[:, :, 0] = x.T
print "x_win.shape", x_win.shape
return x_win
def select_minibatch(x_win, masks, extra, y, window_size, i, minibatch_size, order=None, add_oov_noise=False, oov_noise_prob=0.0):
n = len(masks)
if order is None:
order = range(n)
ms = min(minibatch_size, n-i)
if ms > 1:
minibatch_mask = np.vstack([masks[j] for j in range(i, min(i+ms, n))])
max_len = np.max(np.argmin(minibatch_mask, axis=1))
if max_len == 0:
max_len = len(masks[i])
try:
minibatch_mask = minibatch_mask[:, 0: max_len].reshape((ms, max_len))
except:
e = sys.exc_info()[0]
print e
print max_len
print minibatch_mask
minibatch_x = x_win[0: max_len, order[i: min(i+ms, n)], :]
minibatch_extra = np.vstack([extra[j] for j in range(i, min(i+ms, n))])
minibatch_y = np.vstack([y[j] for j in range(i, min(i+ms, n))])
else:
max_len = np.argmin(masks[i])
if max_len == 0:
max_len = len(masks[i])
minibatch_mask = np.array(masks[i][0: max_len]).reshape((1, max_len))
minibatch_x = x_win[0: max_len, order[i], :].reshape((max_len, 1, window_size))
minibatch_extra = np.array(extra[i]).reshape((1, len(extra[i])))
minibatch_y = np.array(y[i]).reshape((1, len(y[i])))
if add_oov_noise:
draws = np.random.rand(max_len, ms, window_size)
minibatch_x = np.array(minibatch_x * np.array(draws > oov_noise_prob, dtype='int32'), dtype='int32')
return minibatch_x, minibatch_mask, minibatch_extra, minibatch_y
def predict(n, ms, x_win, masks, y, window_size, extra_input_dims, extra, rnn, order=None):
predictions = []
for i in range(0, n, ms):
mb_x, mb_masks, mb_extra, mb_y = select_minibatch(x_win, masks, extra, y, window_size, i, ms, order=order)
if ms > 1:
prediction = rnn.classify(mb_x, mb_masks, window_size, extra_input_dims, mb_extra)
for p in prediction:
predictions.append(p)
else:
prediction = rnn.classify(mb_x, mb_masks, window_size, extra_input_dims, mb_extra)
predictions.append(prediction)
return predictions
if __name__ == '__main__':
report = main()
print report | [
"dcard@andrew.cmu.edu"
] | dcard@andrew.cmu.edu |
99e00483b2fcb94681bab327da53cc9bd655b160 | 9644e6b9b8bf64dac7b95cd527b5364653bf7ea7 | /stuff_map/migrations/0002_car.py | 7969ea652672628e197f29e5bbe827b897c46cb4 | [] | no_license | mittonface/stuff_map | d6e651cb0fc96c3985e4ca4cfa0f75000c4f78e0 | 5b758ee713c4c11c3a5c4ffceac04ea80b5583bb | refs/heads/master | 2020-06-14T03:59:39.848546 | 2016-12-04T17:21:16 | 2016-12-04T17:21:16 | 75,493,410 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 684 | py | # -*- coding: utf-8 -*-
# Generated by Django 1.10.3 on 2016-12-03 17:37
from __future__ import unicode_literals
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('stuff_map', '0001_initial'),
]
operations = [
migrations.CreateModel(
name='Car',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('model', models.CharField(max_length=128)),
('mpg', models.FloatField()),
('fuel_capacity', models.FloatField(max_length=128)),
],
),
]
| [
"bmitton@gatech.edu"
] | bmitton@gatech.edu |
b2cd196a4e77d83e542be25199838e0b8ec80ff9 | ad357cfbec64afb8f4cc4043b212996768f9755c | /api/assessment/automate/formatters.py | dac02f8f9749219cec476cf1e0392f3c9036f96a | [
"MIT"
] | permissive | uktrade/market-access-api | 6b4680e6455eb5c25480ccd3e3d9445654269f36 | 4da26d1be53843d22411577409d9489010bdda09 | refs/heads/master | 2023-08-30T14:47:10.373148 | 2023-08-29T13:58:08 | 2023-08-29T13:58:08 | 131,856,014 | 2 | 3 | MIT | 2023-09-14T08:04:42 | 2018-05-02T13:38:37 | Python | UTF-8 | Python | false | false | 2,065 | py | def rca(import_value, export_value):
if import_value is None or export_value is None:
return "NA"
elif import_value > 0 and export_value > 0:
return "Specialised"
elif import_value < 0 and export_value < 0:
return "Unspecialised"
return "Inconclusive"
def rca_diff(import_value, export_value, country1, country2):
if import_value is None or export_value is None:
return "NA"
elif import_value > 0 and export_value > 0:
return f"{country2} more specialised globally than in {country1}"
elif import_value < 0 and export_value < 0:
return f"{country2} more specialised in {country1} than globally"
return "Inconclusive"
def rca_diff_glob(import_value, export_value, country1, country2):
if import_value is None or export_value is None:
return "NA"
elif import_value > 0 and export_value > 0:
return f"{country2} more specialised globally than {country1}"
elif import_value < 0 and export_value < 0:
return f"{country1} more specialised globally than {country2}"
return "Inconclusive"
def format_value(value):
if value < 1000:
return f"£{round(value, 0)}"
elif value > 1000000000:
return f"£{round(value, -8) / 1000000000}bn"
elif value > 1000000:
return f"£{round(value, -5) / 1000000}m"
return f"£{round(value, -2) / 1000}k"
def value_range(import_value, export_value):
if import_value < export_value:
return f"{format_value(import_value)} - {format_value(export_value)}"
return f"{format_value(export_value)} - {format_value(import_value)}"
def percent_range(import_value, export_value, decimal_places):
import_value *= 100
export_value *= 100
if import_value == export_value:
return f"{round(import_value, decimal_places)}%"
elif import_value < export_value:
return f"{round(import_value, decimal_places)}% - {round(export_value, decimal_places)}%"
return f"{round(export_value, decimal_places)}% - {round(import_value, decimal_places)}%"
| [
"noreply@github.com"
] | uktrade.noreply@github.com |
aecb50d16e4b98d83419ea535baee6629c26a325 | 8060b19318440ff2fbd728afd9435d4b5a3f1da6 | /code/metadata/build_nodelist.py | ae79eb7f27e1ed301c80a478032048397612d20c | [] | no_license | Irallia/IZW-HU-Parasites | dae82db2600a96bdce7cffb228c07cdc6dde5226 | b0d70f671fa489ba132e2cffb81ff6d6fc431fe5 | refs/heads/master | 2021-01-25T08:13:18.646633 | 2018-05-20T20:25:51 | 2018-05-20T20:25:51 | 93,733,091 | 4 | 3 | null | 2018-03-21T13:36:06 | 2017-06-08T09:43:17 | TeX | UTF-8 | Python | false | false | 4,547 | py | import csv
import datetime
import sys
from code.utilities.Helpers import print_time
from code.utilities.nodelist_util import read_tags, tag_node
from time import gmtime, strftime
from Bio import Phylo
from termcolor import colored
# path_freelivings = "./data/interaction_data/reduced_freelivings.csv"
# path_parasites = "./data/interaction_data/reduced_parasites.csv"
path_freelivings = "./data/interaction_data/freelivings.csv"
path_parasites = "./data/interaction_data/parasites.csv"
# input arguments
args = sys.argv
# values from input:
subtree_name = sys.argv[1]
# examples: 'Eukaryota'
# global variables:
START_TIME = datetime.datetime.now().replace(microsecond=0)
CURRENT_TIME = datetime.datetime.now().replace(microsecond=0)
freelivings = []
parasites = []
nr_leave_nodes = 0
nr_used_freelivings = 0
nr_used_parasites = 0
unknown = 0
doubleTagged = 0
nodelist = []
def main():
global START_TIME
global CURRENT_TIME
global freelivings
global parasites
global nr_leave_nodes
global nr_used_freelivings
global nr_used_parasites
global unknown
global nodelist
global doubleTagged
print(colored("------------------------ build nodelists ------------------------", "green"))
print(strftime("%Y-%m-%d %H:%M:%S", gmtime()))
CURRENT_TIME = print_time(START_TIME)
print(colored("---------------- read parasites and freelivings ----------------", "green"))
print("Freelivings:")
freelivings = read_tags(path_freelivings)
print("Parasites:")
parasites = read_tags(path_parasites)
CURRENT_TIME = print_time(CURRENT_TIME)
print(colored("---------------- read tree ----------------", "green"))
subtree_path = './data/subtree/' + subtree_name + '.tre'
print("Build nodelist for:", subtree_name)
tree = Phylo.read(subtree_path, 'newick')
print(colored("---------------- tag tree ----------------", "green"))
fill_tree_with_tags(tree.clade, 0)
print(colored(nr_leave_nodes, 'blue'), "leave nodes are in the tree")
print(colored(nr_used_freelivings, 'blue'), "freeliving tags were used,", colored(nr_used_parasites, 'blue'), "parasite tags were used =>", colored(unknown, 'blue'), "unknown leave nodes")
print("Rootnode, Depth, Heigths: [Min, Max, Mean], Originaltag, Finaltag, Nr_children")
print(nodelist[0])
print(doubleTagged, "are tagged as P, but could also be FL!")
# ---- reset countings ----
nr_leave_nodes = 0
nr_used_freelivings = 0
nr_used_parasites = 0
unknown = 0
nodelist = []
CURRENT_TIME = print_time(CURRENT_TIME)
print(colored("--------------------------------", "green"))
return
def fill_tree_with_tags(subtree, depth):
global nr_leave_nodes
global nr_used_freelivings
global nr_used_parasites
global unknown
global nodelist
global doubleTagged
ott = subtree.name.split("$")[0] # remove index
heights = [1, 1, 1]
# 0 1 2 3 4 5
# nodelist - [id, originaltag, finaltag, depth, heights, nr_children]
nodelist.append([ott, "", "", depth, heights, len(subtree.clades)])
current_list_index = len(nodelist) - 1
if subtree.is_terminal():
stats = [nr_leave_nodes, nr_used_parasites, nr_used_freelivings, unknown, doubleTagged]
stats = tag_node(nodelist, current_list_index, ott, [freelivings, parasites], stats)
nr_leave_nodes = stats[0]
nr_used_parasites = stats[1]
nr_used_freelivings = stats[2]
unknown = stats[3]
doubleTagged = stats[4]
else:
min_heigth = float('inf')
max_heigth = 0
mean_heigth = 0
child_heigth = 0
for clade in subtree.clades:
heights = fill_tree_with_tags(clade, depth + 1)
if heights[0] < min_heigth:
min_heigth = heights[0]
if heights[1] > max_heigth:
max_heigth = heights[1]
child_heigth = child_heigth + heights[2]
mean_heigth = child_heigth/len(subtree.clades) + 1
heights = [min_heigth + 1, max_heigth + 1, mean_heigth]
nodelist[current_list_index][4] = heights
# -------------------------------------------------
csv_title = './data/nodelist/' + subtree_name + '.csv'
nodelist_file = open(csv_title, 'a')
writer = csv.writer(nodelist_file)
writer.writerow((nodelist[current_list_index]))
nodelist_file.close()
# -------------------------------------------------
return heights
main()
| [
"irallia@chrigelyra.de"
] | irallia@chrigelyra.de |
50956f529320e551f07a71600677d930f51f66ab | 4b7883843049d01fa718368f09ee0aa41167f595 | /1HelloWorld.py | e27b7c5f2a359339b94f53dc65f6d1b6e00538f6 | [] | no_license | rickyjreyes/Python_Tutorial | fac324fa94e28e45f8c1293b6c4f0fe8db714052 | 00c1e90e732a6066f1324df52ee6832a85b5d6bb | refs/heads/master | 2021-06-12T03:33:53.140440 | 2017-03-19T08:25:15 | 2017-03-19T08:25:15 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 575 | py | # HelloWorld.py
#
# First Program!
# This prints hello world
print("Hello World!")
# More Practice: Make 5 of your own prints
print("My name is Ricky!!!!")
print("My favorite color is blue!!!!")
print("Video games are cool!!!!")
print("I love to code!!!!")
print("This is Python :)")
# This is a comment
# Make your own comments
"""I am a comment
with multiple lines
"""
# blah blah blah
# The computer doesn't read this
# Practice Input
user = input("Enter input: ")
print("Output: ", user)
# Manipulate Inp ")
print(int(user*10)) | [
"noreply@github.com"
] | rickyjreyes.noreply@github.com |
bb78b536e10ed1c070713095bc6926c0d668fe9b | 680ef089e77f3d510f0fbe7d632c01fb497e2a57 | /manage.py | 6b33c09231ad589a05eba9ffb021c19d258c0cd0 | [] | no_license | kupuk090/idaproject_test | 3fa4ef68ba9ca921edb4ad603ba39e5c9a3f9b7f | e92cbf615bc360fc44f45c746be3f4431b48ee41 | refs/heads/master | 2023-04-09T10:29:22.869203 | 2021-03-21T22:59:41 | 2021-03-21T22:59:41 | 350,136,804 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 669 | py | #!/usr/bin/env python
"""Django's command-line utility for administrative tasks."""
import os
import sys
def main():
"""Run administrative tasks."""
os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'image_resizer.settings')
try:
from django.core.management import execute_from_command_line
except ImportError as exc:
raise ImportError(
"Couldn't import Django. Are you sure it's installed and "
"available on your PYTHONPATH environment variable? Did you "
"forget to activate a virtual environment?"
) from exc
execute_from_command_line(sys.argv)
if __name__ == '__main__':
main()
| [
"kupuk090@gmail.com"
] | kupuk090@gmail.com |
8664746b874b28b034fc07228953772e842c71df | 392b644e8be2bdd5cb0e2656483786a7ce6c1ef9 | /setup.py | ffbe34a51be9405e0e1d23e19a09f3910435ace8 | [
"Apache-2.0",
"LicenseRef-scancode-generic-cla",
"Python-2.0"
] | permissive | pandrey76/python_client | 67287b41a826ed6c2c1c5b87f30582261e35777c | 3b8a8ca471dbb2178687e7febbf216f81b49fcca | refs/heads/master | 2022-04-12T15:29:49.863787 | 2020-02-13T03:31:08 | 2020-02-13T03:31:08 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 2,811 | py | #!/usr/bin/env python
"""
setup.py file for GridDB python client
"""
from distutils.command.build import build
try:
from setuptools import setup, Extension
except ImportError:
from distutils.core import setup, Extension
try:
with open('README.rst') as f:
readme = f.read()
except IOError:
readme = ''
os.environ["CXX"] = "g++"
os.environ["CC"] = "g++"
SOURCES = [
'src/AggregationResult.cpp',
'src/Container.cpp',
'src/ContainerInfo.cpp',
'src/Field.cpp',
'src/PartitionController.cpp',
'src/Query.cpp',
'src/QueryAnalysisEntry.cpp',
'src/RowKeyPredicate.cpp',
'src/RowSet.cpp',
'src/Store.cpp',
'src/StoreFactory.cpp',
'src/TimeSeriesProperties.cpp',
'src/TimestampUtils.cpp',
'src/griddb.i',
'src/Util.cpp',
]
DEPENDENTS = [
'src/AggregationResult.h',
'src/ContainerInfo.h',
'src/Container.h',
'src/ExpirationInfo.h',
'src/Field.h'
'src/GSException.h',
'src/PartitionController.h',
'src/Query.h',
'src/QueryAnalysisEntry.h',
'src/RowKeyPredicate.h',
'src/RowSet.h',
'src/Store.h',
'src/StoreFactory.h',
'src/TimeSeriesProperties.h',
'src/TimestampUtils.h',
'src/gstype_python.i',
'src/gstype.i',
'include/gridstore.h',
'include/Util.h',
]
INCLUDES = [
'include',
'src',
]
COMPILE_ARGS = [
'-std=c++0x'
]
LIBRARIES = [
'rt',
'gridstore',
]
SWIG_OPTS = [
'-DSWIGWORDSIZE64',
'-c++',
'-outdir',
'.',
'-Isrc'
]
class CustomBuild(build):
sub_commands = [
('build_ext', build.has_ext_modules),
('build_py', build.has_pure_modules),
('build_clib', build.has_c_libraries),
('build_scripts', build.has_scripts),
]
griddb_module = Extension('_griddb_python',
sources=SOURCES,
include_dirs=INCLUDES,
libraries=LIBRARIES,
extra_compile_args=COMPILE_ARGS,
swig_opts=SWIG_OPTS,
depends=DEPENDENTS,
)
classifiers = [
"License :: OSI Approved :: Apache Software License",
"Operating System :: POSIX :: Linux",
"Programming Language :: Python :: 3.6",
]
setup(name='griddb_python',
version='0.8.2',
author='Katsuhiko Nonomura',
author_email='contact@griddb.org',
description='GridDB Python Client Library built using SWIG',
long_description=readme,
ext_modules=[griddb_module],
py_modules=['griddb_python'],
url='https://github.com/griddb/python_client/',
license='Apache Software License',
cmdclass={'build': CustomBuild},
long_description_content_type = 'text/x-rst',
classifiers=classifiers,
)
| [
"katsuhiko.nonomura@griddb.org"
] | katsuhiko.nonomura@griddb.org |
840981ee699fd005cdcb4fa9ecbb2214b62fced0 | a6986f430351dcc871d035f8361ca377ddc90edf | /HouseAnalysis/house/migrations/0001_initial.py | afdaea7fc6b90257183f31bc00fc8691e54c02e0 | [] | no_license | qijianchuan/HouseAnalysisWeb | d9f65a91cb1dba2bfea482214c6b532a59e21a8c | 8a619c77bdfb3516c37df912d6fe7bf14d27e706 | refs/heads/master | 2023-03-15T23:23:02.677191 | 2020-04-30T14:59:19 | 2020-04-30T14:59:19 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 8,836 | py | # Generated by Django 2.1.5 on 2020-03-12 07:39
from django.db import migrations, models
class Migration(migrations.Migration):
initial = True
dependencies = [
]
operations = [
migrations.CreateModel(
name='Api',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('title', models.CharField(max_length=500, verbose_name='名称')),
('price', models.DecimalField(decimal_places=1, max_digits=9, verbose_name='总价')),
('unit_price', models.DecimalField(decimal_places=1, max_digits=9, verbose_name='单价')),
('community_name', models.CharField(max_length=100, verbose_name='小区名')),
('region', models.CharField(max_length=50, verbose_name='区域')),
('type', models.CharField(max_length=50, verbose_name='户型')),
('construction_area', models.CharField(max_length=20, verbose_name='建筑面积')),
('orientation', models.CharField(max_length=10, verbose_name='朝向')),
('decoration', models.CharField(max_length=10, verbose_name='装修情况')),
('floor', models.CharField(max_length=15, verbose_name='楼层')),
('elevator', models.CharField(max_length=10, verbose_name='电梯')),
('purposes', models.CharField(max_length=15, verbose_name='房屋类型')),
('release_date', models.DateField(verbose_name='挂牌时间')),
('house_structure', models.CharField(max_length=20, verbose_name='建筑类型')),
('image_urls', models.CharField(max_length=1500, verbose_name='房屋详情图')),
('from_url', models.CharField(max_length=100, verbose_name='房屋链接')),
('idi', models.IntegerField()),
('lat', models.DecimalField(decimal_places=9, max_digits=12, verbose_name='纬度')),
('lng', models.DecimalField(decimal_places=9, max_digits=12, verbose_name='经度')),
],
options={
'verbose_name': 'house',
'verbose_name_plural': 'house',
},
),
migrations.CreateModel(
name='Constructure',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('version', models.CharField(max_length=8, verbose_name='接口版本')),
('title', models.CharField(max_length=12, verbose_name='接口info')),
('layout', models.CharField(max_length=10, verbose_name='建筑类型')),
('num', models.IntegerField(verbose_name='数量')),
],
options={
'verbose_name': 'constructureinfo',
'verbose_name_plural': 'constructureinfo',
},
),
migrations.CreateModel(
name='Decortion',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('version', models.CharField(max_length=8, verbose_name='接口版本')),
('title', models.CharField(max_length=12, verbose_name='接口info')),
('layout', models.CharField(max_length=10, verbose_name='装修情况')),
('num', models.IntegerField(verbose_name='数量')),
('mean_price', models.DecimalField(decimal_places=3, max_digits=8, verbose_name='总价均价')),
('mean_unit_price', models.DecimalField(decimal_places=3, max_digits=8, verbose_name='单价均价')),
],
options={
'verbose_name': 'decorationinfo',
'verbose_name_plural': 'decorationinfo',
},
),
migrations.CreateModel(
name='Elevator',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('version', models.CharField(max_length=8, verbose_name='接口版本')),
('title', models.CharField(max_length=12, verbose_name='接口info')),
('has_el_num', models.IntegerField(verbose_name='存在电梯的房源数')),
('no_el_num', models.IntegerField(verbose_name='不存在电梯的房源数')),
('has_mean_price', models.DecimalField(decimal_places=3, max_digits=8, verbose_name='总价均价')),
('has_mean_unit_price', models.DecimalField(decimal_places=3, max_digits=8, verbose_name='单价均价')),
('no_mean_price', models.DecimalField(decimal_places=3, max_digits=8, verbose_name='总价均价')),
('no_mean_unit_price', models.DecimalField(decimal_places=3, max_digits=8, verbose_name='单价均价')),
],
options={
'verbose_name': 'elevatorinfo',
'verbose_name_plural': 'elevatorinfo',
},
),
migrations.CreateModel(
name='Floor',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('version', models.CharField(max_length=8, verbose_name='接口版本')),
('title', models.CharField(max_length=12, verbose_name='接口info')),
('floor', models.CharField(max_length=20, verbose_name='楼层')),
('num', models.IntegerField(verbose_name='数量')),
],
options={
'verbose_name': 'floorinfo',
'verbose_name_plural': 'floorinfo',
},
),
migrations.CreateModel(
name='Layout',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('version', models.CharField(max_length=8, verbose_name='接口版本')),
('title', models.CharField(max_length=12, verbose_name='接口info')),
('layout', models.CharField(max_length=20, verbose_name='户型')),
('num', models.IntegerField(verbose_name='数量')),
],
options={
'verbose_name': 'layoutinfo',
'verbose_name_plural': 'layoutinfo',
},
),
migrations.CreateModel(
name='Orientation',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('version', models.CharField(max_length=8, verbose_name='接口版本')),
('title', models.CharField(max_length=12, verbose_name='接口info')),
('layout', models.CharField(max_length=15, verbose_name='房屋朝向')),
('num', models.IntegerField(verbose_name='数量')),
],
options={
'verbose_name': 'orientationinfo',
'verbose_name_plural': 'orientationinfo',
},
),
migrations.CreateModel(
name='Purposes',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('version', models.CharField(max_length=8, verbose_name='接口版本')),
('title', models.CharField(max_length=12, verbose_name='接口info')),
('layout', models.CharField(max_length=10, verbose_name='房屋用途')),
('num', models.IntegerField(verbose_name='数量')),
],
options={
'verbose_name': 'purposesinfo',
'verbose_name_plural': 'purposesinfo',
},
),
migrations.CreateModel(
name='Region',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('version', models.CharField(max_length=8, verbose_name='接口版本')),
('title', models.CharField(max_length=12, verbose_name='接口info')),
('layout', models.CharField(max_length=10, verbose_name='行政区划')),
('num', models.IntegerField(verbose_name='数量')),
('mean_price', models.DecimalField(decimal_places=3, max_digits=8, verbose_name='总价均价')),
('mean_unit_price', models.DecimalField(decimal_places=3, max_digits=8, verbose_name='单价均价')),
],
options={
'verbose_name': 'regioninfo',
'verbose_name_plural': 'regioninfo',
},
),
]
| [
"zj20162325@163.com"
] | zj20162325@163.com |
9207dc3cabf5a523ba87298730ea3d3b9f8d7750 | 7a01d168819027ed74021395edf55c79419b1e74 | /miSitio/urls.py | 06f844768f45cce9e92f4a22619de57896d7bf07 | [] | no_license | TeresaHRivas/my-first-blog | 4f2347f53faf222f7279de1d89f282815cf94422 | a6794fca9249ec2d41730f4e420c537e09d045d4 | refs/heads/master | 2020-07-20T15:21:27.431700 | 2020-02-28T22:36:38 | 2020-02-28T22:36:38 | 206,666,510 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 824 | py | """miSitio URL Configuration
The `urlpatterns` list routes URLs to views. For more information please see:
https://docs.djangoproject.com/en/2.2/topics/http/urls/
Examples:
Function views
1. Add an import: from my_app import views
2. Add a URL to urlpatterns: path('', views.home, name='home')
Class-based views
1. Add an import: from other_app.views import Home
2. Add a URL to urlpatterns: path('', Home.as_view(), name='home')
Including another URLconf
1. Import the include() function: from django.urls import include, path
2. Add a URL to urlpatterns: path('blog/', include('blog.urls'))
"""
from django.contrib import admin
from django.urls import path
from django.conf.urls import include
urlpatterns = [
path('admin/', admin.site.urls),
path('', include('miBlog.urls')),
]
| [
"teresahrivas4@gmail.com"
] | teresahrivas4@gmail.com |
b7b979b04b0a3ebb22cfc3527178b9ed7832184d | e104a337a1e3ced474511d258645109641aca01d | /movie/views.py | 71f002084a1d9a6d4d76757bebb7e17068740b8b | [] | no_license | mjkya/MovieVersity | 3be879faa68ce6855cc8e935723a50f7b5ed790f | 7eede648bb9d8bb8108c780feb513bfc8884bc89 | refs/heads/master | 2020-05-19T10:51:18.997948 | 2019-09-01T06:32:26 | 2019-09-01T06:32:26 | 184,977,534 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,015 | py | from django.shortcuts import render,redirect
from django.views.generic import ListView, DetailView
from django.views.generic.edit import CreateView, UpdateView, DeleteView
# Create your views here.
from .models import Movie
from django.urls import reverse_lazy
from .parser import parse_movie
def movie(request):
return render(request, 'home.html')
def parse(request):
parse_movie()
return redirect('home')
class MovieList(ListView):
model = Movie
template_name = 'home.html'
class MovieCreate(CreateView):
model = Movie
fields = '__all__'
template_name = 'movie_form.html'
success_url = reverse_lazy('home')
class MovieDetail(DetailView):
model = Movie
template_name = 'movie_detail.html'
class MovieUpdate(UpdateView):
model = Movie
fields = '__all__'
template_name = 'movie_form.html'
success_url = reverse_lazy('home')
class MovieDelete(DeleteView):
model = Movie
template_name = 'movie_delete.html'
success_url = reverse_lazy('home') | [
"noreply@github.com"
] | mjkya.noreply@github.com |
e706179c11effcfa8f133d63d2655724fca4d1e9 | 0005e05b9d8b8ad0d3c3c0539b2ded9db6e9f1dd | /codechef_client/models/tag.py | 4cdd6e64295823ef02e369ae6ce1a056970ea646 | [] | no_license | termicoder/codechef-client-lib | a3e3de2b300355c5daa5ed3fad03a9859af13d86 | 74d6b21787c75a987e3451751f5554e4cc6cf469 | refs/heads/master | 2020-03-27T17:58:45.298121 | 2018-09-30T18:03:14 | 2018-09-30T18:03:14 | 146,889,644 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 4,094 | py | # coding: utf-8
"""
CodeChef API
CodeChef API to support different applications. # noqa: E501
OpenAPI spec version: 1.0.0
Generated by: https://github.com/swagger-api/swagger-codegen.git
"""
import pprint
import re # noqa: F401
import six
class Tag(object):
"""NOTE: This class is auto generated by the swagger code generator program.
Do not edit the class manually.
"""
"""
Attributes:
swagger_types (dict): The key is attribute name
and the value is attribute type.
attribute_map (dict): The key is attribute name
and the value is json key in definition.
"""
swagger_types = {
'tag': 'str',
'type': 'str',
'count': 'int'
}
attribute_map = {
'tag': 'tag',
'type': 'type',
'count': 'count'
}
def __init__(self, tag=None, type=None, count=None): # noqa: E501
"""Tag - a model defined in Swagger""" # noqa: E501
self._tag = None
self._type = None
self._count = None
self.discriminator = None
if tag is not None:
self.tag = tag
if type is not None:
self.type = type
if count is not None:
self.count = count
@property
def tag(self):
"""Gets the tag of this Tag. # noqa: E501
Value # noqa: E501
:return: The tag of this Tag. # noqa: E501
:rtype: str
"""
return self._tag
@tag.setter
def tag(self, tag):
"""Sets the tag of this Tag.
Value # noqa: E501
:param tag: The tag of this Tag. # noqa: E501
:type: str
"""
self._tag = tag
@property
def type(self):
"""Gets the type of this Tag. # noqa: E501
author/tag # noqa: E501
:return: The type of this Tag. # noqa: E501
:rtype: str
"""
return self._type
@type.setter
def type(self, type):
"""Sets the type of this Tag.
author/tag # noqa: E501
:param type: The type of this Tag. # noqa: E501
:type: str
"""
self._type = type
@property
def count(self):
"""Gets the count of this Tag. # noqa: E501
Count of problems with this tag # noqa: E501
:return: The count of this Tag. # noqa: E501
:rtype: int
"""
return self._count
@count.setter
def count(self, count):
"""Sets the count of this Tag.
Count of problems with this tag # noqa: E501
:param count: The count of this Tag. # noqa: E501
:type: int
"""
self._count = count
def to_dict(self):
"""Returns the model properties as a dict"""
result = {}
for attr, _ in six.iteritems(self.swagger_types):
value = getattr(self, attr)
if isinstance(value, list):
result[attr] = list(map(
lambda x: x.to_dict() if hasattr(x, "to_dict") else x,
value
))
elif hasattr(value, "to_dict"):
result[attr] = value.to_dict()
elif isinstance(value, dict):
result[attr] = dict(map(
lambda item: (item[0], item[1].to_dict())
if hasattr(item[1], "to_dict") else item,
value.items()
))
else:
result[attr] = value
return result
def to_str(self):
"""Returns the string representation of the model"""
return pprint.pformat(self.to_dict())
def __repr__(self):
"""For `print` and `pprint`"""
return self.to_str()
def __eq__(self, other):
"""Returns true if both objects are equal"""
if not isinstance(other, Tag):
return False
return self.__dict__ == other.__dict__
def __ne__(self, other):
"""Returns true if both objects are not equal"""
return not self == other
| [
"diveshuttamchandani@gmail.com"
] | diveshuttamchandani@gmail.com |
0ee27c2b6c2029409b39052286ba40d81a836616 | d3efc82dfa61fb82e47c82d52c838b38b076084c | /Autocase_Result/SjShHBJJMM/YW_HBJJMM_SHSJ_067.py | 4cb90cd9223c79893514c907a5e29a58cc20a03f | [] | no_license | nantongzyg/xtp_test | 58ce9f328f62a3ea5904e6ed907a169ef2df9258 | ca9ab5cee03d7a2f457a95fb0f4762013caa5f9f | refs/heads/master | 2022-11-30T08:57:45.345460 | 2020-07-30T01:43:30 | 2020-07-30T01:43:30 | 280,388,441 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 3,142 | py | #!/usr/bin/python
# -*- encoding: utf-8 -*-
import sys
sys.path.append("/home/yhl2/workspace/xtp_test/xtp/api")
from xtp_test_case import *
sys.path.append("/home/yhl2/workspace/xtp_test/service")
from ServiceConfig import *
from log import *
sys.path.append("/home/yhl2/workspace/xtp_test/MoneyFund/moneyfundservice")
from mfmainService import *
from mfQueryStkPriceQty import *
sys.path.append("/home/yhl2/workspace/xtp_test/MoneyFund/moneyfundmysql")
from mfCaseParmInsertMysql import *
sys.path.append("/home/yhl2/workspace/xtp_test/utils")
from QueryOrderErrorMsg import queryOrderErrorMsg
class YW_HBJJMM_SHSJ_067(xtp_test_case):
# YW_HBJJMM_SHSJ_067
def test_YW_HBJJMM_SHSJ_067(self):
title = '上海Ａ股股票交易日五档即成转限价卖——错误的价格(价格10亿)'
# 定义当前测试用例的期待值
# 期望状态：初始、未成交、部成、全成、部撤已报、部撤、已报待撤、已撤、废单、撤废、内部撤单
# xtp_ID和cancel_xtpID默认为0，不需要变动
case_goal = {
'期望状态': '全成',
'errorID': 0,
'errorMSG': '',
'是否生成报单': '是',
'是否是撤废': '否',
'xtp_ID': 0,
'cancel_xtpID': 0,
}
logger.warning(title)
# 定义委托参数信息------------------------------------------
# 参数：证券代码、市场、证券类型、证券状态、交易状态、买卖方向（Ｂ买Ｓ卖）、期望状态、Api
stkparm = QueryStkPriceQty('999999', '1', '111', '2', '0', 'S', case_goal['期望状态'], Api)
# 如果下单参数获取失败，则用例失败
if stkparm['返回结果'] is False:
rs = {
'用例测试结果': stkparm['返回结果'],
'测试错误原因': '获取下单参数失败,' + stkparm['错误原因'],
}
self.assertEqual(rs['用例测试结果'], True)
else:
wt_reqs = {
'business_type': Api.const.XTP_BUSINESS_TYPE['XTP_BUSINESS_TYPE_CASH'],
'order_client_id':2,
'market': Api.const.XTP_MARKET_TYPE['XTP_MKT_SH_A'],
'ticker': stkparm['证券代码'],
'side': Api.const.XTP_SIDE_TYPE['XTP_SIDE_SELL'],
'price_type': Api.const.XTP_PRICE_TYPE['XTP_PRICE_BEST5_OR_LIMIT'],
'price': 1000000000,
'quantity': 200,
'position_effect': Api.const.XTP_POSITION_EFFECT_TYPE['XTP_POSITION_EFFECT_INIT']
}
ParmIni(Api, case_goal['期望状态'], wt_reqs['price_type'])
CaseParmInsertMysql(case_goal, wt_reqs)
rs = serviceTest(Api, case_goal, wt_reqs)
logger.warning('执行结果为' + str(rs['用例测试结果']) + ','
+ str(rs['用例错误源']) + ',' + str(rs['用例错误原因']))
self.assertEqual(rs['用例测试结果'], True) # 0
if __name__ == '__main__':
unittest.main()
| [
"418033945@qq.com"
] | 418033945@qq.com |
1c5c459904e78434ad22ecd6cb261de1f2c29f83 | 4c378e60497ce892f5c25cbe7d6fc439124141ea | /solutions/226. Invert Binary Tree.py | 96d9017e0473c9f2cd4b047c540206dea030ce6d | [] | no_license | udhavsethi/play | 8008369d4c582f6db33a5737a3d9d68e5afc38c3 | 885d5b6e0a05051554fb61eb501a44a785098f3a | refs/heads/master | 2021-11-27T11:40:52.847607 | 2021-11-23T18:49:40 | 2021-11-23T18:49:40 | 147,461,456 | 1 | 0 | null | null | null | null | UTF-8 | Python | false | false | 441 | py | # Definition for a binary tree node.
# class TreeNode:
# def __init__(self, val=0, left=None, right=None):
# self.val = val
# self.left = left
# self.right = right
class Solution:
def invertTree(self, root: Optional[TreeNode]) -> Optional[TreeNode]:
if root:
root.left, root.right = self.invertTree(root.right), self.invertTree(root.left)
return root
| [
"udhavsethi@users.noreply.github.com"
] | udhavsethi@users.noreply.github.com |
ca9384417c7381029549dc4cefe6c3c2371e83ab | 9547ba5d65029c7eb3975d888bc1c5579bd455c2 | /Spark-DF-Sql/10-AggregationsDemo/AggDemo.py | e8ad9a72a4caf8bdd34de50850b03aef84c65224 | [] | no_license | vinayavs/spark2-python | 53ebd2769525eca2e3727cb171fd43de87fd9d84 | bba1eedd232bd0ea3d1ac72293475d561326c1a4 | refs/heads/main | 2023-04-08T15:29:25.391387 | 2021-04-06T07:32:34 | 2021-04-06T07:32:34 | 352,007,568 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,708 | py | from pyspark.sql import SparkSession
from pyspark.sql import functions as f
from lib.logger import Log4J
if __name__ == '__main__':
spark = SparkSession.builder.master("local[2]").appName("Aggregations Demo") \
.config("spark.sql.shuffle.partitions", 2) \
.getOrCreate()
logger = Log4J(spark)
invoiceDf = spark.read.format("csv") \
.option("header", "true") \
.option("inferSchema", "true") \
.load("data/invoices.csv")
# invoiceDf.printSchema()
# Column Object Expression
invoiceDf.select(f.count("*").alias("Count *"),
f.sum("Quantity").alias("TotalQuantity"),
f.avg("UnitPrice").alias("AvgPrice"),
f.countDistinct("InvoiceNo").alias("CountDistinct")).show()
# Using Sql like string Expression
invoiceDf.selectExpr(
"count(1) as RecordCount", # Includes Null
"count(StockCode) as TickerCount", # Ignores Null
"sum(Quantity) as TotalQuantity",
"avg(UnitPrice) as UnitPrice"
).show()
# Grouping Aggregations
invoiceDf.createOrReplaceTempView("sales")
summarySql = spark.sql("""
SELECT Country, InvoiceNo,
sum(Quantity) as TotalQuantity,
round(sum(Quantity * UnitPrice), 2) as InvoiceValue
FROM sales
GROUP BY Country, InvoiceNo
""")
summarySql.show()
# DF Expressions
summaryDf = invoiceDf \
.groupBy("Country", "InvoiceNo") \
.agg(f.sum("Quantity").alias("TotalQuantity"),
f.expr("round(sum(Quantity * UnitPrice), 2) as InvoiceValue"))
# f.round(f.sum(f.expr("Quantity * UnitPrice")),2).alias("InvoiceValue")
summaryDf.show()
| [
"vinayavs@gmail.com"
] | vinayavs@gmail.com |
0e65d65def490ae82f7776d64b2d70c86abf7d55 | b838b2ecce195d1293b0ff485b3d76bc0adb5d70 | /st36/Film.py | 1b7fe8b7ddd61874118e09afd00ffd4cf7fdb2cb | [] | no_license | BelyaevaSveta/ASM.17.Lab1 | e4d37c8b7600cf3f7bf787dd396678315b19d57d | ee20998877a4e511a778a7afd52cb977b55aa2f5 | refs/heads/master | 2021-05-07T04:52:05.904334 | 2017-11-28T10:30:42 | 2017-11-28T10:30:42 | 111,489,081 | 0 | 0 | null | 2017-11-21T02:36:19 | 2017-11-21T02:36:19 | null | UTF-8 | Python | false | false | 1,363 | py | from .Actor import Actor
import pickle
class Film:
def __init__(self):
self.enter_film_name()
self.actors = {}
def enter_film_name(self):
self.film_name = input('Enter new film name: ')
def print_film_name(self):
print('Film name is %s' % self.film_name)
def print_all_actors(self):
for name, actor_object in self.actors.items():
actor_object.print_name()
def add_new_actor(self):
new_actor = Actor()
self.actors[new_actor.name] = new_actor
def edit_actor(self):
print('\nType name of actor you want to edit:\nAvailable actors:')
for actor_name, actor_object in self.actors.items():
print(actor_name)
actor_name = input()
self.actors[actor_name].edit_bio()
def remove_actor(self):
print('\nType name of actor you want to remove:\nAvailable actors:')
for actor_name, actor_object in self.actors.items():
print(actor_name)
actor_name = input()
self.actors.pop(actor_name)
def remove_all_actors(self):
self.actors.clear()
def save_actors_to_file(self):
with open('Film.txt', 'wb') as f:
pickle.dump(self.actors, f)
def load_actors_from_file(self):
with open('Film.txt', 'rb') as f:
self.actors = pickle.load(f)
| [
"noreply@github.com"
] | BelyaevaSveta.noreply@github.com |
ad0df481b83aad3cb9398d08e94d900100182e33 | 1db17195776328739b465902f6bf2d53bff0f9ac | /logs/2021-09-08-11-39-20/ricequant.py | 24df136b934372a0a551256e7528110b92791d25 | [] | no_license | eulancer/convertible_bond | 01f56d3d318dd7343e2547b3c6a1ad184c25880d | 2778fb5ee2c0145758565e5cfe83392a879d429b | refs/heads/main | 2023-07-28T22:43:45.553292 | 2021-09-18T09:16:40 | 2021-09-18T09:16:40 | 407,878,522 | 1 | 0 | null | 2021-09-18T14:12:34 | 2021-09-18T14:12:33 | null | UTF-8 | Python | false | false | 5,808 | py | # -*- coding: utf-8 -*-
from datetime import date
import rqdatac
import pandas as pd
def read_data(today):
txn_day = rqdatac.get_previous_trading_date(today)
df_all_instruments = rqdatac.convertible.all_instruments(
txn_day).reset_index()
df_latest_bond_price = rqdatac.get_price(
df_all_instruments.order_book_id.tolist(),
start_date=txn_day,
end_date=txn_day,
frequency='1d').reset_index()
df_latest_stock_price = rqdatac.get_price(
df_all_instruments.stock_code.tolist(),
start_date=txn_day,
end_date=txn_day,
frequency='1d').reset_index()
df_conversion_price = rqdatac.convertible.get_conversion_price(
df_all_instruments.order_book_id.tolist(),
end_date=txn_day).reset_index()
df_call_info = rqdatac.convertible.get_call_info(
df_all_instruments.order_book_id.tolist(), end_date=txn_day)
if df_call_info is not None:
df_call_info = df_call_info.reset_index()
df_indicators = rqdatac.convertible.get_indicators(
df_all_instruments.order_book_id.tolist(),
start_date=txn_day,
end_date=txn_day).reset_index()
return txn_day, df_all_instruments, df_conversion_price, df_latest_bond_price, df_latest_stock_price, df_call_info, df_indicators
def process(txn_day, df_all_instruments, df_conversion_price,
df_latest_bond_price, df_latest_stock_price, df_call_info,
df_indicators):
# Data cleaning
# Filter non-conbond, e.g. exchange bond
df_all_instruments = df_all_instruments[df_all_instruments.bond_type ==
'cb']
# Filter bonds that stopped trading by txn_day
df_all_instruments[
'stopped_trading'] = df_all_instruments.stop_trading_date.dt.date <= txn_day
df_all_instruments = df_all_instruments[df_all_instruments.stopped_trading
== False]
df_all_instruments = df_all_instruments[[
'order_book_id',
'symbol',
'stock_code',
]]
df_latest_stock_price = df_latest_stock_price[[
'order_book_id', 'close'
]].rename(columns={
'close': 'stock_price'
}).set_index('order_book_id')
# stock_price
df = df_all_instruments.set_index('stock_code').join(
df_latest_stock_price).reset_index().set_index('order_book_id')
df_latest_bond_price = df_latest_bond_price[[
'order_book_id', 'close'
]].rename(columns={
'close': 'bond_price'
}).set_index('order_book_id')
# bond_price
df = df.join(df_latest_bond_price)
if df_call_info is not None and 'info_date' in df_call_info.columns:
# info_date
df_call_info = df_call_info[pd.notnull(df_call_info.info_date)]
if not df_call_info.empty:
df = df.join(df_call_info[['order_book_id', 'info_date'
]].set_index('order_book_id'))
if df.info_date.dt.date.dtype == date:
df['force_redeem'] = df.info_date.dt.date < txn_day
df = df[df.force_redeem == False]
df_conversion_price = df_conversion_price[[
'order_book_id', 'conversion_price'
]].groupby('order_book_id').min()
# conversion_price
df = df.join(df_conversion_price)
df['convert_premium_rate'] = df.bond_price / (100 / df.conversion_price *
df.stock_price) - 1
return df
# config: Expect to have two keys: weight_bond_price and weight_convert_premium_rate
# df: Expect to have a column named 'double_low', or two columns named 'bond_price' and 'convert_premium_rate'
# index of df is the id for the bond to place order
def double_low(df, config):
assert 'top' in config
top = config['top']
if 'double_low' not in df.columns:
assert 'weight_bond_price' in config
assert 'weight_convert_premium_rate' in config
weight_bond_price = config['weight_bond_price']
weight_convert_premium_rate = config['weight_convert_premium_rate']
assert 'bond_price' in df.columns
assert 'convert_premium_rate' in df.columns
df['double_low'] = df.bond_price * weight_bond_price + df.convert_premium_rate * 100 * weight_convert_premium_rate
dl = df.nsmallest(top, 'double_low')
print(dl)
return set(df.nsmallest(top, 'double_low').index.values.tolist())
def generate_orders(df, strategy, strategy_config, holdings):
candidates = strategy(df, strategy_config)
orders = {}
orders['buy'] = list(candidates - holdings)
orders['sell'] = list(holdings - candidates)
orders['hold'] = list(holdings & candidates)
return orders
def init(context):
context.top = 20
scheduler.run_weekly(rebalance,
tradingday=1,
time_rule=market_open(minute=10))
def rebalance(context, bar_dict):
txn_day, df_all_instruments, df_conversion_price, df_latest_bond_price, df_latest_stock_price, df_call_info, df_indicators = read_data(
context.now)
df = process(txn_day, df_all_instruments, df_conversion_price,
df_latest_bond_price, df_latest_stock_price, df_call_info,
df_indicators)
positions = set()
for p in context.portfolio.get_positions():
positions.add(p.order_book_id)
orders = generate_orders(
df, double_low, {
'weight_bond_price': 0.5,
'weight_convert_premium_rate': 0.5,
'top': context.top,
}, positions)
logger.info("今日操作：%s" % orders)
for code in orders['sell']:
order_target_percent(code, 0)
for op in ['hold', 'buy']:
for code in orders[op]:
order_target_percent(code, 1 / context.top)
| [
"paulhybryant@gmail.com"
] | paulhybryant@gmail.com |
92cd077f58237f62504c76208cda5a489cb6ee9f | 525252226b3c6b76fd0916084e9de7de9db2ae60 | /backend/tests/test_gasfeed.py | d13ec3c809b000f2b5ee40bcdbc9ac1753047945 | [] | no_license | james4388/gas-price | 8215f429249d2985ea964eea6ea7e63b74faf7b8 | 3e380146a8698c84443bba2b11f3829710ad0e69 | refs/heads/master | 2022-12-02T14:17:15.893681 | 2020-08-06T05:29:25 | 2020-08-06T05:29:25 | 278,273,476 | 3 | 0 | null | 2020-07-15T18:17:16 | 2020-07-09T05:41:30 | JavaScript | UTF-8 | Python | false | false | 1,221 | py | from flask import url_for
from tests import BaseTestCase
from gasprice import config, create_app
class GasFeedTestCase(BaseTestCase):
def test_brands(self):
with self.app.app_context():
resp = self.client.get(
url_for(
'GasFeed.station_brands'
)
)
data = resp.json
self.assertIsInstance(
data['stations'], list, 'Should stations is a list')
def test_nearby_stations(self):
with self.app.app_context():
resp = self.client.get(
url_for(
'GasFeed.nearby_stations',
lat='iajsd',
lon='sddsf'
)
)
data = resp.json
self.assertEqual(resp.status_code, 422, 'Validation should work')
resp = self.client.get(
url_for(
'GasFeed.nearby_stations',
lat='37.4131208',
lon='-122.0908522'
)
)
data = resp.json
self.assertIsInstance(
data['stations'], list, 'Should stations is a list') | [
"nhutrinh@leafgroup.com"
] | nhutrinh@leafgroup.com |
1df207e3ac0e742b82e1992b634d3bbf48dbfbe2 | 70331cc864c44a3f30883d40073f048d1a5a8e3f | /main.py | 69d7007bdd8659110ec9a209219a24a98be176d3 | [] | no_license | rmar6544/Day-7-Hangman-3-Start | 5c0f310925b8ba4b3f87db019c7f25e94865cfd8 | ea538f6cff8433bc9ee90c32794593c938d8a4e5 | refs/heads/master | 2023-04-04T23:43:08.966646 | 2021-04-07T23:06:28 | 2021-04-07T23:06:28 | 355,696,406 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 971 | py | #Step 3
import random
word_list = ["aardvark", "baboon", "camel"]
chosen_word = random.choice(word_list)
word_length = len(chosen_word)
#Testing code
print(f'Pssst, the solution is {chosen_word}.')
#Create blanks
display = []
for _ in range(word_length):
display += "_"
#TODO-1: - Use a while loop to let the user guess again. The loop should only stop once the user has guessed all the letters in the chosen_word and 'display' has no more blanks ("_"). Then you can tell the user they've won.
game_on = True
while game_on == True:
guess = input("Guess a letter: ").lower()
#Check guessed letter
for position in range(word_length):
letter = chosen_word[position]
# print(f"Current position: {position}\n Current letter: {letter}\n Guessed letter: {guess}")
if letter == guess:
display[position] = letter
print(display)
print(chosen_word)
if chosen_word == "".join(display):
game_on = False
print("you win") | [
""
] | |
3e0492db360ce01a76f540ff3bf14d2133ae8153 | 9743d5fd24822f79c156ad112229e25adb9ed6f6 | /xai/brain/wordbase/nouns/_bogies.py | e575bb083362fdfd4e25d0bf21f424dc5070f88d | [
"MIT"
] | permissive | cash2one/xai | de7adad1758f50dd6786bf0111e71a903f039b64 | e76f12c9f4dcf3ac1c7c08b0cc8844c0b0a104b6 | refs/heads/master | 2021-01-19T12:33:54.964379 | 2017-01-28T02:00:50 | 2017-01-28T02:00:50 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 226 | py |
from xai.brain.wordbase.nouns._bogy import _BOGY
#calss header
class _BOGIES(_BOGY, ):
def __init__(self,):
_BOGY.__init__(self)
self.name = "BOGIES"
self.specie = 'nouns'
self.basic = "bogy"
self.jsondata = {}
| [
"xingwang1991@gmail.com"
] | xingwang1991@gmail.com |
1fabea699593b6f7717d53f0a54019114c626198 | 470c55bca410969d772593c5b0cc7f63fc97354f | /compiler/tools/conf.py | 3fb06ce1918cb2e16b6a819e5802c9294f1a0a41 | [
"BSD-2-Clause"
] | permissive | knz/restcrumbs | b19fd250a021f1b77c25a48c7163204d4021381d | 494ea1fc5788b0ec8823e0000607da4187c39986 | refs/heads/master | 2016-08-08T15:22:52.301680 | 2011-02-28T18:38:56 | 2011-02-28T18:38:56 | 1,422,383 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 6,928 | py | # -*- coding: utf-8 -*-
#
# Blah documentation build configuration file, created by
# sphinx-quickstart on Tue Nov 9 10:40:39 2010.
#
# This file is execfile()d with the current directory set to its containing dir.
#
# Note that not all possible configuration values are present in this
# autogenerated file.
#
# All configuration values have a default; values that are commented out
# serve to show the default.
import sys, os
# If extensions (or modules to document with autodoc) are in another directory,
# add these directories to sys.path here. If the directory is relative to the
# documentation root, use os.path.abspath to make it absolute, like shown here.
#sys.path.insert(0, os.path.abspath('.'))
# -- General configuration -----------------------------------------------------
# If your documentation needs a minimal Sphinx version, state it here.
#needs_sphinx = '1.0'
# Add any Sphinx extension module names here, as strings. They can be extensions
# coming with Sphinx (named 'sphinx.ext.*') or your custom ones.
extensions = ['sphinx.ext.pngmath']
pngmath_use_preview = True
# Add any paths that contain templates here, relative to this directory.
templates_path = ['_templates']
# The suffix of source filenames.
source_suffix = '.rstr'
source_encoding = 'iso-8859-1'
# The encoding of source files.
#source_encoding = 'utf-8-sig'
# The master toctree document.
master_doc = 'index'
# General information about the project.
project = u'reST Crumbs'
copyright = u'YEAR YOURNAME'
# The version info for the project you're documenting, acts as replacement for
# |version| and |release|, also used in various other places throughout the
# built documents.
#
# The short X.Y version.
version = '2.0'
# The full version, including alpha/beta/rc tags.
release = '2.0'
# The language for content autogenerated by Sphinx. Refer to documentation
# for a list of supported languages.
#language = None
# There are two options for replacing |today|: either, you set today to some
# non-false value, then it is used:
#today = ''
# Else, today_fmt is used as the format for a strftime call.
#today_fmt = '%B %d, %Y'
# List of patterns, relative to source directory, that match files and
# directories to ignore when looking for source files.
exclude_patterns = ['_build']
# The reST default role (used for this markup: `text`) to use for all documents.
#default_role = None
# If true, '()' will be appended to :func: etc. cross-reference text.
#add_function_parentheses = True
# If true, the current module name will be prepended to all description
# unit titles (such as .. function::).
#add_module_names = True
# If true, sectionauthor and moduleauthor directives will be shown in the
# output. They are ignored by default.
#show_authors = False
# The name of the Pygments (syntax highlighting) style to use.
pygments_style = 'sphinx'
# A list of ignored prefixes for module index sorting.
#modindex_common_prefix = []
# -- Options for HTML output ---------------------------------------------------
# The theme to use for HTML and HTML Help pages. See the documentation for
# a list of builtin themes.
html_theme = 'default'
# Theme options are theme-specific and customize the look and feel of a theme
# further. For a list of options available for each theme, see the
# documentation.
#html_theme_options = {}
# Add any paths that contain custom themes here, relative to this directory.
#html_theme_path = []
# The name for this set of Sphinx documents. If None, it defaults to
# "<project> v<release> documentation".
html_title = 'reST Crumbs'
# A shorter title for the navigation bar. Default is the same as html_title.
#html_short_title = None
# The name of an image file (relative to this directory) to place at the top
# of the sidebar.
#html_logo = None
#html_logo = '../im/uva_logo.png'
# The name of an image file (within the static path) to use as favicon of the
# docs. This file should be a Windows icon file (.ico) being 16x16 or 32x32
# pixels large.
#html_favicon = None
# Add any paths that contain custom static files (such as style sheets) here,
# relative to this directory. They are copied after the builtin static files,
# so a file named "default.css" will overwrite the builtin "default.css".
html_static_path = ['_static']
# If not '', a 'Last updated on:' timestamp is inserted at every page bottom,
# using the given strftime format.
#html_last_updated_fmt = '%b %d, %Y'
# If true, SmartyPants will be used to convert quotes and dashes to
# typographically correct entities.
html_use_smartypants = True
# Custom sidebar templates, maps document names to template names.
#html_sidebars = {}
# Additional templates that should be rendered to pages, maps page names to
# template names.
#html_additional_pages = {}
# If false, no module index is generated.
html_domain_indices = False
# If false, no index is generated.
#html_use_index = True
# If true, the index is split into individual pages for each letter.
#html_split_index = False
# If true, links to the reST sources are added to the pages.
#html_show_sourcelink = True
# If true, "Created using Sphinx" is shown in the HTML footer. Default is True.
#html_show_sphinx = True
# If true, "(C) Copyright ..." is shown in the HTML footer. Default is True.
#html_show_copyright = True
# If true, an OpenSearch description file will be output, and all pages will
# contain a <link> tag referring to it. The value of this option must be the
# base URL from which the finished HTML is served.
#html_use_opensearch = ''
# This is the file name suffix for HTML files (e.g. ".xhtml").
#html_file_suffix = None
# Output file base name for HTML help builder.
htmlhelp_basename = 'notesdoc'
# -- Options for LaTeX output --------------------------------------------------
# The paper size ('letter' or 'a4').
#latex_paper_size = 'letter'
# The font size ('10pt', '11pt' or '12pt').
#latex_font_size = '10pt'
# Grouping the document tree into LaTeX files. List of tuples
# (source start file, target name, title, author, documentclass [howto/manual]).
latex_documents = [
]
# The name of an image file (relative to this directory) to place at the top of
# the title page.
#latex_logo = None
# For "manual" documents, if this is true, then toplevel headings are parts,
# not chapters.
#latex_use_parts = False
# If true, show page references after internal links.
#latex_show_pagerefs = False
# If true, show URL addresses after external links.
#latex_show_urls = False
# Additional stuff for the LaTeX preamble.
#latex_preamble = ''
# Documents to append as an appendix to all manuals.
#latex_appendices = []
# If false, no module index is generated.
#latex_domain_indices = True
# -- Options for manual page output --------------------------------------------
# One entry per manual page. List of tuples
# (source start file, name, description, authors, manual section).
man_pages = [
]
| [
"r.c.poss@uva.nl"
] | r.c.poss@uva.nl |
dffede7cbbfa98929853b8241f6a1e945007f560 | e5fb2d912415c302221604126afa7cbbb0a039c0 | /keras_gym/policies/test_special.py | d19afe8e363fc4399127c8f76a179ab42414bef4 | [
"MIT"
] | permissive | KristianHolsheimer/keras-gym | fc034025a1180b1124fe1a25886b54088d2f3552 | 0296ddcc8685e1ce732c3173caaa0fd25af9ef58 | refs/heads/master | 2021-06-28T21:57:50.122753 | 2020-09-30T04:29:15 | 2020-09-30T04:29:15 | 174,637,157 | 17 | 5 | MIT | 2019-08-02T22:48:41 | 2019-03-09T02:09:03 | Python | UTF-8 | Python | false | false | 1,012 | py | from gym.envs.toy_text.frozen_lake import FrozenLakeEnv, RIGHT, DOWN
from .special import UserInputPolicy
class MockInputFunction:
def __init__(self, return_value=None):
self.return_value = return_value
self._orig_input_fn = __builtins__['input']
def _mock_input_fn(self, prompt):
print(prompt + str(self.return_value))
return self.return_value
def __enter__(self):
__builtins__['input'] = self._mock_input_fn
def __exit__(self, type, value, traceback):
__builtins__['input'] = self._orig_input_fn
class TestUserInputPolicy:
def test_expected(self):
env = FrozenLakeEnv(is_slippery=False)
policy = UserInputPolicy(env)
s = env.reset()
env.render()
for i in [RIGHT, RIGHT, DOWN, DOWN, DOWN, RIGHT]:
with MockInputFunction(return_value=i):
a = policy(s)
s, r, done, info = env.step(a)
env.render()
if done:
break
| [
"kristian.holsheimer@gmail.com"
] | kristian.holsheimer@gmail.com |
097439d4e5e15a04cbe777f77fd0434256fd16d1 | a61ca7b89ef5817b2027239ece9dd175f776c8f3 | /rcsb/app/chem/LogFilterUtils.py | 86c6b9113eaef1e38f51a767d80d66d89057586c | [
"Apache-2.0"
] | permissive | rcsb/py-rcsb_app_chem | 7da2941f6e0d0f8ff0f5a802a3edb689d283659b | 64ca10e6ccf8b604fa3d16ab72406408b22c0aca | refs/heads/master | 2023-08-17T21:33:51.660687 | 2023-01-09T17:30:07 | 2023-01-09T17:30:07 | 245,858,180 | 0 | 0 | Apache-2.0 | 2023-01-09T17:30:08 | 2020-03-08T17:31:37 | Python | UTF-8 | Python | false | false | 866 | py | ##
# File: LogFilterUtils.py
# Date: 29-Jun-2020 jdw
#
# Pre-filter for Gunicorn/Uvicorn health check requests -
##
# pylint: disable=E1101
import logging
logger = logging.getLogger(__name__)
class HealthCheckFilter(logging.Filter):
def filter(self, record):
return record.getMessage().find("/healthcheck") == -1
class LogFilterUtils(object):
def __init__(self):
pass
def addFilters(self):
logger.debug("Current loggers are: %r", [name for name in logging.root.manager.loggerDict]) # pylint: disable=no-member
for name in logging.root.manager.loggerDict: # pylint: disable=no-member
if any(x in name for x in ["uvicorn", "gunicorn"]):
logger.debug("Add filter to logger %r", name)
loggerT = logging.getLogger(name)
loggerT.addFilter(HealthCheckFilter())
| [
"john.westbrook@rcsb.org"
] | john.westbrook@rcsb.org |
83da69b3ca2edaf7ca26891e03e9d0eafd7f5cb2 | 3cbf8706ea6655aad48d6bb0c34a4d7f5d6b2fdf | /depth/self_supervised_sfm/train.py | 73671c45601f5f768f1fe3bf22a2e3a02d20caad | [
"MIT"
] | permissive | seo-dev/cvml_project | cf166f7aa513bdbc5b23941d8cb19b53bbdc400b | 7c95ce22db6f31dc4624af9417edffde021b5351 | refs/heads/master | 2022-12-06T07:43:11.906512 | 2020-08-27T03:08:38 | 2020-08-27T03:08:38 | 290,434,881 | 1 | 0 | MIT | 2020-08-26T08:04:37 | 2020-08-26T08:04:36 | null | UTF-8 | Python | false | false | 14,224 | py | import argparse
import datetime
import os
import tensorflow as tf
from dataset import KittiSFMDataset
from disparitynet import DisparityNet
from posenet import PoseNet
from utils import pixel_coord, ssim_loss, smooth_loss, bilinear_sampler, forwardproject, backproject, disp_to_depth
parser = argparse.ArgumentParser(description="Disparity Project")
parser.add_argument('--identifier', default="sfm_resnet18")
parser.add_argument('--data_dir')
parser.add_argument("--input_h", default=192)
parser.add_argument("--input_w", default=640)
parser.add_argument("--batch_size", default=8)
parser.add_argument("--epochs", default=50)
parser.add_argument("--num_scales", default=4)
parser.add_argument("--num_input_frames", default=2, help='num of frames as input to posenet')
parser.add_argument("--frame_ids", default=[0, -1, 1], help='frames to load ')
parser.add_argument("--draw_every_iter", default=1000)
PROJECT_DIR = os.getcwd()
MIN_DEPTH = 1e-3
MAX_DEPTH = 80
class Trainer:
def __init__(self, params, output_dir):
self.params = params
# Models
self.models = {}
self.models['disparity'] = DisparityNet(input_shape=(params.input_h, params.input_w, 3))
self.models['pose'] = PoseNet(input_shape=(params.input_h, params.input_w, 3 * params.num_input_frames),
num_input_frames=params.num_input_frames)
# Datasets
train_dataset = KittiSFMDataset(params.data_dir, 'train',
(params.input_h, params.input_w),
batch_size=params.batch_size,
frame_idx=params.frame_ids)
val_dataset = KittiSFMDataset(params.data_dir, 'val',
(params.input_h, params.input_w),
frame_idx=params.frame_ids,
batch_size=params.batch_size)
self.train_dataset = train_dataset.load_tfdataset()
self.val_dataset = val_dataset.load_tfdataset()
# Optimizer
self.total_iteration = (train_dataset.num_samples // params.batch_size) * params.epochs
learning_rate_fn = tf.keras.optimizers.schedules.PolynomialDecay(0.0002, end_learning_rate=0.000001,
decay_steps=self.total_iteration,
power=0.5)
self.optimizer = tf.keras.optimizers.Adam(learning_rate_fn)
# Tensorboard & Meters
train_log_dir = os.path.join(output_dir, 'train_logs')
val_log_dir = os.path.join(output_dir, 'val_logs')
self.train_summary_writer = tf.summary.create_file_writer(train_log_dir)
self.test_summary_writer = tf.summary.create_file_writer(val_log_dir)
self.train_meter = {
'ssim': tf.keras.metrics.Mean(name='ssim'),
'l1': tf.keras.metrics.Mean(name='l1'),
'smooth': tf.keras.metrics.Mean(name='smooth'),
}
self.val_meter = {
'ssim': tf.keras.metrics.Mean(name='ssim'),
'l1': tf.keras.metrics.Mean(name='l1'),
'smooth': tf.keras.metrics.Mean(name='smooth'),
}
self.step = 0
# Load states from optimiser and model if available
self.ckpt_disp, self.manager_disp = self.setup_logger(self.models['disparity'],
os.path.join(output_dir, 'disparity_model'))
self.ckpt_pose, self.manager_pose = self.setup_logger(self.models['pose'],
os.path.join(output_dir, 'pose_model'))
self.start_epoch = int(self.ckpt_disp.step) + 1 if self.manager_disp.latest_checkpoint else int(
self.ckpt_disp.step)
print("Starting training step {}".format(self.ckpt_disp.step.numpy()))
# Helpers
self.pix_coords = pixel_coord(params.batch_size, params.input_h, params.input_w, True) # [b, 3, npoints]
def setup_logger(self, model, out_dir):
ckpt = tf.train.Checkpoint(step=tf.Variable(0), optimizer=self.optimizer, net=model)
manager = tf.train.CheckpointManager(ckpt, out_dir, max_to_keep=1)
ckpt.restore(manager.latest_checkpoint)
return ckpt, manager
def train(self):
for epoch in range(self.start_epoch, self.params.epochs):
[self.train_meter[k].reset_states() for k, v in self.train_meter.items()]
[self.val_meter[k].reset_states() for k, v in self.val_meter.items()]
# Train
for i, inputs in enumerate(self.train_dataset):
loss, outputs = self.train_step(inputs)
print(
f'\rEpoch: [{epoch}/{self.params.epochs}] | 'f'Iter: [{self.optimizer.iterations.numpy()}/{self.total_iteration}] | '
f'Lr: {self.optimizer._decayed_lr(tf.float32):.5f} | '
f"ssim: {self.train_meter['ssim'].result():.4f} | ",
f"l1: {self.train_meter['l1'].result():.4f} | ",
f"smooth: {self.train_meter['smooth'].result():.10f} | ",
f"total loss: {loss['loss']:.4f} | ",
end="")
if i % self.params.draw_every_iter == 0:
with self.train_summary_writer.as_default():
tf.summary.image('disparity', outputs['disparity0'], step=epoch)
tf.summary.image('depth', outputs['depth0'], step=epoch)
stack_prediction_pred = tf.concat([outputs['pred-10'], inputs['img'], outputs['pred10']],
axis=1)
stack_prediction_gt = tf.concat([inputs['img-1'], inputs['img'], inputs['img1']], axis=1)
tf.summary.image('predictions', stack_prediction_pred, step=epoch)
tf.summary.image('groundtruth', stack_prediction_gt, step=epoch)
# Validation
for i, inputs in enumerate(self.val_dataset):
self.val_step(inputs)
print(
f'\rEpoch: [{epoch}/{params.epochs}] | '
f"ssim: {self.val_meter['ssim'].result():.4f} | ",
f"l1: {self.val_meter['l1'].result():.4f} | ",
f"smooth: {self.val_meter['smooth'].result():.4f} | ",
end="")
with self.train_summary_writer.as_default():
tf.summary.scalar('ssim', self.train_meter['ssim'].result(), step=epoch)
tf.summary.scalar('l1', self.train_meter['l1'].result(), step=epoch)
tf.summary.scalar('smooth', self.train_meter['smooth'].result(), step=epoch)
with self.test_summary_writer.as_default():
tf.summary.scalar('ssim', self.val_meter['ssim'].result(), step=epoch)
tf.summary.scalar('l1', self.val_meter['l1'].result(), step=epoch)
tf.summary.scalar('smooth', self.val_meter['smooth'].result(), step=epoch)
# save and increment
save_path = self.manager_disp.save()
save_path = self.manager_pose.save()
print("Saved checkpoint for step {}: {}".format(int(self.ckpt_disp.step), save_path))
self.ckpt_disp.step.assign_add(1)
self.ckpt_pose.step.assign_add(1)
@tf.function
def train_step(self, inputs):
with tf.GradientTape() as tape:
outputs = self.models['disparity'](inputs['img'], training=True)
outputs.update(self.predict_pose(inputs))
outputs.update(self.view_synthesis(inputs, outputs))
loss = self.criterions(inputs, outputs)
trainable_params = self.models['disparity'].trainable_variables + self.models['pose'].trainable_variables
gradients = tape.gradient(loss['loss'], trainable_params)
self.optimizer.apply_gradients(zip(gradients, trainable_params))
# Update moving average
[self.train_meter[k](loss[k]) for k, v in self.train_meter.items()]
return loss, outputs
@tf.function
def val_step(self, inputs):
outputs = self.models['disparity'](inputs['img'], training=False)
outputs.update(self.predict_pose(inputs))
outputs.update(self.view_synthesis(inputs, outputs))
loss = self.criterions(inputs, outputs)
# Update moving average
[self.val_meter[k](loss[k]) for k, v in self.val_meter.items()]
def criterions(self, inputs, outputs):
loss_dict = {}
total_l1_loss = 0.
total_ssim_loss = 0.
total_smooth_loss = 0.
for scale in range(self.params.num_scales):
l1_losses = []
ssim_losses = []
for f_i in self.params.frame_ids[1:]:
target_rgb = inputs['img']
pred_rgb = outputs[f'pred{f_i}{scale}']
# L1 Loss
abs_diff = tf.abs(target_rgb - pred_rgb)
l1_loss = tf.reduce_mean(abs_diff, axis=-1, keepdims=True) # [b, h, w, 1]
l1_losses.append(l1_loss)
# SSIM Loss
ssim = tf.reduce_mean(ssim_loss(target_rgb, pred_rgb), axis=-1, keepdims=True)
ssim_losses.append(ssim)
ssim_losses = tf.concat(ssim_losses, -1)
l1_losses = tf.concat(l1_losses, -1)
if scale == 0:
outputs['l1_error'] = l1_losses
# Automasking
identity_l1_losses = []
identity_ssim_losses = []
for f_i in self.params.frame_ids[1:]:
target_rgb = inputs['img']
source_rgb = inputs[f'img{f_i}']
# L1 Loss
abs_diff = tf.abs(source_rgb - target_rgb)
l1_loss = tf.reduce_mean(abs_diff, axis=-1, keepdims=True)
identity_l1_losses.append(l1_loss)
# SSIM Loss [b, h, w, 1]
ssim = tf.reduce_mean(ssim_loss(source_rgb, target_rgb), axis=-1, keepdims=True)
identity_ssim_losses.append(ssim)
identity_ssim_losses = tf.concat(identity_ssim_losses, -1)
identity_l1_losses = tf.concat(identity_l1_losses, -1)
identity_l1_losses += tf.random.normal(identity_l1_losses.shape) * 0.00001 # Break ties
identity_ssim_losses += tf.random.normal(identity_ssim_losses.shape) * 0.00001 # Break ties
combined_l1 = tf.concat((identity_l1_losses, l1_losses), axis=-1)
combined_ssim = tf.concat((identity_ssim_losses, ssim_losses), axis=-1)
combined_l1 = tf.reduce_min(combined_l1, axis=-1)
combined_ssim = tf.reduce_min(combined_ssim, axis=-1)
_ssim_loss = tf.reduce_mean(combined_ssim) * 0.85
_l1_loss = tf.reduce_mean(combined_l1) * 0.15
total_l1_loss += _l1_loss
total_ssim_loss += _ssim_loss
# Disparity smoothness
disparity = outputs[f'disparity{scale}']
mean_disp = tf.reduce_mean(disparity, [1, 2], keepdims=True)
norm_disp = disparity / (mean_disp + 1e-7)
h = self.params.input_h // (2 ** scale)
w = self.params.input_w // (2 ** scale)
color_resized = tf.image.resize(target_rgb, (h, w))
smooth = smooth_loss(norm_disp, color_resized) * 1e-3
total_smooth_loss += smooth
total_smooth_loss /= self.params.num_scales
total_ssim_loss /= self.params.num_scales
total_l1_loss /= self.params.num_scales
loss_dict['ssim'] = total_ssim_loss
loss_dict['l1'] = total_l1_loss
loss_dict['smooth'] = total_smooth_loss
loss_dict['loss'] = total_smooth_loss + total_ssim_loss + total_l1_loss
return loss_dict
def predict_pose(self, inputs):
"""
Compute pose wrt to each source frame
"""
output = {}
for f_i in self.params.frame_ids[1:]:
if f_i < 0:
pose_inputs = tf.concat([inputs[f'img{f_i}'], inputs['img']], -1)
else:
pose_inputs = tf.concat([inputs['img'], inputs[f'img{f_i}']], -1)
axisangle, translation, M = self.models['pose'](pose_inputs, invert=(f_i < 0))
output[f'axisangle{f_i}'] = axisangle
output[f'translation{f_i}'] = translation
output[f'M{f_i}'] = M
return output
def view_synthesis(self, inputs, outputs):
"""
Warped prediction based on predicted depth and pose
Args:
inputs:
'disparity': [b, h, w, 1]
'img': [b, h, w, 3]
"""
for scale in range(self.params.num_scales):
disp = outputs[f'disparity{scale}']
disp = tf.image.resize(disp, [self.params.input_h, self.params.input_w])
_, depth = disp_to_depth(disp, min_depth=MIN_DEPTH, max_depth=MAX_DEPTH)
outputs[f'depth{scale}'] = depth
for i, frame_id in enumerate(self.params.frame_ids[1:]):
source = inputs[f'img{frame_id}']
T = outputs[f'M{frame_id}']
# depth2pcl
cam_points = backproject(self.pix_coords, depth, inputs['K_inv'])
# pcl2pix
proj_mat = tf.matmul(inputs['K'], T)
pix_coords = forwardproject(cam_points, proj_mat, self.params.input_h,
self.params.input_w) # [b, h, w, 2]
# Warped source to target
projected_img = bilinear_sampler(source, pix_coords) # [b, h, w, 3]
outputs[f'pred{frame_id}{scale}'] = projected_img
return outputs
if __name__ == '__main__':
params = parser.parse_args()
output_dir = os.path.join(PROJECT_DIR, 'results', params.identifier)
if not os.path.isdir(output_dir):
os.makedirs(output_dir)
print(f'Start: {params.identifier}', datetime.datetime.now())
t = Trainer(params, output_dir)
t.train()
| [
"daryl.tan@easymile.com"
] | daryl.tan@easymile.com |
d5c3dd5a7e5b2f8332dda72ef2737f66f62832b9 | 4f35782ac42f1cc65581b6f21b7a80be8f0164cc | /resources/todo_item.py | 86400c55f8c6821098187dc26050847564cb707d | [] | no_license | Rhemm/berry | 2da3cb202040b130ff566a78f6fb98efac1cace4 | 0c908bc4cbbdf770ac8c5b66d0cbf2b115ac456b | refs/heads/master | 2020-04-23T21:04:40.382398 | 2019-02-21T04:47:18 | 2019-02-21T04:47:18 | 171,459,013 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 3,589 | py | # -*- coding: utf-8 -*-
# pylint: disable=R0201
"""
Module providing todo item resource.
"""
from flask_restful import Resource
from flask_restful.reqparse import RequestParser
from flask_restful.inputs import date, boolean
from bson import ObjectId
from application import mongo
from common.utils import validate_id
item_put_parser = RequestParser()
item_put_parser.add_argument("text", type=str, location="json")
item_put_parser.add_argument("dueDate", type=date, location="json")
item_put_parser.add_argument("finished", type=boolean, location="json")
item_post_parser = item_put_parser.copy()
for arg in item_post_parser.args:
arg.required = True
db = mongo.db.todo
class TodoItem(Resource):
"""
Provides methods for updating and deleting todo items.
Usage::
For updating todo item:
curl -X PUT http://127.0.0.1:5000/todolists/<list_id>/items/<item_id> \
-H "Content-Type: application/json" \
-d '{"text": "sometext", "due_date": "2019-2-12", "finished": "true" }'
For deleting todo item:
curl -X DELETE http://127.0.0.1:5000/todolists/<list_id>/items/<item_id> \
-H "Content-Type: application/json"
"""
def put(self, list_id, item_id):
"""
Updates todo item.
:param list_id: ID of related todo list.
:param item_id: ID of todo item.
"""
if not validate_id(list_id, item_id):
return {"msg": "Invalid id, it should be 24-character string"}, 400
args = item_put_parser.parse_args()
result = db.update_one(
{"_id": ObjectId(list_id), "todos._id": ObjectId(item_id)},
{
"$set": {
"todos.$." + key: args[key] for key in args if args[key] is not None
}
}
)
if result.matched_count == 0:
return {"msg": "No such list found"}, 404
if result.modified_count == 0:
return {"msg": "No item found"}, 404
return {"msg": "Successfuly updated"}
def delete(self, list_id, item_id):
"""
Deletes todo item.
:param list_id: ID of related todo list.
:param item_id: ID of todo item.
"""
if not validate_id(list_id, item_id):
return {"msg": "Invalid id, it should be 24-character string"}, 400
result = db.update_one(
{"_id": ObjectId(list_id)},
{"$pull": {"todos": {"_id": ObjectId(item_id)}}}
)
if result.matched_count == 0:
return {"msg": "No such list found"}, 404
if result.modified_count == 0:
return {"msg": "No item found"}, 404
return {"msg": "Successfuly deleted"}, 204
class TodoItemCollection(Resource):
"""
Provides method for adding new todo item.
Usage::
For creating new todo item:
curl -X POST http://127.0.0.1:5000/todolists/<list_id>/items \
-d '{"text": "sometext", "due_date": "2019-2-12", "finished": "true" }'
"""
def post(self, list_id):
"""
Creates todo item
:param list_id: ID of related todo list.
"""
if not validate_id(list_id):
return {"msg": "Invalid id, it should be 24-character string"}, 400
args = item_post_parser.parse_args()
result = db.update_one(
{"_id": ObjectId(list_id)},
{"$push": {"todos": {"_id": ObjectId(), **args}}}
)
if result.matched_count == 0:
return {"msg": "No such list found"}, 404
return {"msg": "Successfuly added"}, 201
| [
"yslfhe@gmail.com"
] | yslfhe@gmail.com |
a20070b324979ddb978268aa926e7c238120fb98 | 94354828fc025e091165d2a68d692d6645290140 | /apps/user_operation/adminx.py | 8e02327027feb7c3efba6a92eb832823ded15720 | [] | no_license | simon-wxm/MxShop | 02350944a9756887dcd4a95aebf8801fea6df54f | 309b9e52b9e018b9ab5a7631492d59b280b085d6 | refs/heads/master | 2022-12-13T13:41:43.660462 | 2019-07-04T10:40:16 | 2019-07-04T10:40:16 | 191,779,173 | 0 | 0 | null | 2022-12-08T01:04:15 | 2019-06-13T14:31:42 | JavaScript | UTF-8 | Python | false | false | 543 | py | # coding = utf-8
import xadmin
from .models import UserFav ,UserAddress,UserLeavingMessage
class UserFavAdmin(object):
list_display = ['user','goods','add_time']
class UserLeavingMessageAdmin(object):
list_display = ['user','message_type','message','add_time']
class UserAddressAdmin(object):
list_display = ['signer_name','signer_mobile','district','address']
xadmin.site.register(UserFav, UserFavAdmin)
xadmin.site.register(UserLeavingMessage, UserLeavingMessageAdmin)
xadmin.site.register(UserAddress, UserAddressAdmin)
| [
"wangxm5721@163.com"
] | wangxm5721@163.com |
26288ab26b891bd354d684ad66484d83d8fb746a | bf0b7643d9d9f83ea2820df9847a11cf52c8a5bc | /app/models.py | a9b6171b3936f4c683ccee0846d8cfc24309bd39 | [] | no_license | ducknessman/dust | 269851e4f420e01da14247565d65deff7c7c2a17 | 5dad23fd39aa5302f2e17dd286beca7b82446fcc | refs/heads/master | 2023-05-11T07:44:23.914855 | 2020-08-17T12:30:45 | 2020-08-17T12:30:45 | 259,030,166 | 2 | 1 | null | 2023-05-01T21:40:59 | 2020-04-26T12:54:17 | CSS | UTF-8 | Python | false | false | 7,069 | py | #！/usr/bin/env python
#! -*-coding:utf-8 -*-
#!@Author : zhuxx
#!@time : 2020/05/06 19:04
from datetime import datetime
from exts import db
from werkzeug.security import generate_password_hash,check_password_hash
# 用户表
class User(db.Model):
'''
_password:对内密码
password:对外密码
'''
__tablename__ = 'user'
user_id = db.Column(db.Integer,primary_key=True,autoincrement=True) #用户id
username = db.Column(db.String(100),nullable=False,unique=True) # 用户名
_password = db.Column(db.String(500),nullable=False) # 密码
email = db.Column(db.String(100),nullable=False,unique=True) # 邮箱
phone = db.Column(db.String(20),unique=True) # 电话
fullname = db.Column(db.String(100)) #全称
status = db.Column(db.Integer) # 状态
is_super = db.Column(db.SmallInteger) # 是否为管理员，1为管理员
role_id = db.Column(db.Integer, db.ForeignKey('role.id')) # 所属角色
remarks = db.Column(db.String(500)) # 备注
reg_time = db.Column(db.DateTime, default=datetime.now) #注册时间
def __init__(self,username=None,password=None,email=None,phone=None,fullname=None,
status=None,is_super=None,role_id=None,remarks=None,reg_time=None):
self.username = username
self.password = password
self.email = email
self.phone = phone
self.fullname = fullname
self.status = status
self.is_super = is_super
self.role_id = role_id
self.remarks = remarks
self.reg_time = reg_time
#获取密码
@property
def password(self):
return self._password
#设置密码
@password.setter
def password(self,raw_password):
self._password = generate_password_hash(raw_password)
#检查密码
def check_password(self,raw_password):
result = check_password_hash(self.password,raw_password)
return result
#测试用例
class Tasks(db.Model):
__tablename__ = 'tasks'
id = db.Column(db.Integer,primary_key=True,autoincrement=True) #记录id
task_id = db.Column(db.String(100),nullable=False) #用例编号
task_son_id = db.Column(db.String(200),unique=True,nullable=False) #用例子编号
task_name = db.Column(db.String(500),nullable=False) # 用例名称
task_description = db.Column(db.String(4096),nullable=False) # 用例描述
task_url = db.Column(db.String(1024),nullable=False) # 用例地址
task_method = db.Column(db.String(100),nullable=False) #请求方法
task_data = db.Column(db.String(4096)) # 用例数据
task_result = db.Column(db.String(4096),nullable=False) # 预期结果
task_session = db.Column(db.Integer,nullable=False) #是否需要session ,0:不需要，1：需要
sessions = db.Column(db.String(4096)) #登录session
task_auth = db.Column(db.String(1024)) #用例执行人信息
task_env = db.Column(db.Integer,nullable=False) #是否需要环境变量，0：不需要，1：stage，2：alpha，3：real
task_time = db.Column(db.String(4096)) #添加时间
#测试环境变量
class Env(db.Model):
__tablename__ = 'env'
id = db.Column(db.Integer, primary_key=True, autoincrement=True) # 记录id
env_name = db.Column(db.String(4096),nullable=False) # 环境变量名称
env_single = db.Column(db.Integer,nullable=False) #0：不需要，1：stage，2：alpha，3：real
env_url = db.Column(db.String(4096),nullable=False) # 环境变量地址
description = db.Column(db.String(4096),nullable=False)# 环境描述
#测试报告
class TaskReport(db.Model):
__tablename__ = 'taskreport'
id = db.Column(db.Integer, primary_key=True, autoincrement=True) # 记录id
report_name = db.Column(db.String(4096),nullable=False) # 报告名称
success_count = db.Column(db.Integer,nullable=False) # 成功数量
fail_count = db.Column(db.Integer,nullable=False) # 失败数量
error_account = db.Column(db.Integer,nullable=False) # 错误数量
finished_time = db.Column(db.String(100),index=True,default=datetime.now) #生成报告时间
#测试结果
class TaskResult(db.Model):
__tablename__ = 'task_result'
id = db.Column(db.Integer, primary_key=True, autoincrement=True) # 记录id
task_id = db.Column(db.String(100),nullable=False) # 用例编号
task_son_id = db.Column(db.String(100),nullable=False) # 用例子编号
task_url = db.Column(db.String(500),nullable=False) # 用例地址
task_data = db.Column(db.String(1024)) # 用例数据
task_result = db.Column(db.String(2048),nullable=False) #用例结果
task_response = db.Column(db.String(4096),nullable=False) # 请求响应结果
task_status = db.Column(db.Integer,nullable=False) #0:success,1:fail,2:error
finished_time = db.Column(db.String(100),index=True,default=datetime.now) #执行用例结束时间
# 定义角色数据模型
class Role(db.Model):
__tablename__ = 'role'
id = db.Column(db.Integer, primary_key=True) # 编号
name = db.Column(db.String(100), unique=True) # 名称
description = db.Column(db.String(600)) # 角色描述
auths = db.Column(db.String(600)) # 权限列表
add_time = db.Column(db.String(100), index=True, default=datetime.utcnow) # 添加时间
admins = db.relationship("User", backref='role')
# 定义权限数据模型
class Auth(db.Model):
__tablename__ = 'auth'
id = db.Column(db.Integer, primary_key=True) # 编号
name = db.Column(db.String(100),unique=True) # 名称，不能重复
url = db.Column(db.String(255)) # 地址
add_time = db.Column(db.String(100), index=True, default=datetime.utcnow) # 添加时间
#管理员登录日志
class AdminLog(db.Model):
__tablename__ = "admin_log" #定义表名
id = db.Column(db.Integer,primary_key=True) #编号
#定义外键 db.ForeignKey
admin_id = db.Column(db.Integer,db.ForeignKey('user.user_id')) #所属管理员
operate = db.Column(db.String(300)) # 操作行为
ip = db.Column(db.String(100)) #登录IP
time=db.Column(db.String(100))#时间戳
add_time = db.Column(db.String(100),index=True,default=datetime.now) #登录时间 ，默认时间
#操作日志
class OperateLog(db.Model):
__tablename__ = 'operate_log'
id = db.Column(db.Integer, primary_key=True) # 编号
# 定义外键 db.ForeignKey
admin_id = db.Column(db.Integer, db.ForeignKey('user.user_id')) # 所属管理员
ip = db.Column(db.String(100)) # 登录IP
operate = db.Column(db.String(600)) # 操作行为
add_time = db.Column(db.String(100), index=True, default=datetime.now) # 登录时间 ，默认时间
#任务执行表
class TaskRun(db.Model):
__tablename__ = 'task_run'
id = db.Column(db.Integer,primary_key=True,autoincrement=True) #序列编号
running_name = db.Column(db.String(100),nullable=False) #执行名称
running_info = db.Column(db.String(1024),nullable=False) #执行的用例子编号
create_time = db.Column(db.String(100),nullable=False) # 创建时间 | [
"1160154212@qq.com"
] | 1160154212@qq.com |
3084a00a20347e2a00d4110f699cde1dff559340 | d1a5d8bdaf7c27daf3a5aebde84be7cefa371723 | /dlc2_model_training (copy).py | 115e631869346886b255211c96b48c7808389be2 | [] | no_license | sambeettiady/dlc2_he | 1c7b811f3294022edaeb59494a19731badfbf56d | c54d5365b742951524458b92d0b31b028f9e195d | refs/heads/master | 2020-03-30T06:07:49.117344 | 2018-09-29T08:05:31 | 2018-09-29T08:05:31 | 150,840,718 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 6,333 | py | #Import image transformation packages
import skimage
from skimage import filters, io, exposure, color, segmentation, feature, morphology
from skimage.feature import canny
from scipy import ndimage as ndi
from scipy import misc
import skimage.transform as skt
#Import required packages
import numpy as np
import pandas as pd
import os
import glob
#Import Visualisation packages
import matplotlib.pyplot as plt
import graphviz
#Import sklearn modules
import sklearn.metrics as skm
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelEncoder
from sklearn.utils import class_weight
#Import Keras
import keras
from keras import metrics
from keras.models import Sequential
from keras.optimizers import Adam
from keras.losses import categorical_crossentropy,sparse_categorical_crossentropy
from keras.layers import Conv2D, MaxPooling2D, Conv2DTranspose, Input, Concatenate, Dense, Dropout, Flatten, Activation, Merge
from keras.models import Model,load_model
from keras.utils import plot_model
from keras import callbacks
import keras.backend as K
#Change working directory
os.chdir('/home/sambeet/data/hackerearth/deep learning challenge 2/')
#Read train, test and validation packages
#split_string = lambda x : x.split('_')[1]
train_data = pd.read_csv('csv/train_data.csv')
test_data = pd.read_csv('csv/test_data.csv')
val_data = pd.read_csv('csv/val_data.csv')
#train_data.detected = train_data.detected.apply(split_string)
#test_data.detected = test_data.detected.apply(split_string)
#all_data = pd.read_csv('csv/train.csv')
encoder = LabelEncoder()
encoder.fit(train_data.detected.values)
encoded_2 = encoder.transform(train_data.detected.values)
class_weights = class_weight.compute_class_weight('balanced', np.unique(encoded_2), encoded_2)
labels_dict = dict()
for key in np.unique(encoded_2):
labels_dict[key] = class_weights[key]
def data_generator(batch_size = 8, dataset = 'train'):
if dataset == 'train':
df = train_data.copy()
elif dataset == 'test':
df = test_data.copy()
else:
df = val_data.copy()
df = df.sample(frac=1).reset_index(drop=True)
image_list = list(df.image_name.values)
numeric_variables = df[['age','gender_M','view_position']].values
# encode class values as integers
encoded_Y = encoder.transform(df.detected.values)
# convert integers to dummy variables (i.e. one hot encoded)
labels = keras.utils.to_categorical(encoded_Y)
while 1:
for batch_num in range(len(image_list)//batch_size):
start_index = batch_num*batch_size
end_index = (batch_num + 1)*batch_size
batch_images = image_list[start_index:end_index]
numeric_data_1 = numeric_variables[start_index:end_index]
images = np.empty((batch_size, 1024, 1024, 1), dtype = np.float32)
numeric_data_2 = np.empty((batch_size,8), dtype = np.float32)
detected = labels[start_index:end_index]
for i,image_name in zip(range(batch_size),batch_images):
images[i,...,0] = misc.imread('train/train_/' + image_name,flatten=True)/255.
numeric_data_2[i,...] = np.histogram(images[i,...], bins = [0,0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.8, 0.9,1])[0][0:8]/float(1024*1024)
i = i + 1
numeric_data = np.hstack((numeric_data_1,numeric_data_2))
yield [images,numeric_data], detected
train_data_gen = data_generator(2,'train')
test_data_gen = data_generator(2,'test')
input1 = Input(shape=(1024, 1024, 1))
cnn1 = Conv2D(32, (3, 3), activation='relu',padding='same')(input1)
cnn2 = Conv2D(32, (3, 3), activation='relu',padding='same')(cnn1)
pool1 = MaxPooling2D(pool_size=(2, 2))(cnn2)
drop1 = Dropout(0.25)(pool1)
cnn3 = Conv2D(64, (3, 3), activation='relu',padding='same')(drop1)
cnn4 = Conv2D(64, (3, 3), activation='relu',padding='same')(cnn3)
pool2 = MaxPooling2D(pool_size=(2, 2))(cnn4)
drop2 = Dropout(0.25)(pool2)
cnn5 = Conv2D(128, (3, 3), activation='relu',padding='same')(drop2)
cnn6 = Conv2D(128, (3, 3), activation='relu',padding='same')(cnn5)
pool3 = MaxPooling2D(pool_size=(2, 2))(cnn6)
drop3 = Dropout(0.25)(pool3)
cnn7 = Conv2D(256, (3, 3), activation='relu',padding='same')(drop3)
cnn8 = Conv2D(256, (3, 3), activation='relu',padding='same')(cnn7)
pool4 = MaxPooling2D(pool_size=(2, 2))(cnn8)
drop4 = Dropout(0.25)(pool4)
cnn9 = Conv2D(512, (3, 3), activation='relu',padding='same')(drop4)
cnn10 = Conv2D(512, (3, 3), activation='relu',padding='same')(cnn9)
pool5 = MaxPooling2D(pool_size=(2, 2))(cnn10)
drop5 = Dropout(0.25)(pool5)
cnn11 = Conv2D(512, (3, 3), activation='relu',padding='same')(drop5)
cnn12 = Conv2D(512, (5, 5), activation='relu',padding='same')(cnn11)
cnn13 = Conv2D(512, (7, 7), activation='relu',padding='same')(cnn12)
pool6 = MaxPooling2D(pool_size=(2, 2))(cnn13)
drop6 = Dropout(0.25)(pool6)
flatten = Flatten()(drop6)
input2 = Input(shape=(11,))
merged_input = keras.layers.concatenate([flatten, input2])
dense1 = Dense(256, activation='relu')(merged_input)
drop7 = Dropout(0.25)(dense1)
dense2 = Dense(256, activation='relu')(drop7)
drop8 = Dropout(0.25)(dense2)
output = Dense(14, activation='softmax')(drop8)
model = Model(inputs=[input1, input2], outputs=output)
model.compile(loss='categorical_crossentropy', optimizer=Adam(5e-2),metrics=['accuracy'])
model.summary()
#plot_model(model, to_file='dl2_model_1.png')
model_checkpoint = callbacks.ModelCheckpoint(filepath = 'logs/vgg13.{epoch:02d}-{val_loss:.2f}.hdf5', monitor='val_loss', verbose=1, save_best_only=False, save_weights_only=False, mode='auto', period=1)
tensorboard = callbacks.TensorBoard(log_dir='logs', histogram_freq=0, batch_size=2, write_graph=True, write_grads=True, write_images=True, embeddings_freq=0, embeddings_layer_names=None, embeddings_metadata=None)
csv_logger = callbacks.CSVLogger('logs/training.log')
#model.load_weights('dlc2_vgg_13_2fc_1.hd5')#,custom_objects={'dice_loss':dice_loss,'dice_coef':dice_coef})
history = model.fit_generator(train_data_gen, epochs=20, steps_per_epoch= 464*4*4, verbose = 1,
callbacks=[model_checkpoint,tensorboard,csv_logger],class_weight = labels_dict,
validation_data = test_data_gen, validation_steps = 932)
model.save('dlc2_vgg_13_2fc_1.hd5')
| [
"noreply@github.com"
] | sambeettiady.noreply@github.com |
6a08c2f08d79f432c3bf319defed10312a65bde4 | 3501c13c0465b6a4a333840243b583710dbeb959 | /kodēšana.py | 48b2a7a204b83790edb1610465ad2a5f88ac9fcf | [] | no_license | DaigaSarkane/RTR105 | e5032330fe42e08c925013ccc815118bfd23b7cc | 13a619bc8e61938758643be95b163fb58c6758ad | refs/heads/master | 2020-03-28T03:24:01.695389 | 2019-05-03T08:46:25 | 2019-05-03T08:46:25 | 147,642,367 | 0 | 1 | null | null | null | null | UTF-8 | Python | false | false | 2,205 | py | Python 2.7.12 (default, Dec 4 2017, 14:50:18)
[GCC 5.4.0 20160609] on linux2
Type "copyright", "credits" or "license()" for more information.
>>> original = "To be or not to be"
>>> type(original)
<type 'str'>
>>> len(original)
18
>>> original[0]
'T'
>>> original[1]
'o'
>>> original[2]
' '
>>> original[18]
Traceback (most recent call last):
File "<pyshell#6>", line 1, in <module>
original[18]
IndexError: string index out of range
>>> key = 10
>>> original[0] ^ key
Traceback (most recent call last):
File "<pyshell#8>", line 1, in <module>
original[0] ^ key
TypeError: unsupported operand type(s) for ^: 'str' and 'int'
>>> ord(original[0])
84
>>> original[0]
'T'
>>> bin(ord(original[0]))
'0b1010100'
>>> chr(original[0]) ^ key
Traceback (most recent call last):
File "<pyshell#12>", line 1, in <module>
chr(original[0]) ^ key
TypeError: an integer is required
>>> chr(ord(original[0]) ^ key)
'^'
>>> (ord(original[0]) ^ key) ^ key
84
>>> chr(ord(original[0]) ^ key) ^ key
Traceback (most recent call last):
File "<pyshell#15>", line 1, in <module>
chr(ord(original[0]) ^ key) ^ key
TypeError: unsupported operand type(s) for ^: 'str' and 'int'
>>> chr((ord(original[0]) ^ key) ^ key)
'T'
>>> original
'To be or not to be'
>>> key
10
>>> N =len(original)
>>> N
18
\
>>> n
Traceback (most recent call last):
File "<pyshell#21>", line 1, in <module>
n
NameError: name 'n' is not defined
>>> N
18
>>> message = []
>>> for i in range(N):
message.append(chr(ord(original[i]) ^ key))
>>>
>>>
>>>
>>>
>>>
>>>
>>> original
'To be or not to be'
>>> message
['^', 'e', '*', 'h', 'o', '*', 'e', 'x', '*', 'd', 'e', '~', '*', '~', 'e', '*', 'h', 'o']
>>> message = ' '
>>> for i in range(N)
SyntaxError: invalid syntax
>>> for i in range(N):
message = message + (chr(ord(original[i]) ^ key))
>>> message
' ^e*ho*ex*de~*~e*ho'
>>>
>>>
>>>
>>> result = ' '
>>> key1 = 45
>>> for i in range(N):
result = result + (chr(ord(message[i]) ^ key1))
>>>
>>> result
' \rsH\x07EB\x07HU\x07IHS\x07SH\x07E'
>>>
>>>
>>>
>>> key1 = key
>>> result = ' '
>>> for i in range(N):
result = result + (chr(ord(message[i]) ^ key1))
>>> result
' *To be or not to b'
>>>
| [
"daiga.sarkane1@gmail.com"
] | daiga.sarkane1@gmail.com |
f496a31b97699a52d2a516cdc3c0651dbc0f2db5 | ce68b4e79620f8209b22bb72f8896c9a7cc01698 | /dis_train_week.py | 3f6b93b0465af25304fb167aef7fb7d8d25cdf62 | [] | no_license | ajithpad/Indian_railways | 2eb82f093361ce891026e6d88a7adbcff75f76bc | f6d9d2908127e3f945180c95b47191d74949add9 | refs/heads/master | 2021-01-20T17:07:12.057935 | 2016-11-11T07:57:01 | 2016-11-11T07:57:01 | 62,805,963 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,440 | py | #Code to use bokeh to plot the distribution of trains for a given station across the week
import pickle
import numpy as np
from bokeh.charts import Bar, output_file, show
import pylab as pl
import seaborn as sbn
from bokeh import mpl as mpl
from bokeh.io import output_notebook, show
dis_trains = pickle.load(open("data/dep_times_lat_lon.p","rb"))
gradient = []
for ii in range(7):
gradient += range(25)
gradient = np.array(gradient)
gradient = gradient/24.
gradient = gradient.reshape(1,-1)
def get_trains_week(stat_code):
sbn.set_style("white")
stat_vals = dis_trains[dis_trains.code == stat_code]
all_trains = stat_vals.times.values
xx = all_trains
days = np.array(xx[0])/1440
tot_mins = np.array(xx[0])%1440
hour = tot_mins/60
mins = tot_mins % 60
train_time = zip(days,hour,mins)
fig = pl.figure(2)
ax1 = fig.add_subplot(111)
aa,bb = np.histogram(xx[0], bins = range(0,10081,120))
ax1.hist(xx[0], bins = range(0,10081,120), alpha = 0.5)
ax1.imshow(np.sin(3*gradient), extent=[0, 10080, 0, max(aa)+1], aspect='auto', cmap='gray')
ax1.set_xlim(0,24*60*7)
ax1.set_xticks(range(0,10081,1440))
ax1.set_xticklabels(['Sun','Mon', 'Tue','Wed','Thu','Fri','Sat'],size = 16, rotation = 90)
ax1.set_ylabel("Number of trains departing", size = 16)
ax1.set_yticks(range(0,max(aa)+1,2))
ax1.set_yticklabels(range(0,max(aa)+1,2), size = 16)
pl.show()
| [
"ajith.physics@gmail.com"
] | ajith.physics@gmail.com |
8ada09c41d2c212d35513c2278b4de86e8462604 | 1619eff6f62daf2109134cf49f1a1f8c4a45d639 | /product/urls.py | eea12a79437bfc40a6f0fb188604935c3db8daec | [] | no_license | Loay159/esntls.co | aa92a4647cdecaa159f69b175aefa813c5af358d | 5cafef4267e1bdf115c5a49a0fdf66580721a478 | refs/heads/master | 2023-08-03T10:12:27.188706 | 2021-09-23T09:15:01 | 2021-09-23T09:15:01 | 403,773,260 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 811 | py | from django.urls import path
from .views import *
from dynamic_rest.routers import DynamicRouter
urlpatterns = [
# path('', ProductsAPI.as_view(), name='All_products'),
# path('<int:id>', ProductItem.as_view(), name='specefic_products'),
# path('category/<slug:category>', CategoryAPI.as_view(), name='Men_products'),
path('products/<slug:slug>/', ProductItemAPI.as_view(), name='product_detail'),
path('categories/<slug:slug>/', CategoryAPI.as_view(), name='category_detail'),
path('cart/<slug:slug>/', CartAPI.as_view(), name='cart_detail'),
]
router = DynamicRouter()
router.register(r'products', AllProduct)
router.register(r'categories', AllCategories)
router.register(r'colors', AllProductColors)
# router.register(r'cart', Cart)
app_name = "product"
urlpatterns += router.urls
| [
"88399225+LoayAshraf@users.noreply.github.com"
] | 88399225+LoayAshraf@users.noreply.github.com |
5da9adf25b89d369656bd244e97b7be62c895405 | ce918cd9b23a4cd44861a8ba7448abac218c9761 | /0x03-python-data_structures/11-delete_at.py | 22abba5f9291ba9766a7213758d301401397b5fc | [] | no_license | adrielt07/holbertonschool-higher_level_programming | 80318056730a5c1bea0e95f7308b1499fab93a22 | adb04252e63ba714d2c65f25597778c89f6878a9 | refs/heads/master | 2020-03-09T14:48:29.225685 | 2018-10-07T00:48:45 | 2018-10-07T00:48:45 | 128,843,771 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 157 | py | #!/usr/bin/python3
def delete_at(my_list=[], idx=0):
if idx < 0 or idx > len(my_list)-1:
return my_list
del my_list[idx]
return my_list
| [
"adrieltolentino@outlook.com"
] | adrieltolentino@outlook.com |
b739d2efc981f15f251d102759aa0a3696382421 | 975a3b8189fffde47256b52901bcce9b5d8b1001 | /app.py | 6e0e28db9d423cdf8e340b69678b4ee67aaaa5b4 | [] | no_license | FrancisGKing/RedditToMessengerBot | f7db66e7b6b3150dd42473002ddd7eb11efc47df | 25c4f09da5c5b456f88738b00cd6674bedb984ef | refs/heads/master | 2020-04-17T03:39:27.733581 | 2019-01-17T09:09:19 | 2019-01-17T09:09:19 | 166,194,326 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 6,438 | py | from flask import Flask, request
from flask_sqlalchemy import SQLAlchemy
import json
import requests
import os
import praw
app = Flask(__name__)
app.config['SQLALCHEMY_DATABASE_URI'] = os.environ['DATABASE_URL']
db = SQLAlchemy(app)
reddit = praw.Reddit(client_id='QhZh0AZf4HlrXg',
client_secret='g7vBRIAfYRFEBJtXrHF-P_lh0RQ',
user_agent='web:reddit_to_messenger_bot:v1.0.0 (by u/ThisMeansFreedom)')
#This must be filled with the Page Access Token that will be provided by the
#Facebook app that will be created
PAT = 'EAAfla2Qkli8BAHwBWlkFIjE6IpLiBNCe7WhDhVPyqqtDXQfjNIb8ZC9rRbU4ZCOsKTBaLSmshDsv3ZC3SneVcZCrEtSqSaKRqNqdXApe3lEoqcNhUrQgzTKZAwkA03anfV5mZCdVZA8eXPK3LKAsZBXbtvb4RKJtmEB6ZApuYM33whAZDZD'
quick_replies_list = [{
"content_type":"text",
"title":"Meme",
"payload":"meme",
},
{
"content_type":"text",
"title":"Motivation",
"payload":"motivation",
},
{
"content_type":"text",
"title":"Shower Thought",
"payload":"Shower_Thought",
},
{
"content_type":"text",
"title":"Jokes",
"payload":"Jokes",
}
]
@app.route('/', methods=['GET'])
def handle_verification():
print("Handling Verification.")
if request.args.get('hub.verify_token', '') == 'my_voice_is_my_password_verify_me':
print("Verification successful!")
return request.args.get('hub.challenge', '')
else:
print("Verification failed!")
return 'Error, wrong validation token'
@app.route('/', methods=['POST'])
def handle_messages():
print("Handling Messages")
payload = request.get_data()
print(payload)
for sender, message in messaging_events(payload):
print("Incoming from %s: %s" % (sender, message))
send_message(PAT, sender, message)
return "ok"
def messaging_events(payload):
"""Generate tuples of (sender_id, message_text) from the provided payload. """
data = json.loads(payload)
messaging_events = data["entry"][0]["messaging"]
for event in messaging_events:
if "message" in event and "text" in event["message"]:
yield event["sender"]["id"], event["message"]["text"].encode('unicode_escape')
else:
yield event["sender"]["id"], "I can't echo this"
def send_message(token, recipient, text):
"""Send the message text to recipient with id recipient"""
if "meme" in text.lower():
subreddit_name = "memes"
elif "shower" in text.lower():
subreddit_name = "Showerthoughts"
elif "joke" in text.lower():
subreddit_name = "Jokes"
else:
subreddit_name = "GetMotivated"
myUser = get_or_create(db.session, Users, name=recipient)
if subreddit_name == "Showerthoughts":
for submission in reddit.subreddit(subreddit_name).hot(limit=None):
if (submission.is_self == True):
query_result = Posts.query.filter(Posts.name == submission.id).first()
if query_result is None:
myPost = Posts(submission.id, submission.title)
myUser.posts.append(myPost)
db.session.commit()
payload = submission.title
break
elif myUser not in query_result.users:
myUser.posts.append(query_result)
db.session.commit()
payload = submission.title
break
else:
continue
r = requests.post("https://graph.facebook.com/v2.6/me/messages",
params={"access_token": token},
data=json.dumps({
"recipient": {"id": recipient},
"message": {"text": payload, "quick_replies":quick_replies_list}
}),
headers={'Content-type': 'application/json'})
elif subreddit_name == "Jokes":
for submission in reddit.subreddit(subreddit_name).hot(limit=None):
if ((submission.is_self == True) and (submission.link_flair_text is None)):
query_result = Posts.query.filter(Posts.name == submission.id).first()
if query_result is None:
myPost = Posts(submission.id, submission.title)
myUser.posts.append(myPost)
db.session.commit()
payload = submission.title
payload_text = submission.selftext
break
elif myUser not in query_result.users:
myUser.posts.append(query_result)
db.session.commit()
payload = submission.title
payload_text = submission.selftext
break
else:
continue
r = requests.post("https://graph.facebook.com/v2.6/me/messages",
params={"access_token": token},
data=json.dumps({
"recipient": {"id": recipient},
"message": {"text": payload, "quick_replies":quick_replies_list}
}),
headers={'Content-type': 'application/json'})
else:
payload = "http://imgur.com/WeyNGtQ.jpg"
for submission in reddit.subreddit(subreddit_name).hot(limit=None):
if (submission.link_flair_css_class == 'image') or ((submission.is_self != True) and ((".jpg" in submission.url) or (".png" in submission.url))):
query_result = Posts.query.filter(Posts.name == submission.id).first()
if query_result is None:
myPost = Posts(submission.id, submission.url)
myUser.posts.append(myPost)
db.session.commit()
payload = submission.url
break
elif myUser not in query_result.users:
myUser.posts.append(query_result)
db.session.commit()
payload = submission.url
break
else:
continue
r = requests.post("https://graph.facebook.com/v2.6/me/messages",
params={"access_token": token},
data=json.dumps({
"recipient": {"id": recipient},
"message": {"attachment": {
"type": "image",
"payload": {"url": payload}},
"quick_replies":quick_replies_list}
}),
headers={'Content-type': 'application/json'})
if r.status_code != requests.codes.ok:
print(r.text)
def get_or_create(session, model, **kwargs):
instance = session.query(model).filter_by(**kwargs).first()
if instance:
return instance
else:
instance = model(**kwargs)
session.add(instance)
session.commit()
return instance
relationship_table=db.Table('relationship_table',
db.Column('user_id', db.Integer,db.ForeignKey('users.id'), nullable=False),
db.Column('post_id', db.Integer,db.ForeignKey('posts.id'), nullable=False),
db.PrimaryKeyConstraint('user_id', 'post_id') )
class Users(db.Model):
id = db.Column(db.Integer, primary_key=True)
name = db.Column(db.String(255),nullable=False)
posts=db.relationship('Posts', secondary=relationship_table, backref='users' )
def __init__(self, name=None):
self.name = name
class Posts(db.Model):
id=db.Column(db.Integer, primary_key=True)
name=db.Column(db.String, unique=True, nullable=False)
url=db.Column(db.String, nullable=False)
def __init__(self, name=None, url=None):
self.name = name
self.url = url
if __name__ == '__main__':
app.run() | [
"francis.kingjr@gmail.com"
] | francis.kingjr@gmail.com |
2e3fcba553d7bb1fd1bf38c8ca6fd92ca74e91d4 | be862c96024320595afd7736605f94928be5f90d | /Crawler/私活/person_info.py | a8ea63dff59472dc088f8a2670225911586b8e2d | [] | no_license | SmasterZheng/leetcode | 1b35b72efc6ea6add605d08b13d7abcffbdf05e8 | 11b33fcbab47969ebd0bfc2f41cb50a976881910 | refs/heads/master | 2023-04-24T14:45:53.285565 | 2021-05-15T06:52:29 | 2021-05-15T06:52:29 | 264,981,844 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 13,091 | py | # -*- coding: utf-8 -*-
# @Time : 20201228
# @Author : zhengxz
import requests
import json
from bs4 import BeautifulSoup
import pandas as pd
def get_html():
'''获取html'''
headers = {
'Accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,image/apng,*/*;q=0.8,application/signed-exchange;v=b3',
'Accept-Encoding':'gzip, deflate',
'Accept-Language':'zh-CN,zh;q=0.9',
'Cache-Control':'max-age=0',
'Connection': 'keep-alive',
'Content-Type':'application/x-www-form-urlencoded',
'Upgrade-Insecure-Requests':'1',
'User-Agent': 'Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/76.0.3809.87 Safari/537.36',
'Referer':'http://data.gdcic.net/Dop/Open/PersonList.aspx'
}
viestate=['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',#第一页
'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', #第三页
'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', #第二页
'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',
'',
]
data={
'__VIEWSTATE': viestate[3] #根据__VIEWSTATE实现翻页，加密算法未知
}
url="http://data.gdcic.net/Dop/Open/PersonList.aspx"
strhtml = requests.post(url,headers=headers,data=data,verify=False)
return strhtml.text
def bs_parse_html(html):
'''解析页面'''
soup =BeautifulSoup(html,'lxml')
table = soup.select('.data-list td')
info_list=[]
n=1
for tr in table:
if tr.a != None:
name=tr.a.string
info_list.append(name)
else:
infos=tr.string.strip()
info_list.append(infos)
info_lists=clip_list(a=info_list,c=4)
df=pd.DataFrame(info_lists,columns=['姓名','身份证号码','性别','学历'])
print(df)
def clip_list(a,c): #a为原列表，c为等分长度
clip_back=[]
if len(a)>c:
for i in range(int(len(a) / c)):
# print(i)
clip_a = a[c * i:c * (i + 1)]
clip_back.append(clip_a)
# print(clip_a)
# last 剩下的单独为一组
last = a[int(len(a) / c) * c:]
if last:
clip_back.append(last)
else: #如果切分长度不小于原列表长度，那么直接返回原列表
clip_back = a
return clip_back
def main():
html=get_html()
bs_parse_html(html)
if __name__ == '__main__':
main() | [
"xiaozhangzxz@163.com"
] | xiaozhangzxz@163.com |
b696c2d48b27c31a6a2b374d40a31649b1a5774c | 28d5b9d208a861703840837ad6302cd7e3f84d42 | /process.py | 87a80d181d435e34caf88251aacfe4543cab462d | [] | no_license | jagmeet787/Malicious-App-Detection | d4f5ee9b747d05799886483b157c62a15a94420b | c5925a36aafcbc60ccd30c69685220644652c34c | refs/heads/master | 2020-05-14T17:58:33.647278 | 2019-04-18T18:26:04 | 2019-04-18T18:26:04 | 181,902,329 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,421 | py | import time
from permissions import PermissionsResource
from database import database
from androwarn.warn.report.report import generate_report
from androwarn.warn.analysis.analysis import perform_analysis
from androwarn.warn.search.search import grab_application_package_name
from androguard.misc import AnalyzeAPK
class process(object):
permissions_service = PermissionsResource()
database_service = database()
upload_directory_base = "./Files"
def process_apk(self):
taks_list = self.database_service.get_processing()
for records in taks_list:
apk_id = records[0]
apk_name = records[3]
file_dir = self.upload_directory_base + "/" + str(apk_id) + "/"
file_path = file_dir + apk_name
a, d, dx = AnalyzeAPK(file_path)
permissions = self.permissions_service.getPermissions(a)
# print permissions
package_name = grab_application_package_name(a)
data = perform_analysis(file_path, a, d, dx, False)
# 'Verbosity level (ESSENTIAL 1, ADVANCED 2, EXPERT 3) (default 1)'
generate_report(package_name, data, 1, 'html', file_dir + 'index.html')
# maldrolyzer report and send back in the body
self.database_service.update_record(apk_id,"Finished")
process_ins = process()
while (True):
process_ins.process_apk()
# time.sleep(100) | [
"jagmeet787@gmail.com"
] | jagmeet787@gmail.com |
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