blob_id stringlengths 40 40 | directory_id stringlengths 40 40 | path stringlengths 3 281 | content_id stringlengths 40 40 | detected_licenses listlengths 0 57 | license_type stringclasses 2 values | repo_name stringlengths 6 116 | snapshot_id stringlengths 40 40 | revision_id stringlengths 40 40 | branch_name stringclasses 313 values | visit_date timestamp[us] | revision_date timestamp[us] | committer_date timestamp[us] | github_id int64 18.2k 668M ⌀ | star_events_count int64 0 102k | fork_events_count int64 0 38.2k | gha_license_id stringclasses 17 values | gha_event_created_at timestamp[us] | gha_created_at timestamp[us] | gha_language stringclasses 107 values | src_encoding stringclasses 20 values | language stringclasses 1 value | is_vendor bool 2 classes | is_generated bool 2 classes | length_bytes int64 4 6.02M | extension stringclasses 78 values | content stringlengths 2 6.02M | authors listlengths 1 1 | author stringlengths 0 175 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
323801345ddf65b4f2d46b72eae6c8c91ab6e767 | 11147666888ce8a0d6bd540b1d1d4bf7a788f4bb | /server/botserver.py | 84f5477d38903f2a824ba9d8243e8d3f9b0b299b | [
"MIT"
] | permissive | Kileak/Kileak-Slack-Base-Bot | 4ef4312955db30e54dd36e2f50e279c33af7fce2 | 5a1c14b96383f4d5bbbde87670d4f080530c4fdf | refs/heads/master | 2021-01-20T08:24:33.079050 | 2017-09-07T12:20:43 | 2017-09-07T12:20:43 | 101,556,351 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 5,200 | py | import json
import threading
import time
from slackclient import SlackClient
from handlers.handler_factory import *
from handlers import *
from util.loghandler import *
from bottypes.invalid_console_command import *
class BotServer(threading.Thread):
# global lock for locking global data in bot server
threadLock = threading.Lock()
userList = []
def __init__(self):
log.debug("Parse config file and initialize threading...")
threading.Thread.__init__(self)
def lock(self):
"""Acquire global lock for working with global (not thread-safe) data."""
BotServer.threadLock.acquire()
def release(self):
"""Release global lock after accessing global (not thread-safe) data."""
BotServer.threadLock.release()
def updateUserList(self, slack_client):
self.lock()
log.debug("Retrieving user list")
api_call = slack_client.api_call("users.list")
if api_call.get('ok'):
BotServer.userList = api_call.get('members')
self.release()
def getUser(self, userName):
self.lock()
foundUser = None
for user in BotServer.userList:
if 'name' in user and user.get('name') == userName:
foundUser = user
break
self.release()
return foundUser
def quit(self):
log.info("Shutting down")
self.running = False
def sendMessage(self, channelID, msg):
self.slack_client.api_call(
"chat.PostMessage", channel=channelID, text=msg, as_user=True)
def load_config(self):
self.lock()
with open("./config.json") as f:
self.config = json.load(f)
self.release()
def get_config_option(self, option):
self.lock()
result = None
if option in self.config:
result = self.config[option]
self.release()
return result
def set_config_option(self, option, value):
self.lock()
try:
if option in self.config:
self.config[option] = value
log.info("Updated configuration: {} => {}".format(option, value))
with open("./config.json", "w") as f:
json.dump(self.config, f)
else:
raise InvalidConsoleCommand("The specified configuration option doesn't exist: {}".format(option))
finally:
self.release()
def parseSlackMessage(self, slackMessage):
"""
The Slack Real Time Messaging API is an events firehose.
Return (message, channel, user) if the message is directed at the bot,
otherwise return (None, None, None).
"""
message_list = slackMessage
for msg in message_list:
if msg.get("type") == "message":
if self.botAT in msg.get("text", ""):
# return text after the @ mention, whitespace removed
return msg['text'].split(self.botAT)[1].strip().lower(), msg['channel'], msg['user']
elif msg.get("text", "").startswith('!'):
# return text after the !
return msg['text'][1:].strip().lower(), msg['channel'], msg['user']
return None, None, None
def searchBotUser(self, botName):
log.debug("Trying to resolve bot user in slack")
self.updateUserList(self.slack_client)
self.botName = botName
botUser = self.getUser(self.botName)
if botUser:
self.botID = botUser['id']
self.botAT = "<@%s>" % self.botID
log.debug("Found bot user %s (%s)" % (self.botName, self.botID))
self.running = True
else:
log.error("Could not find bot user. Abort...")
self.running = False
def run(self):
log.info("Starting server thread...")
self.load_config()
self.slack_client = SlackClient(self.get_config_option('api_key'))
self.searchBotUser(self.get_config_option('bot_name'))
if self.botID:
# 1 second delay between reading from firehose
READ_WEBSOCKET_DELAY = 1
if self.slack_client.rtm_connect():
log.info("Connection successful...")
log.info("Initializing handlers...")
# Might even pass the bot server for handlers?
HandlerFactory.initialize(
self.slack_client, self.botID)
# Main loop
while self.running:
command, channel, user = self.parseSlackMessage(
self.slack_client.rtm_read())
if command:
log.debug("Received bot command : %s (%s)" %
(command, channel))
HandlerFactory.process(
self.slack_client, self, command, channel, user)
time.sleep(READ_WEBSOCKET_DELAY)
else:
log.error("Connection failed. Invalid slack token or bot id?")
log.info("Shutdown complete...")
| [
"razor99@gmx.de"
] | razor99@gmx.de |
9782fd2180f9d64e673c362aad00d0e19916f9e3 | dcdde01af29567eba920ffcf340605251aa726be | /flask/flsk.py | 7964838ff06455a8dc7653023413917e3b9739b6 | [] | no_license | MeherMS/python | b9f3d77ef97a72f4e68deb77f767fa45f7160a43 | 66296d552755a11600a8fba4d241efa13e0f3b50 | refs/heads/main | 2023-03-25T11:07:41.007216 | 2021-03-27T23:40:59 | 2021-03-27T23:40:59 | 352,113,082 | 1 | 0 | null | null | null | null | UTF-8 | Python | false | false | 855 | py | #!/usr/bin/env python
# coding: utf-8
# In[1]:
import pandas as pd
from flask import Flask, jsonify, request
import pickle
import json
# load model
model = pickle.load(open('model.pkl','rb'))
data = {'Pclass': 3
, 'Age': 2
, 'SibSp': 1
, 'Fare': 50}
data = json.dumps(data)
# app
app = Flask(__name__)
# routes
@app.route('/', methods=['POST'])
def predict():
# get data
#data = request.get_json(force=True)
# convert data into dataframe
data.update((x, [y]) for x, y in data.items())
data_df = pd.DataFrame.from_dict(data)
# predictions
result = model.predict(data_df)
# send back to browser
output = {'results': int(result[0])}
# return data
return jsonify(results=output)
if __name__ == '__main__':
app.run(port = 5000, debug=True)
# In[ ]:
# In[ ]:
# In[ ]:
| [
"noreply@github.com"
] | noreply@github.com |
d78e4bb780d288b7d29be51d0fba08d43575ed04 | e0e60b1efc1d91e39dfba9b612d82cf32b5efcda | /mysite/Grade_A/models.py | 4be9ab3e9110a63784716f2acd792856cbe9f654 | [] | no_license | Apaisley/Grade-A | f187b76c1a74a3edca35712dd361f6c49d60fbe6 | d750372c7e0e6c31efb1eb2699b84f7c56df3887 | refs/heads/master | 2021-09-23T03:54:51.311184 | 2020-03-12T00:56:45 | 2020-03-12T00:56:45 | 238,564,849 | 0 | 0 | null | 2021-09-22T19:40:01 | 2020-02-05T22:54:50 | Python | UTF-8 | Python | false | false | 5,374 | py | from django.db import models
from taggit.managers import TaggableManager
from django.utils.datetime_safe import datetime
from django.dispatch import receiver
from django.db.models.signals import post_save
import numpy as np
from django.contrib.auth.models import User
# Create your models here.
#////////////////////-------User and profile////////////////////////////////////
class Profile(models.Model):
user = models.OneToOneField(User, on_delete=models.CASCADE)
bio = models.TextField(max_length=500, blank=True)
location = models.CharField(max_length=30, blank=True)
birth_date = models.DateField(null=True, blank=True)
@receiver(post_save, sender=User)
def create_user_profile(sender, instance, created, **kwargs):
if created:
Profile.objects.create(user=instance)
@receiver(post_save, sender=User)
def save_user_profile(sender, instance, **kwargs):
instance.profile.save()
#////////////////////----Product-Model -----////////////////////////////////////////////////////////////////////////////////
class Products(models.Model):
# effect choices
UPLIFTED = 'UP'
HAPPY = 'HA'
RELAXED ='RE'
ENERGETIC='EN'
CREATIVE ='CE'
FOCUSED ='FO'
TALKATIVE ='TA'
EUPHORIC='EU'
GIGGLY ='GI'
HUNGRY ='HU'
AROUSED ='AR'
TINGLY= 'TI'
SLEEPY= 'SL'
Effects_Choices = [
(UPLIFTED, 'Uplifted'),
(HAPPY, 'Happy'),
(RELAXED, 'Relaxed'),
(ENERGETIC, 'Energetic'),
(CREATIVE,'Creative' ),
(FOCUSED,'Focused'),
(TALKATIVE,'Talkative'),
(EUPHORIC, 'Euphoric'),
(GIGGLY, 'Giggly'),
(HUNGRY, 'Hungry'),
(AROUSED, 'Aroused'),
(TINGLY, 'Tingy'),
(SLEEPY, 'Sleepy')]
#catagories choices ///////////////////////////////////////////////////////////////////////////////
POLLEN ='PL'
FEM_POLLEN ='FP'
SEEDS = 'SD'
FEM_SEEDS ='FS'
AUTO_SEEDS ='AS'
FLOWER ='FL'
CONCENTRATES ='CS'
EDIBLES ='ES'
Catagories_Choices =[
(POLLEN,'Pollen'),
(FEM_POLLEN, 'Feminized Pollen'),
(SEEDS, 'Seeds'),
(FEM_SEEDS,'Feminized Seeds'),
(AUTO_SEEDS, 'Auto Flowering Seeds'),
(FLOWER, 'Flower'),
(CONCENTRATES,'Concentrates'),
(EDIBLES, 'Edibles')]
#variety
SATIVA ='SA'
INDICA ='IN'
HYBRID ='HY'
Variety_choices =[
(SATIVA, 'Sativa'),
(INDICA, 'Indica'),
(HYBRID, 'Hybrid')]
name = models.CharField(max_length = 30)
catagory = models.CharField(max_length = 30, choices=Catagories_Choices)
variety = models.CharField(max_length= 2, choices=Variety_choices)
price = models.DecimalField(decimal_places=2, max_digits=20, default=0.00)
quantity = models.IntegerField()
Image = models.ImageField(default="Null")
description = models.CharField(max_length= 256, blank=True, null=True)
effects = models.CharField(max_length= 2 ,choices=Effects_Choices)
objects =models.Manager()
Review = models.ForeignKey('Review', on_delete=models.CASCADE,blank=True ,null=True)
tags = TaggableManager()
def average_rating(self):
all_ratings = map(lambda x: x.rating, self.review_set.all())
return np.mean(all_ratings)
def __str__(self):
return self.name,self.price
class Meta:
indexes =[
models.Index(fields=['name'])
]
#///////////////////////----Review-Model---/////////////////////////////////////
class Review(models.Model):
rating_choices = zip(range(6), range(6))
item = models.ForeignKey(Products, on_delete=models.CASCADE)
pub_date = models.DateTimeField('date published')
user = models.CharField(max_length=100)
comment = models.CharField(max_length=200)
rating = models.IntegerField(choices=rating_choices)
def __str__(self):
return ( )
#////////////////////////////-----Cart--------///////////////////////////////////////////////////////////////////
class Cart(models.Model):
user = models.ForeignKey(User, null=True, blank=True, on_delete=models.CASCADE)
count = models.PositiveIntegerField(default=0)
total = models.DecimalField(default=0.00, max_digits=10, decimal_places=2)
updated = models.DateTimeField(auto_now_add=True)
timestamp = models.DateTimeField(auto_now_add=True)
def __str__(self):
return "User: {} has {} items in their cart. Their total is ${}".format(self.user , self.count, self.total)
#/////////////////////----------Entry-------------------///////////////////////////////////////
class Entry(models.Model):
product = models.ForeignKey(Products, null=True, on_delete=models.CASCADE)
cart = models.ForeignKey(Cart, null=True, on_delete=models.CASCADE)
quantity = models.PositiveIntegerField()
def __str__(self):
return"This entry contains {} {}(s).".format(self.quantity, self.product.name)
@receiver(post_save, sender=Entry)
def update_cart(sender, instance, **kwargs):
line_cost = instance.quantity * instance.product.line.cost
instance.cart.total += line_cost
instance.cart.count += instance.quantity
instance.cart.updated = datetime.now()
#////////////////////////////////////////////////////////////////////////////////////////////// | [
"apaisley2017@gmail.com"
] | apaisley2017@gmail.com |
cffd64491c65e0ffc66d3cb53f6958c2596eb53b | 7bdb103ab024adf67fdd209af6377e86659656dc | /pydicom/errors.py | 8872313e29db0a934a18f8302a83025a4f3b3365 | [
"MIT",
"LicenseRef-scancode-unknown-license-reference"
] | permissive | glemaitre/pydicom | b58b71f3a0082595a4d62586be21b574c63a76f9 | d88f78e90a8df1cc314858049edaa403be3f5526 | refs/heads/master | 2020-02-26T14:13:25.970610 | 2017-07-31T22:35:55 | 2017-08-01T12:41:22 | 58,641,730 | 0 | 0 | NOASSERTION | 2023-04-29T22:22:23 | 2016-05-12T12:59:33 | Python | UTF-8 | Python | false | false | 876 | py | # errors.py
"""Module for pydicom exception classes"""
#
# Copyright (c) 2013 Darcy Mason
# This file is part of pydicom, released under a modified MIT license.
# See the file LICENSE included with this distribution, also
# available at https://github.com/pydicom/pydicom
#
class InvalidDicomError(Exception):
"""Exception that is raised when the the file does not seem
to be a valid dicom file, usually when the four characters
"DICM" are not present at position 128 in the file.
(According to the dicom specification, each dicom file should
have this.)
To force reading the file (because maybe it is a dicom file without
a header), use read_file(..., force=True).
"""
def __init__(self, *args):
if not args:
args = ('The specified file is not a valid DICOM file.', )
Exception.__init__(self, *args)
| [
"darcymason@gmail.com"
] | darcymason@gmail.com |
0e185c34961eff4289c4c9d576294c3a43be2ae6 | 4dd9a94828221013244d56443ce988b49c749523 | /neural_net.py | 589a7d86c407d6c653e2bd7c4fd28d4e7a919928 | [] | no_license | dhorrall/deep_learning_foundations | e452084d91c684ebc9d54de452041ecff2704733 | 714457299508bf9b7a5b3b18dd260c842c432cfe | refs/heads/master | 2021-01-09T06:16:20.121329 | 2017-06-06T14:27:49 | 2017-06-06T14:27:49 | 80,946,135 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 3,106 | py | '''
Created on Jan 29, 2017
@author: derekh1
'''
from numpy import exp, array, random, dot
class NeuralNetwork():
def __init__(self):
# Seed the random number generator, so it generates the same numbers
# every time the program runs.
random.seed(1)
# We model a single neuron, with 3 input connections and 1 output connection.
# We assign random weights to a 3 x 1 matrix, with values in the range -1 to 1
# and mean 0.
self.synaptic_weights = 2 * random.random((3, 1)) - 1
# The Sigmoid function, which describes an S shaped curve.
# We pass the weighted sum of the inputs through this function to
# normalise them between 0 and 1.
def __sigmoid(self, x):
return 1 / (1 + exp(-x))
# The derivative of the Sigmoid function.
# This is the gradient of the Sigmoid curve.
# It indicates how confident we are about the existing weight.
def __sigmoid_derivative(self, x):
return x * (1 - x)
# We train the neural network through a process of trial and error.
# Adjusting the synaptic weights each time.
def train(self, training_set_inputs, training_set_outputs, number_of_training_iterations):
for iteration in xrange(number_of_training_iterations):
# Pass the training set through our neural network (a single neuron).
output = self.think(training_set_inputs)
# Calculate the error (The difference between the desired output
# and the predicted output).
error = training_set_outputs - output
# Multiply the error by the input and again by the gradient of the Sigmoid curve.
# This means less confident weights are adjusted more.
# This means inputs, which are zero, do not cause changes to the weights.
adjustment = dot(training_set_inputs.T, error * self.__sigmoid_derivative(output))
# Adjust the weights.
self.synaptic_weights += adjustment
# The neural network thinks.
def think(self, inputs):
# Pass inputs through our neural network (our single neuron).
return self.__sigmoid(dot(inputs, self.synaptic_weights))
if __name__ == "__main__":
#Intialise a single neuron neural network.
neural_network = NeuralNetwork()
print "Random starting synaptic weights: "
print neural_network.synaptic_weights
# The training set. We have 4 examples, each consisting of 3 input values
# and 1 output value.
training_set_inputs = array([[0, 0, 1], [1, 1, 1], [1, 0, 1], [0, 1, 1]])
training_set_outputs = array([[0, 1, 1, 0]]).T
# Train the neural network using a training set.
# Do it 10,000 times and make small adjustments each time.
neural_network.train(training_set_inputs, training_set_outputs, 100000)
print "New synaptic weights after training: "
print neural_network.synaptic_weights
# Test the neural network with a new situation.
print "Considering new situation [1, 0, 0] -> ?: "
print neural_network.think(array([1, 0, 0]))
| [
"derekh1@oc7043351046.ibm.com"
] | derekh1@oc7043351046.ibm.com |
082b2931c6c4dbd7173131856b1c308a119963b8 | 18740007244035abf66efb0c4c37eca457806cce | /infrastructure/cloudformation/troposphere/storage.py | 5ce481321753c61486729cc7dacb45fbe8b5d2a6 | [] | no_license | khueue/khueue-diary | 7773b34fc0214cdd04133cd8cb38ec5e8096318c | 90de164b997ebde328d4f116b94f588a3c394cec | refs/heads/master | 2020-04-07T02:28:26.654045 | 2019-01-22T12:37:23 | 2019-01-22T12:37:23 | 157,977,843 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 2,135 | py | import troposphere
import troposphere.cloudfront
import troposphere.s3
import awacs
import awacs.s3
CONFIG = {
'app_bucket': {
'name': 'khueue-diary-app',
},
'pipeline_bucket': {
'name': 'khueue-diary-pipeline',
'object_lifetime_days': 30,
},
'log_bucket': {
'name': 'khueue-diary-logs',
'object_lifetime_days': 30,
},
}
template = troposphere.Template()
log_bucket = troposphere.s3.Bucket(
'LogBucket',
template=template,
BucketName=CONFIG['log_bucket']['name'],
AccessControl='LogDeliveryWrite',
LifecycleConfiguration=troposphere.s3.LifecycleConfiguration(
Rules=[
troposphere.s3.LifecycleRule(
Id='DeleteOldObjects',
Status='Enabled',
ExpirationInDays=CONFIG['log_bucket']['object_lifetime_days'],
),
],
),
)
pipeline_bucket = troposphere.s3.Bucket(
'PipelineBucket',
template=template,
BucketName=CONFIG['pipeline_bucket']['name'],
LoggingConfiguration=troposphere.s3.LoggingConfiguration(
DestinationBucketName=troposphere.Ref(log_bucket),
LogFilePrefix='pipeline-s3/',
),
LifecycleConfiguration=troposphere.s3.LifecycleConfiguration(
Rules=[
troposphere.s3.LifecycleRule(
Id='DeleteOldObjects',
Status='Enabled',
ExpirationInDays=CONFIG['pipeline_bucket']['object_lifetime_days'],
),
],
),
)
app_bucket = troposphere.s3.Bucket(
'AppBucket',
template=template,
BucketName=CONFIG['app_bucket']['name'],
LoggingConfiguration=troposphere.s3.LoggingConfiguration(
DestinationBucketName=troposphere.Ref(log_bucket),
LogFilePrefix='app-s3/',
),
WebsiteConfiguration=troposphere.s3.WebsiteConfiguration(
IndexDocument="index.html",
),
)
app_bucket_policy = troposphere.s3.BucketPolicy(
'AppBucketPolicy',
template=template,
Bucket=CONFIG['app_bucket']['name'],
PolicyDocument=awacs.aws.Policy(
Statement=[
awacs.aws.Statement(
Effect=awacs.aws.Allow,
Principal=awacs.aws.Principal('AWS', [
'*',
]),
Action=[
awacs.aws.Action('s3', 'GetObject'),
],
Resource=[
awacs.s3.ARN('/'.join([
CONFIG['app_bucket']['name'],
'*',
])),
],
),
],
),
)
print(template.to_json())
| [
"khueue@gmail.com"
] | khueue@gmail.com |
6093d2129fcc9b86264e32f2199313f4ee2360fc | bdf86d69efc1c5b21950c316ddd078ad8a2f2ec0 | /venv/Lib/site-packages/twisted/web/_http2.py | 1a425a7729ffd88e8e0c2c4b4f98294cbdc6f975 | [
"LicenseRef-scancode-unknown-license-reference",
"MIT"
] | permissive | DuaNoDo/PythonProject | 543e153553c58e7174031b910fd6451399afcc81 | 2c5c8aa89dda4dec2ff4ca7171189788bf8b5f2c | refs/heads/master | 2020-05-07T22:22:29.878944 | 2019-06-14T07:44:35 | 2019-06-14T07:44:35 | 180,941,166 | 1 | 1 | null | 2019-06-04T06:27:29 | 2019-04-12T06:05:42 | Python | UTF-8 | Python | false | false | 45,541 | py | # -*- test-case-name: twisted.web.test.test_http2 -*-
# Copyright (c) Twisted Matrix Laboratories.
# See LICENSE for details.
"""
HTTP2 Implementation
This is the basic server-side protocol implementation used by the Twisted
Web server for HTTP2. This functionality is intended to be combined with the
HTTP/1.1 and HTTP/1.0 functionality in twisted.web.http to provide complete
protocol support for HTTP-type protocols.
This API is currently considered private because it's in early draft form. When
it has stabilised, it'll be made public.
"""
from __future__ import absolute_import, division
import io
import sys
import warnings
from collections import deque
import h2.config
import h2.connection
import h2.errors
import h2.events
import h2.exceptions
import priority
from twisted.internet._producer_helpers import _PullToPush
from twisted.internet.defer import Deferred
from twisted.internet.error import ConnectionLost
from twisted.internet.interfaces import (
IProtocol, ITransport, IConsumer, IPushProducer, ISSLTransport
)
from twisted.internet.protocol import Protocol
from twisted.logger import Logger
from twisted.protocols.policies import TimeoutMixin
from twisted.python.failure import Failure
from zope.interface import implementer
# This API is currently considered private.
__all__ = []
_END_STREAM_SENTINEL = object()
# Python versions 2.7.3 and older don't have a memoryview object that plays
# well with the struct module, which h2 needs. On those versions, just refuse
# to import.
if sys.version_info < (2, 7, 4):
warnings.warn(
"HTTP/2 cannot be enabled because this version of Python is too "
"old, and does not fully support memoryview objects.",
UserWarning,
stacklevel=2,
)
raise ImportError("HTTP/2 not supported on this Python version.")
@implementer(IProtocol, IPushProducer)
class H2Connection(Protocol, TimeoutMixin):
"""
A class representing a single HTTP/2 connection.
This implementation of L{IProtocol} works hand in hand with L{H2Stream}.
This is because we have the requirement to register multiple producers for
a single HTTP/2 connection, one for each stream. The standard Twisted
interfaces don't really allow for this, so instead there's a custom
interface between the two objects that allows them to work hand-in-hand here.
@ivar conn: The HTTP/2 connection state machine.
@type conn: L{h2.connection.H2Connection}
@ivar streams: A mapping of stream IDs to L{H2Stream} objects, used to call
specific methods on streams when events occur.
@type streams: L{dict}, mapping L{int} stream IDs to L{H2Stream} objects.
@ivar priority: A HTTP/2 priority tree used to ensure that responses are
prioritised appropriately.
@type priority: L{priority.PriorityTree}
@ivar _consumerBlocked: A flag tracking whether or not the L{IConsumer}
that is consuming this data has asked us to stop producing.
@type _consumerBlocked: L{bool}
@ivar _sendingDeferred: A L{Deferred} used to restart the data-sending loop
when more response data has been produced. Will not be present if there
is outstanding data still to send.
@type _consumerBlocked: A L{twisted.internet.defer.Deferred}, or L{None}
@ivar _outboundStreamQueues: A map of stream IDs to queues, used to store
data blocks that are yet to be sent on the connection. These are used
both to handle producers that do not respect L{IConsumer} but also to
allow priority to multiplex data appropriately.
@type _outboundStreamQueues: A L{dict} mapping L{int} stream IDs to
L{collections.deque} queues, which contain either L{bytes} objects or
C{_END_STREAM_SENTINEL}.
@ivar _sender: A handle to the data-sending loop, allowing it to be
terminated if needed.
@type _sender: L{twisted.internet.task.LoopingCall}
@ivar abortTimeout: The number of seconds to wait after we attempt to shut
the transport down cleanly to give up and forcibly terminate it. This
is only used when we time a connection out, to prevent errors causing
the FD to get leaked. If this is L{None}, we will wait forever.
@type abortTimeout: L{int}
@ivar _abortingCall: The L{twisted.internet.base.DelayedCall} that will be
used to forcibly close the transport if it doesn't close cleanly.
@type _abortingCall: L{twisted.internet.base.DelayedCall}
"""
factory = None
site = None
abortTimeout = 15
_log = Logger()
_abortingCall = None
def __init__(self, reactor=None):
config = h2.config.H2Configuration(
client_side=False, header_encoding=None
)
self.conn = h2.connection.H2Connection(config=config)
self.streams = {}
self.priority = priority.PriorityTree()
self._consumerBlocked = None
self._sendingDeferred = None
self._outboundStreamQueues = {}
self._streamCleanupCallbacks = {}
self._stillProducing = True
if reactor is None:
from twisted.internet import reactor
self._reactor = reactor
# Start the data sending function.
self._reactor.callLater(0, self._sendPrioritisedData)
# Implementation of IProtocol
def connectionMade(self):
"""
Called by the reactor when a connection is received. May also be called
by the L{twisted.web.http._GenericHTTPChannelProtocol} during upgrade
to HTTP/2.
"""
self.setTimeout(self.timeOut)
self.conn.initiate_connection()
self.transport.write(self.conn.data_to_send())
def dataReceived(self, data):
"""
Called whenever a chunk of data is received from the transport.
@param data: The data received from the transport.
@type data: L{bytes}
"""
self.resetTimeout()
try:
events = self.conn.receive_data(data)
except h2.exceptions.ProtocolError:
# A remote protocol error terminates the connection.
dataToSend = self.conn.data_to_send()
self.transport.write(dataToSend)
self.transport.loseConnection()
self.connectionLost(Failure())
return
for event in events:
if isinstance(event, h2.events.RequestReceived):
self._requestReceived(event)
elif isinstance(event, h2.events.DataReceived):
self._requestDataReceived(event)
elif isinstance(event, h2.events.StreamEnded):
self._requestEnded(event)
elif isinstance(event, h2.events.StreamReset):
self._requestAborted(event)
elif isinstance(event, h2.events.WindowUpdated):
self._handleWindowUpdate(event)
elif isinstance(event, h2.events.PriorityUpdated):
self._handlePriorityUpdate(event)
elif isinstance(event, h2.events.ConnectionTerminated):
self.transport.loseConnection()
self.connectionLost(ConnectionLost("Remote peer sent GOAWAY"))
dataToSend = self.conn.data_to_send()
if dataToSend:
self.transport.write(dataToSend)
def timeoutConnection(self):
"""
Called when the connection has been inactive for
L{self.timeOut<twisted.protocols.policies.TimeoutMixin.timeOut>}
seconds. Cleanly tears the connection down, attempting to notify the
peer if needed.
We override this method to add two extra bits of functionality:
- We want to log the timeout.
- We want to send a GOAWAY frame indicating that the connection is
being terminated, and whether it was clean or not. We have to do this
before the connection is torn down.
"""
self._log.info(
"Timing out client {client}", client=self.transport.getPeer()
)
# Check whether there are open streams. If there are, we're going to
# want to use the error code PROTOCOL_ERROR. If there aren't, use
# NO_ERROR.
if (self.conn.open_outbound_streams > 0 or
self.conn.open_inbound_streams > 0):
error_code = h2.errors.ErrorCodes.PROTOCOL_ERROR
else:
error_code = h2.errors.ErrorCodes.NO_ERROR
self.conn.close_connection(error_code=error_code)
self.transport.write(self.conn.data_to_send())
# Don't let the client hold this connection open too long.
if self.abortTimeout is not None:
# We use self.callLater because that's what TimeoutMixin does, even
# though we have a perfectly good reactor sitting around. See
# https://twistedmatrix.com/trac/ticket/8488.
self._abortingCall = self.callLater(
self.abortTimeout, self.forceAbortClient
)
# We're done, throw the connection away.
self.transport.loseConnection()
def forceAbortClient(self):
"""
Called if C{abortTimeout} seconds have passed since the timeout fired,
and the connection still hasn't gone away. This can really only happen
on extremely bad connections or when clients are maliciously attempting
to keep connections open.
"""
self._log.info(
"Forcibly timing out client: {client}",
client=self.transport.getPeer()
)
# We want to lose track of the _abortingCall so that no-one tries to
# cancel it.
self._abortingCall = None
self.transport.abortConnection()
def connectionLost(self, reason):
"""
Called when the transport connection is lost.
Informs all outstanding response handlers that the connection has been
lost, and cleans up all internal state.
"""
self._stillProducing = False
self.setTimeout(None)
for stream in self.streams.values():
stream.connectionLost(reason)
for streamID in list(self.streams.keys()):
self._requestDone(streamID)
# If we were going to force-close the transport, we don't have to now.
if self._abortingCall is not None:
self._abortingCall.cancel()
self._abortingCall = None
# Implementation of IPushProducer
#
# Here's how we handle IPushProducer. We have multiple outstanding
# H2Streams. Each of these exposes an IConsumer interface to the response
# handler that allows it to push data into the H2Stream. The H2Stream then
# writes the data into the H2Connection object.
#
# The H2Connection needs to manage these writes to account for:
#
# - flow control
# - priority
#
# We manage each of these in different ways.
#
# For flow control, we simply use the equivalent of the IPushProducer
# interface. We simply tell the H2Stream: "Hey, you can't send any data
# right now, sorry!". When that stream becomes unblocked, we free it up
# again. This allows the H2Stream to propagate this backpressure up the
# chain.
#
# For priority, we need to keep a backlog of data frames that we can send,
# and interleave them appropriately. This backlog is most sensibly kept in
# the H2Connection object itself. We keep one queue per stream, which is
# where the writes go, and then we have a loop that manages popping these
# streams off in priority order.
#
# Logically then, we go as follows:
#
# 1. Stream calls writeDataToStream(). This causes a DataFrame to be placed
# on the queue for that stream. It also informs the priority
# implementation that this stream is unblocked.
# 2. The _sendPrioritisedData() function spins in a tight loop. Each
# iteration it asks the priority implementation which stream should send
# next, and pops a data frame off that stream's queue. If, after sending
# that frame, there is no data left on that stream's queue, the function
# informs the priority implementation that the stream is blocked.
#
# If all streams are blocked, or if there are no outstanding streams, the
# _sendPrioritisedData function waits to be awoken when more data is ready
# to send.
#
# Note that all of this only applies to *data*. Headers and other control
# frames deliberately skip this processing as they are not subject to flow
# control or priority constraints.
def stopProducing(self):
"""
Stop producing data.
This tells the L{H2Connection} that its consumer has died, so it must
stop producing data for good.
"""
self.connectionLost(ConnectionLost("Producing stopped"))
def pauseProducing(self):
"""
Pause producing data.
Tells the L{H2Connection} that it has produced too much data to process
for the time being, and to stop until resumeProducing() is called.
"""
self._consumerBlocked = Deferred()
def resumeProducing(self):
"""
Resume producing data.
This tells the L{H2Connection} to re-add itself to the main loop and
produce more data for the consumer.
"""
if self._consumerBlocked is not None:
d = self._consumerBlocked
self._consumerBlocked = None
d.callback(None)
def _sendPrioritisedData(self, *args):
"""
The data sending loop. This function repeatedly calls itself, either
from L{Deferred}s or from
L{reactor.callLater<twisted.internet.interfaces.IReactorTime.callLater>}
This function sends data on streams according to the rules of HTTP/2
priority. It ensures that the data from each stream is interleved
according to the priority signalled by the client, making sure that the
connection is used with maximal efficiency.
This function will execute if data is available: if all data is
exhausted, the function will place a deferred onto the L{H2Connection}
object and wait until it is called to resume executing.
"""
# If producing has stopped, we're done. Don't reschedule ourselves
if not self._stillProducing:
return
stream = None
while stream is None:
try:
stream = next(self.priority)
except priority.DeadlockError:
# All streams are currently blocked or not progressing. Wait
# until a new one becomes available.
assert self._sendingDeferred is None
self._sendingDeferred = Deferred()
self._sendingDeferred.addCallback(self._sendPrioritisedData)
return
# Wait behind the transport.
if self._consumerBlocked is not None:
self._consumerBlocked.addCallback(self._sendPrioritisedData)
return
self.resetTimeout()
remainingWindow = self.conn.local_flow_control_window(stream)
frameData = self._outboundStreamQueues[stream].popleft()
maxFrameSize = min(self.conn.max_outbound_frame_size, remainingWindow)
if frameData is _END_STREAM_SENTINEL:
# There's no error handling here even though this can throw
# ProtocolError because we really shouldn't encounter this problem.
# If we do, that's a nasty bug.
self.conn.end_stream(stream)
self.transport.write(self.conn.data_to_send())
# Clean up the stream
self._requestDone(stream)
else:
# Respect the max frame size.
if len(frameData) > maxFrameSize:
excessData = frameData[maxFrameSize:]
frameData = frameData[:maxFrameSize]
self._outboundStreamQueues[stream].appendleft(excessData)
# There's deliberately no error handling here, because this just
# absolutely should not happen.
# If for whatever reason the max frame length is zero and so we
# have no frame data to send, don't send any.
if frameData:
self.conn.send_data(stream, frameData)
self.transport.write(self.conn.data_to_send())
# If there's no data left, this stream is now blocked.
if not self._outboundStreamQueues[stream]:
self.priority.block(stream)
# Also, if the stream's flow control window is exhausted, tell it
# to stop.
if self.remainingOutboundWindow(stream) <= 0:
self.streams[stream].flowControlBlocked()
self._reactor.callLater(0, self._sendPrioritisedData)
# Internal functions.
def _requestReceived(self, event):
"""
Internal handler for when a request has been received.
@param event: The Hyper-h2 event that encodes information about the
received request.
@type event: L{h2.events.RequestReceived}
"""
stream = H2Stream(
event.stream_id,
self, event.headers,
self.requestFactory,
self.site,
self.factory
)
self.streams[event.stream_id] = stream
self._streamCleanupCallbacks[event.stream_id] = Deferred()
self._outboundStreamQueues[event.stream_id] = deque()
# Add the stream to the priority tree but immediately block it.
try:
self.priority.insert_stream(event.stream_id)
except priority.DuplicateStreamError:
# Stream already in the tree. This can happen if we received a
# PRIORITY frame before a HEADERS frame. Just move on: we set the
# stream up properly in _handlePriorityUpdate.
pass
else:
self.priority.block(event.stream_id)
def _requestDataReceived(self, event):
"""
Internal handler for when a chunk of data is received for a given
request.
@param event: The Hyper-h2 event that encodes information about the
received data.
@type event: L{h2.events.DataReceived}
"""
stream = self.streams[event.stream_id]
stream.receiveDataChunk(event.data, event.flow_controlled_length)
def _requestEnded(self, event):
"""
Internal handler for when a request is complete, and we expect no
further data for that request.
@param event: The Hyper-h2 event that encodes information about the
completed stream.
@type event: L{h2.events.StreamEnded}
"""
stream = self.streams[event.stream_id]
stream.requestComplete()
def _requestAborted(self, event):
"""
Internal handler for when a request is aborted by a remote peer.
@param event: The Hyper-h2 event that encodes information about the
reset stream.
@type event: L{h2.events.StreamReset}
"""
stream = self.streams[event.stream_id]
stream.connectionLost(
ConnectionLost("Stream reset with code %s" % event.error_code)
)
self._requestDone(event.stream_id)
def _handlePriorityUpdate(self, event):
"""
Internal handler for when a stream priority is updated.
@param event: The Hyper-h2 event that encodes information about the
stream reprioritization.
@type event: L{h2.events.PriorityUpdated}
"""
try:
self.priority.reprioritize(
stream_id=event.stream_id,
depends_on=event.depends_on or None,
weight=event.weight,
exclusive=event.exclusive,
)
except priority.MissingStreamError:
# A PRIORITY frame arrived before the HEADERS frame that would
# trigger us to insert the stream into the tree. That's fine: we
# can create the stream here and mark it as blocked.
self.priority.insert_stream(
stream_id=event.stream_id,
depends_on=event.depends_on or None,
weight=event.weight,
exclusive=event.exclusive,
)
self.priority.block(event.stream_id)
def writeHeaders(self, version, code, reason, headers, streamID):
"""
Called by L{twisted.web.http.Request} objects to write a complete set
of HTTP headers to a stream.
@param version: The HTTP version in use. Unused in HTTP/2.
@type version: L{bytes}
@param code: The HTTP status code to write.
@type code: L{bytes}
@param reason: The HTTP reason phrase to write. Unused in HTTP/2.
@type reason: L{bytes}
@param headers: The headers to write to the stream.
@type headers: L{twisted.web.http_headers.Headers}
@param streamID: The ID of the stream to write the headers to.
@type streamID: L{int}
"""
headers.insert(0, (b':status', code))
try:
self.conn.send_headers(streamID, headers)
except h2.exceptions.StreamClosedError:
# Stream was closed by the client at some point. We need to not
# explode here: just swallow the error. That's what write() does
# when a connection is lost, so that's what we do too.
return
else:
self.transport.write(self.conn.data_to_send())
def writeDataToStream(self, streamID, data):
"""
May be called by L{H2Stream} objects to write response data to a given
stream. Writes a single data frame.
@param streamID: The ID of the stream to write the data to.
@type streamID: L{int}
@param data: The data chunk to write to the stream.
@type data: L{bytes}
"""
self._outboundStreamQueues[streamID].append(data)
# There's obviously no point unblocking this stream and the sending
# loop if the data can't actually be sent, so confirm that there's
# some room to send data.
if self.conn.local_flow_control_window(streamID) > 0:
self.priority.unblock(streamID)
if self._sendingDeferred is not None:
d = self._sendingDeferred
self._sendingDeferred = None
d.callback(streamID)
if self.remainingOutboundWindow(streamID) <= 0:
self.streams[streamID].flowControlBlocked()
def endRequest(self, streamID):
"""
Called by L{H2Stream} objects to signal completion of a response.
@param streamID: The ID of the stream to write the data to.
@type streamID: L{int}
"""
self._outboundStreamQueues[streamID].append(_END_STREAM_SENTINEL)
self.priority.unblock(streamID)
if self._sendingDeferred is not None:
d = self._sendingDeferred
self._sendingDeferred = None
d.callback(streamID)
def abortRequest(self, streamID):
"""
Called by L{H2Stream} objects to request early termination of a stream.
This emits a RstStream frame and then removes all stream state.
@param streamID: The ID of the stream to write the data to.
@type streamID: L{int}
"""
self.conn.reset_stream(streamID)
self.transport.write(self.conn.data_to_send())
self._requestDone(streamID)
def _requestDone(self, streamID):
"""
Called internally by the data sending loop to clean up state that was
being used for the stream. Called when the stream is complete.
@param streamID: The ID of the stream to clean up state for.
@type streamID: L{int}
"""
del self._outboundStreamQueues[streamID]
self.priority.remove_stream(streamID)
del self.streams[streamID]
cleanupCallback = self._streamCleanupCallbacks.pop(streamID)
cleanupCallback.callback(streamID)
def remainingOutboundWindow(self, streamID):
"""
Called to determine how much room is left in the send window for a
given stream. Allows us to handle blocking and unblocking producers.
@param streamID: The ID of the stream whose flow control window we'll
check.
@type streamID: L{int}
@return: The amount of room remaining in the send window for the given
stream, including the data queued to be sent.
@rtype: L{int}
"""
# TODO: This involves a fair bit of looping and computation for
# something that is called a lot. Consider caching values somewhere.
windowSize = self.conn.local_flow_control_window(streamID)
sendQueue = self._outboundStreamQueues[streamID]
alreadyConsumed = sum(
len(chunk) for chunk in sendQueue
if chunk is not _END_STREAM_SENTINEL
)
return windowSize - alreadyConsumed
def _handleWindowUpdate(self, event):
"""
Manage flow control windows.
Streams that are blocked on flow control will register themselves with
the connection. This will fire deferreds that wake those streams up and
allow them to continue processing.
@param event: The Hyper-h2 event that encodes information about the
flow control window change.
@type event: L{h2.events.WindowUpdated}
"""
streamID = event.stream_id
if streamID:
if not self._streamIsActive(streamID):
# We may have already cleaned up our stream state, making this
# a late WINDOW_UPDATE frame. That's fine: the update is
# unnecessary but benign. We'll ignore it.
return
# If we haven't got any data to send, don't unblock the stream. If
# we do, we'll eventually get an exception inside the
# _sendPrioritisedData loop some time later.
if self._outboundStreamQueues.get(streamID):
self.priority.unblock(streamID)
self.streams[streamID].windowUpdated()
else:
# Update strictly applies to all streams.
for stream in self.streams.values():
stream.windowUpdated()
# If we still have data to send for this stream, unblock it.
if self._outboundStreamQueues.get(stream.streamID):
self.priority.unblock(stream.streamID)
def getPeer(self):
"""
Get the remote address of this connection.
Treat this method with caution. It is the unfortunate result of the
CGI and Jabber standards, but should not be considered reliable for
the usual host of reasons; port forwarding, proxying, firewalls, IP
masquerading, etc.
@return: An L{IAddress} provider.
"""
return self.transport.getPeer()
def getHost(self):
"""
Similar to getPeer, but returns an address describing this side of the
connection.
@return: An L{IAddress} provider.
"""
return self.transport.getHost()
def openStreamWindow(self, streamID, increment):
"""
Open the stream window by a given increment.
@param streamID: The ID of the stream whose window needs to be opened.
@type streamID: L{int}
@param increment: The amount by which the stream window must be
incremented.
@type increment: L{int}
"""
self.conn.acknowledge_received_data(increment, streamID)
data = self.conn.data_to_send()
if data:
self.transport.write(data)
def _isSecure(self):
"""
Returns L{True} if this channel is using a secure transport.
@returns: L{True} if this channel is secure.
@rtype: L{bool}
"""
# A channel is secure if its transport is ISSLTransport.
return ISSLTransport(self.transport, None) is not None
def _send100Continue(self, streamID):
"""
Sends a 100 Continue response, used to signal to clients that further
processing will be performed.
@param streamID: The ID of the stream that needs the 100 Continue
response
@type streamID: L{int}
"""
headers = [(b':status', b'100')]
self.conn.send_headers(headers=headers, stream_id=streamID)
self.transport.write(self.conn.data_to_send())
def _respondToBadRequestAndDisconnect(self, streamID):
"""
This is a quick and dirty way of responding to bad requests.
As described by HTTP standard we should be patient and accept the
whole request from the client before sending a polite bad request
response, even in the case when clients send tons of data.
Unlike in the HTTP/1.1 case, this does not actually disconnect the
underlying transport: there's no need. This instead just sends a 400
response and terminates the stream.
@param streamID: The ID of the stream that needs the 100 Continue
response
@type streamID: L{int}
"""
headers = [(b':status', b'400')]
self.conn.send_headers(
headers=headers,
stream_id=streamID,
end_stream=True
)
self.transport.write(self.conn.data_to_send())
stream = self.streams[streamID]
stream.connectionLost(ConnectionLost("Invalid request"))
self._requestDone(streamID)
def _streamIsActive(self, streamID):
"""
Checks whether Twisted has still got state for a given stream and so
can process events for that stream.
@param streamID: The ID of the stream that needs processing.
@type streamID: L{int}
@return: Whether the stream still has state allocated.
@rtype: L{bool}
"""
return streamID in self.streams
@implementer(ITransport, IConsumer, IPushProducer)
class H2Stream(object):
"""
A class representing a single HTTP/2 stream.
This class works hand-in-hand with L{H2Connection}. It acts to provide an
implementation of L{ITransport}, L{IConsumer}, and L{IProducer} that work
for a single HTTP/2 connection, while tightly cleaving to the interface
provided by those interfaces. It does this by having a tight coupling to
L{H2Connection}, which allows associating many of the functions of
L{ITransport}, L{IConsumer}, and L{IProducer} to objects on a
stream-specific level.
@ivar streamID: The numerical stream ID that this object corresponds to.
@type streamID: L{int}
@ivar producing: Whether this stream is currently allowed to produce data
to its consumer.
@type producing: L{bool}
@ivar command: The HTTP verb used on the request.
@type command: L{unicode}
@ivar path: The HTTP path used on the request.
@type path: L{unicode}
@ivar producer: The object producing the response, if any.
@type producer: L{IProducer}
@ivar site: The L{twisted.web.server.Site} object this stream belongs to,
if any.
@type site: L{twisted.web.server.Site}
@ivar factory: The L{twisted.web.http.HTTPFactory} object that constructed
this stream's parent connection.
@type factory: L{twisted.web.http.HTTPFactory}
@ivar _producerProducing: Whether the producer stored in producer is
currently producing data.
@type _producerProducing: L{bool}
@ivar _inboundDataBuffer: Any data that has been received from the network
but has not yet been received by the consumer.
@type _inboundDataBuffer: A L{collections.deque} containing L{bytes}
@ivar _conn: A reference to the connection this stream belongs to.
@type _conn: L{H2Connection}
@ivar _request: A request object that this stream corresponds to.
@type _request: L{twisted.web.iweb.IRequest}
@ivar _buffer: A buffer containing data produced by the producer that could
not be sent on the network at this time.
@type _buffer: L{io.BytesIO}
"""
# We need a transport property for t.w.h.Request, but HTTP/2 doesn't want
# to expose it. So we just set it to None.
transport = None
def __init__(self, streamID, connection, headers,
requestFactory, site, factory):
"""
Initialize this HTTP/2 stream.
@param streamID: The numerical stream ID that this object corresponds
to.
@type streamID: L{int}
@param connection: The HTTP/2 connection this stream belongs to.
@type connection: L{H2Connection}
@param headers: The HTTP/2 request headers.
@type headers: A L{list} of L{tuple}s of header name and header value,
both as L{bytes}.
@param requestFactory: A function that builds appropriate request
request objects.
@type requestFactory: A callable that returns a
L{twisted.web.iweb.IRequest}.
@param site: The L{twisted.web.server.Site} object this stream belongs
to, if any.
@type site: L{twisted.web.server.Site}
@param factory: The L{twisted.web.http.HTTPFactory} object that
constructed this stream's parent connection.
@type factory: L{twisted.web.http.HTTPFactory}
"""
self.streamID = streamID
self.site = site
self.factory = factory
self.producing = True
self.command = None
self.path = None
self.producer = None
self._producerProducing = False
self._hasStreamingProducer = None
self._inboundDataBuffer = deque()
self._conn = connection
self._request = requestFactory(self, queued=False)
self._buffer = io.BytesIO()
self._convertHeaders(headers)
def _convertHeaders(self, headers):
"""
This method converts the HTTP/2 header set into something that looks
like HTTP/1.1. In particular, it strips the 'special' headers and adds
a Host: header.
@param headers: The HTTP/2 header set.
@type headers: A L{list} of L{tuple}s of header name and header value,
both as L{bytes}.
"""
gotLength = False
for header in headers:
if not header[0].startswith(b':'):
gotLength = (
_addHeaderToRequest(self._request, header) or gotLength
)
elif header[0] == b':method':
self.command = header[1]
elif header[0] == b':path':
self.path = header[1]
elif header[0] == b':authority':
# This is essentially the Host: header from HTTP/1.1
_addHeaderToRequest(self._request, (b'host', header[1]))
if not gotLength:
if self.command in (b'GET', b'HEAD'):
self._request.gotLength(0)
else:
self._request.gotLength(None)
self._request.parseCookies()
expectContinue = self._request.requestHeaders.getRawHeaders(b'expect')
if expectContinue and expectContinue[0].lower() == b'100-continue':
self._send100Continue()
# Methods called by the H2Connection
def receiveDataChunk(self, data, flowControlledLength):
"""
Called when the connection has received a chunk of data from the
underlying transport. If the stream has been registered with a
consumer, and is currently able to push data, immediately passes it
through. Otherwise, buffers the chunk until we can start producing.
@param data: The chunk of data that was received.
@type data: L{bytes}
@param flowControlledLength: The total flow controlled length of this
chunk, which is used when we want to re-open the window. May be
different to C{len(data)}.
@type flowControlledLength: L{int}
"""
if not self.producing:
# Buffer data.
self._inboundDataBuffer.append((data, flowControlledLength))
else:
self._request.handleContentChunk(data)
self._conn.openStreamWindow(self.streamID, flowControlledLength)
def requestComplete(self):
"""
Called by the L{H2Connection} when the all data for a request has been
received. Currently, with the legacy L{twisted.web.http.Request}
object, just calls requestReceived unless the producer wants us to be
quiet.
"""
if self.producing:
self._request.requestReceived(self.command, self.path, b'HTTP/2')
else:
self._inboundDataBuffer.append((_END_STREAM_SENTINEL, None))
def connectionLost(self, reason):
"""
Called by the L{H2Connection} when a connection is lost or a stream is
reset.
@param reason: The reason the connection was lost.
@type reason: L{str}
"""
self._request.connectionLost(reason)
def windowUpdated(self):
"""
Called by the L{H2Connection} when this stream's flow control window
has been opened.
"""
# If we don't have a producer, we have no-one to tell.
if not self.producer:
return
# If we're not blocked on flow control, we don't care.
if self._producerProducing:
return
# We check whether the stream's flow control window is actually above
# 0, and then, if a producer is registered and we still have space in
# the window, we unblock it.
remainingWindow = self._conn.remainingOutboundWindow(self.streamID)
if not remainingWindow > 0:
return
# We have a producer and space in the window, so that producer can
# start producing again!
self._producerProducing = True
self.producer.resumeProducing()
def flowControlBlocked(self):
"""
Called by the L{H2Connection} when this stream's flow control window
has been exhausted.
"""
if not self.producer:
return
if self._producerProducing:
self.producer.pauseProducing()
self._producerProducing = False
# Methods called by the consumer (usually an IRequest).
def writeHeaders(self, version, code, reason, headers):
"""
Called by the consumer to write headers to the stream.
@param version: The HTTP version.
@type version: L{bytes}
@param code: The status code.
@type code: L{int}
@param reason: The reason phrase. Ignored in HTTP/2.
@type reason: L{bytes}
@param headers: The HTTP response headers.
@type: Any iterable of two-tuples of L{bytes}, representing header
names and header values.
"""
self._conn.writeHeaders(version, code, reason, headers, self.streamID)
def requestDone(self, request):
"""
Called by a consumer to clean up whatever permanent state is in use.
@param request: The request calling the method.
@type request: L{twisted.web.iweb.IRequest}
"""
self._conn.endRequest(self.streamID)
def _send100Continue(self):
"""
Sends a 100 Continue response, used to signal to clients that further
processing will be performed.
"""
self._conn._send100Continue(self.streamID)
def _respondToBadRequestAndDisconnect(self):
"""
This is a quick and dirty way of responding to bad requests.
As described by HTTP standard we should be patient and accept the
whole request from the client before sending a polite bad request
response, even in the case when clients send tons of data.
Unlike in the HTTP/1.1 case, this does not actually disconnect the
underlying transport: there's no need. This instead just sends a 400
response and terminates the stream.
"""
self._conn._respondToBadRequestAndDisconnect(self.streamID)
# Implementation: ITransport
def write(self, data):
"""
Write a single chunk of data into a data frame.
@param data: The data chunk to send.
@type data: L{bytes}
"""
self._conn.writeDataToStream(self.streamID, data)
return
def writeSequence(self, iovec):
"""
Write a sequence of chunks of data into data frames.
@param iovec: A sequence of chunks to send.
@type iovec: An iterable of L{bytes} chunks.
"""
for chunk in iovec:
self.write(chunk)
def loseConnection(self):
"""
Close the connection after writing all pending data.
"""
self._conn.endRequest(self.streamID)
def abortConnection(self):
"""
Forcefully abort the connection by sending a RstStream frame.
"""
self._conn.abortRequest(self.streamID)
def getPeer(self):
"""
Get information about the peer.
"""
return self._conn.getPeer()
def getHost(self):
"""
Similar to getPeer, but for this side of the connection.
"""
return self._conn.getHost()
def isSecure(self):
"""
Returns L{True} if this channel is using a secure transport.
@returns: L{True} if this channel is secure.
@rtype: L{bool}
"""
return self._conn._isSecure()
# Implementation: IConsumer
def registerProducer(self, producer, streaming):
"""
Register to receive data from a producer.
This sets self to be a consumer for a producer. When this object runs
out of data (as when a send(2) call on a socket succeeds in moving the
last data from a userspace buffer into a kernelspace buffer), it will
ask the producer to resumeProducing().
For L{IPullProducer} providers, C{resumeProducing} will be called once
each time data is required.
For L{IPushProducer} providers, C{pauseProducing} will be called
whenever the write buffer fills up and C{resumeProducing} will only be
called when it empties.
@param producer: The producer to register.
@type producer: L{IProducer} provider
@param streaming: L{True} if C{producer} provides L{IPushProducer},
L{False} if C{producer} provides L{IPullProducer}.
@type streaming: L{bool}
@raise RuntimeError: If a producer is already registered.
@return: L{None}
"""
if self.producer:
raise ValueError(
"registering producer %s before previous one (%s) was "
"unregistered" % (producer, self.producer))
if not streaming:
self.hasStreamingProducer = False
producer = _PullToPush(producer, self)
producer.startStreaming()
else:
self.hasStreamingProducer = True
self.producer = producer
self._producerProducing = True
def unregisterProducer(self):
"""
@see: L{IConsumer.unregisterProducer}
"""
# When the producer is unregistered, we're done.
if self.producer is not None and not self.hasStreamingProducer:
self.producer.stopStreaming()
self._producerProducing = False
self.producer = None
self.hasStreamingProducer = None
# Implementation: IPushProducer
def stopProducing(self):
"""
@see: L{IProducer.stopProducing}
"""
self.producing = False
self.abortConnection()
def pauseProducing(self):
"""
@see: L{IPushProducer.pauseProducing}
"""
self.producing = False
def resumeProducing(self):
"""
@see: L{IPushProducer.resumeProducing}
"""
self.producing = True
consumedLength = 0
while self.producing and self._inboundDataBuffer:
# Allow for pauseProducing to be called in response to a call to
# resumeProducing.
chunk, flowControlledLength = self._inboundDataBuffer.popleft()
if chunk is _END_STREAM_SENTINEL:
self.requestComplete()
else:
consumedLength += flowControlledLength
self._request.handleContentChunk(chunk)
self._conn.openStreamWindow(self.streamID, consumedLength)
def _addHeaderToRequest(request, header):
"""
Add a header tuple to a request header object.
@param request: The request to add the header tuple to.
@type request: L{twisted.web.http.Request}
@param header: The header tuple to add to the request.
@type header: A L{tuple} with two elements, the header name and header
value, both as L{bytes}.
@return: If the header being added was the C{Content-Length} header.
@rtype: L{bool}
"""
requestHeaders = request.requestHeaders
name, value = header
values = requestHeaders.getRawHeaders(name)
if values is not None:
values.append(value)
else:
requestHeaders.setRawHeaders(name, [value])
if name == b'content-length':
request.gotLength(int(value))
return True
return False
| [
"teadone@naver.com"
] | teadone@naver.com |
075026177bd5d8967534c7183b390a63941c8beb | f4205df28c7b9817acd03aa78c24adcc501a65b8 | /run.py | 766037e23811d8ad0a4f210904cad02d954fcc1e | [
"MIT"
] | permissive | chelseaayoo/Password-manager | d8133f6827b30aa919f2cd179ffd5c0ecfb3ae23 | e7abe7c334427e40e78515066845e8cf742b367d | refs/heads/master | 2023-08-28T09:45:20.495179 | 2021-10-26T06:15:49 | 2021-10-26T06:15:49 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 5,661 | py | #!/usr/bin/env python3.8
#!/usr/bin/env python3.6
from credential import Credential
from user import User
def create_credential(login_username,password):
'''
Function to create a new credential
'''
new_credential = Credential(login_username,password)
return new_credential
def save_credentials(credential):
'''
Function to save credential
'''
credential.save_credential()
def del_credential(credential):
'''
Function to delete a credential
'''
credential.delete_credential()
def find_credential(login_username):
'''
Function that finds a credential by email and returns the credential
'''
return Credential.find_by_login_username(login_username)
def check_existing_credentials(login_username):
'''
Function that check if a credential exists with that login_username and return a Boolean
'''
return Credential.credential_exist(login_username)
def display_credentials():
'''
Function that returns all the saved credentials
'''
return Credential.display_credentials()
def create_user(email,first_name,last_name):
'''
Function to create a new user
'''
new_user = User(email,first_name,last_name)
return new_user
def save_users(user):
'''
Function to save user
'''
user.save_user()
def del_user(user):
'''
Function to delete a user
'''
user.delete_user()
def find_user(email):
'''
Function that finds a user by email and returns the user
'''
return User.find_by_email(email)
def check_existing_users(email):
'''
Function that check if a user exists with email and return a Boolean
'''
return User.user_exist(email)
def display_users():
'''
Function that returns all the saved users
'''
return User.display_users()
def main():
print("Hello Welcome to your credential list. What is your name?")
login_username = input()
print(f"Hello {login_username}. what would you like to do?")
print('\n')
while True:
print("Use these short codes : cc - create a new credential, dc - display credentials, fc -find a credential, delc -delete credential, ex -exit the credential list ")
short_code = input().lower()
if short_code == 'cc':
print("New Credential")
print("-"*10)
print ("First name ....")
first_name = input()
print("Last name ...")
last_name = input()
print("Login username ...")
login_username = input()
print("Password ...")
password = input()
print("Email address for account login ...")
email = input()
save_credentials(create_credential(login_username,password)) # create and save new credential.
print ('\n')
print(f"New Credential with login username: {login_username} with password: {password} created")
print ('\n')
elif short_code == 'dc':
if display_credentials():
print("Here is a list of all your credentials")
print('\n')
for credential in display_credentials():
print(f"Login Username : {credential.login_username} Password: {credential.password}")
print('\n')
else:
print('\n')
print("You dont seem to have any credentials saved yet")
print('\n')
elif short_code == 'fc':
print("Enter the login username you used while creating your credential")
search_login_username = input()
if check_existing_credentials(search_login_username):
search_credential = find_credential(search_login_username)
print(f"{search_credential.login_username} Your password is {search_credential.password}")
print('-' * 20)
# print(f"Phone number.......{search_contact.phone_number}")
# print(f"Email address.......{search_contact.email}")
else:
print("That credential does not exist")
elif short_code == 'delc':
if del_credential(create_credential):
print("choose credentials to delete")
print('\n')
for credential in del_credential():
print("{credential}")
else:
print('\n')
print("no credential deleted")
print('\n')
elif short_code == "ex":
print("Bye .......")
break
else:
print("I really didn't get that. Please use the short codes")
if __name__ == '__main__':
main() | [
"chelsea.ayoo@student.moringaschool.com"
] | chelsea.ayoo@student.moringaschool.com |
54256fc282316da722359a762ee5dc1ab76d62a0 | 09c4fef0e8941a40b5cdd1b1f689031361852727 | /csvwriter.py | d1aef78e356fdc96b6f3a281462513cd9776bdd1 | [] | no_license | jw910731/NTNU_TextProcessing_Final | c109fa3d1c391638817902c2fb5073e9063ad1a8 | 3ce60778b4868251e9de4e2a980b3f118a9c0352 | refs/heads/main | 2023-02-12T20:06:20.516885 | 2021-01-12T07:31:01 | 2021-01-12T07:31:01 | 307,621,039 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 114 | py | import pandas as pd
def csvwrite(DICT):
pd.DataFrame(DICT).to_csv('output.csv', header=True,encoding='utf-8') | [
"adam20001002@gmail.com"
] | adam20001002@gmail.com |
a7e699f06dbaa2415ab8f0ce348214b552cfc045 | 2c5302a8f6962c2de4849c0a49271033ca4fbfbd | /Day-30/Day_30_VishwaPatel.py | 24b68063b1058a4c94682b5d6b1d95b172b53b5a | [] | no_license | 136tejas/30DayOfPython | 4db35033e413b9ec7dc6c0c0871d12b6a1e39555 | 20df271cd8ed3170aad2ee359a6a28f9f682a5b5 | refs/heads/main | 2023-08-11T10:51:39.357383 | 2021-10-04T07:34:44 | 2021-10-04T07:34:44 | 379,897,363 | 0 | 0 | null | 2021-06-24T11:10:35 | 2021-06-24T11:10:35 | null | UTF-8 | Python | false | false | 445 | py | import numpy as np
m = int(input('Enter rows'))
n = int(input('Enter columns'))
l = []
b = []
for i in range(m):
l = []
for j in range(n):
e = int(input('Enter element: '))
l.append(e)
b.append(l)
print("Original matrix")
print(np.matrix(b))
for i in range(m):
for j in range(n):
if i > j or i == j:
continue
else:
b[i][j] = 0
print("Changed matrix")
print(np.matrix(b))
| [
"noreply@github.com"
] | noreply@github.com |
6abcb0d176f43e109d8555a90281fa679fce1a72 | 64ac3af07036f240bc333f58f9ff9924d6fea06a | /modelrerank/src/main.py | 4502decd697e2341bdca9cb97474a8a809929668 | [
"Apache-2.0"
] | permissive | stevencdang/AutoML-DS-Components | b9e12011fd958f4734f323f4882806e8be4b64f2 | b0490262d3db5307c37f82c92e25cd938dd3a242 | refs/heads/master | 2023-01-10T13:06:27.167187 | 2019-03-28T18:11:40 | 2019-03-28T18:11:40 | 175,680,072 | 0 | 0 | Apache-2.0 | 2023-01-04T23:30:19 | 2019-03-14T18:42:27 | Python | UTF-8 | Python | false | false | 2,992 | py |
# Author: Steven C. Dang
# Main script for importing provided D3M dataset schemas for operation on datasets
import logging
import os.path as path
import sys
import json
import pprint
import argparse
import csv
import pandas as pd
# Workflow component specific imports
from ls_utilities.ls_logging import setup_logging
from ls_utilities.cmd_parser import get_default_arg_parser
from ls_utilities.ls_wf_settings import *
from ls_dataset.d3m_dataset import D3MDataset
from modeling.models import *
from modeling.component_out import *
__version__ = '0.1'
if __name__ == '__main__':
# Parse argumennts
parser = get_default_arg_parser("Rerank Model")
parser.add_argument('-model_id', type=str,
help='the name of the dataset to import')
parser.add_argument('-new_rank', type=int,
help='the new rank to resort the specified model')
parser.add_argument('-file0', type=argparse.FileType('r'),
help='the tab-separated list of models to select from')
args = parser.parse_args()
if args.is_test is not None:
is_test = args.is_test == 1
else:
is_test = False
# Get config file
config = SettingsFactory.get_settings(path.join(args.programDir, 'program', 'settings.cfg'),
program_dir=args.programDir,
working_dir=args.workingDir,
is_test=is_test
)
# Setup Logging
setup_logging(config)
logger = logging.getLogger('model_rerank')
### Begin Script ###
logger.info("Reranking the models according to new given rank")
logger.debug("Running Model Rerank with arguments: %s" % str(args))
if args.is_test is not None:
is_test = args.is_test == 1
else:
is_test = False
# Decode the models from file
logger.debug("ModelRank file input: %s" % args.file0)
m_index, ranked_models = ModelRankSetIO.from_file(args.file0)
selected_mid = m_index[int(args.model_id)]
model = ranked_models[selected_mid]
logger.debug("Seleted Model ID:\t %s" % selected_mid)
logger.debug("Seleted Ranked Model:\t%s" % str(model.to_dict()))
new_rank = int(args.new_rank)
logger.debug("Previous Rank %i\t New rank: %i" % (model.rank, new_rank))
# Resort the ranked models
ranks = range(1, len(ranked_models)+1)
mid_ranks = {mdl.rank: mid for mid, mdl in ranked_models.items()}
ordered_models = [mid_ranks[i] for i in ranks if mid_ranks[i] != selected_mid]
ordered_models.insert(new_rank - 1, selected_mid)
# Update new ranks for all models
for i, mid in enumerate(ordered_models):
ranked_models[mid].update_rank(i+1)
# Write dataset info to output file
out_file_path = path.join(args.workingDir, config.get('Output', 'out_file'))
ModelRankSetIO.to_file(out_file_path, ranked_models, m_index)
| [
"stevencdang@gmail.com"
] | stevencdang@gmail.com |
d005e74749c88692012dd32e899d20852ffbc130 | c223e858c9ebf1b734221e4db4b3d594993a5536 | /thespian/system/timing.py | ec5f22afa79083a0f4d2f9c23f08117403194959 | [
"MIT"
] | permissive | jfasenfest/Thespian | 17f9738aff648328a40f94d3225427d82fe27e39 | 5979a2c9791b774fb620253bb62253c95cf7d4b5 | refs/heads/master | 2020-12-26T00:27:09.446001 | 2016-11-28T21:48:20 | 2016-11-28T21:48:20 | 48,067,029 | 0 | 0 | null | 2016-11-21T23:17:52 | 2015-12-15T20:24:12 | Python | UTF-8 | Python | false | false | 6,566 | py | from datetime import datetime, timedelta
###
### Time Management
###
def timePeriodSeconds(basis, other=None):
if isinstance(basis, datetime):
if isinstance(other, datetime):
return timePeriodSeconds(other - basis)
if isinstance(basis, timedelta):
try:
return basis.total_seconds()
except AttributeError:
# Must be Python 2.6... which doesn't have total_seconds yet
return (basis.days * 24.0 * 60 * 60) + basis.seconds + (basis.microseconds / 1000.0 / 1000)
raise TypeError('Cannot determine time from a %s argument'%str(type(basis)))
def toTimeDeltaOrNone(timespec):
if timespec is None: return None
if isinstance(timespec, timedelta): return timespec
if isinstance(timespec, int): return timedelta(seconds=timespec)
if isinstance(timespec, float):
return timedelta(seconds=int(timespec),
microseconds = int((timespec - int(timespec)) * 1000 * 1000))
raise TypeError('Unknown type for timespec: %s'%type(timespec))
class ExpiryTime(object):
def __init__(self, duration):
self._time_to_quit = None if duration is None else (datetime.now() + duration)
def expired(self):
return False if self._time_to_quit is None else (datetime.now() >= self._time_to_quit)
def remaining(self, forever=None):
return forever if self._time_to_quit is None else \
(timedelta(seconds=0) if datetime.now() > self._time_to_quit else \
(self._time_to_quit - datetime.now()))
def remainingSeconds(self, forever=None):
return forever if self._time_to_quit is None else \
(0 if datetime.now() > self._time_to_quit else \
timePeriodSeconds(self._time_to_quit - datetime.now()))
def __str__(self):
if self._time_to_quit is None: return 'Forever'
if self.expired():
return 'Expired_for_%s'%(datetime.now() - self._time_to_quit)
return 'Expires_in_' + str(self.remaining())
def __eq__(self, o):
if isinstance(o, timedelta):
o = ExpiryTime(o)
if self._time_to_quit == o._time_to_quit: return True
if self._time_to_quit == None or o._time_to_quit == None: return False
if self.expired() and o.expired(): return True
return abs(self._time_to_quit - o._time_to_quit) < timedelta(microseconds=1)
def __lt__(self, o):
try:
if self._time_to_quit is None and o._time_to_quit is None: return False
except Exception: pass
if self._time_to_quit is None: return False
if isinstance(o, timedelta):
o = ExpiryTime(o)
if o._time_to_quit is None: return True
return self._time_to_quit < o._time_to_quit
def __gt__(self, o):
try:
if self._time_to_quit is None and o._time_to_quit is None: return False
except Exception: pass
return not self.__lt__(o)
def __le__(self, o): return self.__eq__(o) or self.__lt__(o)
def __ge__(self, o): return self.__eq__(o) or self.__gt__(o)
def __ne__(self, o): return not self.__eq__(o)
def __bool__(self): return self.expired()
def __nonzero__(self): return self.expired()
class ExpirationTimer(object):
"""Keeps track of a duration relative to an original time and
indicates whether that duration has expired or how much time is
left before it expires. As an optimization, this object will
not call datetime.now() itself and must be updated via the
`update_time_now()` method to accurately measure elapsed
time.
May also be initialized with a duration of None, indicating
that it should never timeout and that `remaining()` should
return the forever value (defaulting to None).
"""
def __init__(self, duration, timenow=None):
self._time_now = timenow or datetime.now()
self._time_to_quit = None if duration is None else (self._time_now + duration)
def update_time_now(self, timenow):
"Call this to update the elapsed time."
self._time_now = timenow
def expired(self):
"Returns true if the indicated duration has passed since this was created."
return False if self._time_to_quit is None else (self._time_now >= self._time_to_quit)
def remaining(self, forever=None):
"""Returns a timedelta of remaining time until expiration, or 0 if the
duration has already expired. Returns forever if no timeout."""
return forever if self._time_to_quit is None else \
(timedelta(seconds=0) if self._time_now > self._time_to_quit else \
(self._time_to_quit - self._time_now))
def remainingSeconds(self, forever=None):
"""Similar to `remaining()`, but returns an floating point value of the
number of remaining seconds instead of returning a
timedelta object.
"""
return forever if self._time_to_quit is None else \
(0 if self._time_now > self._time_to_quit else \
timePeriodSeconds(self._time_to_quit - self._time_now))
def __str__(self):
if self._time_to_quit is None: return 'Forever'
if self.expired():
return 'Expired_for_%s'%(self._time_now - self._time_to_quit)
return 'Expires_in_' + str(self.remaining())
def __eq__(self, o):
if isinstance(o, timedelta):
o = ExpiryTime(o)
if self._time_to_quit == o._time_to_quit: return True
if self._time_to_quit == None or o._time_to_quit == None: return False
if self.expired() and o.expired(): return True
return abs(self._time_to_quit - o._time_to_quit) < timedelta(microseconds=1)
def __lt__(self, o):
try:
if self._time_to_quit is None and o._time_to_quit is None: return False
except Exception: pass
if self._time_to_quit is None: return False
if isinstance(o, timedelta):
o = ExpiryTime(o)
if o._time_to_quit is None: return True
return self._time_to_quit < o._time_to_quit
def __gt__(self, o):
try:
if self._time_to_quit is None and o._time_to_quit is None: return False
except Exception: pass
return not self.__lt__(o)
def __le__(self, o): return self.__eq__(o) or self.__lt__(o)
def __ge__(self, o): return self.__eq__(o) or self.__gt__(o)
def __ne__(self, o): return not self.__eq__(o)
def __bool__(self): return self.expired()
def __nonzero__(self): return self.expired()
| [
"kquick@godaddy.com"
] | kquick@godaddy.com |
8cea560fba02030a0a26133fc24eb1c13946c0a5 | ae6177cf2ebe87c3749f03e0ffaade2dac8b8688 | /AulasPython/Parte1/Semana4/decrescente.py | 275cbedae0412d30178947b70b66b0b611437eff | [] | no_license | jmarq76/Learning_Programming | 8a7c598a733c1ba9983103e4aa284bed80ffabbe | bf15d351e239529645fb74a355e296d085683921 | refs/heads/master | 2022-11-17T23:03:32.236684 | 2020-07-07T12:05:56 | 2020-07-07T12:05:56 | 277,804,012 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 417 | py |
decrescente = True
anterior = int(input("Digite o primeiro número da sequência: "))
valor = 1
while valor != 0 and decrescente:
valor = int(input("Digite o próximo da sequência: "))
if valor > anterior:
decrescente = False
anterior = valor
if decrescente:
print("A sequência está em ordem decrescente! :-) ")
else:
print("A sequência não está em ordem decrescente! :-( ") | [
"58978254+jmarq76@users.noreply.github.com"
] | 58978254+jmarq76@users.noreply.github.com |
d019acbd04f6f92c44b1c6b5ef4f6c1d988e6d74 | c50c22c8f814c8d9b697337891904aa0be0edf56 | /shortest_string.py | b26e0a4d78a888e7c2d4a6252bf9b0d4e510728a | [] | no_license | mhiloca/Codewars | a6dc6e8ea5e5c1e97fb4a3d01a059b3120b556b7 | 3155e4b20fbd96c8e7fbe6564014a136d095c079 | refs/heads/master | 2020-07-11T12:41:19.997254 | 2019-11-01T12:44:38 | 2019-11-01T12:44:38 | 204,541,593 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 222 | py | """
Simple, given a string of words, return the length of the shortest word(s).
String will never be empty and you do not need to account for different data types.
"""
def find_short(s):
return min(len(x) for x in s) | [
"mhiloca@gmail.com"
] | mhiloca@gmail.com |
c5d4d2f46c24a51bf6824c2c8735e80bc1f67f80 | 3f4464c932403615c1fbbaf82eaec096426b1ef5 | /StartOutPy4/CH6 Files and Exceptions/write_sales.py | ecb7c7a8611babac6e9db4c42a7bbdc92ed31f8e | [] | no_license | arcstarusa/prime | 99af6e3fed275982bf11ada7bf1297294d527e91 | 5f1102aa7b6eaba18f97eb388525d48ab4cac563 | refs/heads/master | 2020-03-22T14:07:08.079963 | 2019-05-09T11:45:21 | 2019-05-09T11:45:21 | 140,154,408 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 740 | py | # This program prompts the user for sales amounts
# and writes those amounts to the sales.txt file. 6-8
def main():
# Get the numbers of days.
num_days = int(input('For how many days do ' + 'you have sales? '))
# Open a nuew file named sales.txt.
sales_file = open('sales.txt', 'w')
# Get the amount of sales for each day and write
# it to the file.
for count in range (1, num_days + 1):
# Get the sales for a day.
sales = float(input('Enter the sales for day #' + str(count) + ': '))
# Write the sales amount to the file.
sales_file.write(str(sales) + '\n')
# Close the file.
sales_file.close()
print('Data written to sales.txt')
# Call the main function.
main() | [
"40938410+edwardigarashi@users.noreply.github.com"
] | 40938410+edwardigarashi@users.noreply.github.com |
0c2357d7adb5b24a1223a63d777fbf1484ae81ab | 6bcb73fc72587d24927eb9f1450ba3e676c421cf | /setup.py | 9d6ec105a703c31f0bd3be9f32394427a8abd00f | [
"MIT",
"LicenseRef-scancode-unknown-license-reference"
] | permissive | SorchaYang/Scopy | ed26cb9026de898e89e1d39bb7a8af2d041a34a9 | 897f9f0f41a90e0cff1e2fd28a7e82cfac051306 | refs/heads/master | 2022-06-09T19:14:14.962262 | 2020-05-07T10:16:07 | 2020-05-07T10:16:07 | 262,022,320 | 4 | 0 | MIT | 2020-05-07T10:40:58 | 2020-05-07T10:40:58 | null | UTF-8 | Python | false | false | 1,702 | py | # -*- coding: utf-8 -*-
"""
Created on Fri Mar 15 13:21:07 2019
@Author: Zhi-Jiang Yang, Dong-Sheng Cao
@Institution: CBDD Group, Xiangya School of Pharmaceutical Science, CSU, China
@Homepage: http://www.scbdd.com
@Mail: yzjkid9@gmail.com; oriental-cds@163.com
@Blog: https://blog.moyule.me
I love my senpai forerver!:P
"""
from __future__ import absolute_import
from distutils.core import setup
# print（__doc__）
package_data= {'scopy':['structure_alert/*','druglikeness/*','fingerprint/*',
'visualize/*','pretreat/*','data/SMARTS/*','data/PATT/*',
'data/ACID/*','data/*','data/Crippen/*','data/EFG/*','data/Demo/*',
'data/MC/mcloud/*','data/MC/mcloud/ertl/mcloud/*',
'data/MOL/*','data/PubChem/*',]}
#package_data= {'scopy':['structure_alert/*','druglikeness/*','test/*','data/SMARTS/*','data/PATT/*','data/ACID/*','data/*','data/Crippen/*','fingerprint/*']}
setup(name="cbdd-scopy",
version="1.1.2",
license="MIT",
description="A filter tool for HTS and VS",
long_description="Scopy (Screening COmpounds in PYthon), based on RDKit, is an integrated negative design python library designed for screening out undesiable compounds in the early drug discovery.",
author="Zhi-Jiang Yang (Kotori), Dong-Sheng Cao",
author_email="yzjkid9@gmail.com",
maintainer="Zhi-Jiang Yang (Kotori)",
maintainer_email="kotori@cbdd.me",
url="https://github.com/kotori-y/Scopy",
package_data=package_data,
# include_package_data=True,
package_dir={'scopy':'scopy'},
py_modules = ['scopy.ScoConfig'],
packages=['scopy']
)
| [
"yzjkid9@gmail.com"
] | yzjkid9@gmail.com |
d906c6552a6f100ecb942e4634c4b7e28aa9643d | e5cf384933ccc0b54edfc1e775e56586bc100f74 | /pixiv_downloader/pixiv_downloader.py | 1f858e4e6da6edd7f92feb021b5f5213814f07ce | [] | no_license | Einsbon/DeepLearning-with-Pixiv | bda902c07a955fe3bff081fb10986ca7e2fdaad0 | 642aadc892766623a5f861ab448975f36deab8ce | refs/heads/master | 2020-03-28T14:30:19.162858 | 2018-09-12T18:48:56 | 2018-09-12T18:48:56 | 148,493,338 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 5,885 | py | from selenium import webdriver
from bs4 import BeautifulSoup
import time
import urllib.request
import urllib
import getpass
import os
import shutil
phantompath = r'C:\Users\Einsbon\Documents\Python_Projects\python_window\crawler\pixiv_low_quality_image_crawler\phantomjs.exe'
chromepath = 'C:\\Users\\Einsbon\\Documents\\Python Scripts\\crawler\\pixiv_low_quality_image_crawler\\chromedriver.exe'
urlVocaloid = r"https://www.pixiv.net/search.php?s_mode=s_tag_full&word=VOCALOID&type=illust&blt=100&mode=safe"
urlMiku = r'https://www.pixiv.net/search.php?s_mode=s_tag_full&word=%E5%88%9D%E9%9F%B3%E3%83%9F%E3%82%AF&type=illust&blt=100&mode=safe'
urlLogin = 'https://accounts.pixiv.net/login?lang=ko&source=pc&view_type=page&ref=wwwtop_accounts_index'
def driverSetup(web, userId, userpd, urlFirst):
web.get(urlFirst)
web.implicitly_wait(10)
web.find_element_by_xpath(
'/html/body/div[3]/div[3]/div/form/div[1]/div[1]/input').send_keys(
userId)
web.find_element_by_xpath(
'/html/body/div[3]/div[3]/div/form/div[1]/div[2]/input').send_keys(
userpd)
web.find_element_by_xpath(
'/html/body/div[3]/div[3]/div/form/button').click()
web.set_window_size(1020, 960)
def scrollUpToDown(web):
web.execute_script("window.scrollTo(0, 400);")
time.sleep(0.15)
web.execute_script("window.scrollTo(0, 800);")
time.sleep(0.15)
web.execute_script("window.scrollTo(0, 1200);")
time.sleep(0.15)
web.execute_script("window.scrollTo(0, 1600);")
time.sleep(0.15)
web.execute_script("window.scrollTo(0, 2000);")
time.sleep(0.15)
web.execute_script("window.scrollTo(0, 2400);")
time.sleep(0.15)
web.execute_script("window.scrollTo(0, 2800);")
time.sleep(0.15)
web.execute_script("window.scrollTo(0, 3200);")
def checkLoaded(elements):
for element in elements:
est = str(element)
if est.find('p0_master1200') == -1:
print(est)
return False
return True
opener = urllib.request.URLopener()
opener.addheader('Referer', 'https://www.pixiv.net/')
def downloadImage(url, name):
image = opener.open(url)
data = image.read()
f = open(name, 'wb')
f.write(data)
f.close()
image.close()
'''
headers = {'User-Agent': 'Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.11 (KHTML, like Gecko) Chrome/23.0.1271.64 Safari/537.11',
'Accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,*/*;q=0.8',
'Accept-Charset': 'ISO-8859-1,utf-8;q=0.7,*;q=0.3',
'Accept-Encoding': 'none',
'Accept-Language': 'en-US,en;q=0.8',
'Connection': 'keep-alive'}
req = urllib.request.Request(url, headers=headers) # The assembled request
response = urllib.request.urlopen(req) # store the response
# create a new file and write the image
f = open(name, 'wb')
f.write(response.read())
f.close()'''
def crawling(web, repeatNum, savePath):
while repeatNum > 0:
print('\n\nNumber of pages remaining: ' + str(repeatNum))
print('Current_url:' + str(web.current_url))
scrollUpToDown(web)
web.implicitly_wait(20)
html = web.page_source
soup = BeautifulSoup(html, 'html.parser')
count = 0
elements = soup.find_all('div', {'class': '_309ad3C'})
print(checkLoaded(elements))
while checkLoaded(elements) == False:
print('waiting')
time.sleep(0.8)
elements = soup.find_all('div', {'class': '_309ad3C'})
if count == 5:
scrollUpToDown(web)
if count > 12:
web.refresh()
scrollUpToDown(web)
count = 0
count += 1
urlList = []
downloadedWell = True
downloadcount = 0
for element in elements:
# print(element)
est = str(element)
if (est.find('(') != -1 & est.find(')') != -1):
est = est[est.find('(')+2: est.find(')')-1]
urlList.append(est)
filename = savePath + "\\" + est.split('/')[-1]
print(est)
print(filename)
try:
if os.path.isfile(filename):
print(': already')
else:
downloadImage(est, filename)
print(': downloaded')
downloadcount += 1
except:
print('error, refrestart this page')
downloadedWell = False
break
if downloadedWell == False:
web.refresh()
continue
print('Downloaded images in this page: ' + str(downloadcount))
web.find_element_by_xpath(
'//*[@id="wrapper"]/div[1]/div/nav/div/span[2]/a').click()
repeatNum -= 1
def main():
# print("launch path" + os.getcwd())
# print('phantom path' + phantompath)
print(' ')
#userId = input('Id:')
#userPd = getpass.getpass('Password:')
userId = 'sbkim0316@naver.com'
userPd = 'airplane0316'
web = webdriver.Chrome(os.path.abspath(os.path.dirname(__file__)) + '/chromedriver.exe')
driverSetup(web, userId, userPd, urlLogin)
#startUrl = input('Start url:')
startUrl = r'https://www.pixiv.net/search.php?s_mode=s_tag_full&word=VOCALOID&type=illust&blt=1000&mode=safe'
#savePath = input('Path to save:')
savePath = r'D:\picture\miku300-999'
pageNumber = int(input('Number of pages to download:'))
web.get(startUrl)
crawling(web, pageNumber, savePath)
if __name__ == "__main__":
main()
| [
"noreply@github.com"
] | noreply@github.com |
d547c8a2ec1e142851a69e65f88b8dd40d883aaf | 5bf143f904a19a8ff94cf4a7ef8da8da25de3458 | /analyze_sentiment.py | 8d744c14a23fb55013f1cb8285781cf4c8a71f11 | [] | no_license | kurobeko1259/multsum | 7d51a181e43ae8ce28aab7af6e8dee9a48d276ad | cfd4fe98c8371578c804ec85197a75a59fa92869 | refs/heads/master | 2020-04-06T23:00:45.765070 | 2018-11-21T06:34:18 | 2018-11-21T06:34:18 | 157,854,903 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 2,649 | py | #!/usr/bin/python
# -*- coding: utf-8 -*-
import os, sys, re, math, numpy
from sets import Set
f = open(os.path.dirname(__file__)+"/emotion_words_positive.txt")
#positive_emotions = Set(f.readlines())
positive_emotions = set()
for pos in f.readlines():
#print pos
positive_emotions.add(pos.replace("\n", ""))
f = open(os.path.dirname(__file__)+"/emotion_words_negative.txt")
negative_emotions = set()
for neg in f.readlines():
#print neg
negative_emotions.add(neg.replace("\n", ""))
def analyze_sentiment(sentences):
emo_vectors = []
for s in sentences:
#print s
positive_count = 0
negative_count = 0
positive_frac = 0.0
negative_frac = 0.0
#words = s.split()
#words = re.findall(r"[\w']+", s)
if len(s) > 0:
for w in s:
#"print w
w = filter(str.isalnum, w)
if w.lower() in positive_emotions:
#print "positive! "+w
positive_count += 1
elif w.lower() in negative_emotions:
#print "negative! "+w
negative_count += 1
positive_frac = float(positive_count) / float(len(s))
negative_frac = float(negative_count) / float(len(s))
emo_vec = [positive_frac, negative_frac]
emo_vectors.append(emo_vec)
# cosinesim is btw -1 and 1.
# use:
# 1+cosinesimilarity(vec1,vec2)/2
positive_matrix = numpy.zeros((len(sentences), len(sentences)))
negative_matrix = numpy.zeros((len(sentences), len(sentences)))
min_simpos = 1.0
max_simpos = 0.0
min_simneg = 1.0
max_simneg = 0.0
#print "Number of lines: "+str(len(emo_vectors))
for normalize in [1,0]:
for i in range(len(emo_vectors)):
for j in range(len(emo_vectors)):
simpos = 1-abs(emo_vectors[i][0]-emo_vectors[j][0])
simneg = 1-abs(emo_vectors[i][1]-emo_vectors[j][1])
sim = simpos
if normalize == 1:
if simpos > max_simpos:
max_simpos = simpos
if simpos < min_simpos:
min_simpos = simpos
if simneg > max_simneg:
max_simneg = simneg
if simneg < min_simneg:
min_simneg = simneg
else:
normalized_simpos = 0.0
normalized_simneg = 0.0
if len(sentences[i]) > 0 and len(sentences[j]) > 1:
if max_simpos-min_simpos > 0.0:
normalized_simpos = (simpos-min_simpos)/(max_simpos-min_simpos)
if max_simneg-min_simneg > 0.0:
normalized_simneg = (simneg-min_simneg)/(max_simneg-min_simneg)
positive_matrix[i][j] = normalized_simpos
negative_matrix[i][j] = normalized_simneg
return (positive_matrix, negative_matrix)
| [
"[s.u.zzz1259@icloud.com]"
] | [s.u.zzz1259@icloud.com] |
40cf04c7d362189c7309e4ba6a220708a877cf9a | a4e2bd84a06e41a446b521a4a16ecf85e8aee76a | /locate_chromedriver.py | 968593b2dcbe3e79d6acfe7d62da1c0bde8ea2ec | [] | no_license | 526avijitgupta/Bookaway-testing | 273715bb07ab2b5216e4231b1ca63024846c0953 | cb3babd4b60de2e5b1e931fc63880987385ad052 | refs/heads/master | 2021-01-22T09:04:12.689869 | 2014-10-31T13:35:52 | 2014-10-31T13:35:52 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 387 | py | #!/usr/bin/python
# Filename: local_settings.py
import os
print "Locating chromedriver on your machine.."
chromedriver = os.popen("find / -name 'chromedriver' -not -name '*.*' 2>/dev/null").read()
chromedriver = str(chromedriver.split("\n")[0])
if(chromedriver):
print ("chromedriver found! Executing Script..")
else:
print ("Unable to find chromedriver!")
# End of local_settings.py
| [
"526avijitgupta@gmail.com"
] | 526avijitgupta@gmail.com |
d4ec50f6dfaad60ee5b78a209f3dfea8cc4a1456 | eabe353d086aacfe2953eaf5ec8cf71668ae1448 | /backend/card/migrations/0001_initial.py | 3dbd597c01a62d3b027b59fd1094d179e39c1c4d | [] | no_license | epc91/oblique-strategies | d5961e7b0ac92b3338cc6bddf213673d2779661a | de9ac2b1a75b44944534750178641ae1b34604a0 | refs/heads/main | 2023-07-10T11:30:05.971381 | 2021-08-24T23:16:38 | 2021-08-24T23:16:38 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 554 | py | # Generated by Django 3.2.6 on 2021-08-14 01:17
from django.db import migrations, models
class Migration(migrations.Migration):
initial = True
dependencies = [
]
operations = [
migrations.CreateModel(
name='Card',
fields=[
('id', models.BigAutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('own', models.BooleanField(default=False)),
('description', models.CharField(max_length=70)),
],
),
]
| [
"eduardopeschke@gmail.com"
] | eduardopeschke@gmail.com |
07774d6b330c67e1c6a3bd8784a7181f14658bae | 329e8d1ac5b2d38fef727921fdcebc9435dc8842 | /sysdetails.py | 419b8d78f0c693d716f6e4f0dec0d2d730574463 | [
"MIT"
] | permissive | ChankitSaini/DaisyX-Extra | 082a9a008df458bd3aeb765c068055598bea5978 | e1cafbbb4f5a7845ffd70e16e4395e7e797b2cf7 | refs/heads/main | 2023-04-24T02:25:15.715245 | 2021-05-14T05:18:19 | 2021-05-14T05:18:19 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 2,673 | py | import platform
import sys
from datetime import datetime
import psutil
from telethon import __version__
from DaisyX.utils import admin_cmd, sudo_cmd
from DaisyX import ALIVE_NAME
# ================= CONSTANT =================
DEFAULTUSER = str(ALIVE_NAME) if ALIVE_NAME else bot.me.first_name
# ============================================
@bot.on(admin_cmd(outgoing=True, pattern=r"spc$"))
@bot.on(sudo_cmd(allow_sudo=True, pattern=r"spc$"))
async def psu(event):
uname = platform.uname()
softw = "**System Information**\n"
softw += f"`System : {uname.system}`\n"
softw += f"`Release : {uname.release}`\n"
softw += f"`Version : {uname.version}`\n"
softw += f"`Machine : {uname.machine}`\n"
# Boot Time
boot_time_timestamp = psutil.boot_time()
bt = datetime.fromtimestamp(boot_time_timestamp)
softw += f"`Boot Time: {bt.day}/{bt.month}/{bt.year} {bt.hour}:{bt.minute}:{bt.second}`\n"
# CPU Cores
cpuu = "**CPU Info**\n"
cpuu += "`Physical cores : " + str(psutil.cpu_count(logical=False)) + "`\n"
cpuu += "`Total cores : " + str(psutil.cpu_count(logical=True)) + "`\n"
# CPU frequencies
cpufreq = psutil.cpu_freq()
cpuu += f"`Max Frequency : {cpufreq.max:.2f}Mhz`\n"
cpuu += f"`Min Frequency : {cpufreq.min:.2f}Mhz`\n"
cpuu += f"`Current Frequency: {cpufreq.current:.2f}Mhz`\n\n"
# CPU usage
cpuu += "**CPU Usage Per Core**\n"
for i, percentage in enumerate(psutil.cpu_percent(percpu=True)):
cpuu += f"`Core {i} : {percentage}%`\n"
cpuu += "**Total CPU Usage**\n"
cpuu += f"`All Core: {psutil.cpu_percent()}%`\n"
# RAM Usage
svmem = psutil.virtual_memory()
memm = "**Memory Usage**\n"
memm += f"`Total : {get_size(svmem.total)}`\n"
memm += f"`Available : {get_size(svmem.available)}`\n"
memm += f"`Used : {get_size(svmem.used)}`\n"
memm += f"`Percentage: {svmem.percent}%`\n"
# Bandwidth Usage
bw = "**Bandwith Usage**\n"
bw += f"`Upload : {get_size(psutil.net_io_counters().bytes_sent)}`\n"
bw += f"`Download: {get_size(psutil.net_io_counters().bytes_recv)}`\n"
help_string = f"{str(softw)}\n"
help_string += f"{str(cpuu)}\n"
help_string += f"{str(memm)}\n"
help_string += f"{str(bw)}\n"
help_string += "**Engine Info**\n"
help_string += f"`Python {sys.version}`\n"
help_string += f"`Telethon {__version__}`"
await event.edit(help_string)
def get_size(inputbytes, suffix="B"):
factor = 1024
for unit in ["", "K", "M", "G", "T", "P"]:
if inputbytes < factor:
return f"{inputbytes:.2f}{unit}{suffix}"
inputbytes /= factor
| [
"noreply@github.com"
] | noreply@github.com |
c7a1545c04ec79435caf4f721bcbd105da6ab6dc | 18e6cf3e0f748b55738d927c9ea0b0dda71dd60c | /modelo mvc/main.py | fa3118ceea937e8e73a8b6cd0e7db5ad20be8448 | [] | no_license | Adjailson/AulasETE | d0d94697d2c74afb4b38c176d34d6264e2e12ff3 | 0a223e41e8680a7eddac6443a4d9c21ab5d808b9 | refs/heads/main | 2023-09-04T18:52:38.252095 | 2021-11-23T14:48:22 | 2021-11-23T14:48:22 | 399,248,907 | 5 | 1 | null | null | null | null | UTF-8 | Python | false | false | 48 | py | from view.telaView import TelaView
TelaView()
| [
"noreply@github.com"
] | noreply@github.com |
7afc571991601facfe9836680cdc867c586faa8b | ae3b0909aa4b9f96c17385c1c2e62721817121fe | /policy/task_decomposition_all_without_task.py | 580d0829db47525b4d360c18566c1892655c7fe7 | [] | no_license | saki-37/StarCraft | 9f58912f8006e60559d174fdb046c7fc16c15728 | a915116526478a00690393554f7ddf11b5acda5e | refs/heads/master | 2023-07-13T08:17:26.665932 | 2021-08-19T02:32:16 | 2021-08-19T02:32:16 | 398,439,928 | 1 | 0 | null | null | null | null | UTF-8 | Python | false | false | 16,091 | py | '''不传0。没有task score。按列求和选择最优动作。
'''
import torch
import os
from network.task_rnn_all import TaskRNNAll, TaskRNNAllwoTask
from network.task_decomposition_all import TaskDecompositionAll
import torch.nn.functional as F
import time
class TDAll:
def __init__(self, args):
self.n_actions = args.n_actions
self.n_agents = args.n_agents
self.state_shape = args.state_shape
self.obs_shape = args.obs_shape
self.n_tasks = args.n_tasks
input_shape = self.obs_shape
# 根据参数决定RNN的输入维度
if args.last_action:
input_shape += self.n_actions
if args.reuse_network:
input_shape += self.n_agents
# 神经网络
self.eval_rnn = TaskRNNAllwoTask(input_shape, args) # 每个agent选动作的网络
self.target_rnn = TaskRNNAllwoTask(input_shape, args)
self.eval_task_net = TaskDecompositionAll(args) # 把agentsQ值加起来的网络
self.target_task_net = TaskDecompositionAll(args)
self.args = args
if self.args.cuda:
self.eval_rnn.cuda()
self.target_rnn.cuda()
self.eval_task_net.cuda()
self.target_task_net.cuda()
self.model_dir = args.model_dir + '/' + args.alg + '/' + args.map
# 如果存在模型则加载模型
if self.args.load_model:
if os.path.exists(self.model_dir + '/rnn_net_params.pkl'):
path_rnn = self.model_dir + '/rnn_net_params.pkl'
path_qmix = self.model_dir + '/td_net_params.pkl'
map_location = 'cuda:0' if self.args.cuda else 'cpu'
self.eval_rnn.load_state_dict(torch.load(path_rnn, map_location=map_location))
self.eval_task_net.load_state_dict(torch.load(path_qmix, map_location=map_location))
print('Successfully load the model: {} and {}'.format(path_rnn, path_qmix))
else:
raise Exception("No model!")
# 让target_net和eval_net的网络参数相同
self.target_rnn.load_state_dict(self.eval_rnn.state_dict())
self.target_task_net.load_state_dict(self.eval_task_net.state_dict())
self.eval_parameters = list(self.eval_task_net.parameters()) + list(self.eval_rnn.parameters())
if args.optimizer == "RMS":
self.optimizer_rnn = torch.optim.RMSprop(self.eval_rnn.parameters(), lr=args.lr)
self.optimizer_mix = torch.optim.RMSprop(self.eval_task_net.parameters(), lr=args.lr*10)
# 执行过程中，要为每个agent都维护一个eval_hidden
# 学习过程中，要为每个episode的每个agent都维护一个eval_hidden、target_hidden
self.eval_hidden = None
self.target_hidden = None
print('Init alg Task Decomposition')
def learn(self, batch, max_episode_len, train_step, epsilon=None): # train_step表示是第几次学习，用来控制更新target_net网络的参数
'''
在learn的时候，抽取到的数据是四维的，四个维度分别为 1——第几个episode 2——episode中第几个transition
3——第几个agent的数据 4——具体obs维度。因为在选动作时不仅需要输入当前的inputs，还要给神经网络输入hidden_state，
hidden_state和之前的经验相关，因此就不能随机抽取经验进行学习。所以这里一次抽取多个episode，然后一次给神经网络
传入每个episode的同一个位置的transition
'''
episode_num = batch['o'].shape[0]
self.init_hidden(episode_num)
for key in batch.keys(): # 把batch里的数据转化成tensor
if key == 'u':
batch[key] = torch.tensor(batch[key], dtype=torch.long)
else:
batch[key] = torch.tensor(batch[key], dtype=torch.float32)
s, s_next, u, r, avail_u, avail_u_next, terminated = batch['s'], batch['s_next'], batch['u'], \
batch['r'], batch['avail_u'], batch['avail_u_next'],\
batch['terminated']
mask = 1 - batch["padded"].float() # 用来把那些填充的经验的TD-error置0，从而不让它们影响到学习
# 得到每个agent对应的Q值，维度为(episode个数, max_episode_len， n_agents， n_tasks , n_actions )
# i_task shape: (episode个数, max_episode_len， n_agents)
q_evals, q_targets, u_max_q_targets = self.get_q_values(batch, max_episode_len)
if self.args.cuda:
s = s.cuda()
u = u.cuda()
r = r.cuda()
s_next = s_next.cuda()
terminated = terminated.cuda()
mask = mask.cuda()
# 取每个agent动作对应的Q值，并且把最后不需要的一维去掉，因为仅采取一个动作。(n_episode, episode_len, n_agent)
u_shape = list(u.shape) # u.shape : (n_episode, episode_len, n_agent, 1)
u_shape.insert(3, self.n_tasks)
u = u.unsqueeze(-2).expand(u_shape) # u shape: (n_episode, episode_len, n_agent, n_tasks, 1)
q_evals = torch.gather(q_evals, dim=4, index=u).squeeze(4) #q shape: (n_episode, episode_len, n_agent, n_tasks)
# 得到target_q
avail_u_shape = u_shape
avail_u_shape[-1] = self.n_actions
avail_u_next = avail_u_next.unsqueeze(-2).expand(avail_u_shape)
q_targets[avail_u_next == 0.0] = - 9999999
# 通过 i_task_target 选择对应行，找出最大价值的动作，选出该动作对应的所有任务的q值。
i_task_target_shape = list(q_targets.shape) # q_targets.shape: (n_episode, episode_len, n_agent, n_tasks, n_actions)
# i_task_target_shape[-2] = self.n_tasks
i_task_target_shape[-1] = 1 # i_task_target_shape.shape: (n_episode, episode_len, n_agent, n_tasks, 1)
q_targets_seletor = u_max_q_targets.unsqueeze(-2).expand(i_task_target_shape) # shape: (n_episode, episode_len, n_agents, n_tasks, 1)
q_targets = torch.gather(q_targets, dim=-1, index=q_targets_seletor).squeeze(-1) # shape: (n_episode, episode_len, n_agents, n_tasks)
hyper_networks, q_values_list = self.eval_task_net(q_evals, s)
hyper_networks_target, q_targets_list = self.target_task_net(q_targets, s_next)
q_total_eval = sum(self.calc_q_total(q_values_list, hyper_networks))
q_total_target = sum(self.calc_q_total(q_targets_list, hyper_networks_target))
# q_total_target = self.target_qmix_net(q_targets, s_next, i_task_target)
targets = r.sum(dim=-1).unsqueeze(-1) + self.args.gamma * q_total_target * (1 - terminated)
td_error = (q_total_eval - targets.detach())
masked_td_error = mask * td_error # 抹掉填充的经验的td_error
# 不能直接用mean，因为还有许多经验是没用的，所以要求和再比真实的经验数，才是真正的均值
loss1 = (masked_td_error ** 2).sum() / mask.sum()
q_tasks = self.calc_q_total(q_values_list, hyper_networks, is_grad4rnn=False)
q_tasks_targets = self.calc_q_total(q_targets_list, hyper_networks_target, is_grad4rnn=False)
q_tasks = torch.cat(q_tasks, dim=-1)
q_tasks_targets = torch.cat(q_tasks_targets, dim=-1)
q_tasks_targets = r + self.args.gamma * q_tasks_targets * (1 - terminated)
td_task_error = (q_tasks - q_tasks_targets.detach())
task_mask = mask.expand(td_task_error.shape)
masked_td_task_error = task_mask * td_task_error
loss2 = (masked_td_task_error ** 2).sum() / task_mask.sum()
# loss = loss1+loss2
self.optimizer_rnn.zero_grad()
self.optimizer_mix.zero_grad()
# loss1.backward(retain_graph=True)
loss1.backward()
# for parm in self.eval_task_net.parameters():
# x =parm.grad.data.cpu().numpy()
loss2.backward()
# loss.backward()
torch.nn.utils.clip_grad_norm_(self.eval_parameters, self.args.grad_norm_clip)
self.optimizer_rnn.step()
self.optimizer_mix.step()
if train_step > 0 and train_step % self.args.target_update_cycle == 0:
self.target_rnn.load_state_dict(self.eval_rnn.state_dict())
self.target_task_net.load_state_dict(self.eval_task_net.state_dict())
def calc_q_total(self, q_list, hyper_networks, is_grad4rnn=True):
'''
Params:
q_list: n_task长list.代表第i任务输入的q。
'''
Qi_list = []
# 把计算过程搬到这里
for i in range(self.n_tasks):
qi = q_list[i]
w1, b1, w2, b2 = hyper_networks[i]
# 传入的q_values是三维的，shape为(episode_num, max_episode_len， n_agents)
episode_num = qi.size(0)
qi = qi.view(-1, 1, self.args.n_agents) # (episode_num * max_episode_len, 1, n_agents) = (1920,1,5)
if is_grad4rnn:
hidden = F.elu(torch.bmm(qi, w1.detach()) + b1.detach()) # (1920, 1, 32)
Qi = torch.bmm(hidden, w2.detach()) + b2.detach() # (1920, 1, 1)
# hidden = F.elu(torch.bmm(qi, w1) + b1) # (1920, 1, 32)
# Qi = torch.bmm(hidden, w2) + b2 # (1920, 1, 1)
else:
hidden = F.elu(torch.bmm(qi.detach(), w1) + b1) # (1920, 1, 32)
Qi = torch.bmm(hidden, w2) + b2 # (1920, 1, 1)
Qi = Qi.view(episode_num, -1, 1) # (32, 60, 1)
Qi_list.append(Qi)
return Qi_list
def _get_inputs(self, batch, transition_idx):
'''
Return:
inputs: (n_episode*n_agent, n_obs+n_actions+n_agent)
'''
# 取出所有episode上该transition_idx的经验，u_onehot要取出所有，因为要用到上一条
obs, obs_next, u_onehot = batch['o'][:, transition_idx], \
batch['o_next'][:, transition_idx], batch['u_onehot'][:]
# obs: (n_episode, n_agent, n_obs)
episode_num = obs.shape[0]
inputs, inputs_next = [], []
inputs.append(obs)
inputs_next.append(obs_next)
# 给obs添加上一个动作、agent编号
if self.args.last_action:
# inputs append (n_episode, n_agent, n_action) tensor
if transition_idx == 0: # 如果是第一条经验，就让前一个动作为0向量
inputs.append(torch.zeros_like(u_onehot[:, transition_idx]))
else:
inputs.append(u_onehot[:, transition_idx - 1])
inputs_next.append(u_onehot[:, transition_idx])
if self.args.reuse_network:
# 因为当前的obs三维的数据，每一维分别代表(episode编号，agent编号，obs维度)，直接在dim_1上添加对应的向量
# 即可，比如给agent_0后面加(1, 0, 0, 0, 0)，表示5个agent中的0号。而agent_0的数据正好在第0行，那么需要加的
# agent编号恰好就是一个单位矩阵，即对角线为1，其余为0
inputs.append(torch.eye(self.args.n_agents).unsqueeze(0).expand(episode_num, -1, -1))
inputs_next.append(torch.eye(self.args.n_agents).unsqueeze(0).expand(episode_num, -1, -1))
# 要把obs中的三个拼起来，并且要把episode_num个episode、self.args.n_agents个agent的数据拼成40条(40,96)的数据，
# 因为这里所有agent共享一个神经网络，每条数据中带上了自己的编号，所以还是自己的数据
inputs = torch.cat([x.reshape(episode_num * self.args.n_agents, -1) for x in inputs], dim=1)
inputs_next = torch.cat([x.reshape(episode_num * self.args.n_agents, -1) for x in inputs_next], dim=1)
return inputs, inputs_next
def get_q_values(self, batch, max_episode_len, require_grad=True):
# 按照时间顺序将q拼接起来
episode_num = batch['o'].shape[0]
q_evals, q_targets = [], []
u_max_q_targets= []
for transition_idx in range(max_episode_len):
# inputs, inputs_next：(episode_num * n_agents, n_obs+n_actions+n_agent).表示
inputs, inputs_next = self._get_inputs(batch, transition_idx) # 给obs加last_action、agent_id
if self.args.cuda:
inputs = inputs.cuda()
inputs_next = inputs_next.cuda()
self.eval_hidden = self.eval_hidden.cuda()
self.target_hidden = self.target_hidden.cuda()
# q_eval维度为 (n_episode*n_agent, n_tasks, n_action)
q_eval, self.eval_hidden = self.eval_rnn(inputs, self.eval_hidden)
q_target, self.target_hidden= self.target_rnn(inputs_next, self.target_hidden)
# 把q_eval维度重新变回(episode_num, n_agents, n_tasks, n_actions)
q_eval = q_eval.view(episode_num, self.n_agents, self.n_tasks,-1)
q_target = q_target.view(episode_num, self.n_agents, self.n_tasks, -1)
# u_max_q_targets (n_episode, n_agents, 1 )
u_max_q_target = q_target.sum(dim=2).argmax(dim=-1).unsqueeze(-1)
q_evals.append(q_eval)
q_targets.append(q_target)
u_max_q_targets.append(u_max_q_target)
# 得的q_eval和q_target是一个列表，列表里装着max_episode_len个数组，数组的的维度是(episode_num, n_agents, n_tasks, n_actions)
# q_evals： (episode个数, max_episode_len， n_agents, n_tasks, n_actions)的数组
q_evals = torch.stack(q_evals, dim=1)
q_targets = torch.stack(q_targets, dim=1)
u_max_q_targets = torch.stack(u_max_q_targets, dim=1) # u_max_q_targets (n_episode, max_episode_len， n_agents, 1 )的数组
return q_evals, q_targets, u_max_q_targets
def find_task_q(self, q):
'''
Params:
q: (n_episode, n_tasks, n_actions+1)
Returns:
q: (n_episode, n_tasks, n_actions)
i_task: (n_episode, n_tasks, n_actions)
'''
q_shape = q.shape
# 1. 取最后一个维度第一个数作为task selector, 选择最擅长的task
_, i_task = q[...,0].max(dim = -1) # i_task shape: (n_episode)
# 2. 取对应项相乘，计算
i_task = i_task.unsqueeze(-1).unsqueeze(-1) # i_task shape: (n_episode, 1, 1)
i_task_shape = list(q_shape)
i_task_shape[-2] = 1
i_task = i_task.expand(i_task_shape) # i_task_shape: (n_episode, 1, n_action + 1)
q = torch.gather(q, dim=-2, index=i_task) # q shape (n_episode, 1, n_action + 1)。 选择q中仅与最高分任务的一行
q = (q[...,0].unsqueeze(-1) *q[...,1:]).squeeze(-2) # task_score * 对应动作q值。 q shape : (n_episode, n_action)
i_task = i_task.squeeze(1) # 将 i_task task维度去掉(因为该维度值必为1)
i_task = i_task[..., 0] # i_task shape (n_episode)
return q, i_task
def init_hidden(self, episode_num):
# 为每个episode中的每个agent都初始化一个eval_hidden、target_hidden
self.eval_hidden = torch.zeros((episode_num, self.n_agents, self.args.rnn_hidden_dim))
self.target_hidden = torch.zeros((episode_num, self.n_agents, self.args.rnn_hidden_dim))
def save_model(self, train_step):
num = str(train_step // self.args.save_cycle)
if not os.path.exists(self.model_dir):
os.makedirs(self.model_dir)
time_n = time.time()
torch.save(self.eval_task_net.state_dict(), self.model_dir + '/' + num +str(time_n) + '_td_net_params.pkl')
torch.save(self.eval_rnn.state_dict(), self.model_dir + '/' + num +str(time_n) + '_rnn_net_params.pkl')
| [
"852488062@qq.com"
] | 852488062@qq.com |
ee3ca22e9c0f5e05bbf59b552966f070d8a674d9 | 8613ec7f381a6683ae24b54fb2fb2ac24556ad0b | /20~29/ABC021/honest.py | 9c7f3d43c9ab13670129a435bb52b6b7b42038ab | [] | no_license | Forest-Y/AtCoder | 787aa3c7dc4d999a71661465349428ba60eb2f16 | f97209da3743026920fb4a89fc0e4d42b3d5e277 | refs/heads/master | 2023-08-25T13:31:46.062197 | 2021-10-29T12:54:24 | 2021-10-29T12:54:24 | 301,642,072 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 296 | py | n = int(input())
a, b = map(int, input().split())
m = int(input())
x, y = [0] * m, [0] * m
data = [[] for _ in range(n)]
for i in range(m):
x, y = map(int, input().split())
data[x - 1].append(y - 1)
data[y - 1].append(x - 1)
dist = [[-1] * n for i in range(n)]
dist[a - 1][b - 1] = 0 | [
"yuuya15009@gmail.com"
] | yuuya15009@gmail.com |
62a6d539be9bbfbbbbed23228177058ae2f177d0 | 189d590adf09e309862e5f3aef1012c8a6e0b337 | /userbot/plugin/rename.py | d34526cdf9e8f7d497224a26489c06d9e3e5c380 | [
"MIT"
] | permissive | LegitWoLf/BlackShadowBot | dbabec745ccbb6dac5615dfaa5dd8a24849c9c4b | be3e3e5f390141944ce2f412274cbb0e1463c493 | refs/heads/master | 2022-07-02T21:30:25.346111 | 2020-05-15T11:50:04 | 2020-05-15T11:50:04 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 7,728 | py | """Rename Telegram Files
Syntax:
.rename file.name
.rnupload file.name
.rnstreamupload file.name
By @Ck_ATR"""
import aiohttp
import asyncio
from datetime import datetime
from hachoir.metadata import extractMetadata
from hachoir.parser import createParser
import json
import os
import requests
import subprocess
from telethon import events
from telethon.tl.types import DocumentAttributeVideo
from telethon.errors import MessageNotModifiedError
import time
from userbot.utils import progress, humanbytes, time_formatter, admin_cmd
import io
import math
import os
from pySmartDL import SmartDL
thumb_image_path = Config.TMP_DOWNLOAD_DIRECTORY + "/thumb_image.jpg"
def get_video_thumb(file, output=None, width=90):
metadata = extractMetadata(createParser(file))
p = subprocess.Popen([
'ffmpeg', '-i', file,
'-ss', str(int((0, metadata.get('duration').seconds)[metadata.has('duration')] / 2)),
'-filter:v', 'scale={}:-1'.format(width),
'-vframes', '1',
output,
], stdout=subprocess.PIPE, stderr=subprocess.DEVNULL)
if not p.returncode and os.path.lexists(file):
return output
@borg.on(admin_cmd("rename (.*)"))
async def _(event):
if event.fwd_from:
return
await event.edit("Renaming in process 🙄🙇♂️🙇♂️🙇♀️ It might take some time if file size is big")
input_str = event.pattern_match.group(1)
if not os.path.isdir(Config.TMP_DOWNLOAD_DIRECTORY):
os.makedirs(Config.TMP_DOWNLOAD_DIRECTORY)
if event.reply_to_msg_id:
start = datetime.now()
file_name = input_str
reply_message = await event.get_reply_message()
# c_time = time.time()
to_download_directory = Config.TMP_DOWNLOAD_DIRECTORY
downloaded_file_name = os.path.join(to_download_directory, file_name)
downloaded_file_name = await borg.download_media(
reply_message,
downloaded_file_name
)
end = datetime.now()
ms = (end - start).seconds
if os.path.exists(downloaded_file_name):
await event.edit("Downloaded to `{}` in {} seconds.".format(downloaded_file_name, ms))
else:
await event.edit("Error Occurred\n {}".format(input_str))
else:
await event.edit("Syntax // `.rename file.name` as reply to a Telegram media")
@borg.on(admin_cmd("rnupload (.*)"))
async def _(event):
if event.fwd_from:
return
thumb = None
if os.path.exists(thumb_image_path):
thumb = thumb_image_path
await event.edit("Rename & Upload in process 🙄🙇♂️🙇♂️🙇♀️ It might take some time if file size is big")
input_str = event.pattern_match.group(1)
if not os.path.isdir(Config.TMP_DOWNLOAD_DIRECTORY):
os.makedirs(Config.TMP_DOWNLOAD_DIRECTORY)
if event.reply_to_msg_id:
start = datetime.now()
file_name = input_str
reply_message = await event.get_reply_message()
to_download_directory = Config.TMP_DOWNLOAD_DIRECTORY
downloaded_file_name = os.path.join(to_download_directory, file_name)
downloaded_file_name = await borg.download_media(
reply_message,
downloaded_file_name
)
end = datetime.now()
ms_one = (end - start).seconds
if os.path.exists(downloaded_file_name):
c_time = time.time()
await borg.send_file(
event.chat_id,
downloaded_file_name,
force_document=False,
supports_streaming=False,
allow_cache=False,
reply_to=event.message.id,
thumb=thumb,
)
end_two = datetime.now()
os.remove(downloaded_file_name)
ms_two = (end_two - end).seconds
await event.edit("Downloaded in {} seconds. Uploaded in {} seconds.".format(ms_one, ms_two))
else:
await event.edit("File Not Found {}".format(input_str))
else:
await event.edit("Syntax // .rnupload file.name as reply to a Telegram media")
@borg.on(admin_cmd("rnstreamupload (.*)"))
async def _(event):
if event.fwd_from:
return
await event.edit("Rename & Upload as Streamable in process 🙄🙇♂️🙇♂️🙇♀️ It might take some time if file size is big")
input_str = event.pattern_match.group(1)
if not os.path.isdir(Config.TMP_DOWNLOAD_DIRECTORY):
os.makedirs(Config.TMP_DOWNLOAD_DIRECTORY)
if event.reply_to_msg_id:
start = datetime.now()
file_name = input_str
reply_message = await event.get_reply_message()
c_time = time.time()
to_download_directory = Config.TMP_DOWNLOAD_DIRECTORY
downloaded_file_name = os.path.join(to_download_directory, file_name)
downloaded_file_name = await borg.download_media(
reply_message,
downloaded_file_name
)
end_one = datetime.now()
ms_one = (end_one - start).seconds
if os.path.exists(downloaded_file_name):
thumb = None
if not downloaded_file_name.endswith((".mkv", ".mp4", ".mp3", ".flac")):
await event.edit("Sorry. But I don't think {} is a streamable file. Please try again.\n**Supported Formats**: MKV, MP4, MP3, FLAC".format(downloaded_file_name))
return False
if os.path.exists(thumb_image_path):
thumb = thumb_image_path
else:
thumb = get_video_thumb(downloaded_file_name, thumb_image_path)
start = datetime.now()
metadata = extractMetadata(createParser(downloaded_file_name))
duration = 0
width = 0
height = 0
if metadata.has("duration"):
duration = metadata.get('duration').seconds
if os.path.exists(thumb_image_path):
metadata = extractMetadata(createParser(thumb_image_path))
if metadata.has("width"):
width = metadata.get("width")
if metadata.has("height"):
height = metadata.get("height")
# Telegram only works with MP4 files
# this is good, since with MKV files sent as streamable Telegram responds,
# Bad Request: VIDEO_CONTENT_TYPE_INVALID
# c_time = time.time()
try:
await borg.send_file(
event.chat_id,
downloaded_file_name,
thumb=thumb,
caption="reuploaded by [IndianBot](https://github.com/blackshadow98/BlackShadowBot",
force_document=False,
allow_cache=False,
reply_to=event.message.id,
attributes=[
DocumentAttributeVideo(
duration=duration,
w=width,
h=height,
round_message=False,
supports_streaming=True
)
]
)
except Exception as e:
await event.edit(str(e))
else:
end = datetime.now()
os.remove(downloaded_file_name)
ms_two = (end - end_one).seconds
await event.edit("Downloaded in {} seconds. Uploaded in {} seconds.".format(ms_one, ms_two))
else:
await event.edit("File Not Found {}".format(input_str))
else:
await event.edit("Syntax // .rnstreamupload file.name as reply to a Telegram media")
| [
"noreply@github.com"
] | noreply@github.com |
08b555c7ec6ff9ee16e439791eb8fbf841af3b69 | 8d74ac2b0acad1a10bc916d8566181ac801c7597 | /articles/migrations/0007_auto_20210305_2120.py | 4b9e92250ae194fa86adcf0379f40449a515f1d4 | [] | no_license | PatrickBoynton/news-app | e459b08f651258388d0d20074d94a685ad57d44f | 125464d4ff89d7a2297536e39ba4dd770e5089fa | refs/heads/main | 2023-08-22T00:17:30.512303 | 2021-09-24T05:19:32 | 2021-09-24T05:19:32 | 343,518,497 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 832 | py | # Generated by Django 3.1.7 on 2021-03-05 21:20
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('articles', '0006_auto_20210305_0416'),
]
operations = [
migrations.AlterField(
model_name='article',
name='article_status',
field=models.CharField(choices=[('pending', 'pending'), ('published', 'published'), ('rejected', 'rejected'), ('archived', 'archived')], default='draft', max_length=80),
),
migrations.AlterField(
model_name='article',
name='article_type',
field=models.CharField(choices=[('astronomy', 'astronomy'), ('cosmology', 'cosmology'), ('exoplanets', 'exoplanets'), ('editorial', 'editorial')], default='astronomy', max_length=80),
),
]
| [
"jpboynton3@gmail.com"
] | jpboynton3@gmail.com |
ba928fba58eaca0bce8982e702f44206ea394ad4 | 631d667ad3f5ef759923e63644e82af5e6bdebf6 | /Cryptology/Cyphers/Cyphers/CypherGeneration/Bifid/BifidUtil.py | 7f6969a258ced04ba6d69ce894d5b5c5178752f2 | [] | no_license | frankbryce/First | e808537593f96fb693e65bb909cd1a84dc2fa76e | a69ccb715a65d7647272402790674336e72431f0 | refs/heads/master | 2020-12-28T23:34:58.777395 | 2014-05-15T00:15:52 | 2014-05-15T00:15:52 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,195 | py | import StrUtil as s
alphabet = 'ABCDEFGHIKLMNOPQRSTUVWXYZ' # intentionally missing 'j'
Table = []
Result = []
def SetKey(keyword):
global Table
keyword = jtoi(s.StripStr(keyword))
keyalph = s.GenerateKeyedAlphabet(keyword,alphabet)
Table = [keyalph[i:i+5] for i in xrange(0, len(keyalph), 5)]
return Table
def jtoi(c):
return c.upper().replace("J","I")
def GetRow(c):
global Table
c = jtoi(c)
return [i for i,row in enumerate(Table) if c in row][0]
def GetCol(c):
global Table
c = jtoi(c)
return [row.index(c) for _,row in enumerate(Table) if c in row][0]
def GetChr(r,c):
global Table
return Table[r][c]
def GenerateResult(str):
str = jtoi(s.StripStr(str))
nums = [GetRow(c) for c in str]+[GetCol(c) for c in str]
numtuples = [nums[i:i+2] for i in xrange(0, len(nums), 2)]
return ''.join([GetChr(r,c) for (r,c) in numtuples])
def GenerateInput(str):
str = jtoi(s.StripStr(str))
tuples = [[GetRow(c),GetCol(c)] for c in str]
nums = [item for tuple in tuples for item in tuple]
coords = [(nums[i],nums[i+len(nums)/2]) for i in range(len(nums)/2)]
return ''.join([GetChr(r,c) for (r,c) in coords]) | [
"jonnyjack7@gmail.com"
] | jonnyjack7@gmail.com |
4bbad83e050e46e0bd882f7147d3faa597ef6614 | 26d6c34df00a229dc85ad7326de6cb5672be7acc | /msgraph-cli-extensions/beta/files_beta/setup.py | 917c1b376b39507335d14d388323f62f011a8a2c | [
"MIT"
] | permissive | BrianTJackett/msgraph-cli | 87f92471f68f85e44872939d876b9ff5f0ae6b2c | 78a4b1c73a23b85c070fed2fbca93758733f620e | refs/heads/main | 2023-06-23T21:31:53.306655 | 2021-07-09T07:58:56 | 2021-07-09T07:58:56 | 386,993,555 | 0 | 0 | NOASSERTION | 2021-07-17T16:56:05 | 2021-07-17T16:56:05 | null | UTF-8 | Python | false | false | 1,858 | py | #!/usr/bin/env python
# --------------------------------------------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License. See License.txt in the project root for license information.
# --------------------------------------------------------------------------------------------
from codecs import open
from setuptools import setup, find_packages
# HISTORY.rst entry.
VERSION = '0.1.0'
try:
from azext_files_beta.manual.version import VERSION
except ImportError:
pass
# The full list of classifiers is available at
# https://pypi.python.org/pypi?%3Aaction=list_classifiers
CLASSIFIERS = [
'Development Status :: 4 - Beta',
'Intended Audience :: Developers',
'Intended Audience :: System Administrators',
'Programming Language :: Python',
'Programming Language :: Python :: 3',
'Programming Language :: Python :: 3.6',
'Programming Language :: Python :: 3.7',
'Programming Language :: Python :: 3.8',
'License :: OSI Approved :: MIT License',
]
DEPENDENCIES = []
try:
from azext_files_beta.manual.dependency import DEPENDENCIES
except ImportError:
pass
with open('README.md', 'r', encoding='utf-8') as f:
README = f.read()
with open('HISTORY.rst', 'r', encoding='utf-8') as f:
HISTORY = f.read()
setup(
name='files_beta',
version=VERSION,
description='Microsoft Azure Command-Line Tools Files Extension',
author='Microsoft Corporation',
author_email='azpycli@microsoft.com',
url='https://github.com/Azure/azure-cli-extensions/tree/master/files_beta',
long_description=README + '\n\n' + HISTORY,
license='MIT',
classifiers=CLASSIFIERS,
packages=find_packages(),
install_requires=DEPENDENCIES,
package_data={'azext_files_beta': ['azext_metadata.json']},
)
| [
"japhethobalak@gmail.com"
] | japhethobalak@gmail.com |
62604aec9fec8d853af4fd5bdc81a077ae397e7c | 4e567fc53288f53cdcfa37c0f5490a29cf4bc0cd | /projects-inventory/inventory/inventorya.py | dc89e6b234d8d28aaf3a41b5b98a5039f3ac59d3 | [] | no_license | sidarmawan/ansible_training | 3e4bba3bde4557a23c92735314f09328319463c9 | 77d3acb3430143458de07ca2b8257f3a2637f59b | refs/heads/master | 2022-04-08T06:01:31.043044 | 2020-01-16T09:56:41 | 2020-01-16T09:56:41 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 103 | py | htttps://materials.example.com/labs/projects-inventory/inventorya.py: Unsupported scheme ‘htttps’.
| [
"student@workstation.lab.example.com"
] | student@workstation.lab.example.com |
db7d00585385b6589b6b11d0e3b16814d349cc17 | a61ebd1507eeaa334aff44800b022ef0a258752a | /Code/CodeChef/remainder.py | 37c511c002d48cc5390d47f937a2ec559c35e257 | [
"MIT"
] | permissive | Jimut123/competitive_programming | 14ce0ab65414e6086763519f95487cddc91205a9 | b4cdebaceee719c1a256921829ebafda11c515f5 | refs/heads/master | 2023-03-05T15:42:57.194176 | 2022-04-08T08:53:26 | 2022-04-08T08:53:26 | 156,541,142 | 1 | 0 | null | 2019-05-29T17:10:28 | 2018-11-07T12:09:55 | C++ | UTF-8 | Python | false | false | 152 | py | #jimutbahanpal@yahoo.com
t = int(input())
l = []
for i in range(t):
m,n = map(int,input().split())
l.append(m%n)
for item in l:
print(item)
| [
"jimutbahanpal@yahoo.com"
] | jimutbahanpal@yahoo.com |
33bb22d6d151a3b8a644cd786b43bd9822fdbecd | 8f02ed34ca9d4c82cf3b6305792e0b43cb032456 | /motor_descuento/test/test_articulos.py | e8008d54a3a3d2ce86cabe415c54fdde4a89b02c | [
"Apache-2.0"
] | permissive | angelquin1986/tiptopDescuentos | 977569c15a9e5d3632177f4f7ce39c156931be3a | 6bca61d3142b75eee0e72f3932aeabcf1457526a | refs/heads/master | 2023-07-30T02:09:35.119053 | 2020-06-19T17:11:47 | 2020-06-19T17:11:47 | 272,595,003 | 0 | 0 | Apache-2.0 | 2021-09-22T19:15:28 | 2020-06-16T02:53:13 | TypeScript | UTF-8 | Python | false | false | 1,509 | py | """
@author aquingaluisa
"""
# Cargamos el módulo unittest
import os
import random
import pytest
from django.test import TestCase
# Creamos una clase heredando de TestCase
from motor_descuento.logica_negocio import ln_articulo as ln_articulo, ln_descuento
from motor_descuento.modelo.modelo_productos import Product
from motor_descuento.test import util_test
class TestArticulos(TestCase):
fixtures = ['db.json', 'descuento.json']
def setUp(self):
util_test.product();
util_test.stok_items()
# Creamos una prueba para probar un valor inicial
@pytest.mark.django_db
def test_crear_articulos(self):
categorias_list = list(ln_articulo.consultar_categoria_id_list())
producto_list = list(ln_articulo.consultar_product_id_list())
stok_list = list(ln_articulo.consultar_stock_id_list())
discount_list = list(ln_descuento.obtener_descuento_list())
print(categorias_list)
print(producto_list)
print(stok_list)
print(discount_list[0].json_data)
brand_id_list = [1, 2, 3, 4, 5]
# product_list = []
# is_create = False
# for numero in [10000]:
# product = Product(name='Producto_' + str(numero), description='Producto_' + str(numero), tax_rate=12)
# product.brand_id = random.choice(brand_id_list)
# product_list.append(product)
# is_create = ln_articulo.crear_producto_list(product_list)
# self.assertEqual(True, is_create)
| [
"aquingaluisa@galapagosislands.com"
] | aquingaluisa@galapagosislands.com |
ea6f324c9eaa7e365c7e8d1385843b1e0080dd62 | 5f0ce32209a69ca934174631df447b8d4d13caaa | /bibliotecaMath.py | 408d5eaecc1fb58bbb5282697c3d4545a2ba33a6 | [] | no_license | arthurcardosof/praticando-python | 8fea1da81ab09402d35db9f8cebfeaae664e90bb | f3c997e1963ddf1caad0396a6ec52803dc309583 | refs/heads/master | 2020-12-21T07:48:17.650179 | 2020-01-26T19:26:00 | 2020-01-26T19:26:00 | 236,363,509 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 459 | py | '''
Exercicio: Crie uma função para verificar se uma palavra é palíndrome. Retorna true se for uma palavra palíndrome, e false se não for.
Exemplos de palavras palindromes: ovo, arara
'arara' -> [0], [1], [2]
'''
def palindrome(palavra): return palavra == palavra[::-1]
print(palindrome('arara'))
print(palindrome('murilo'))
print(palindrome('OVO'))
def contaAte1000():
for i in range(1000):
print(i)
contaAte1000()
| [
"panchobauer@gmail.com"
] | panchobauer@gmail.com |
65a4c9be5fe8054649cd3ece5dbe367bc18a3e9e | 2dc17d12ff6ea9794177c81aa4f385e4e09a4aa5 | /archive/55JumpGame.py | f503b433be7298c6acd75a571e1e0b2c43dd3322 | [] | no_license | doraemon1293/Leetcode | 924b19f840085a80a9e8c0092d340b69aba7a764 | 48ba21799f63225c104f649c3871444a29ab978a | refs/heads/master | 2022-10-01T16:20:07.588092 | 2022-09-08T02:44:56 | 2022-09-08T02:44:56 | 122,086,222 | 0 | 0 | null | null | null | null | WINDOWS-1252 | Python | false | false | 398 | py | # coding=utf-8
'''
Created on 2017�3�7�
@author: Administrator
'''
class Solution(object):
def canJump(self, nums):
"""
:type nums: List[int]
:rtype: bool
"""
mini_true = len(nums) - 1
for i in xrange(len(nums) - 1, -1, -1):
if nums[i] >= (mini_true - i):
mini_true = i
return mini_true == 0
| [
"yanhuang1293@gmail.com"
] | yanhuang1293@gmail.com |
c80f4c821974aa8bd95e8d011cc9d0f71baae2a6 | 19e4d45d574d6dedd46fa9fac20faa1151fa2601 | /Euler49-PrimePermutations.py | a15d07ba65b2b5b3933211a5860e9f535f18255b | [] | no_license | zzflux/ProjectEuler | bba88d5e42232aa0d39d8325a7752f0983084135 | 796d0ed72738031a300a0e950bb792d046518e73 | refs/heads/master | 2021-01-01T05:14:19.125012 | 2016-04-25T22:45:17 | 2016-04-25T22:45:17 | 56,016,380 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,861 | py | #Project Euler problem #49
#Prime permutations
#Jon McMahon 2016
import DivisibilityTools as dtools
import itertools as itools
import math
DIGITS = 4
SEQ_LEN = 3
BLACKLIST = ((1487, 4817, 8147),)
PRIMELIST = dtools.primeListWithDigits(DIGITS)
PRIMESET = frozenset(PRIMELIST)
GREATEST_PRIME = max(PRIMELIST)
SMALLEST_PRIME = min(PRIMELIST)
def sequenceGenerator(prefix = None):
#print('Called with prefix=', prefix)
if not prefix:
for p in PRIMELIST:
for grp in sequenceGenerator((p,)):
if len(grp) == SEQ_LEN:
yield grp
elif len(prefix) == 1:
#print('Set1:', set(int(''.join(_)) for _ in itools.permutations(str(prefix[-1]))))
#print('Set2:', '*primes*')
#print('Set3:', set(range(prefix[-1] + 1, GREATEST_PRIME + 1)))
#print('Intersection:', sorted(set(int(''.join(_)) for _ in itools.permutations(str(prefix[-1]))) & PRIMESET & set(range(prefix[-1] + 1, GREATEST_PRIME + 1))))
for nextprime in sorted(set(int(''.join(_)) for _ in itools.permutations(str(prefix[-1]))) & PRIMESET & set(range(prefix[-1] + 1, GREATEST_PRIME + 1))):
#print('Supposedly calling on', prefix + (nextprime,))
for g in sequenceGenerator(prefix + (nextprime,)):
yield g
elif len(prefix) == SEQ_LEN:
yield prefix
else:
#print('Seq len:', len(prefix))
np = 2 * prefix[-1] - prefix[0]
if np in (PRIMESET & set(int(''.join(_)) for _ in itools.permutations(str(prefix[-1])))):
for g in sequenceGenerator(prefix + (np,)):
yield g
else:
#print('Impossible')
pass
if __name__ == '__main__':
for seq in sequenceGenerator():
if not seq in BLACKLIST:
print('Answer:', ''.join(str(_) for _ in seq))
| [
"jonmcmahon21@gmail.com"
] | jonmcmahon21@gmail.com |
7d4cf1affeb7426291e784ea8cf7e185ac843347 | 3bd3441d6cf42b50ce627db32495004d1e515d63 | /Lesson2/weather_station.py | e9c0349c1930f32517de816f5972e5bfe26f3c5e | [] | no_license | MikalaiMikalalai/head_first_design_patterns | cf8006a3b7d8f531a4872d150a73b5ed77b88a23 | 8792eae3d73e30912f93bf56c7d7b2d844e3d5ec | refs/heads/master | 2021-02-27T02:22:25.057364 | 2020-04-18T02:22:43 | 2020-04-18T02:22:43 | 245,570,150 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 4,367 | py | ######################################################
# Observer #
######################################################
import abc
import collections
######################################################
# Interfaces #
######################################################
WeatherParams = collections.namedtuple('WeatherParams', ['temperature', 'humidity', 'pressure'])
class Subject(abc.ABC):
@abc.abstractmethod
def register_observer(self, observer):
pass
@abc.abstractmethod
def remove_observer(self, observer):
pass
@abc.abstractmethod
def notify_observers(self):
pass
class Observer(abc.ABC):
@abc.abstractmethod
def update(self):
pass
class DisplayElement(abc.ABC):
@abc.abstractmethod
def display(self, weather_params: WeatherParams):
pass
######################################################
# Implementations #
######################################################
class WeatherData(Subject):
def __init__(self):
self.observers = []
self.__temperature: float
self.__humidity: float
self.__pressure: float
def register_observer(self, observer):
self.observers.append(observer)
def remove_observer(self, observer):
self.observers.remove(observer)
def notify_observers(self):
for obs in self.observers:
obs.update()
def measurements_changed(self):
self.notify_observers()
def set_measurments(self, temperature, humidity, pressure):
self.__temperature = temperature
self.__humidity = humidity
self.__pressure = pressure
self.measurements_changed()
def get_weather_params(self):
return WeatherParams(self.__temperature,
self.__humidity,
self.__pressure)
class CurrentConditionsDisplay(Observer, DisplayElement):
def __init__(self, weather_data):
self.weather_data = weather_data
self.weather_data.register_observer(self)
def update(self):
self.display(self.weather_data.get_weather_params())
def display(self, weather_params):
print(f'Current conditions: {weather_params.temperature}F degrees'
f' and {weather_params.humidity}% humidity')
class StatisticsDisplay(Observer, DisplayElement):
def __init__(self, weather_data):
self.weather_data = weather_data
self.weather_data.register_observer(self)
def update(self):
curr_weather_params = self.weather_data.get_weather_params()
statistics_weather_params = WeatherParams(curr_weather_params.temperature - 2,
curr_weather_params.humidity - 2,
curr_weather_params.pressure - 2)
self.display(statistics_weather_params)
def display(self, weather_params):
print(f'Previous conditions: {weather_params.temperature}F degrees'
f' and {weather_params.humidity}% humidity')
class ForecastDisplay(Observer, DisplayElement):
def __init__(self, weather_data):
self.weather_data = weather_data
self.weather_data.register_observer(self)
def update(self):
curr_weather_params = self.weather_data.get_weather_params()
forecast_weather_params = WeatherParams(curr_weather_params.temperature + 2,
curr_weather_params.humidity + 2,
curr_weather_params.pressure + 2)
self.display(forecast_weather_params)
def display(self, weather_params):
print(f'Tomorrow conditions: {weather_params.temperature}F degrees'
f' and {weather_params.humidity}% humidity')
if __name__ == '__main__':
# Initializing objects
weather_data = WeatherData()
current_display = CurrentConditionsDisplay(weather_data)
statistics_display = StatisticsDisplay(weather_data)
forecast_display = ForecastDisplay(weather_data)
# Starting weather station
weather_data.set_measurments(80, 65, 30.4)
weather_data.set_measurments(82, 70, 29.2)
weather_data.set_measurments(78, 90, 29.2)
| [
"mikalaip@google.com"
] | mikalaip@google.com |
71402662a43efd9f3ece9bfc6b5fb824add27987 | c676bf5e77ba43639faa6f17646245f9d55d8687 | /tests/ut/python/ops/test_tuple_slice.py | ea5112995c06203210d7c6ca569e2949187c6f26 | [
"Apache-2.0",
"BSD-3-Clause-Open-MPI",
"MPL-2.0-no-copyleft-exception",
"LGPL-2.1-only",
"BSD-3-Clause",
"MPL-2.0",
"MPL-1.0",
"Libpng",
"AGPL-3.0-only",
"MPL-1.1",
"LicenseRef-scancode-proprietary-license",
"MIT",
"IJG",
"LicenseRef-scancode-unknown-license-reference",
"Unlicense",
"Z... | permissive | zhengnengjin/mindspore | 1e2644e311f54a8bd17010180198a46499e9c88f | 544b859bb5f46611882749088b44c5aebae0fba1 | refs/heads/master | 2022-05-13T05:34:21.658335 | 2020-04-28T06:39:53 | 2020-04-28T06:39:53 | 259,522,589 | 2 | 0 | Apache-2.0 | 2020-04-28T03:35:33 | 2020-04-28T03:35:33 | null | UTF-8 | Python | false | false | 4,665 | py | # Copyright 2020 Huawei Technologies Co., Ltd
#
# 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.
# ============================================================================
""" test_tuple_slice """
import numpy as np
import pytest
from mindspore import Tensor
from mindspore.nn import Cell
import mindspore.ops.operations as P
from ....mindspore_test_framework.mindspore_test import mindspore_test
from ....mindspore_test_framework.pipeline.forward.compile_forward \
import pipeline_for_compile_forward_ge_graph_for_case_by_case_config
from ....mindspore_test_framework.pipeline.forward.verify_exception \
import pipeline_for_verify_exception_for_case_by_case_config
class NetWork_1(Cell):
""" NetWork_1 definition """
def __init__(self):
super(NetWork_1, self).__init__()
self.addN = P.AddN()
def construct(self, tensor_tuple):
tensor_tuple_slice0 = tensor_tuple[:]
tensor_tuple_slice1 = tensor_tuple[:3]
tensor_tuple_slice2 = tensor_tuple[1:]
tensor_tuple_slice3 = tensor_tuple[2:5:1]
sum0 = self.addN(tensor_tuple_slice0)
sum1 = self.addN(tensor_tuple_slice1)
sum2 = self.addN(tensor_tuple_slice2)
sum3 = self.addN(tensor_tuple_slice3)
ret = sum0 + sum1 + sum2 + sum3
return ret
class NetWork_2(Cell):
""" NetWork_2 definition """
def __init__(self):
super(NetWork_2, self).__init__()
self.addN = P.AddN()
def construct(self, tensor_tuple):
tensor_tuple_slice0 = tensor_tuple[::-1]
tensor_tuple_slice1 = tensor_tuple[-1::-1]
tensor_tuple_slice2 = tensor_tuple[:-4:-1]
tensor_tuple_slice3 = tensor_tuple[-6:3]
tensor_tuple_slice4 = tensor_tuple[-1:-6:-2]
sum0 = self.addN(tensor_tuple_slice0)
sum1 = self.addN(tensor_tuple_slice1)
sum2 = self.addN(tensor_tuple_slice2)
sum3 = self.addN(tensor_tuple_slice3)
sum4 = self.addN(tensor_tuple_slice4)
ret = sum0 + sum1 + sum2 + sum3 + sum4
return ret
class NetWork_3(Cell):
""" NetWork_3 definition """
def __init__(self):
super(NetWork_3, self).__init__()
self.addN = P.AddN()
def construct(self, tensor_tuple, start, stop, step=1):
tensor_tuple_slice0 = tensor_tuple[start:stop:step]
res = self.addN(tensor_tuple_slice0)
return res
test_cases = [
('SlicePositive', {
'block': NetWork_1(),
'desc_inputs': [(Tensor(np.ones([2, 3, 4], np.int32)),
Tensor(np.zeros([2, 3, 4], np.int32)),
Tensor(np.ones([2, 3, 4], np.int32)),
Tensor(np.ones([2, 3, 4], np.int32)),
Tensor(np.zeros([2, 3, 4], np.int32)),
Tensor(np.ones([2, 3, 4], np.int32)))],
}),
('SliceNegative', {
'block': NetWork_2(),
'desc_inputs': [(Tensor(np.ones([2, 3, 4], np.int32)),
Tensor(np.zeros([2, 3, 4], np.int32)),
Tensor(np.ones([2, 3, 4], np.int32)),
Tensor(np.ones([2, 3, 4], np.int32)),
Tensor(np.zeros([2, 3, 4], np.int32)),
Tensor(np.ones([2, 3, 4], np.int32)))],
}),
]
test_cases_for_verify_exception = [
('SliceStartCross', {
'block': (NetWork_3(), {'exception': RuntimeError}),
'desc_inputs': [*(Tensor(np.ones([2, 3, 4], np.int32)),
Tensor(np.zeros([2, 3, 4], np.int32)),
Tensor(np.ones([2, 3, 4], np.int32)))],
}),
('SliceStepZero', {
'block': (NetWork_3(), {'exception': RuntimeError}),
'desc_inputs': [*(Tensor(np.ones([2, 3, 4], np.int32)),
Tensor(np.zeros([2, 3, 4], np.int32)),
Tensor(np.ones([2, 3, 4], np.int32)))],
}),
]
@mindspore_test(pipeline_for_compile_forward_ge_graph_for_case_by_case_config)
def test_compile():
return test_cases
@mindspore_test(pipeline_for_verify_exception_for_case_by_case_config)
def test_check_exception():
return test_cases_for_verify_exception
| [
"leon.wanghui@huawei.com"
] | leon.wanghui@huawei.com |
a34d691e65e99a6c67292407136f05cfbeaeb546 | bafacfb2290ba3d32963ecfbfb6a749e71438050 | /cli_site/cli/migrations/0002_auto_20190705_1248.py | 004b28b80608f1b9385ba5bd9682755af98f08ac | [] | no_license | OneTallProgrammer/django-cli | 185066a564d52afe9f07301b0e94383b72735f64 | 8107b68b57c101fb20cdf857fbd6e5fe9bcbc69a | refs/heads/master | 2020-06-14T05:24:05.038459 | 2019-07-10T19:20:21 | 2019-07-10T19:20:21 | 194,916,074 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 841 | py | # Generated by Django 2.2.2 on 2019-07-05 12:48
from django.db import migrations, models
import django.db.models.deletion
class Migration(migrations.Migration):
dependencies = [
('cli', '0001_initial'),
]
operations = [
migrations.CreateModel(
name='Agent',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('agent_name', models.CharField(max_length=200)),
('agent_type', models.IntegerField()),
],
),
migrations.AddField(
model_name='command',
name='agent_id',
field=models.ForeignKey(default=-1, on_delete=django.db.models.deletion.CASCADE, to='cli.Agent'),
preserve_default=False,
),
]
| [
"jmcauthen@live.com"
] | jmcauthen@live.com |
8d4588530f69c619168a4cc1e6f9fb07ba1e6326 | d2c4934325f5ddd567963e7bd2bdc0673f92bc40 | /tests/artificial/transf_RelativeDifference/trend_Lag1Trend/cycle_12/ar_12/test_artificial_128_RelativeDifference_Lag1Trend_12_12_20.py | b92931e266c853cfe294b5ace5bc7d11ca7edc8c | [
"BSD-3-Clause",
"LicenseRef-scancode-unknown-license-reference"
] | permissive | jmabry/pyaf | 797acdd585842474ff4ae1d9db5606877252d9b8 | afbc15a851a2445a7824bf255af612dc429265af | refs/heads/master | 2020-03-20T02:14:12.597970 | 2018-12-17T22:08:11 | 2018-12-17T22:08:11 | 137,104,552 | 0 | 0 | BSD-3-Clause | 2018-12-17T22:08:12 | 2018-06-12T17:15:43 | Python | UTF-8 | Python | false | false | 280 | py | import pyaf.Bench.TS_datasets as tsds
import pyaf.tests.artificial.process_artificial_dataset as art
art.process_dataset(N = 128 , FREQ = 'D', seed = 0, trendtype = "Lag1Trend", cycle_length = 12, transform = "RelativeDifference", sigma = 0.0, exog_count = 20, ar_order = 12); | [
"antoine.carme@laposte.net"
] | antoine.carme@laposte.net |
ec63ed048f6211cd69e8bc2bc40d3e6f418eaf0d | 336cd9225281befde93e01858ede15f70d3e5b47 | /params/cartpole_obs/shm_default copy.py | 2ab5c02726c7c6586d11fff9f5736ea8bffe8c5f | [] | no_license | GuancongLuo/mpc-mpnet-py | 7d6ba9f0c954185a724421091b1b098ec6d148e6 | 3d8d8ef743fd467fd2ffe177021edc6e852fd094 | refs/heads/master | 2023-02-06T03:49:06.072105 | 2020-12-07T11:01:08 | 2020-12-07T11:01:08 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 3,333 | py | import numpy as np
def get_params():
params = {
'solver_type': "cem",
'n_problem': 1,
'n_sample': 32,
'n_elite': 8,
'n_t': 1,
'max_it': 5,
'converge_r': 0.1,
'dt': 2e-3,
'mu_u': [0],
'sigma_u': [400],
'mu_t': 0.5,
'sigma_t': 0.5,
't_max': 1,
'verbose': False, # True,#
'step_size': 1,
"goal_radius": 1.5,
"sst_delta_near": .3,
"sst_delta_drain": 0.1,
"goal_bias": 0.05,
"width": 4,
"hybrid": False,
"hybrid_p": 0.0,
"cost_samples": 1,
"mpnet_weight_path":"mpnet/exported/output/cartpole_obs/mpnet_10k_external_small_model.pt",
#"mpnet_weight_path":"mpnet/exported/output/cartpole_obs/mpnet_10k_external_v2_deep.pt",
# "mpnet_weight_path":"mpnet/exported/output/cartpole_obs/mpnet_10k.pt",
# "mpnet_weight_path":"mpnet/exported/output/cartpole_obs/mpnet_10k_nonorm.pt",
# "mpnet_weight_path":"mpnet/exported/output/cartpole_obs/mpnet_subsample0.5_10k.pt",
# "cost_predictor_weight_path": "mpnet/exported/output/cartpole_obs/cost_10k.pt",
"cost_predictor_weight_path": "mpnet/exported/output/cartpole_obs/cost_10k.pt",
"cost_to_go_predictor_weight_path": "mpnet/exported/output/cartpole_obs/cost_to_go_obs.pt",
"refine": False,
"using_one_step_cost": False,
"refine_lr": 0,
"refine_threshold": 0,
"device_id": "cuda:3",
"cost_reselection": False,
"number_of_iterations": 100000,
"weights_array": [1, 1, 1, 0.5],
'max_planning_time': 50,
'shm_max_steps': 40
}
cuda_batch_params = {
'solver_type' : "cem",
'n_problem' : 1,
'n_sample': 32,
'n_elite': 2,
'n_t': 1,
'max_it': 5,
'converge_r': 1e-1,
'dt': 2e-3,
'mu_u': [0],
'sigma_u': [400],
'mu_t': 0.4,
'sigma_t': 0.5,
't_max': 1,
'verbose': False,#True,#
'step_size': 1,
"goal_radius": 1.5,
"sst_delta_near": .6,
"sst_delta_drain": .3,
"goal_bias": 0.05,
"width": 4,
"hybrid": False,
"hybrid_p": 0.0,
"cost_samples": 5,
"mpnet_weight_path":"mpnet/exported/output/cartpole_obs/mpnet_10k_external_small_model.pt",
#"mpnet_weight_path":"mpnet/exported/output/cartpole_obs/mpnet_10k_external_v2_deep.pt",
# "mpnet_weight_path":"mpnet/exported/output/cartpole_obs/mpnet_10k.pt",
# "mpnet_weight_path":"mpnet/exported/output/cartpole_obs/mpnet_10k_nonorm.pt",
# "mpnet_weight_path":"mpnet/exported/output/cartpole_obs/mpnet_subsample0.5_10k.pt",
"cost_predictor_weight_path": "mpnet/exported/output/cartpole_obs/cost_10k.pt",
"cost_to_go_predictor_weight_path": "mpnet/exported/output/cartpole_obs/cost_to_go_obs.pt",
"refine": False,
"using_one_step_cost": False,
"refine_lr": 0.0,
"refine_threshold": 0.0,
"device_id": "cuda:0",
"cost_reselection": False,
"number_of_iterations": 40000,
"weights_array": [1, 1, 1, .5],
'max_planning_time': 50,
'shm_max_steps': 40
}
return cuda_batch_params
| [
"you@example.com"
] | you@example.com |
89474e153defaaa9938f24de4429e92defcd0542 | d723b9c2dcfc9e3366928fd0ea18ee5ee19c2b3c | /backend/apps/detections/upload_sets.py | 140c2d9bcfbee0781d15cc18a2aab00c879d9188 | [] | no_license | skarzi/yb_hackathon_2019 | ff8266e89ae6fa74d57c61e4117d6fc176dba825 | 83c3d96795f6b14f97683ad5c998579adb3faaf4 | refs/heads/master | 2020-09-11T01:34:55.206979 | 2020-07-19T07:50:16 | 2020-07-19T07:50:16 | 221,895,345 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 219 | py | from flask import current_app
from flask_uploads import (
IMAGES,
UploadSet,
configure_uploads,
)
detections = UploadSet(name='detections', extensions=IMAGES)
configure_uploads(current_app, (detections,))
| [
"skarzynski_lukasz@protonmail.com"
] | skarzynski_lukasz@protonmail.com |
81aa064e876bd2b4d52c97a334447ea805b84532 | e32f87eeccab48b690af53cc9e9c5558c21ce589 | /brownian_tree/tools/size.py | b27caad8c830dc1ab0fcbb3d859a992928c9627a | [] | no_license | mblicharz/diffusion-limited-aggregation | c822f21f7d0c0e7aba8c0dbe870bcff8ec650b7e | 40ed87c9d909ddf154483365a3c8bf525adb2b5b | refs/heads/master | 2023-05-31T11:38:11.878870 | 2021-07-02T15:14:00 | 2021-07-02T15:14:00 | 378,947,926 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 368 | py | class Size:
def __init__(self, max_x: int, max_y: int):
self._validate(max_x, max_y)
self.max_x = max_x
self.max_y = max_y
def _validate(self, max_x, max_y) -> bool:
if max_x < 0 or max_y < 0:
raise ValueError(
"Both dimensions should be equal or greater than 0."
)
return True
| [
"maciej.blicharz@protonmail.com"
] | maciej.blicharz@protonmail.com |
f63c373acfcbf528968315471e17ac98b2ea26b0 | 595471f4acc7c6db2b414019e069bcb510f24edc | /blog/migrations/0001_initial.py | 41c9e2d8887c36b9e654ce6c7e8239d8ee358c7c | [] | no_license | UshieChris/Blog-PROJECT | 9ffcbde536283443e6743d8b729691d18e4e1432 | 3bd751c49f95101579a9d6d0dc59c5f730ad1ec6 | refs/heads/main | 2023-03-30T03:39:48.214184 | 2021-04-09T16:29:01 | 2021-04-09T16:29:01 | 356,331,503 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 785 | py | # Generated by Django 3.0.5 on 2020-12-18 10:44
from django.conf import settings
from django.db import migrations, models
import django.db.models.deletion
class Migration(migrations.Migration):
initial = True
dependencies = [
migrations.swappable_dependency(settings.AUTH_USER_MODEL),
]
operations = [
migrations.CreateModel(
name='Post',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('title', models.CharField(max_length=200)),
('body', models.TextField()),
('author', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to=settings.AUTH_USER_MODEL)),
],
),
]
| [
"chrisushie301@gmail.com"
] | chrisushie301@gmail.com |
4b4a097a95f1da6da6dfa3927d7c83d66941ecdf | d8f0761acc94f9f1c0365e5a1716c9e17c6e4e16 | /scrapers/bs4_selectors/selector.py | cabcd3ea0bed6889945755aac7fe5cf0cdf9cd8c | [] | no_license | lesleyfon/one-time-scrapers | 75ca851107d59b4f2b7cd816b2ae46ecd11d6bc0 | 6ee5443497c9e05924abf5704c16112beb740064 | refs/heads/master | 2023-05-02T12:58:21.693133 | 2021-05-21T13:09:57 | 2021-05-21T13:09:57 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,990 | py | ##############################
#
# Beautiful Soup cheat sheet
#
# by
#
# Code Monkey King
#
##############################
# Step 1: import packages
import requests
from bs4 import BeautifulSoup
# Step 2: define target URL
url = 'https://podsearch.com/listing/car-talk.html'
# Step 3: make HTTP request to the target URL
response = requests.get(url)
# Step 4: parse entire HTML document
content = BeautifulSoup(response.text, 'lxml')
# Step 5: parse PARENT element conteining needed data
parent = content.find('div', {'class': 'col-md-8 col-sm-12 col-xs-12 pdl0'})
# Step 6: parse CHILD element containing the exact data we need
child = parent.find('span').text
# Step 7: split the target string if needed
data = child.split(': ')[-1]
# Step 8: print data to console
print(data)
#####################################
#
# Useful data extraction techniques
#
#####################################
# extract FIRST data occurence by unique class
description = content.find('p', {'class': 'pre-line'}).text
print('\n', description)
# extract ALL data occurences by unique class
text = [
item.text
for item in
content.find_all('p', {'class': 'pre-line'})
]
print('\n', text)
# reference similar data occurences by index
print('\n', text[0])
print('\n', text[1])
# join list elements into one single string by whatever character
print('\n', '\n joined by new line \n'.join(text))
# reference element by whatever attribute (ID in this case)
button = content.find('button', {'id': 'headerSearchButton'}).text
print(button)
# extract FIRST other but textual node data element, e.g. HREF attribute or whatever
link = content.find('a')['href']
print(link)
# extract ALL other but textual node data elements, e.g. HREF attribute or whatever
links = [
link['href']
for link in
content.find_all('a')
# filter on condition if needed
#if link['href'] == 'https://podsearch.com/listing/rethinking-weight-loss.html'
]
print(links)
| [
"freesoft.for.people@gmail.com"
] | freesoft.for.people@gmail.com |
aa9c2bf1b305cc6403a880948c9ce34f01af5268 | 2d19317ab9af09be9e6c8f0a25d4a43d4632b680 | /first_project/urls.py | 44eee9ee4a82b0a41d31cece1c0325b3b2218316 | [] | no_license | rudiq4/first_project | 73837d297b21ccd7c706fc08373473e9e4cd8b29 | ba0e987f863f599da9700c355875af76158b76f0 | refs/heads/master | 2021-01-25T13:41:56.102967 | 2018-03-27T14:15:26 | 2018-03-27T14:15:26 | 123,607,387 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,132 | py | """first_project URL Configuration
The `urlpatterns` list routes URLs to views. For more information please see:
https://docs.djangoproject.com/en/1.11/topics/http/urls/
Examples:
Function views
1. Add an import: from my_app import views
2. Add a URL to urlpatterns: url(r'^$', views.home, name='home')
Class-based views
1. Add an import: from other_app.views import Home
2. Add a URL to urlpatterns: url(r'^$', Home.as_view(), name='home')
Including another URLconf
1. Import the include() function: from django.conf.urls import url, include
2. Add a URL to urlpatterns: url(r'^blog/', include('blog.urls'))
"""
from django.conf.urls import url, include
from django.contrib import admin
from django.conf.urls.static import static
from django.conf import settings
urlpatterns = [
url(r'^admin/', admin.site.urls),
url(r'^', include('Auto.urls')),
url(r'^', include('Post.urls')),
url(r'user/', include('User.urls')),
] \
+ static(settings.STATIC_URL, document_root=settings.STATIC_ROOT) \
+ static(settings.MEDIA_URL, document_root=settings.MEDIA_ROOT) | [
"rudikvovan@gmail.com"
] | rudikvovan@gmail.com |
80a957f639ad9152f7478c2f7c5faf20dff26992 | 3907034857319c47efd09429f994ff4c8a34a642 | /bienes/urls.py | 9cb1ec71e2006a9673df3a25d19c1a410aaa861f | [] | no_license | xcarlx/bienes | c018af7582f725e06d354144bac8641e348192ab | 48a69798ead5d627b38a2d9afe84ad484ca4e7da | refs/heads/master | 2020-04-17T02:36:14.054458 | 2019-02-19T20:44:15 | 2019-02-19T20:44:15 | 166,143,291 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,038 | py | """bienes URL Configuration
The `urlpatterns` list routes URLs to views. For more information please see:
https://docs.djangoproject.com/en/2.1/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, include
from django.conf import settings
from django.conf.urls.static import static
urlpatterns = [
path('admin/', admin.site.urls),
path('', include('apps.home.urls')),
path('', include('apps.servicio.urls'))
] + static(settings.MEDIA_URL, document_root=settings.MEDIA_ROOT)
| [
"cruiz@regioncajamarca.gob.pe"
] | cruiz@regioncajamarca.gob.pe |
f3b980f098f1f78d08ed82f4a62c12949fb78bde | 5c3fcfd1c30036154af34de9fbf11b77ebd31777 | /9444/ass1/hw1/src/part3.py | 05bd53f4097cb69ca9688d6c2d276e110cb6ed30 | [] | no_license | Skylerliutian/UNSW_19T3 | d5fc13a744aa6193c904404b9ef37010e89261a0 | cdc6324b5622a7326869f58c0cc53dd9976f22cb | refs/heads/master | 2022-04-16T13:37:43.400815 | 2020-03-27T09:46:43 | 2020-03-27T09:46:43 | 250,228,085 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 7,778 | py | #!/usr/bin/env python3
"""
part3.py
UNSW COMP9444 Neural Networks and Deep Learning
ONLY COMPLETE METHODS AND CLASSES MARKED "TODO".
DO NOT MODIFY IMPORTS. DO NOT ADD EXTRA FUNCTIONS.
DO NOT MODIFY EXISTING FUNCTION SIGNATURES.
DO NOT IMPORT ADDITIONAL LIBRARIES.
DOING SO MAY CAUSE YOUR CODE TO FAIL AUTOMATED TESTING.
"""
import torch
from torchvision import datasets, transforms
from torch import nn, optim
import torch.nn.functional as F
import matplotlib.pyplot as plt
import numpy as np
class Linear(nn.Module):
"""
DO NOT MODIFY
Linear (10) -> ReLU -> LogSoftmax
"""
def __init__(self):
super().__init__()
self.fc1 = nn.Linear(784, 10)
def forward(self, x):
x = x.view(x.shape[0], -1) # make sure inputs are flattened
x = F.relu(self.fc1(x))
x = F.log_softmax(x, dim=1) # preserve batch dim
return x
class FeedForward(nn.Module):
"""
TODO: Implement the following network structure
Linear (256) -> ReLU -> Linear(64) -> ReLU -> Linear(10) -> ReLU-> LogSoftmax
"""
def __init__(self):
super().__init__()
self.fc1 = nn.Linear(784, 256)
self.fc2 = nn.Linear(256, 64)
self.fc3 = nn.Linear(64, 10)
def forward(self, x):
x = x.view(x.shape[0], -1)
x = F.relu(self.fc1(x))
x = F.relu(self.fc2(x))
x = F.relu(self.fc3(x))
x = F.log_softmax(x, dim=1)
return x
class CNN(nn.Module):
"""
TODO: Implement CNN Network structure
conv1 (channels = 10, kernel size= 5, stride = 1) -> Relu -> max pool (kernel size = 2x2) ->
conv2 (channels = 50, kernel size= 5, stride = 1) -> Relu -> max pool (kernel size = 2x2) ->
Linear (256) -> Relu -> Linear (10) -> LogSoftmax
Hint: You will need to reshape outputs from the last conv layer prior to feeding them into
the linear layers.
"""
def __init__(self):
super().__init__()
# in_channels = 1 gray_scale
self.conv1 = nn.Conv2d(1, 10, 5, 1)
# in_channels = the first conv1 out_channels
self.conv2 = nn.Conv2d(10, 50, 5, 1)
self.fc1 = nn.Linear(50 * 4 * 4, 256)
self.fc2 = nn.Linear(256, 10)
self.max_pool = nn.MaxPool2d((2, 2))
def forward(self, x):
x = F.relu(self.conv1(x))
x = F.max_pool2d(x, (2, 2))
x = F.relu(self.conv2(x))
x = F.max_pool2d(x, (2, 2))
x = x.view(x.shape[0], -1)
x = F.relu(self.fc1(x))
x = self.fc2(x)
x = F.log_softmax(x, dim=1)
return x
class NNModel:
def __init__(self, network, learning_rate):
"""
Load Data, initialize a given network structure and set learning rate
DO NOT MODIFY
"""
# Define a transform to normalize the data
transform = transforms.Compose([transforms.ToTensor(),
transforms.Normalize((0.5,), (0.5,))])
# Download and load the training data
trainset = datasets.KMNIST(root='./data', train=True, download=True, transform=transform)
self.trainloader = torch.utils.data.DataLoader(trainset, batch_size=64, shuffle=False)
# Download and load the test data
testset = datasets.KMNIST(root='./data', train=False, download=True, transform=transform)
self.testloader = torch.utils.data.DataLoader(testset, batch_size=64, shuffle=False)
self.model = network
"""
TODO: Set appropriate loss function such that learning is equivalent to minimizing the
cross entropy loss. Note that we are outputting log-softmax values from our networks,
not raw softmax values, so just using torch.nn.CrossEntropyLoss is incorrect.
Hint: All networks output log-softmax values (i.e. log probabilities or.. likelihoods.).
"""
self.lossfn = nn.NLLLoss()
self.optimizer = optim.Adam(self.model.parameters(), lr=learning_rate)
self.num_train_samples = len(self.trainloader)
self.num_test_samples = len(self.testloader)
def view_batch(self):
"""
TODO: Display first batch of images from trainloader in 8x8 grid
Do not make calls to plt.imshow() here
Return:
1) A float32 numpy array (of dim [28*8, 28*8]), containing a tiling of the batch images,
place the first 8 images on the first row, the second 8 on the second row, and so on
2) An int 8x8 numpy array of labels corresponding to this tiling
"""
train_x, train_y = next(iter(self.trainloader))
x = train_x.view(8, 8, 28, 28).permute(0, 2, 1, 3).reshape(8 * 28, 8 * 28).numpy()
y = train_y.reshape(8, 8)
return x, y.numpy()
# dataiter = iter(self.trainloader)
# images, labels = dataiter.next()
# labels = labels.view(8, 8).numpy()
# image_grid = torch.empty(0)
# for i in range(8):
# img_row = images.numpy()[i * 8:(i + 1) * 8][:][:][:].reshape(8, 28, 28)
# imgs = torch.from_numpy(img_row)
# row = imgs[0]
# for j in range(1, 8):
# row = torch.cat((row, imgs[j]), dim=1)
# image_grid = torch.cat((image_grid, row))
# return image_grid, labels
def train_step(self):
"""
Used for submission tests and may be usefull for debugging
DO NOT MODIFY
"""
self.model.train()
for images, labels in self.trainloader:
log_ps = self.model(images)
loss = self.lossfn(log_ps, labels)
self.optimizer.zero_grad()
loss.backward()
self.optimizer.step()
return
def train_epoch(self):
self.model.train()
for images, labels in self.trainloader:
log_ps = self.model(images)
loss = self.lossfn(log_ps, labels)
self.optimizer.zero_grad()
loss.backward()
self.optimizer.step()
return
def eval(self):
self.model.eval()
accuracy = 0
with torch.no_grad():
for images, labels in self.testloader:
log_ps = self.model(images)
ps = torch.exp(log_ps)
top_p, top_class = ps.topk(1, dim=1)
equals = top_class == labels.view(*top_class.shape)
accuracy += torch.mean(equals.type(torch.FloatTensor))
return accuracy / self.num_test_samples
def plot_result(results, names):
"""
Take a 2D list/array, where row is accuracy at each epoch of training for given model, and
names of each model, and display training curves
"""
for i, r in enumerate(results):
plt.plot(range(len(r)), r, label=names[i])
plt.legend()
plt.title("KMNIST")
plt.xlabel("Epoch")
plt.ylabel("Test accuracy")
plt.grid(True)
plt.tight_layout()
plt.show()
plt.savefig("./part_2_plot.png")
def main():
models = [Linear(), FeedForward(), CNN()] # Change during development
epochs = 10
results = []
# Can comment the below out during development
images, labels = NNModel(Linear(), 0.003).view_batch()
print(labels)
plt.imshow(images, cmap="Greys")
plt.show()
for model in models:
print(f"Training {model.__class__.__name__}...")
m = NNModel(model, 0.003)
accuracies = [0]
for e in range(epochs):
m.train_epoch()
accuracy = m.eval()
print(f"Epoch: {e}/{epochs}.. Test Accuracy: {accuracy}")
accuracies.append(accuracy)
results.append(accuracies)
plot_result(results, [m.__class__.__name__ for m in models])
if __name__ == "__main__":
main()
| [
"skyler151096@gmail.com"
] | skyler151096@gmail.com |
4bbf1ff6013d7a48ce63d817454fb8940a26487f | 7d23fff61314842d6d7d8ca106382d163a04f139 | /watch/models.py | 3a423333ca2230cec766903c543e4a2e444032de | [
"MIT"
] | permissive | GeGe-K/Neighbourhood | 8b71bc789a72d34769436a5a912ffde87b3c014b | 366667dff147141558732e5c6f5004fe4cff221e | refs/heads/master | 2022-12-09T18:26:27.536704 | 2019-01-16T14:13:37 | 2019-01-16T14:13:37 | 165,236,886 | 0 | 0 | MIT | 2022-12-08T01:32:31 | 2019-01-11T12:00:41 | Python | UTF-8 | Python | false | false | 3,462 | py | from django.db import models
from django.contrib.auth.models import User
import datetime as dt
# Create your models here.
class Location(models.Model):
name = models.CharField(max_length=40)
def __str__(self):
return self.name
class Neighbourhood(models.Model):
'''
Neighbourhood class has the following properties
'''
neighbourhood_name = models.CharField(max_length = 30)
neighborhood_location = models.ForeignKey('Location', on_delete = models.CASCADE, null = True, blank =True)
occupants = models.IntegerField(null = True)
admin = models.ForeignKey(User, on_delete = models.CASCADE)
def create_neighbourhood(self):
self.save
def delete_neighbourhood(self):
self.delete()
def __str__(self):
return self.neighbourhood_name
@classmethod
def find_neighbourhood(cls, neighbourhood_id):
neighbourhood = cls.objects.get(id = neighbourhood_id)
return neighbourhood
def update_nighbourhood(self):
self.save()
def update_occupants(self):
self.occupants +=1
self.save()
class UserProfile(models.Model):
'''
UserProfile class has the following properties
'''
first_name = models.CharField(max_length=20, blank=True)
last_name = models.CharField(max_length=20,blank=True)
email = models.EmailField()
user = models.ForeignKey(User,on_delete=models.CASCADE)
neighborhood = models.ForeignKey('Neighbourhood', on_delete=models.CASCADE, null=True, blank=True)
def assign_neighbourhood(self, neighbourhood):
self.neighbourhood = neighborhood
self.save()
def save_profile(self):
self.save()
def delete_profile(self):
self.delete()
def __str__(self):
return f'{self.user.username}'
class Business(models.Model):
'''
Business class has the following properties
'''
business_name = models.CharField(max_length = 50)
owner = models.ForeignKey(User, on_delete = models.CASCADE)
business_neighbourhood = models.ForeignKey(
'Neighbourhood', on_delete = models.CASCADE)
email = models.EmailField()
def create_business(self):
self.save()
def delete_business(self):
self.delete()
@classmethod
def find_business(cls, business_id):
business = cls.objects.get(id = business_id)
return business
def update_business(self, business_name):
self.name = business_name
self.save()
def __str__(self):
return self.business_name
class EmergencyContacts(models.Model):
'''
Emergency contact class has the following properties
'''
name = models.CharField(max_length = 30)
contacts = models.CharField(max_length = 20)
email = models.EmailField()
neighbourhood_contact = models.ForeignKey(
'Neighbourhood', on_delete = models.CASCADE)
def __str__(self):
return f'{self.name},{self.email}'
class Post(models.Model):
'''
Post class has the following properties
'''
title = models.CharField(max_length=40)
post_description = models.TextField(blank = True)
posted_by = models.ForeignKey(User, on_delete = models.CASCADE)
post_hood = models.ForeignKey('Neighbourhood', on_delete = models.CASCADE)
posted_on = models.DateTimeField(auto_now_add = True)
def __str__(self):
return f'{self.title},{self.post_hood.neighbourhood_name}'
| [
"gloriagivondo@gmail.com"
] | gloriagivondo@gmail.com |
3bb37917a0398704646cc7328ac2b1f67c1f8a85 | 1ca5995b3f1debd011cf4c9d3382979ad103ff40 | /ros_workspace/src/regions/src/applytransformations.py | 167f015c21a78140e0270e815319a39cff8b1325 | [] | no_license | rcxking/cv_final_project | 06e864554affd19d062909c6b6b3d5e907f19f4f | 8751cd15d23afe721e4c523675dcd4648a4e2308 | refs/heads/master | 2020-06-01T17:53:19.579281 | 2015-05-15T16:24:24 | 2015-05-15T16:24:24 | 34,287,134 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 3,160 | py | #!/usr/bin/python
'''
applytransformations.py - This script applies a series of transformations
to an input side of images and pickles the data.
Bryant Pong / Micah Corah
CSCI-4962
5/10/15
Last Updated: Bryant Pong: 5/14/15 - 5:10 PM
'''
# Python Imports:
import cv2
import numpy as np
from matplotlib import pyplot as plt
import cPickle as pickle # cPickle is faster for Python 2.x
import os # listdir()
import transformations as tf # Custom transformations
'''
This function loads pickled data of images and features
to process.
'''
def loadImages(imgFolder):
return [pickle.load(open(imgFolder+"/20150423_152021.dat", "rb"))]
# Main function:
def transform():
# Load the images to run transformations on:
data = loadImages("../../../../data/pickle")
print("There are: " + str(len(data)) + " datasets to process")
# Global dataset:
globalData = []
datasetNum = 1
imgNum = 1
for dataset in data:
print("Now extracting images from dataset: " + str(datasetNum))
print("There are: " + str(len(dataset)) + " images in this dataset")
for img in dataset:
print("Now extracting features for image : " + str(imgNum) + " of dataset: " + str(datasetNum))
# Extract the image and feature set from each image:
image = img[0]
features = img[1]
imgWidth = image.shape[1]
imgHeight = image.shape[0]
# Resize the images and features:
resizedImage = cv2.resize(image, (int(0.5*imgWidth),int(0.5*imgHeight)))
resizedFeatures = features * 0.5
globalData.append( (resizedImage, resizedFeatures) )
'''
Apply the following transformations to each image:
Transformations 1 - 12: Rotate the image from -30 degrees to 30 degrees
in increments of 5 degrees
Transformations 13 - 14: Generate two perspective transformations with added
random Gaussian noise to stimulate vibrations in the
robot while traveling
Transformations 15 - 16: Generate two saturation transformations with
increasing/decreasing saturation levels to simulate
shadows and sunlight
'''
# Generate the rotated images:
rotImg, rotFeatures = tf.rotateCenter(resizedImage, resizedFeatures, -10)
rotImg2, rotFeatures2 = tf.rotateCenter(resizedImage, resizedFeatures, 10)
globalData.append( (rotImg, rotFeatures) )
globalData.append( (rotImg2, rotFeatures2) )
# Generate transformations 13 - 14 (Perspective Transformations):
trans13, trans13Features = tf.hTrans(resizedImage, resizedFeatures)
trans14, trans14Features = tf.hTrans(resizedImage, resizedFeatures)
globalData.append( (trans13, trans13Features))
globalData.append( (trans14, trans14Features))
# Generate transformations 15 - 16 (Saturation Transformations):
trans15 = tf.sTran(resizedImage, -50)
trans16 = tf.sTran(resizedImage, 50)
globalData.append( (trans15, resizedFeatures) )
globalData.append( (trans16, resizedFeatures) )
imgNum += 1
imgNum = 0
datasetNum += 1
print("data dump complete")
return globalData
# Main function runner:
if __name__ == "__main__":
transform()
| [
"rcxking@gmail.com"
] | rcxking@gmail.com |
4ac983d36e1373bef3ae2040efb427f6810916b4 | 83e85a05d87d784d5beffe8d3c7c709bda9ffe6b | /py_src/samoa/request/replication_state.py | f1edef830b6e914167e2526762a1c227ed5af975 | [] | no_license | jgraettinger/samoa | 42c3015e525f3fdad61725ff4be569e758b32cc9 | 36d17dddeea0cc28a223e839ebf3cfb0bf3f7c07 | refs/heads/master | 2020-04-09T10:11:22.750684 | 2011-10-22T22:32:23 | 2011-10-22T22:32:23 | 533,368 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 38 | py | from _request import ReplicationState
| [
"johng@boomer.(none)"
] | johng@boomer.(none) |
a04bc47a13938f0f6f6d24f671bdcad4533f47f9 | 362efa5644c13e9a370e4d6ce4730833358e0ff0 | /MyWebsite/AGR/AGR/settings.py | 8bcd4ef8e2ed8fe4edddf75d747689f7b2af9f82 | [] | no_license | ISS-IS-IRSPM-AGR/-IRSPM | f07d7133d3005fbb14e784d8a2e719613f17dc0a | 556fc45cb31ddf56dd557305a603a514eaab672c | refs/heads/master | 2023-04-01T03:11:23.162711 | 2021-03-14T16:24:38 | 2021-03-14T16:24:38 | 347,680,073 | 0 | 1 | null | 2021-03-14T16:21:41 | 2021-03-14T15:51:06 | JavaScript | UTF-8 | Python | false | false | 3,153 | py | """
Django settings for AGR project.
Generated by 'django-admin startproject' using Django 3.1.7.
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 = 'whfxmu35o6%2)9z71og9*efq^7++so1%@i-nwekkj2d8&fi=$t'
# SECURITY WARNING: don't run with debug turned on in production!
DEBUG = True
ALLOWED_HOSTS = []
# Application definition
INSTALLED_APPS = [
'django.contrib.admin',
'django.contrib.auth',
'django.contrib.contenttypes',
'django.contrib.sessions',
'django.contrib.messages',
'django.contrib.staticfiles',
'AGRApi.apps.ApiConfig',
'rest_framework',
'AGRFrontend.apps.FrontendConfig'
]
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 = 'AGR.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 = 'AGR.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 = '/AGRFrontend/static/'
| [
"21048103+antoniadevina@users.noreply.github.com"
] | 21048103+antoniadevina@users.noreply.github.com |
5d8565f123ea80979f9cd6a4454521fd2ddff15c | b0de612c2f7d03399c0d02c5aaf858a72c9ad818 | /armi/nuclearDataIO/cccc/tests/test_rzflux.py | 93771c4e863ba214363f58516bdda65027c1eb5c | [
"GPL-1.0-or-later",
"BSD-3-Clause",
"LicenseRef-scancode-free-unknown",
"Apache-2.0"
] | permissive | wangcj05/armi | 2007e7abf4b422caca0157fc4405b7f45fc6c118 | 8919afdfce75451b291e45ca1bc2e03c044c2090 | refs/heads/master | 2022-12-22T00:05:47.561722 | 2022-12-13T16:46:57 | 2022-12-13T16:46:57 | 277,868,987 | 0 | 0 | Apache-2.0 | 2020-07-07T16:32:40 | 2020-07-07T16:32:39 | null | UTF-8 | Python | false | false | 2,673 | py | # Copyright 2019 TerraPower, LLC
#
# 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.
"""
Test rzflux reading and writing.
"""
# pylint: disable=missing-function-docstring,missing-class-docstring,protected-access,invalid-name,no-self-use,no-method-argument,import-outside-toplevel
import os
import unittest
from armi.nuclearDataIO.cccc import rzflux
from armi.utils.directoryChangers import TemporaryDirectoryChanger
THIS_DIR = os.path.dirname(__file__)
# This RZFLUX was made by DIF3D 11 in a Cartesian test case.
SIMPLE_RZFLUX = os.path.join(THIS_DIR, "fixtures", "simple_cartesian.rzflux")
class TestRzflux(unittest.TestCase):
"""Tests the rzflux class"""
def test_readRzflux(self):
"""Ensure we can read a RZFLUX file."""
flux = rzflux.readBinary(SIMPLE_RZFLUX)
self.assertEqual(
flux.groupFluxes.shape, (flux.metadata["NGROUP"], flux.metadata["NZONE"])
)
def test_writeRzflux(self):
"""Ensure that we can write a modified RZFLUX file."""
with TemporaryDirectoryChanger():
flux = rzflux.readBinary(SIMPLE_RZFLUX)
rzflux.writeBinary(flux, "RZFLUX2")
self.assertTrue(binaryFilesEqual(SIMPLE_RZFLUX, "RZFLUX2"))
# perturb off-diag item to check row/col ordering
flux.groupFluxes[2, 10] *= 1.1
flux.groupFluxes[12, 1] *= 1.2
rzflux.writeBinary(flux, "RZFLUX3")
flux2 = rzflux.readBinary("RZFLUX3")
self.assertAlmostEqual(flux2.groupFluxes[12, 1], flux.groupFluxes[12, 1])
def test_rwAscii(self):
"""Ensure that we can read/write in ascii format."""
with TemporaryDirectoryChanger():
flux = rzflux.readBinary(SIMPLE_RZFLUX)
rzflux.writeAscii(flux, "RZFLUX.ascii")
flux2 = rzflux.readAscii("RZFLUX.ascii")
self.assertTrue((flux2.groupFluxes == flux.groupFluxes).all())
def binaryFilesEqual(fn1, fn2):
"""True if two files are bytewise identical."""
with open(fn1, "rb") as f1, open(fn2, "rb") as f2:
for byte1, byte2 in zip(f1, f2):
if byte1 != byte2:
return False
return True
| [
"noreply@github.com"
] | noreply@github.com |
58a803cde41cd78a494ce62be9283c642fa86e83 | eaea162301e30aece2bafc08a868c33ebe753324 | /main.py | d5db9e0a139f8043fbf0bd4dfdd9e6903ff8179d | [] | no_license | LAEQ/Pollution_StructurationTools | a56616638e20368c2d1fa41634d6cc277376b378 | 5439f9e148a0055781100fd9164c9068dd260199 | refs/heads/master | 2020-04-21T04:10:47.345888 | 2019-07-24T14:35:54 | 2019-07-24T14:35:54 | 169,305,682 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,072 | py | # -*- coding: utf-8 -*-
"""
Created on Wed Mar 07 14:15:16 2018
@author: GelbJ
"""
Racine = "E:/Datas/_Montreal 2019/A)_FieldData"
######################################################
### Config Steph
######################################################
#Users = ["ID1_JR"]
#Users=["ID2_LN"]
#Users=["ID3_MS"]
#Users=["ID4_TA"]
Avoid=[]
#####################################################
## Programme principal
#####################################################
from Config import Config
from JG_Structuring_BD import PollutionBD
import sys
sys.path.append(Config["JBasicsPath"])
for User in Users :
Path = Racine+"/"+User
#generation de la BD (depuis le debut si necessaire)
BD = PollutionBD(Path,Config,Erase=True)
#nettoyage des CSV
BD.CleanCSVs()
# #creation de la BD SQLITE
BD.Fill()
## #Generation des fichiers SHP
## Avoid = ["ID3_VJ_2019-02-25_TRAJET03",#pas de gps...
## ]
BD.GenerateShps(Avoid = Avoid)
BD.EvaluateShps()
BD.PrepareTimeExcel() | [
"noreply@github.com"
] | noreply@github.com |
0ee4eca75016f2b835c19c87122bb9b245e074d6 | f69580425242d3c2e27881f0c85be73915a6f81e | /pydnd/character/models.py | 84079f946cf1dddd30273502390f5c778fe56e77 | [] | no_license | pyasi/pydnd | 83c9a8626e60b32797d9c6abbdf9ba7ab36bdb4e | a2e880c8a7264d313cad56f9ea0eec72309ac20e | refs/heads/master | 2020-03-07T08:37:43.767559 | 2018-05-03T13:57:53 | 2018-05-03T13:57:53 | 127,384,097 | 0 | 0 | null | 2018-05-03T13:57:54 | 2018-03-30T05:09:48 | Python | UTF-8 | Python | false | false | 1,770 | py | from django.db import models
from pydnd.mechanics.models import MagicSchool
class AbilityScore(models.Model):
name = models.CharField(max_length=100, unique=True)
full_name = models.CharField(max_length=100)
desc = models.CharField(max_length=10000)
def __str__(self):
return self.full_name
class Skill(models.Model):
name = models.CharField(max_length=100, unique=True)
desc = models.CharField(max_length=10000)
ability_score = models.ForeignKey(AbilityScore, on_delete=models.CASCADE, null=True)
def __str__(self):
return self.name
class Spell(models.Model):
name = models.CharField(max_length=100, unique=True)
desc = models.CharField(max_length=10000)
higher_level = models.CharField(max_length=10000, null=True)
range = models.CharField(max_length=1000)
components = models.CharField(max_length=1000)
ritual = models.CharField(max_length=10000)
duration = models.CharField(max_length=1000)
concentration = models.CharField(max_length=1000)
casting_time = models.CharField(max_length=1000)
level = models.IntegerField()
school = models.ForeignKey(MagicSchool, on_delete=models.CASCADE, null=True)
class SpellCastingClass(models.Model):
name = models.CharField(max_length=100, unique=True)
ability_score = models.ManyToManyField(AbilityScore)
cantrips = models.CharField(max_length=10000, null=True)
preparing_and_casting = models.CharField(max_length=10000, null=True)
spellcasting_ability = models.CharField(max_length=10000, null=True)
ritual_casting = models.CharField(max_length=10000, null=True)
spellcasting_focus = models.CharField(max_length=10001, null=True)
spell_slots = models.CharField(max_length=10000, null=True)
| [
"pyasi8192@gmail.com"
] | pyasi8192@gmail.com |
82f795bf9a7875dc84a5097c204ab284d9770801 | d232b3aa9449ad13a5b33f141d432c5a9f46aa7e | /Day3-1.py | bd0c791662db9a061f1774f4ef8d4bda64cf922f | [] | no_license | stephanie19950405/Python200805 | e75ba082faa65367a977652e65b2088e3d4669c5 | ad8a71874b0919aad49e6f9fe5cf214121a050a7 | refs/heads/master | 2022-11-26T02:59:39.331128 | 2020-08-05T08:41:29 | 2020-08-05T08:41:29 | 285,170,878 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 172 | py | # -*- coding: utf-8 -*-
"""
Created on Wed Aug 5 09:26:48 2020
@author: AE401
"""
for i in range(1,10):
for j in range(1,10):
print(i,"x",j,"=",i*j) | [
"noreply@github.com"
] | noreply@github.com |
3f710f824a9ba3fc05f946ea786168e280edb9f3 | 05040f0dce123be0d88e760808fdf6b1bbf1ac43 | /backend/manage.py | 38f19f1341ecef8ebd95be44a20975de9823d12e | [] | no_license | crowdbotics-apps/mobile-8-dec-dev-16453 | 8026421c163ab200a0106faaf3567faf469b348f | 3cc2feeba0d1a753a98db7167491e5a28d7ae6fe | refs/heads/master | 2023-01-23T10:02:48.817260 | 2020-12-08T15:42:47 | 2020-12-08T15:42:47 | 319,665,608 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 642 | py | #!/usr/bin/env python
"""Django's command-line utility for administrative tasks."""
import os
import sys
def main():
os.environ.setdefault("DJANGO_SETTINGS_MODULE", "mobile_8_dec_dev_16453.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()
| [
"team@crowdbotics.com"
] | team@crowdbotics.com |
72a405e1ba0507690cafee76a320b4682d94aac3 | b236c7ce962b3f35d70589993833f24eaf3ce948 | /train.py | 1f188e44283e9df69675b39c5da3d4783585b862 | [
"MIT"
] | permissive | Wblossom/Retinaface--without-landm | 499e11ef4fe51c7684c04769e4b4b3f0480f828e | 53c3574c95abab3665ce51de81cbf54b10d97fc9 | refs/heads/main | 2023-01-01T06:39:54.481816 | 2020-10-21T10:48:09 | 2020-10-21T10:48:09 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 6,341 | py | # -*- coding: utf-8 -*-
from __future__ import print_function
import os
import torch
import torch.optim as optim
import torch.backends.cudnn as cudnn
import argparse
import torch.utils.data as data
from data import WiderFaceDetection, detection_collate, preproc, cfg_mnet, cfg_re50
from layers.modules import MultiBoxLoss
from layers.functions.prior_box import PriorBox
from tqdm import tqdm
import time
import datetime
import math
from models.retinaface import RetinaFace
parser = argparse.ArgumentParser(description='Retinaface Training')
parser.add_argument('--training_dataset', default='/home/disk/zhy/widerface/label.txt', help='Training dataset directory')
parser.add_argument('--network', default='resnet50', help='Backbone network mobile0.25 or resnet50')
parser.add_argument('--num_workers', default=4, type=int, help='Number of workers used in dataloading')
parser.add_argument('--lr', '--learning-rate', default=1e-4, type=float, help='initial learning rate')
parser.add_argument('--momentum', default=0.9, type=float, help='momentum')
parser.add_argument('--resume_net', default='./weights/Resnet50_Final_1.pth', help='resume net for retraining')
# parser.add_argument('--resume_net', default=None, help='resume net for retraining')
parser.add_argument('--resume_epoch', default=0, type=int, help='resume iter for retraining')
parser.add_argument('--weight_decay', default=5e-4, type=float, help='Weight decay for SGD')
parser.add_argument('--gamma', default=0.1, type=float, help='Gamma update for SGD')
parser.add_argument('--save_folder', default='./weights/', help='Location to save checkpoint models')
args = parser.parse_args()
if not os.path.exists(args.save_folder):
os.mkdir(args.save_folder)
cfg = None
if args.network == "mobile0.25":
cfg = cfg_mnet
elif args.network == "resnet50":
cfg = cfg_re50
rgb_mean = (128)
# rgb_mean = (104, 117, 123) # bgr order
num_classes = 2
img_dim = cfg['image_size']
num_gpu = cfg['ngpu']
batch_size = cfg['batch_size']
max_epoch = cfg['epoch']
gpu_train = cfg['gpu_train']
num_workers = args.num_workers
momentum = args.momentum
weight_decay = args.weight_decay
initial_lr = args.lr
gamma = args.gamma
training_dataset = args.training_dataset
save_folder = args.save_folder
net = RetinaFace(cfg=cfg)
print("Printing net...")
print(net)
if args.resume_net is not None:
print('Loading resume network...')
state_dict = torch.load(args.resume_net)
# create new OrderedDict that does not contain `module.`
from collections import OrderedDict
new_state_dict = OrderedDict()
for k, v in state_dict.items():
head = k[:7]
if head == 'module.':
name = k[7:] # remove `module.`
else:
name = k
new_state_dict[name] = v
net.load_state_dict(new_state_dict,strict=False)
if num_gpu > 1 and gpu_train:
net = torch.nn.DataParallel(net).cuda()
else:
net = net.cuda()
cudnn.benchmark = True
# setup optimizer
params = filter(lambda p: p.requires_grad, net.parameters())
# optimizer = optim.SGD(params, lr=initial_lr, momentum=momentum, weight_decay=weight_decay)
optimizer = optim.Adam(params, lr=initial_lr, betas=(0.9, 0.99))
criterion = MultiBoxLoss(num_classes, 0.35, True, 0, True, 7, 0.35, False)
priorbox = PriorBox(cfg, image_size=(img_dim, img_dim))
with torch.no_grad():
priors = priorbox.forward()
priors = priors.cuda()
def train():
net.train()
epoch = 0 + args.resume_epoch
print('Loading Dataset...')
dataset = WiderFaceDetection( training_dataset,preproc(img_dim, rgb_mean))
epoch_size = math.ceil(len(dataset) / batch_size)
max_iter = max_epoch * epoch_size
stepvalues = (cfg['decay1'] * epoch_size, cfg['decay2'] * epoch_size, cfg['decay3'] * epoch_size)
step_index = 0
if args.resume_epoch > 0:
start_iter = args.resume_epoch * epoch_size
else:
start_iter = 0
for iteration in range(start_iter, max_iter):
if iteration % epoch_size == 0:
# create batch iterator
batch_iterator = iter(data.DataLoader(dataset, batch_size, shuffle=True, num_workers=num_workers, collate_fn=detection_collate))
if (epoch % 5 == 0 and epoch > 0) or (epoch % 5 == 0 and epoch > cfg['decay1']):
torch.save(net.state_dict(), save_folder + cfg['name']+ '_epoch_' + str(epoch) + '.pth')
epoch += 1
load_t0 = time.time()
if iteration in stepvalues:
step_index += 1
lr = adjust_learning_rate(optimizer, gamma, epoch, step_index, iteration, epoch_size)
# load train data
images, targets = next(batch_iterator)
# print(targets)
images = images.cuda()
targets = [anno.cuda() for anno in targets]
# forward
out = net(images)
# backprop
optimizer.zero_grad()
# loss_l, loss_c, loss_landm = criterion(out, priors, targets)
loss_l, loss_c = criterion(out, priors, targets)
# loss = cfg['loc_weight'] * loss_l + loss_c + loss_landm
loss = cfg['loc_weight'] * loss_l + loss_c
loss.backward()
optimizer.step()
load_t1 = time.time()
batch_time = load_t1 - load_t0
eta = int(batch_time * (max_iter - iteration))
print('Epoch:{}/{} || Epochiter: {}/{} || Iter: {}/{} || Loc: {:.4f} Cla: {:.4f} || LR: {:.8f} || Batchtime: {:.4f} s || ETA: {}'
.format(epoch, max_epoch, (iteration % epoch_size) + 1,
epoch_size, iteration + 1, max_iter, loss_l.item(), loss_c.item(), lr, batch_time, str(datetime.timedelta(seconds=eta))))
torch.save(net.state_dict(), save_folder + cfg['name'] + '_Final.pth')
# torch.save(net.state_dict(), save_folder + 'Final_Retinaface.pth')
def adjust_learning_rate(optimizer, gamma, epoch, step_index, iteration, epoch_size):
"""Sets the learning rate
# Adapted from PyTorch Imagenet example:
# https://github.com/pytorch/examples/blob/master/imagenet/main.py
"""
warmup_epoch = -1
if epoch <= warmup_epoch:
lr = 1e-6 + (initial_lr-1e-6) * iteration / (epoch_size * warmup_epoch)
else:
lr = initial_lr * (gamma ** (step_index))
for param_group in optimizer.param_groups:
param_group['lr'] = lr
return lr
if __name__ == '__main__':
train()
| [
"kylin33@outlook.com"
] | kylin33@outlook.com |
14539757dac6949ee5971550e5ce037dd82f2b1f | d1a6f7a61035e8e85f70663677eef0794ed42b8f | /create dictonary.py | 3c5eb2d0df31c49d8ad7a5a6594eefab23a1db46 | [] | no_license | shubhamfursule/Create-Dictonry | aaf9a665dc14c950c54c9713b2f9d879f239f822 | d251528ae1b654a72964fb7daf43d404d4a3c5ba | refs/heads/main | 2023-02-09T03:20:07.039319 | 2020-12-28T18:15:44 | 2020-12-28T18:15:44 | 325,084,068 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 396 | py | dicts={}
while True:
try:
key,value=map(str,input().split(","))
except:
print("Please Enter proper way")
else:
if key=="STOP":
break
else:
try:
dicts.update({key:int(value)})
except:
print("please Enter proper type!")
print(dicts)
| [
"noreply@github.com"
] | noreply@github.com |
f974ed4dfce8a8ce9c6813f917c8078319276785 | d6de09317cf3aeba4af26ae9ebacb3669f85899b | /oAuth/venv/bin/pip3 | 9c8aec44a34ad396320fcc678c1c0230d62a0182 | [] | no_license | Vaibhav-Kotadiya/Flask-oAuth | 66a0fd57ab3242cc81ecdef250d69b166248ff59 | 817ef470328cd5341fdb7ffa0354b3574d3dd936 | refs/heads/master | 2022-12-09T07:02:49.437622 | 2020-09-15T16:03:54 | 2020-09-15T16:03:54 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 411 | #!/Users/mac/PycharmProjects/Flask-oAuth/oAuth/venv/bin/python
# EASY-INSTALL-ENTRY-SCRIPT: 'pip==19.0.3','console_scripts','pip3'
__requires__ = 'pip==19.0.3'
import re
import sys
from pkg_resources import load_entry_point
if __name__ == '__main__':
sys.argv[0] = re.sub(r'(-script\.pyw?|\.exe)?$', '', sys.argv[0])
sys.exit(
load_entry_point('pip==19.0.3', 'console_scripts', 'pip3')()
)
| [
"mac@MACs-MacBook-Pro.local"
] | mac@MACs-MacBook-Pro.local | |
8b7bcd726beabde390de9d1928fa4f6a36307508 | 2e0a18c571b5f8000e9900e9a332eca5aff54f0f | /guppe/Seção 7/Deque.py | f759ccf0d293c473ffd25d117524a60bebc7daff | [] | no_license | LucasFerreiraB/Teste | bbf57e92db2bcc4795316c513959d2674252264b | d2b542aff96a27a727706df887506336062fbbda | refs/heads/master | 2022-09-27T11:38:07.755660 | 2020-06-05T18:38:22 | 2020-06-05T18:38:22 | 269,750,327 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 435 | py | """
Modulo Collections - Deque
Podemos dizer que o Deque é uma lista de alto performance.
"""
# Import
from collections import deque
# Criando deques
deq = deque('lucas')
print(deq)
# Adicionando elementos no deque
deq.append('y') # adiciona no final
print(deq)
deq.appendleft('k') # adiciona no comeco
print(deq)
# Remover elementos
print(deq.pop()) # Remove o ultimo elemento
print(deq)
print(deq.popleft())
print(deq)
| [
"lucasferreira9b@gmail.com"
] | lucasferreira9b@gmail.com |
27818e0cbd1150b9c136e42d2890085eb3918eeb | e5a1e766d32fa2475b23e8bda93462ea98938a8f | /new_chat/wsgi.py | 2f4a3672370594a951791355a78a5ed6f3af4d72 | [] | no_license | ShoaibMoeen/Django_Chat_App | 7993efeb603d6df7315108822dff1a2513df8dba | 27c8c99d5f7308c710741b0454f81ec85ff3707a | refs/heads/master | 2023-03-05T20:29:35.349574 | 2021-02-17T09:27:02 | 2021-02-17T09:27:02 | 338,788,856 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 393 | py | """
WSGI config for new_chat 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/3.1/howto/deployment/wsgi/
"""
import os
from django.core.wsgi import get_wsgi_application
os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'new_chat.settings')
application = get_wsgi_application()
| [
"shoaibmoeen4343@gmail.com"
] | shoaibmoeen4343@gmail.com |
2dda85a9ba04d01eb6f79efbf26e1aa3f5fe73a8 | b23f9b54f622032e71a80a497ca2d7dbd48469ad | /setup.py | d7560750ca56a0500f4ae92ea7dba81932711c29 | [] | no_license | h4ck3rm1k3/pycparserext | 15cf0a02429f3fd6bad977cd612e74ca7b20b891 | 489fd9c4804e7b3f17760b0800cf81a930a2ec7e | refs/heads/master | 2021-01-21T08:32:39.114178 | 2016-04-03T05:22:38 | 2016-04-03T05:22:38 | 55,293,358 | 0 | 0 | null | 2016-04-02T12:29:40 | 2016-04-02T12:29:40 | null | UTF-8 | Python | false | false | 893 | py | #!/usr/bin/env python
# -*- coding: latin1 -*-
from setuptools import setup
setup(name="pycparserext",
version="2016.1",
description="Extensions for pycparser",
long_description=open("README.rst", "r").read(),
classifiers=[
'Development Status :: 4 - Beta',
'Intended Audience :: Developers',
'Intended Audience :: Other Audience',
'Intended Audience :: Science/Research',
'License :: OSI Approved :: MIT License',
'Natural Language :: English',
'Programming Language :: Python',
'Topic :: Utilities',
],
install_requires=[
"ply>=3.4",
"pycparser>=2.14",
],
author="Andreas Kloeckner",
url="http://pypi.python.org/pypi/pycparserext",
author_email="inform@tiker.net",
license="MIT",
packages=["pycparserext"])
| [
"inform@tiker.net"
] | inform@tiker.net |
701090d667fe4a9f5daf26aa08d32a2d7dde0dcb | cda5f9506ff9a05ca6cc5c6fd29a56bb645f03e3 | /Grasshopper-Terminal-game-combat-function.py | 1265b0149a59ea7ccf489495107d14273b4188b1 | [] | no_license | zecollokaris/toy-problems | bc28799f41cdbddfa5a4a56c613031b3f52430bc | 66f6462ace04afbe025d90956573ef295d66f41f | refs/heads/master | 2020-03-25T00:20:17.099894 | 2018-08-04T20:12:16 | 2018-08-04T20:12:16 | 143,180,896 | 2 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,004 | py | #############################################################################################################
######Question######
# Create a combat function that takes the player's current health and the amount of damage recieved,
# and returns the player's new health.
# Health can't be less than 0.
#############################################################################################################
######BDD######
# 1. Subtract health - damage
# 2. If health - damge is less than 0 we must return 0
# 3. return health - damage
#############################################################################################################
######Solution#####
def combat(health,damage):
if health-damage < 0:
return 0
return health - damage
#############################################################################################################
############################################################################################################# | [
"collo.kariss@gmail.com"
] | collo.kariss@gmail.com |
f4ef0d969896be33f79af58aaa8261cb2e9aec27 | f112dfe38732f131156556ab724e2b9a01d317ae | /week6/12-olimp-results.py | 84111b344ca01350a77d270fb1eafcce9b46a3ef | [] | no_license | pharick/python-coursera | 2a92bf467e0ddd35a573ea4e29fff9a37e45bd24 | 3e24ac9385eada126e7c4753f71cd38181987fbf | refs/heads/master | 2020-04-04T03:44:45.067099 | 2019-03-20T07:10:22 | 2019-03-20T07:10:22 | 155,724,086 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 227 | py | n = int(input())
students = []
for i in range(n):
student = input().split()
students.append((student[0], int(student[1])))
students.sort(key=lambda student: -student[1])
for student in students:
print(student[0])
| [
"artemforkunov@gmail.com"
] | artemforkunov@gmail.com |
ea9bdeb9e2025531d7f315d480fb6d55a59df027 | ab36c659808a9c6f7b4dc873fbffd63d4b7a54cc | /functions.py | 0b8ce7451229eecd8741cb36884c4b506c1cb54f | [] | no_license | Mixpap/Ptyxiaki | 697da20d66f48df1adea1961871b3fdc4ac06913 | 6cb2dd1196f2bab275b81d17a35a85044d64b40f | refs/heads/master | 2021-01-22T13:42:16.877514 | 2015-07-28T13:02:45 | 2015-07-28T13:02:45 | 27,761,005 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 30,905 | py | from astropy.io import fits
from astropy import wcs
from astropy.table import Table
import numpy as np
import pywcsgrid2
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
from matplotlib.colors import LogNorm
from matplotlib.patches import Rectangle
from mpl_toolkits.axes_grid1.inset_locator import inset_axes,zoomed_inset_axes,mark_inset
from scipy.optimize import curve_fit
from IPython.display import clear_output, display, HTML
#===================Constants==================================================
ab12CO=77. ##Abundancy ratio of 12co to 13co (Schoier et al. 2002 )
ab13CO=8. ##Abundancy ratio of 13co to c18o (Schoier et al. 2002)
ab12CO_H2=8.5e-5 ##Abundancy ratio of 12CO to H2 (Frerking et al. 1982)
ab13CO_H2=1.25e-6 ##Abundancy ratio of 13CO to H2 (Moore et al. 2007)
ab18CO_H2=1.7e-7 ##Abundancy ratio of C18O to H2 (Frerking et al. 1982)
hk=0.0479924335 #* 10e9
h=6.626e-34 ##(J*s) Planck's const
k=1.381e-23 ##(J/K) Boltzman const
eo=8.85e-12 ##(F/m) permitivity const
Tbg=2.7 ##(K) Background Temp
c=3.e8 ##(m/s) speed of light
Msol=1.98892e30 ##(kg) Solar mass in kg
mH=1.67e-27 ##atomic hydrogen mass in kg
mH2=2*mH ##(kg) mass of H2 in kg
mmw=2.33 ##mean mol weight wrt no of Hydrogen mols
SQARSCTSR=206265.**2 ##no of stradians in arcsec squared
dp=0.112*3.3365e30 ##(C*m) CO dipole moment (0.112 debye)
pac=3.08568e16 ##(m) one parsec in metres
v_co12_j32=345.796 #GHz
v_co13_j32=330.5879 #GHz
v_c18o_j32=329.331 #GHz
m_CO12=28.01 #atomic units
m_CO13=29.02 #atomic units
m_C18O=29.999 #atomic units
amu=1.66e-24 #g
k_b=1.38e-16 #erg/K
vres=0.83
velocity=np.array([-20.06933116, -20.90281358, -21.736296 , -22.56977842,
-23.40326084, -24.23674326, -25.07022567, -25.90370809,
-26.73719051, -27.57067293, -28.40415535, -29.23763777,
-30.07112019, -30.90460261, -31.73808503, -32.57156745,
-33.40504986, -34.23853228, -35.0720147 , -35.90549712,
-36.73897954, -37.57246196, -38.40594438, -39.2394268 ,
-40.07290922, -40.90639164, -41.73987405, -42.57335647,
-43.40683889, -44.24032131, -45.07380373, -45.90728615,
-46.74076857, -47.57425099, -48.40773341, -49.24121582,
-50.07469824, -50.90818066, -51.74166308, -52.5751455 ,
-53.40862792, -54.24211034, -55.07559276, -55.90907518,
-56.7425576 , -57.57604001, -58.40952243, -59.24300485,
-60.07648727, -60.90996969, -61.74345211, -62.57693453,
-63.41041695, -64.24389937, -65.07738179, -65.9108642 ,
-66.74434662, -67.57782904, -68.41131146, -69.24479388, -70.0782763 ])
#==============================================================================
def open_map(fits_file):
"""
Usage: Pixel_map, Wcs_Coords = open_map('fits_file')
Input: A 2D/3D fits file
Return: Numpy Masked Array of pixel Map,
WCS object of Map
"""
image = fits.getdata(fits_file)
image= np.ma.masked_array(image , np.isnan(image))
w = wcs.WCS(fits_file)
return image,w
def denoise_map(m,rms):
"""
Usage: DeNoised_map = denoise_map(map,RMS_map)
Input: A 2D pixel map
Return: Numpy Masked Array where snr>3
"""
return np.ma.masked_where(m<3.0*rms,m)
def cube_to_max(cube,option=1):
"""
Input: A 3D Masked Cube_Map
Option 1: (X,Y)=(X-Pixels,Y-Pixels)
Option 2: (X,Y)=(Y-Pixels,Velocity)
Option 3: (X,Y)=(X-Pixels,Velocity)
Return: Masked Maximum Intensity Map
"""
xx=1 if option==2 else 2
yy=1 if option==1 else 0
X=cube.shape[xx]
Y=cube.shape[yy]
mapmax=np.ma.masked_array(np.zeros((Y,X)),mask=np.ones((Y,X)))
for y in range(Y):
for x in range(X):
if (option==1):
value = cube[:,y,x].max()
elif (option==2):
value = cube[y,x,:].max()
elif (option==3):
value = cube[y,:,x].max()
else:
print 'Options Only (1,2,3)'
break
if (value.dtype=='float32'):
mapmax[y,x]=value
return mapmax
def plot_map(image,coords,title='title',cm ='RdPu'):
"""
Usage: plot_map(pixel_map,WCS_Coords)
Input: 2D pixel Map, WCS object of the Map
Output: Plot of The Image with a WCS Compass
"""
ax1 = pywcsgrid2.subplot(111, wcs=coords)
im = ax1.imshow(image,origin='low',cmap=cm)
ax1.add_compass(loc=5,color='black')
ax1.set_title(title)
plt.colorbar(im)
def save_to_fits(image,coords,filename):
wheader=coords.to_header()
hdu=fits.PrimaryHDU(data=image,header=wheader)
hdu.writeto(filename)
def plot_maps(coords,fn,cm='coolwarm',norm='log',fs=(20,10),**kwargs):
"""
Usage: plot_maps(maps=[list_of_maps],titles=[list_of_titles],fn=filename(put f for not saving),norm='log',fs=figsize,coords=wcs,cm=colormap)
Input: 2D pixel Maps, WCS object of the Maps
Output: Plot all Subplots of The Images with WCS Compass
"""
plt.rcParams['figure.figsize'] = fs
nm=len(kwargs['maps'])
a=[[]]*nm
for i,m in enumerate(kwargs['maps']):
vmax=m.max()
a[i]=(pywcsgrid2.subplot(1,nm,i,wcs=coords))
a[i].grid()
if norm=='log':
im=a[i].imshow(m,origin='low',cmap=cm,norm=LogNorm(),aspect=1.)
else:
im=a[i].imshow(m,origin='low',cmap=cm,aspect=1.)
a[i].set_title(kwargs['titles'][i])
a[i].add_compass(loc=5,color='black')
ains = inset_axes(a[i], width='2%', height='37%', loc=1)
cb=plt.colorbar(im,cax=ains)
plt.tight_layout()
if (fn !='f'):
plt.savefig(fn,bbox_inches='tight')
def initial_est(map_thick,map_thin,abundance):
"""
Initial Estimation of Optical Thickness using
two Isotopes and its abundance ratio
Input: Pixel_map of one isotope, Pixel_map of Optical Thick Isotope,
abudance ratio
Return: Masked Pixel_map of Optical Thickness
"""
map_thick=np.ma.masked_where(map_thick==0.0,map_thick)
ratio = map_thin/map_thick
tau=-abundance*np.log(1-ratio)
return tau
def tau_new(tau,map_thick,map_thin,abundance):
ratio=map_thick/map_thin
e=np.exp(-tau)
eab=np.exp(-tau/abundance)
return tau-(ratio*(1-eab)-(1-e))/(ratio*eab/abundance-e)
def final_est(map_thick,map_thin,T0,abundance,maxiter=5):
"""
Final Estimation of Optical Thickness using two Isotopes,
its abundance ratio and the Initial Estimation
Input: Pixel_map of Optical Thick isotope, Pixel_map of Optical Thin Isotope,
Initial estimation Pixel_map, abudance ratio and MaxIterations = 5
Return: Pixel_map of Optical Thickness
"""
for i in range(maxiter):
tau=tau_new(T0,map_thick,map_thin,abundance)
return tau
def Tr_est(Ta,nfss=0.77):
"""
True Temperature Estimation
Input: Pixel_map of Optical Thick Isotope, fss parameter
Return: Pixel_map of True Temperature
"""
return Ta/nfss
def gaussian(x, a, x0, sigma):
return a*np.exp(-(x-x0)**2/(2*sigma**2))
def gauss_fit(T,N):
"""
!! Warning: Extreme Slow !!
Runs a Scipy Curve Gaussian fit through every pixel of non-Masked Map
Input: A 3D pixel map
Return: 2D Map of GPeak Position, 2D Map of FWHM
"""
#Z,Y,X=T.shape[0],N,N
Z,Y,X=T.shape[0],T.shape[1],T.shape[2]
xx=np.linspace(0,1,61)
Peak_Map=np.zeros((Y,X))
HW=np.zeros((Y,X))
for y in range(Y):
for x in range(X):
s=Spectra(T,y,x)
if np.ma.is_masked(s):
Peak_Map[y,x]=0.0
FW[y,x]=0.0
else:
try:
popt, pcov = curve_fit(gaussian,xx,s)
Peak_Map[y,x]=popt[1]
FW[y,x]=2.355*np.abs(popt[2])
except:
Peak_Map[y,x]=0.0
FW[y,x]=0.0
pass
return Peak_Map,FW
def Tx_est(v,Tr,Tbg=2.7):
"""
Excitation Temperature Estimation
Input: Frequency of observation in GHz, Pixel_map of Optical Thick Isotope, Cosmic Background Temperature
Return: Pixel_map of Excitation Temperature
"""
T0=hk*v
A=Tr+T0/(np.exp(T0/Tbg)-1)
Tx = T0/np.log(1+T0/A)
return Tx
def Spectra(cube_map,y,x):
"""
Spectrum of Selected Coordinates
Input: Cube Map, pixel Coordinates
Return: Vector of Spectrum Values
"""
return cube_map[:,y,x]
def Spectra9(cube_map,y,x):
"""
Mean Spectrum of 3x3 Selected Coordinates
Input: Cube Map, pixel Coordinates
Return: Mean Spectrum, Standard Deviation Spectrum
"""
a=[]
for j in [y-1,y,y+1]:
for i in [x-1,x,x+1]:
a.append(np.nan_to_num(cube_map[:,j,i]))
a=np.array(a)
return np.mean(a, axis=0),np.std(a, axis=0)
def MassEst(od,TR,Tex,d,res,j,i,X,fco):
"""
od=optical depth
TR=Integral
Tex=Excitation Temp
d=distance in kpc
res=pixel resolution
j,i=line
X=CO to H2 abundance
fco=frequency of line in Hz
MG=MassEst(tau12_f,Integral12_fix,Tx12,d=2.,res=7.7,j=3,i=2,X=functions.ab12CO_H2,fco=functions.v_co12_j32*1e9)
"""
dist=d*1e3*pac ##distance to the cloud in metres
sq_pxsz=(res**2)*SQARSCTSR ##squared pixel size in steradian
ODcor=od/(1-np.exp(-od)) ##Optical depth correction
"""****************************************
Set the Nij equation terms (result in m^-2)
****************************************"""
term1=1.49*1e25 ##
term2=0.364*(Tex+0.922) ## the partition function Z
term3=2.765*1e-9*(i*(i+1)) ## h*vio/k
term4=4.798*1e-11 ## h/k
Nco=(term1/(fco*j))*term2*np.exp(term3/Tex)*(1-np.exp(-term4*fco/Tex))*((1/(np.exp(term4*fco/Tex)-1))-(1/(np.exp(term4*fco/Tbg)-1)))**(-1)*ODcor*1e3*TR ##should be in cm^-2
MGas=(1E-4)*(1E-6)*2.8*mH2*dist*dist*sq_pxsz*X*Nco/Msol
return MGas
def Integral_to_mass(integral,Tx,tau):
d=2000.*pac
X=1./(8.5e-5)
A=1.28e14
T10=h *115.271*10**9 /k
T32=h *v_co12_j32*10**9 /k
Tbg=2.7
Z=0.36156*(Tx+0.922)
BB=np.exp(T10/Tx)
B=(1.-np.exp(-T32/Tx))
C=(1./(np.exp(T32/Tx)-1.)-1./(np.exp(T32/Tbg)-1.))**(-1)
D=tau/(1.-np.exp(-tau))
integ=integral*1000.
N=A*Z*BB*B*C*D*integ
M=N*2.8*(3.35e-27)*(7.7/206265)**2 *X *d**2
return M/Msol
def map_showXY(map12,map12m,map12my,map12mx,map13,map13m,map18,map18m,ta12,ta13,Tx12,Tx13,Tx18,wcs,y,x,dy,dx,dv,gf,s):
"""
To use with IPython interact
"""
#===================================Fitting======================
#gf=0.2 #gooud fit parameter ~10%
index_deviation=[10,2] #Max and Min Index Deviation for Second Derivative Mask
xx=velocity
#===========CO12=======================
s12=Spectra(map12,y,x)
#----mask------
der2=np.diff(s12,2.) #Second Derivative
m = np.ones(len(s12), dtype=bool)
ind1=np.argsort(der2)[0]+1
ind2=np.argsort(der2)[1]+1
i=0
while (np.abs(ind1-ind2)>index_deviation[0] or np.abs(ind1-ind2)<index_deviation[1]):
#print np.abs(ind1-ind2),np.abs(ind1-ind2)>15
ind2=np.argsort(der2)[i]+1
i=i+1
m[ind1:ind2]=False
m[ind2:ind1]=False
#---end of mask-----
try:
popt12, pcov12 = curve_fit(gaussian, xx[m], s12[m],p0=[s12.max(),xx[np.argmax(s12)],1.5],diag=(0.01,0.01))
except:
popt12,pcov12=np.zeros((3)),np.zeros((3,3))
sd12= np.sqrt(np.diag(pcov12)) #Standard Deviation
fit12 = (sd12<gf*np.abs(popt12)).all() #Good Fit?
FWHM12=2.355*np.abs(popt12[2]) #Fitted Full Width Half Maximum
FWTM12=1.865*FWHM12
outflow=False
if (FWTM12>4.):
outflow=True
FWHM12t=0.00001*2.355*np.sqrt(k_b*Tx12[y,x]/(m_CO12*amu)) #theoretical (thermal)
########################################
# s912=Spectra9(map12,y,x)[0]
# popt912, pcov912 = curve_fit(gaussian, xx, s912,p0=[s912.max(),xx[np.argmax(s12)],1.5],diag=(0.01,0.01))
# sd912= np.sqrt(np.diag(pcov912))
#===========CO13=======================
s13=Spectra(map13,y,x)
try:
popt13, pcov13 = curve_fit(gaussian, xx, s13,p0=[0.25*popt12[0],popt12[1],popt12[2]],diag=(0.005,0.005))
except:
popt13,pcov13=np.zeros((3)),np.zeros((3,3))
sd13= np.sqrt(np.diag(pcov13)) #Standard Deviation
fit13 = (sd13<gf*np.abs(popt13)).all() #Good Fit?
FWHM13=2.355*np.abs(popt13[2]) #Fitted Full Width Half Maximum
FWHM13t=0.00001*2.355*np.sqrt(k_b*Tx13[y,x]/(m_CO13*amu)) #theoretical (thermal)
########################################################
# s913=Spectra9(map13,y,x)[0]
# popt913, pcov913 = curve_fit(gaussian, xx, s913,p0=[0.25*popt912[0],popt912[1],popt912[2]],diag=(0.1,0.1))
# sd913= np.sqrt(np.diag(pcov913))
#===========CO18=======================
s18=Spectra(map18,y,x)
try:
popt18, pcov18 = curve_fit(gaussian, xx, s18,p0=[0.25*popt13[0],popt13[1],popt13[2]],diag=(0.001,0.001))
except:
popt18,pcov18=np.zeros((3)),np.zeros((3,3))
sd18= np.sqrt(np.diag(pcov18)) #Standard Deviation
fit18 = (sd18<gf*np.abs(popt18)).all() #Good Fit?
FWHM18=2.355*np.abs(popt18[2]) #Fitted Full Width Half Maximum
FWHM18t=0.00001*2.355*np.sqrt(k_b*Tx18[y,x]/(m_C18O*amu)) #theoretical (thermal)
#================TABLE========================================
col1=['$^{12}CO$ $T_{B}$','$^{13}CO$ $T_{B}$','$C^{18}O$ $T_{B}$',r'$\tau^{12}$',r'$\tau^{13}$','$^{12}CO$ $T_{X}$','$^{13}CO$ $T_{X}$',r'$^{12}CO$ FWHM',r'$^{12}CO$ FWTM',r'$^{13}CO$ FWHM',r'$C^{18}O$ FWHM',r'$^{12}CO$ Integral',r'$^{12}CO$ Core Integral', r'$^{12}CO$ Wings Integral','$^{12}CO$ Mass','$^{12}CO$ Core Mass','$^{12}CO$ Wings Mass',r'$^{12}CO$ Blue Wing Speed (Absolute)',r'$^{12}CO$ Red Wing Speed (Absolute)',r'$^{12}CO$ (Blue,Right) Wings Momentum']
i12all=s12.sum()*vres
i12b=s12[xx<popt13[1]-FWHM13/2].sum()*vres if fit13 else np.nan
i12r=s12[xx>popt13[1]+FWHM13/2].sum()*vres if fit13 else np.nan
i12=i12b+i12r
#i12=(s12[xx<popt13[1]-FWHM13/2].sum()+s12[xx>popt13[1]+FWHM13/2].sum())*vres if fit13 else np.nan
i12core=i12all-i12 if fit13 else np.nan
Mb=Integral_to_mass(i12b,Tx12[y,x],ta12[y,x]) if i12b else np.nan
Mr=Integral_to_mass(i12r,Tx12[y,x],ta12[y,x]) if i12r else np.nan
MG= Integral_to_mass(i12,Tx12[y,x],ta12[y,x]) if i12 else np.nan
MGall=Integral_to_mass(i12all,Tx12[y,x],ta12[y,x]) if i12 else np.nan
MGcore=Integral_to_mass(i12core,Tx12[y,x],ta12[y,x]) if i12 else np.nan
Vrel=popt13[1]
xx1=np.logical_and(xx<=Vrel-FWHM13/2,xx>=Vrel-10.)
xx2=np.logical_and(xx>=Vrel+FWHM13/2,xx<=Vrel+10.)
Vb=(xx[xx1]*s12[xx1]).sum()/s12[xx1].sum()
Vr=(xx[xx2]*s12[xx2]).sum()/s12[xx2].sum()
Pb=Mb*Vb/np.cos(57.3)
Pr=Mr*Vr/np.cos(57.3)
if outflow:
si='%0.2f $K\,km\,s^{-1}$'%i12
sm='%0.2f $M_{\odot}$ ($M_{B}$: %0.2f, $M_{R}$: %0.2f) // Ratio (all): %0.2f (core): %0.2f'%(MG,Mb,Mr,MG/MGall,MG/MGcore)
siall='%0.2f $K\,km\,s^{-1}$'%i12all
small='%0.2f $M_{\odot}$'%MGall
sicore='%0.2f $K\,km\,s^{-1}$'%i12core
smcore='%0.2f $M_{\odot}$'%MGcore
else:
si='0.0 (%0.2f) $K\,km\,s^{-1}$'%i12
sm='0.0 (%0.2f) $M_{\odot}$'%MG
siall='0.0 (%0.2f) $K\,km\,s^{-1}$'%i12all
small='0.0 (%0.2f) $M_{\odot}$'%MGall
sicore='0.0 (%0.2f) $K\,km\,s^{-1}$'%i12core
smcore='0.0 (%0.2f) $M_{\odot}$'%MGcore
col2=np.array(['%0.2f'%map12m[y,x],'%0.2f'%map13m[y,x],'%0.2f'%map18m[y,x],'%0.2f'%ta12[y,x],'%0.2f'%ta13[y,x],'%0.2f K'%Tx12[y,x],'%0.2f K'%Tx13[y,x],'%0.2f $km\,s^{-1}$ (thermal: %0.2f)'%(FWHM12,FWHM12t),'%0.2f $km\,s^{-1}$'%FWTM12,'%0.2f $km\,s^{-1}$ (thermal: %0.2f)'%(FWHM13,FWHM13t),'%0.2f $km\,s^{-1}$ (thermal: %0.2f)'%(FWHM18,FWHM18t),siall,sicore,si,small,smcore,sm,'%0.2f (%0.2f) $km\,s^{-1}$'%(Vb-Vrel,Vb),'%0.2f (%0.2f) $km\,s^{-1}$'%(Vr-Vrel,Vr),'%0.2f,%0.2f // %0.2f $M_{\odot} km\,s^{-1}$'%(Pb,Pr,Pb+Pr)])
t=Table([col1,col2],names=('Name','Value'),meta={'name': 'first table'})
display(t)
#col1=np.array(['$^{12}CO$ $T_{B}$','%0.2f'%map12m[y,x]])
#col2=np.array(['$^{13}CO$ $T_{B}$','%0.2f'%map13m[y,x]])
#col3=np.array(['$C^{18}O$ $T_{B}$','%0.2f'%map18m[y,x]])
#col4=np.array([r'$\tau^{12}$','%0.2f'%ta12[y,x]])
#col5=np.array([r'$\tau^{13}$','%0.2f'%ta13[y,x]])
#col6=np.array(['$^{12}CO$ $T_{X}$','%0.2f K'%Tx12[y,x]])
#col7=np.array(['$^{13}CO$ $T_{X}$','%0.2f K'%Tx13[y,x]])
#col8=np.array([r'$^{12}CO$ FWHM','%0.2f $km\,s^{-1}$ (thermal: %0.2f)'%(FWHM12,FWHM12t)])
#col9=np.array([r'$^{13}CO$ FWHM','%0.2f $km\,s^{-1}$'%FWTM12])
#col10=np.array(['$^{13}CO$ $T_{B}$','%0.2f $km\,s^{-1}$ (thermal: %0.2f)'%(FWHM13,FWHM13t)])
#col11=np.array([r'$C^{18}O$ FWHM','%0.2f $km\,s^{-1}$ (thermal: %0.2f)'%(FWHM18,FWHM18t)])
#col12=np.array([r'$^{12}CO$ Wings Integral',si])
#col13=np.array(['$^{12}CO$ Wings Mass',sm])
#t=Table([col1,col2,col3,col4,col5,col6,col7,col8,col9,col10,col11,col12,col13])
#display(t)
#t.write('test.tex',format='latex')
#================Print==============================================
# print 'tau12: %0.3f'%ta12[y,x]
# print 'tau13: %0.3f'%ta13[y,x]
# print '12CO Excitation Temperature: %0.2f'%Tx12[y,x]
# print '13CO Excitation Temperature: %0.2f'%Tx13[y,x]
# print 'C18O Excitation Temperature: %0.2f'%Tx18[y,x]
# print '12CO FWHM: %0.2f km/s || Theoretical (thermal): %0.2f km/s (Ratio:%0.2f) || Larson L: %0.2f pc || Larson M: %0.2f Mo'%(FWHM12,FWHM12t,FWHM12/FWHM12t,(FWHM12/1.1)**(1/0.38),(FWHM12/0.42)**(1/0.2))
# print '13CO FWHM: %0.2f km/s || Theoretical (thermal): %0.2f km/s (Ratio:%0.2f) || Larson L: %0.2f pc || Larson M: %0.2f Mo'%(FWHM13,FWHM13t,FWHM13/FWHM13t,(FWHM13/1.1)**(1/0.38),(FWHM13/0.42)**(1/0.2))
# print 'C18O FWHM: %0.2f km/s || Theoretical (thermal): %0.2f km/s (Ratio:%0.2f) || Larson L: %0.2f pc || Larson M: %0.2f Mo'%(FWHM18,FWHM18t,FWHM18/FWHM18t,(FWHM18/1.1)**(1/0.38),(FWHM18/0.42)**(1/0.2))
# if fit13:
# print 'Wings Integral in $^{12}CO$: %0.2f K'%(s12[xx<popt13[1]-FWHM13/2].sum()+s12[xx>popt13[1]+FWHM13/2].sum())
#===========================================================================
#===========PLOTS=======================
#===========================================================================
plt.rcParams['figure.figsize'] = 22, 35
#dy,dx,dv=20,20,10
vmax=np.argmax(s13)
v1,v2=vmax-dv,vmax+dv
#===========================================
#===Map=====================================
gs = gridspec.GridSpec(11, 8)
#====ax1-Main Map===============================
ax1=plt.subplot(gs[1:5,2:7]) #Axis for Main Map
ax1.set_title('$^{12}CO$ Excitation Temperatures')
#mim=ax1.imshow(map12m,origin='low',cmap='coolwarm')
mim=ax1.imshow(Tx12.data,origin='low',cmap='coolwarm') #Excitation Map
ax1.axvline(x=x,color='k',ls='dashed',linewidth=1.0,alpha=0.6) #Current X
ax1.annotate('$x$=%d'%x,(x+5,5),color='k',size=20)
ax1.axhline(y=y,color='k',ls='dashed',linewidth=1.0,alpha=0.6) #Current Y
ax1.annotate('$y$=%d'%y,(5,y+5),color='k',size=20)
ax1bar = inset_axes(ax1, width='2%', height='40%', loc=4) #axis for colorbar
ax1.annotate(s='', xy=(530,40), xytext=(579,40), arrowprops={'arrowstyle':'|-|','linewidth':2.0})
ax1.text(542,43,'3 pc',color='white',fontsize=15)
plt.colorbar(mim,cax=ax1bar) #colorbar
#=====================================
axv0=plt.subplot(gs[0,2:7],sharex=ax1) #Axis for (X,Velocity) Map
axv0.set_title(' $^{12}CO$ Max $T_{B}$')
lev=np.linspace(0,map12mx.max(),20) #Contourf Levels
mimx=axv0.contourf(np.arange(0,map12mx.shape[1]),velocity,map12mx,levels=lev,cmap='coolwarm')
axv0.xaxis.tick_top()
axv0.axvline(x=x,color='k',ls='dashed',linewidth=1.0,alpha=0.75) #Current X
axv1=plt.subplot(gs[1:5,7],sharey=ax1) #Axis for (X,Velocity) Map
axv1.set_title('$^{12}CO$ Max $T_{B}$')
lev=np.linspace(0,map12my.max(),20) #Contourf Levels
mimx=axv1.contourf(velocity,np.arange(0,map12my.shape[1]),np.rot90(map12my,3),levels=lev,cmap='coolwarm')
axv1.yaxis.tick_right()
axv1.axhline(y=y,color='k',ls='dashed',linewidth=1.0,alpha=0.75) #Current Y
#===========================
#==Zoom=======
x1, x2, y1, y2 = x-dx, x+dx, y-dy, y+dy #Define Region
region12=map12m[y1:y2,x1:x2]
region13=map13m[y1:y2,x1:x2]
if ((x+dx>350) and (y+dy>250)): #dx=20, 7
axins = zoomed_inset_axes(ax1, 140./np.max([dx,dy]), loc=3) #Axis for Zoom
else:
axins = zoomed_inset_axes(ax1, 140./np.max([dx,dy]), loc=1) #Axis for Zoom
#axins.set_title('$^{12}CO$ and $^{13}CO$ (contours) \n max$T_{B}$ $(K)$')
c12=axins.contourf(map12m,levels=np.linspace(region12.min(),region12.max(),15))
c13=axins.contour(map13m,cmap='gnuplot',levels=np.linspace(region13.min(),region13.max(),7),alpha=0.75)
axins.set_xlim(x1, x2)
axins.set_ylim(y1, y2)
mark_inset(ax1, axins, loc1=2, loc2=3, fc="none", ec="1.",lw=2.)
axins.set_title('Max $T_{B}$ Contours:$^{13}CO$ \n Max $T_{B}$ Filled Contours:$^{12}CO$ ')
# axins12 = inset_axes(axins, width='2%', height='25%', loc=2)
# cbar12=plt.colorbar(c12,cax=axins12)
# cbar12.ax.set_title('$^{12}CO$ \n maxT $(K)$')
#
# axins13 = inset_axes(axins, width='2%', height='25%', loc=1)
# cbar13=plt.colorbar(c13,cax=axins13)
# cbar13.ax.set_title('$^{13}CO$ \n maxT $(K)$')
#==Map-Rectangles
dd=0.5
rect1 = [Rectangle((x-dx,y-dd), width=2.*dx, height=2.*dd, fill=False,color='red',linewidth=1.2,alpha=0.75)]
axins.add_artist(rect1[0])
rect2 = [Rectangle((x-dd,y-dy), width=2.*dd, height=2.*dy, fill=False,color='red',linewidth=1.2,alpha=0.75)]
axins.add_artist(rect2[0])
rect3= [Rectangle((x-1.5,y-1.5), width=3, height=3, fill=False,color='green',linewidth=1.5,alpha=0.95)]
axins.add_artist(rect3[0])
#=================3D Velocities==================================================
#Parameters
hl_lw=3 #Highlight LineWidth
lev_18 = [popt18[0]/2.] #C18O Contour Display
lev_13 = popt13[0]/2. #CO13 Contour Display
min13=0.75
min12=0.
num_levels_12=10
num_levels_13=8
#======X-line=========================
ax1y=plt.subplot(gs[5:7,2:])
ax1y.set_ylabel('Velocity $(km\,s^{-1})$')
ax1y.set_xlabel('X-Pixel')
region13y=map13[v1:v2,y,x-dx:x+dx] #CO13 zoom region
region12y=map12[v1:v2,y,x-dx:x+dx] #CO12 zoom region
region18y=map18[v1:v2,y,x-dx:x+dx] #CO18 zoom region
lev13y = np.linspace(np.abs(region13y.min())+min13,region13y.max(),num_levels_13)
lev12y = np.linspace(min12,region12y.max(),num_levels_12)
lev18y=lev_18
cy=ax1y.contour(np.arange(x-dx,x+dx,1),velocity[v1:v2],region13y,levels=lev13y,linewidths=2.1,cmap='gnuplot',alpha=0.8)
if fit13:
ax1y.contour(np.arange(x-dx,x+dx,1),velocity[v1:v2],region13y,[lev_13],linestyles='--',linewidths=hl_lw,colors='red',alpha=1.)
c2y=ax1y.contourf(np.arange(x-dx,x+dx,1),velocity[v1:v2],region12y,levels=lev12y)
if fit18:
ax1y.contour(np.arange(x-dx,x+dx,1),velocity[v1:v2],region18y,lev18y,linestyles='--',linewidths=hl_lw,colors='black',alpha=0.8)
ax1y.contour(np.arange(x-dx,x+dx,1),velocity[v1:v2],region18y,[s18.max()*0.8],linewidths=hl_lw+1,colors='black',alpha=0.8)
ax1y.plot(np.ones(velocity[v1:v2].shape)*x,velocity[v1:v2],'--')
#===CO12 Colorbar Hacks======================================
axinsy12 = inset_axes(ax1y, width='35%', height='3%', loc=2)
cbary12=plt.colorbar(c2y,cax=axinsy12,orientation='horizontal')
cbary12.ax.set_title('$^{12}CO$ $T_{B}$ $(K)$')
#===CO13 Colorbar Hacks======================================
axinsy13 = inset_axes(ax1y, width='30%', height='3%', loc=1)
cbary13=plt.colorbar(cy,cax=axinsy13,orientation='horizontal')
cbary13.ax.set_title('$^{13}CO$ $T_{B}$ $(K)$')
#==========Y-line===============================================================
ax1x=plt.subplot(gs[1:5,:2])
ax1x.set_xlabel('Velocity $(km\,s^{-1})$')
ax1x.set_ylabel('Y-Pixel')
region13x=map13[v1:v2,y-dy:y+dy,x]
region12x=map12[v1:v2,y-dy:y+dy,x]
region18x=map18[v1:v2,y-dy:y+dy,x]
lev13x = np.linspace(np.abs(region13x.min())+min13,region13x.max(),num_levels_13)
lev12x = np.linspace(min12,region12x.max(),num_levels_12)
lev18x=lev_18
cx=ax1x.contour(velocity[v1:v2],np.arange(y-dy,y+dy),np.rot90(region13x,3),levels=lev13x,linewidths=2.1,cmap='gnuplot',alpha=0.8)
if fit13:
ax1x.contour(velocity[v1:v2],np.arange(y-dy,y+dy),np.rot90(region13x,3),[lev_13],linestyles='--',linewidths=hl_lw,colors='red',alpha=1.)
c2x=ax1x.contourf(velocity[v1:v2],np.arange(y-dy,y+dy),np.rot90(region12x,3),levels=lev12x)
if fit18:
ax1x.contour(velocity[v1:v2],np.arange(y-dy,y+dy),np.rot90(region18x,3),lev18x,linestyles='--',linewidths=hl_lw,colors='black',alpha=0.8)
ax1x.contour(velocity[v1:v2],np.arange(y-dy,y+dy),np.rot90(region18y,3),[s18.max()*0.8],linewidths=hl_lw+1,colors='black',alpha=0.8)
ax1x.plot(velocity[v1:v2],np.ones(velocity[v1:v2].shape)*y,'--')
#===CO12 Colorbar Hacks======================================
axinsx12 = inset_axes(ax1x, width='3%', height='30%', loc=2)
cbarx12=plt.colorbar(c2x,cax=axinsx12)
cbarx12.ax.set_title('$^{12}CO$ \n $T_{B}$ $(K)$')
#===CO13 Colorbar Hacks======================================
axinsx13 = inset_axes(ax1x, width='3%', height='30%', loc=1)
cbarx13=plt.colorbar(cx,cax=axinsx13)
cbarx13.ax.set_title('$^{13}CO$ \n $T_{B}$ $(K)$')
axinsx13.yaxis.set_ticks_position("left")
#++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
#============================================================================
x_p=xx.min()
#x_mean= xx.mean() #For Text Annotation
ax2=plt.subplot(gs[7,:])
y_p=s12.max()
ax2.set_title('$^{12}CO$ // $FWHM=$%0.3f'%(FWHM12))
ax2.plot(xx,s12,label='CO12')
ax2.plot(xx[m],s12[m],'ko',label='Masked CO12 Data')
ax2.plot(xx,gaussian(xx,popt12[0],popt12[1],popt12[2]),label='Fit to Masked Data')
if fit12:
ax2.fill_between(xx,gaussian(xx,popt12[0]-sd12[0],popt12[1]-sd12[1],popt12[2]-sd12[2]),gaussian(xx,popt12[0]+sd12[0],popt12[1]+sd12[1],popt12[2]+sd12[2]),alpha=0.25)
ax2.annotate(r'Fit Parameters for CO12: $A=$%0.3f +/-%0.3f, $x_0=$%0.3f +/-%0.3f, $\sigma=$%0.3f +/-%0.3f'%(popt12[0],sd12[0],popt12[1],sd12[1],popt12[2],sd12[2]),(x_p,y_p))
hline=np.linspace(popt12[1]-FWHM12/2,popt12[1]+FWHM12/2,10)
ax2.plot(hline,np.ones(hline.shape)*popt12[0]/2,color='r',label='HalfMaximum of fit CO12')
ax2.plot(xx,s13,alpha=0.7,label='CO13')
ax2.plot(xx,s18,alpha=0.5,label='C18O')
if fit18:
ax2.axvspan(popt18[1]-FWHM18/2.,popt18[1]+FWHM18/2.,alpha=0.15)
if fit13:
ax2.axvspan(popt13[1]-FWHM13/2.,popt13[1]+FWHM13/2.,alpha=0.15,color='green')
ax2.legend()
ax3=plt.subplot(gs[8,:],sharex=ax2)
y_p=s13.max()
ax3.set_title('$^{13}CO$ // $FWHM=$%0.3f'%(FWHM13))
ax3.plot(xx,s13,'ko',label='CO13')
ax3.plot(xx,gaussian(xx,popt13[0],popt13[1],popt13[2]),label='Fit to CO13')
if fit13:
ax3.fill_between(xx,gaussian(xx,popt13[0]-sd13[0],popt13[1]-sd13[1],popt13[2]-sd13[2]),gaussian(xx,popt13[0]+sd13[0],popt13[1]+sd13[1],popt13[2]+sd13[2]),alpha=0.25)
ax3.annotate(r'Fit Parameters for CO13: $A=$%0.3f +/-%0.3f, $x_0=$%0.3f +/-%0.3f, $\sigma=$%0.3f +/-%0.3f'%(popt13[0],sd13[0],popt13[1],sd13[1],popt13[2],sd13[2]),(x_p,y_p))
hline=np.linspace(popt13[1]-FWHM13/2,popt13[1]+FWHM13/2,10)
ax3.plot(hline,np.ones(hline.shape)*popt13[0]/2,color='r',label='HalfMaximum of fit CO13')
ax3.plot(xx,s18,alpha=0.5,label='C18O')
if fit18:
ax3.axvspan(popt18[1]-FWHM18/2,popt18[1]+FWHM18/2,alpha=0.15)
if fit13:
ax3.axvspan(popt13[1]-FWHM13/2.,popt13[1]+FWHM13/2.,alpha=0.15,color='green')
ax3.legend()
ax4=plt.subplot(gs[9,:],sharex=ax2)
y_p=s18.max()
ax4.set_title(r'$C^{18}O$ // $FWHM=$%0.3f'%(FWHM18))
ax4.plot(xx,s18,'ko',label='C18O')
if fit18:
ax4.plot(xx,gaussian(xx,popt18[0],popt18[1],popt18[2]),label='Fit to CO18')
ax4.fill_between(xx,gaussian(xx,popt18[0]-sd18[0],popt18[1]-sd18[1],popt18[2]-sd18[2]),gaussian(xx,popt18[0]+sd18[0],popt18[1]+sd18[1],popt18[2]+sd18[2]),alpha=0.25)
ax4.annotate(r'Fit Parameters for CO18: $A=$%0.3f +/-%0.3f, $x_0=$%0.3f +/-%0.3f, $\sigma=$%0.3f +/-%0.3f'%(popt18[0],sd18[0],popt18[1],sd18[1],popt18[2],sd18[2]),(x_p,y_p))
hline=np.linspace(popt18[1]-FWHM18/2,popt18[1]+FWHM18/2,10)
ax4.plot(hline,np.ones(hline.shape)*popt18[0]/2,color='r',label='HalfMaximum of fit CO18')
ax4.axvspan(popt18[1]-FWHM18/2,popt18[1]+FWHM18/2,alpha=0.15)
ax4.legend()
ax5=plt.subplot(gs[10,:],sharex=ax2)
ax5.set_title(r'$^{12}CO$ Wings')
ax5.fill_between(xx[xx1],s12[xx1],alpha=0.7,color='blue')
ax5.fill_between(xx[xx2],s12[xx2],alpha=0.7,color='red')
if outflow:
ax5.annotate(r'Outflow Mass Estimation: %0.2f $M_{\odot}$'%MG,(x_p,s12.max()-2),fontsize=17)
ax5.annotate(r'Outflow Momentum Estimation: %0.2f $M_{\odot}\, km \, s^{-1}$'%(Pb+Pr),(x_p,s12.max()-0.4*s12.max()),fontsize=17)
ax5.annotate(r'Blue Outflow: %0.2f $M_{\odot}$'%Mb,(popt13[1]-10,y_p),fontsize=13)
ax5.annotate(r'$V_B:$ %0.2f $km\,s^{-1}$'%Vb,(Vb,y_p+5),fontsize=12)
ax5.annotate(r'$V_R:$ %0.2f $km\,s^{-1}$'%Vr,(Vr,y_p+5),fontsize=12)
ax5.annotate(r'Red Outflow: %0.2f $M_{\odot}$'%Mr,(popt13[1]+5,y_p),fontsize=13)
ax5.vlines(Vb,0,s12.max())
ax5.vlines(Vr,0,s12.max())
#ax6=plt.subplot(gs[11:,:],sharex=ax2)
#ax6.set_title('3X3 Mean and Standard Deviation Spectrum')
#ave12=Spectra9(map12,y,x)
#ave13=Spectra9(map13,y,x)
#ave18=Spectra9(map18,y,x)
#ax6.plot(xx,ave12[0],color='blue',label='CO12 Mean')
#ax6.fill_between(xx,ave12[0]-ave12[1],ave12[0]+ave12[1],color='blue',alpha=0.25)
#ax6.plot(xx,ave13[0],color='green',label='CO12 Mean')
#ax6.fill_between(xx,ave13[0]-ave13[1],ave13[0]+ave13[1],color='green',alpha=0.25)
#ax6.plot(xx,ave18[0],color='red',label='CO12 Mean')
#ax6.fill_between(xx,ave18[0]-ave18[1],ave18[0]+ave18[1],color='red',alpha=0.25)
# ax6.plot(xx,gaussian(xx,popt913[0],popt913[1],popt913[2]),'--',linewidth=3.,label='Fit')
#ax6.legend()
plt.tight_layout()
if s:
plt.savefig('full%d-%d'%(y,x),bbox_inches='tight')
#t.write('t%d-%d.tex'%(y,x),format='latex')
| [
"mighalis@gmail.com"
] | mighalis@gmail.com |
c961d180e49b2f37329b439a63ed3caf39182499 | 61febabc6aa34b7c47208aa7be5dfca88287ddaf | /Ch. 5 If Statements/Alien_Colors_2.py | f3d2b2cede94db66aee11d3245b9b9571a6284d5 | [] | no_license | chrisstophere/Python-Crash-Course | 4be7262acc2ff8ad26d99aceb028c25e4c7f9b0b | 702c44734e93df68ec55831626fb7a7a22ce2b8d | refs/heads/master | 2021-05-24T10:31:54.679224 | 2020-05-07T21:10:09 | 2020-05-07T21:10:09 | 253,520,738 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 215 | py | alien_color = 'red'
if alien_color == 'green':
print(f"You shot the {alien_color} alien. You earned 5 points.")
elif alien_color != 'green':
print(f"You shot the {alien_color} alien. You earned 10 points.") | [
"chris@ewentech.com"
] | chris@ewentech.com |
70dd6b6891e4793418f9b327dcf8ddb1de563ef7 | 52b5773617a1b972a905de4d692540d26ff74926 | /.history/clouds_20200703183549.py | deecd69a4f52fe13e3dd7c9a278d545d91b636a2 | [] | no_license | MaryanneNjeri/pythonModules | 56f54bf098ae58ea069bf33f11ae94fa8eedcabc | f4e56b1e4dda2349267af634a46f6b9df6686020 | refs/heads/master | 2022-12-16T02:59:19.896129 | 2020-09-11T12:05:22 | 2020-09-11T12:05:22 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 439 | py | def jumpingClouds(c):
i = 0
jumps = 0
while i < len(c)-2:
if c[i] == 0 and c[i+2] == 0:
print('here')
print('c---->',c[i],'i-->',i)
jumps +=1
i +=2
elif c[i] == 0 and c[i+1] == 0:
print('here2')
print('c---->',c[i],'i-->',i)
jumps +=1
i +=1
print(jumps)
jumpingClouds([0 ,0, 1, 0, 0, 1, 0]) | [
"mary.jereh@gmail.com"
] | mary.jereh@gmail.com |
14b6e041b02bb82c853acc82a39ce2540e479dbf | 71d1c48161fadaaed2e9c4437e1194ce13b18622 | /working_with_api/english_vocabulary.py | b3b6503565d79ed20257c0d739fc6b1499e9b953 | [] | no_license | ikventure/some_projects | 2f43eef7ebe91e7d2ffaf4f96f2b55a4a8b7c141 | c87a1cab28662de8524ac3d77ba1884bd1fc53b9 | refs/heads/main | 2023-08-16T17:26:53.033466 | 2021-09-14T07:24:42 | 2021-09-14T07:24:42 | 401,194,468 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 636 | py | # -*- coding: utf-8 -*-
"""
Created on Fri Sep 11 16:15:44 2020
@author: ikventure
"""
filename = 'SUM_of_cet4+6+toefl+gre.txt'
txt = open(filename, "r").read()
txt = txt.lower()
all_words = txt.split()
words1, words2 = [], []
for word in all_words:
if len(word) == 6 and word[0] == word[-1] and word[1] != word[4]:
words1.append(word)
if len(word) == 5 and word[0] == word[-1] and word[1] != word[3]:
words2.append(word)
print(len(words1))
print(len(words2))
for word1 in words1:
for word2 in words2:
if word1[0:2] == word2[0:2] and word1[4:6] == word2[3:5]:
print(word1 + " " + word2) | [
"ikventurelove@gmail.com"
] | ikventurelove@gmail.com |
f0ec7fdedb65b26187793048fe26edf04bd719b9 | b06b79983d2dbe596e9ad2171694f9124cdc7fc1 | /Python/memoryExample.py | 432ad951664f2b50a485e499b2e98253b74d72f5 | [] | no_license | ljthink/TDC-2 | 87e89124eb08c3b181f0aca2bed4a5beca43dd88 | b09ed8783a2401e8eaa35eb99a3bff79aaa382c3 | refs/heads/master | 2022-11-19T06:44:14.588905 | 2020-07-23T00:24:32 | 2020-07-23T00:24:32 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 702 | py | from QuartusMemory import QuartusMemory
q = QuartusMemory()
# Find index of instance named RAM1
inst = q.find_instance('RAM1')
# Read memory from device
arr = q.read_mem(inst,True)
# Print contents of address 0x04
print('Arr1:')
for k in arr[4]:
print(str(k) + ' ',end='')
print()
# Copy the array and change one of the bits in 0x04
arr2 = arr
arr2[4][1] = 1
# Print the new array
print('Arr2:')
for k in arr2[4]:
print(str(k) + ' ',end='')
print()
# Write the memory to the device
q.write_mem(inst,arr2,True)
# Read memory into a new array
arr3 = q.read_mem(inst,True)
# Print to confirm that memory was changed
print('Arr3:')
for k in arr3[4]:
print(str(k) + ' ',end='')
print()
| [
"me@lramsey.com"
] | me@lramsey.com |
3ef2026eb83017aa5c24665674b8d15767fb2008 | 51d8f003828d6ee6e6611f0e133b1e35cf400601 | /ipaxi/ixbr_api/core/tests/use_cases_tests/test_service_use_case.py | 830992ce3f08fe190f0264ea26fd19099f6e8a39 | [
"Apache-2.0"
] | permissive | tatubola/xpto | 23b5f7a42c13c7d39eb321e52b9b4b2d1ef76c4c | 6ed8cec23b06bccb1edf57e6b67af017f9a162d3 | refs/heads/master | 2020-04-02T11:05:24.560009 | 2018-10-23T17:41:10 | 2018-10-23T17:41:10 | 154,370,519 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,817 | py | from unittest.mock import patch
from django.core.exceptions import ValidationError
from django.test import TestCase
from model_mommy import mommy
from ...models import ContactsMap, MLPAv4, Tag
from ...use_cases.service_use_case import delete_service_use_case
from ..login import DefaultLogin
class ServiceUseCaseTest(TestCase):
def setUp(self):
DefaultLogin.__init__(self)
p = patch('ixbr_api.core.models.HistoricalTimeStampedModel.full_clean')
p.start()
self.addCleanup(p.stop)
p = patch('ixbr_api.core.models.create_all_ips')
p.start()
self.addCleanup(p.stop)
p = patch('ixbr_api.core.models.create_tag_by_channel_port')
p.start()
self.addCleanup(p.stop)
def test_delete_service_use_case(self):
tag = mommy.make(Tag, status='PRODUCTION')
contacts_map = mommy.make(ContactsMap)
service_mlpav4 = mommy.make(MLPAv4, tag=tag, make_m2m=True)
service_mlpav4.asn.contactsmap_set.add(contacts_map)
self.assertEqual(MLPAv4.objects.filter(pk=service_mlpav4.pk).count(), 1)
delete_service_use_case(pk=service_mlpav4.pk)
self.assertEqual(MLPAv4.objects.filter(pk=service_mlpav4.pk).count(), 0)
def test_fail_delete_service_use_case(self):
tag = mommy.make(Tag, status='PRODUCTION')
contacts_map = mommy.make(ContactsMap)
service_mlpav4 = mommy.make(MLPAv4, tag=tag, make_m2m=True)
service_mlpav4.asn.contactsmap_set.add(contacts_map)
self.assertEqual(MLPAv4.objects.filter(pk=service_mlpav4.pk).count(), 1)
with self.assertRaisesMessage(ValidationError, "Invalid service primary key"):
delete_service_use_case(pk=tag.pk)
self.assertEqual(MLPAv4.objects.filter(pk=service_mlpav4.pk).count(), 1)
| [
"dmoniz@nic.br"
] | dmoniz@nic.br |
a6262547823f4b422a3298d7263130a495f95dc7 | 14825e285a5637d0c7e981e3c32c3b961e89f981 | /Identification/Rider.py | e6a641da3302e9d3b322b635a0a257877b712f91 | [] | no_license | simohn/Ride_on_Time | ef43627f6b65732309c41bf51692066e7920f8fc | 7e9ca19ba1c4c82f8423819b61345c686affb454 | refs/heads/master | 2020-04-21T16:31:26.765286 | 2019-08-27T18:10:28 | 2019-08-27T18:10:28 | 169,704,386 | 1 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,571 | py | # Code written by Simon Schauppenlehner
# Last change: 22.06.2019
import numpy as np
class Rider:
# Private class variable
_unique_id = 1000
# Public methods
def __init__(self, id=-1):
if id == -1:
self.id = self._get_unique_id()
else:
self.id = id
self.images = []
self.features_average = []
self.features_variance = []
def add_image(self, image):
self.images.append(image)
def get_total_distance(self, image):
total_dist = np.linalg.norm(self.get_features_average()-image.get_features())
return total_dist
def get_id(self):
return self.id
def calc_features_average(self):
images_cnt = len(self.images)
average = np.zeros(2048)
for image in self.images:
average += image.get_features()
average = np.true_divide(average, images_cnt)
self.features_average = average
def calc_features_variance(self):
images_cnt = len(self.images)
variance = np.zeros(2048)
for image in self.images:
variance += np.power((image.get_features()-self.features_average), 2)
variance = np.true_divide(variance, images_cnt)
self.features_variance = variance
def get_features_average(self):
return self.features_average
def get_features_variance(self):
return self.features_variance
# Private methods
@classmethod
def _get_unique_id(cls):
cls._unique_id += 1
return cls._unique_id - 1
| [
"schauppinger@gmail.com"
] | schauppinger@gmail.com |
2cf7a4b05dd7a1a4b8cd95fbc9af7fcfbc6af2ce | b42af24fae93c62573256c47bc10df396a098c01 | /snakegameyash.py | 3547106350dc9cc09401cc31f74ed7a3425382c4 | [] | no_license | YashVardhan-444/snakegame | ac597e284e4b4eb74038dd06200538dc930de494 | ac5807808dd45cfcb90898ab0112e45db1b9ff98 | refs/heads/main | 2023-07-13T11:18:31.361490 | 2021-08-13T16:11:17 | 2021-08-13T16:11:17 | 308,942,232 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 4,348 | py | import pygame
from pygame import mixer
import time
import random
pygame.init()
white = (255, 255, 255) # constants defined to be used in the code ahead
# These parameters are color code for red,green and blue respectively
yellow = (255, 255, 102)
black = (0, 0, 0)
red = (213, 50, 80)
green = (0, 255, 0)
blue = (50, 153, 213)
dis_width = 1100 # width of display screen
dis_height = 600 # height of display screen
pygame.display.set_mode()
bgimg = pygame.image.load("tree.jpg")
bgimg = pygame.transform.scale(bgimg, (dis_width, dis_height)).convert_alpha()
# creating window with width and height as parameters
dis = pygame.display.set_mode((dis_width, dis_height))
# providing caption at the top of the window
pygame.display.set_caption('Snake Game by Yash vardhan')
# to keep tack of time we built an object 'clock' for predefined function clock()
clock = pygame.time.Clock()
snake_block = 10 # size of block
snake_speed = 20 # speed of snake
font_style = pygame.font.SysFont(
"bahnschrift", 25) # for font play again or quit
score_font = pygame.font.SysFont("calibri", 25) # for font of score
def Your_score(score): # method for score
value = score_font.render("Your Score: " + str(score), True, black)
dis.blit(value, [0, 0])
def our_snake(snake_block, snake_list):
for x in snake_list:
pygame.draw.rect(dis, black, [x[0], x[1], snake_block, snake_block])
def message(msg, color):
mesg = font_style.render(msg, True, color)
dis.blit(mesg, [dis_width / 3, dis_height / 2])
def gameLoop():
mixer.music.load("background.wav")
mixer.music.play(-1)
game_over = False
game_close = False
x1 = dis_width / 2
y1 = dis_height / 2
x1_change = 0
y1_change = 0
snake_List = []
Length_of_snake = 1
foodx = round(random.randrange(0, dis_width - snake_block) / 10.0) * 10.0
foody = round(random.randrange(0, dis_height - snake_block) / 10.0) * 10.0
while not game_over:
dis.blit(bgimg, (0, 0))
while game_close == True:
dis.fill(black)
message("You Lost! Press P-Play Again or Q-Quit", yellow)
Your_score(Length_of_snake - 1)
pygame.display.update()
for event in pygame.event.get():
if event.type == pygame.KEYDOWN:
if event.key == pygame.K_q:
game_over = True
game_close = False
if event.key == pygame.K_p:
gameLoop()
for event in pygame.event.get():
if event.type == pygame.QUIT:
game_over = True
if event.type == pygame.KEYDOWN:
if event.key == pygame.K_LEFT:
x1_change = -snake_block
y1_change = 0
elif event.key == pygame.K_RIGHT:
x1_change = snake_block
y1_change = 0
elif event.key == pygame.K_UP:
y1_change = -snake_block
x1_change = 0
elif event.key == pygame.K_DOWN:
y1_change = snake_block
x1_change = 0
if x1 >= dis_width or x1 < 0 or y1 >= dis_height or y1 < 0:
game_close = True
x1 += x1_change
y1 += y1_change
pygame.draw.rect(dis, red, [foodx, foody, snake_block, snake_block])
snake_Head = []
snake_Head.append(x1)
snake_Head.append(y1)
snake_List.append(snake_Head)
if len(snake_List) > Length_of_snake:
del snake_List[0]
for x in snake_List[:-1]:
if x == snake_Head:
game_close = True
our_snake(snake_block, snake_List)
Your_score(Length_of_snake - 1)
pygame.display.update()
if x1 == foodx and y1 == foody:
foodx = round(random.randrange(
0, dis_width - snake_block) / 10.0) * 10.0
foody = round(random.randrange(
0, dis_height - snake_block) / 10.0) * 10.0
Length_of_snake += 1
clock.tick(snake_speed)
pygame.quit()
quit()
gameLoop()
| [
"noreply@github.com"
] | noreply@github.com |
cc578e23762a3824d2011e2c493327ac6fa7534f | 40361071089b4f243962c5dd2e0bd6144f76acbd | /cell_count_utils.py | 769ebe2450f13b3cdaf08e2d1dc651be248d2d58 | [] | no_license | liaorongfan/cell-counting | d555d06e8113ffc2dcf4c19e1e4003a9c61f8fac | 0f0640225fa3f6884efdd315158bf14dc47bedc1 | refs/heads/main | 2023-05-05T20:17:46.539954 | 2021-05-23T14:39:40 | 2021-05-23T14:39:40 | 370,076,359 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 4,942 | py | import cv2
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
def plt_show(img, figsize=(24, 6), gray=True):
"""默认画灰度图"""
plt.figure(figsize=figsize)
plt.grid(False)
if gray:
plt.imshow(img, 'gray')
else:
plt.imshow(img)
plt.show()
pass
def plt_show_(img, figsize=(24, 6), gray=True):
"""默认画灰度图"""
plt.figure(figsize=figsize)
plt.grid(False)
if gray:
plt.imshow(img, 'gray')
else:
plt.imshow(img)
plt.show()
pass
def prt(*args):
# print(*args)
pass
class SegCircle:
"""找出图片中的培养皿并擦除其他部分"""
def __call__(self, input_img):
"""
args:
input_img:array; rgb格式图像
ret:
seged_img:ndarray; 检测出的培养皿图片，格式RGB，
尺寸固定到(nh, nw) = (900, 900 * (w / h))
"""
# img = cv2.imread(img_path)
img = self.resize(input_img)
seged_img = self._seg_circle(img)
return seged_img
def _seg_circle(self, img):
"""
args:
img:ndarray; rgb格式图片
"""
img_hw = img.shape[:2]
circles = self.detect_circle(img) # ; print('r = ', circles[0][2])
center_hw, radius = (circles[0][1], circles[0][0]), (circles[0][2]) # 圆心格式：(y, x) (h, w) (行， 列)
mask = self.dis_map(img_hw, center_hw, radius)
mask = mask.astype(np.uint8)
res = cv2.add(img, np.zeros(np.shape(img), dtype=np.uint8), mask=mask)
# plt_show(res, figsize=(12,12))
return res
@staticmethod
def detect_circle(img):
gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
gaussian = cv2.GaussianBlur(gray, (5, 5), 0)
circles_ = cv2.HoughCircles(
gaussian, cv2.HOUGH_GRADIENT, dp=1, minDist=500,
param1=200, param2=75, minRadius=100, maxRadius=500
)
circles = circles_[0, :, :]
circles = np.uint16(np.around(circles))
return circles
@staticmethod
def dis_map(img_shape, center, r):
img_h, img_w = img_shape
ch, cw = center
tensor_h, tensor_w = np.meshgrid(np.arange(img_h), np.arange(img_w), indexing='ij')
tensor = np.zeros((img_h, img_w, 2))
tensor_mask = np.zeros((img_h, img_w))
tensor[:, :, 0] = tensor_h
tensor[:, :, 1] = tensor_w
distances = np.sqrt((tensor[:, :, 0] - ch) ** 2 + (tensor[:, :, 1] - cw) ** 2)
tensor_mask[distances <= r] = 255
return tensor_mask
@staticmethod
def resize(img, fixed_size=900):
h, w, _ = img.shape # opencv.readimg 格式(h, w)
nh, nw = fixed_size, int(fixed_size * (w / h))
img_resize = cv2.resize(img, (nw, nh), interpolation=cv2.INTER_CUBIC) # resize格式(w, h)
return img_resize
def count_metric(count_num, labeled_num):
count_num = np.array(count_num)
labeled_num = np.array(labeled_num)
error = np.abs(count_num - labeled_num) / labeled_num
error = np.round(error, 2)
return error
def count_record(config, batch, label, count_num, error):
with open("count_log.txt", "a") as f:
mean_error = np.array(error).mean().round(3)
f.write("Image list:" + str(batch) + "\n")
f.write("config:\n")
f.write("min_cell_area:" + str(config["min_cell_area"]) + "|" +
"max_cell_area:" + str(config["max_cell_area"]) + "\n")
f.write("hsv_low:" + str(config["hsv_low"]) + "|" +
"hsv_up:" + str(config["hsv_up"]) + "\n")
f.write("\tmin_cell_area:" + str(config["min_cell_area"]) + "|" +
"good_cell_area:" + str(config["good_cell_area"]) + "\n")
f.write("\tadjust_ratio_low:" + str(config["adjust_ratio_low"]) + "|" +
"adjust_ratio_up:" + str(config["adjust_ratio_up"]) + "\n")
f.write("cell label num:" + str(label) + "\n")
f.write("cell count num:" + str(count_num) + "\n")
f.write("cell count err:" + str(error) + '\nmean error:' + str(mean_error) + "\n\n")
def hsv_dist_plot(hsv):
plt.figure(figsize=(15, 8))
sns.distplot(hsv[:, :, 0].flatten(), color="Y")
sns.distplot(hsv[:, :, 1].flatten(), color="G")
sns.distplot(hsv[:, :, 2].flatten(), color="Black")
plt.show()
def hsv_select(img, hsv, h=[0, 255], s=[0, 255], v=[0, 255], show_mask=False):
"""拿到h[]， s[], v[]数值范围内的图像"""
low_purple = np.array([h[0], s[0], v[0]]) # [h, s, v]
high_purple = np.array([h[1], s[1], v[1]])
mask = cv2.inRange(hsv, low_purple, high_purple)
# print(mask.shape) # 二维 二值（0/255）
if show_mask:
plt_show(mask, figsize=(15, 15))
masked_image = cv2.add(img, np.zeros(np.shape(img), dtype=np.uint8), mask=mask)
plt_show(masked_image, figsize=(15, 15))
return masked_image
| [
"15670381505@163.com"
] | 15670381505@163.com |
841c1efe6325c2b67960e896a83526bf52ba892d | eb7541314c5368b8fc7d9264b25ae1f6813829bc | /Longest_Palindromic_Subsequence.py | e0801b6f9927041d8e62d26fc6a1196e8fabf181 | [] | no_license | BibekKoirala/DynamicProgramming | 7f7fbf18a5f2b8113dc232dbd3ba047d898727f1 | 81ca3e83ac4bc320c0cd12e2b638b5a044f7ed09 | refs/heads/master | 2022-12-03T18:08:42.392955 | 2020-08-19T07:30:58 | 2020-08-19T07:30:58 | 283,597,971 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 463 | py | def LPS(s):
Lookup = [[0 if i != j else 1 for i in range(len(s))] for j in range(len(s))]
k = 1
while k < len(s):
i = 0
j = k
while j < len(s):
if s[i] == s[j]:
Lookup[i][j] = Lookup[i + 1][j - 1] + 2
else:
Lookup[i][j] = max(Lookup[i][j - 1], Lookup[i + 1][j])
j += 1
i += 1
k += 1
for i in Lookup:
print(i)
LPS('BABCBAB')
| [
"bibek.high@gmail.com"
] | bibek.high@gmail.com |
89fe832ad18539d9ffcc91dc818c1d4a5827b99c | 0f2391cb82f218ed7505266c5f708725ed064427 | /roman-numerals-take-two/roman.py | d47eb096b46c40035d14e9d809a97c71d456881a | [] | no_license | tomviner/tdd-dojo | e34c6ecb1fd27113b0a0b33a5609c23d6695d226 | 3e35847b2e1d7b73132ee3a07a546abe26322e74 | refs/heads/master | 2021-01-22T02:34:06.146674 | 2014-11-25T14:07:51 | 2014-11-25T14:07:51 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 518 | py | """
Roman Numerals
Write a function to convert from normal numbers to Roman Numerals: e.g.
1 => I
4 => IV
7 => VII
10 => X
99 => XCIX
"""
LOOKUP_NUMERALS = (
(1000, 'M'),
(900, 'CM'),
(500, 'D'),
(400, 'CD'),
(100, 'C'),
(90, 'XC'),
(50, 'L'),
(40, 'XL'),
(10, 'X'),
(9, 'IX'),
(5, 'V'),
(4, 'IV'),
(1, 'I'),
)
def to_roman(num):
for digit, letter in LOOKUP_NUMERALS:
if num >= digit:
return letter + (to_roman(num - digit) or '')
| [
"tom.viner@hogarthww.com"
] | tom.viner@hogarthww.com |
5656efc34e8254aae61d10bea0f54846789da243 | 338062cc2bb422f1364fd18ad5e721f6f713907a | /30. Библиотеки Python. Встроенные модули/Классная работа/Дни рождения друзей.py | 901cba0c8631ab75c84e2788ce36d59850346786 | [] | no_license | rady1337/FirstYandexLyceumCourse | f3421d5eac7e7fbea4f5e266ebeb6479b89941cf | 0d27e452eda046ddd487d6471eeb7d9eb475bd39 | refs/heads/master | 2022-06-17T03:07:51.017888 | 2020-05-12T22:17:34 | 2020-05-12T22:17:34 | 263,459,364 | 0 | 1 | null | null | null | null | UTF-8 | Python | false | false | 119 | py | import datetime as dtdin = dt.datetime.now()dn = dt.timedelta(days=int(input()))print((din + dn).day, (din + dn).month) | [
"noreply@github.com"
] | noreply@github.com |
87edc282a225d250961e93168a4da4a287edcf14 | 97f0f649b007f0ac9f7e2c7f4efdec1f06d6b154 | /decision_implementation.py | 9314086d4dd4f91519211d6d8a107c731d0b37a2 | [] | no_license | ajaymalik2592/projects | de877ea8aebde28900158a6c9a1f576caf64ccd5 | 0cabd9f85f6b056d82c28243157a06c64f45afc8 | refs/heads/master | 2020-05-14T08:24:03.121768 | 2019-04-16T16:03:59 | 2019-04-16T16:03:59 | 181,722,431 | 1 | 0 | null | null | null | null | UTF-8 | Python | false | false | 4,056 | py | import numpy as np
import math
import csv
import random
data = []
with open('shuffled_data.csv', "rt") as filereader:
fil = csv.reader(filereader, delimiter= ' ' )
for rows in fil:
data.append(rows)
formated_data = []
i = 0
for da in data:
if(i == 207):
break
i+=1
da = da[0]
da = da.split(",")
data_new = []
j = 0
for x in da:
j+=1
data_new.append(float(x))
if( j == 60):
break
if(da[-1] == 'M'):
data_new.append(1)
else:
data_new.append(0)
formated_data.append(data_new)
rows = len(formated_data)
cols = len(formated_data[0])
""" here onward analysis part on decision tree of the project, data set in list form easy to compute """
rock = 0
metal = 0
for x in formated_data:
if(x[-1] == 0):
rock += 1
else:
metal += 1
value = []
for x in range(cols - 1):
value.append(0)
for x in range(rows):
for y in range(cols - 1):
value[y] += formated_data[x][y]
distributed, min_max, gain = [], [], []
for x in range(cols ):
distributed.append([0, 0])
min_max.append([100, 0])
if(cols-1 == x):
break
gain.append([1, 0, 0])
test_rows = 170
for x in range(int(test_rows)):
for y in range(cols ):
if(y != cols-1):
distributed[y][formated_data[x][-1]] += formated_data[x][y]
min_max[y][0] = min(min_max[y][0], formated_data[x][y])
min_max[y][1] = max(min_max[y][1], formated_data[x][y])
else:
distributed[ y ][formated_data[x] [ -1 ] ] += 1
matels_in_test = distributed[cols-1][1]
solid_in_test = distributed[cols -1][0]
def gain_ratio(di):
r = 0
if(di[0][0] == 0 or di[0][1] == 0):
pass
else:
r -= di[0][0] * math.log( di[0][0]/ (di[0][0] + di[0][1]) , 2) + di[0][1] * math.log(di[0][1]/ (di[0][0] + di[0][1]) , 2)
if(di[1][0] ==0 or di[1][1] == 0):
pass
else:
r -= di[1][0] * math.log( di[1][0]/ (di[1][0] + di[1][1]) , 2) + di[0][1] * math.log(di[1][1]/ (di[1][0] + di[1][1]) , 2)
p1 = sum(di[0])
p2 = sum(di[1])
print(p1 , " " , p2)
total = p1 + p2
p = 0
if(p1 != 0):
p -= p1 * math.log(p1 / total , 2)
if(p2 != 0):
p -=p2 * math.log(p2 / total,2 )
if(p == 0):
return r * pow(10,100)
return (r / test_rows) / p
for x in range(cols -1):
l = min_max[x][0]
r = min_max[x][1]
i = l * 10000
temp = []
while(i <= 10000 * r):
mid = i / 10000
i += 1
divided = [[0, 0], [0, 0]]
for y in range(test_rows):
if(formated_data[y][x] >= mid):
divided[1][formated_data[y][-1]] += 1
else:
divided[0][formated_data[y][-1]] += 1
temp.append( [gain_ratio(divided), mid] )
i = 100000
index = 0
for xx in temp:
if(xx[0] < i):
i = xx[0]
index = xx[1]
gain[x][0], gain[x][1], gain[x][2] = i, x, index
i = 0
equalize = 0
for x in gain:
equalize = max(equalize, x[0])
final_weighted_for_decisiontree = []
for x in range(cols - 1 ):
final_weighted_for_decisiontree.append([ equalize / gain[x][0] ,gain[x][2] ] )
print(final_weighted_for_decisiontree)
i = 0
aa = ['R', 'M']
tr = 0
tp , fn , fp , tn = 0 ,0, 0, 0
output = []
for index in range(170, 207):
i = 1
j = 1
for x in range(cols -1):
if(formated_data[index][x] >= final_weighted_for_decisiontree[x][1]):
i *= final_weighted_for_decisiontree[x][0]
else:
j *= final_weighted_for_decisiontree[x][0]
if i < j :
if("R" == aa[formated_data[index][-1]] ):
tn += 1
tr += 1
output.append(1)
else : fp += 1; output.append(0)
else:
if("M" == aa[formated_data[index][-1]]):
tr += 1
tp += 1
output.append(1)
else : fn += 1; output.append(0)
print(output)
print(tp, fn)
print(fp, tn)
print(tr * 100/ (207 - 171)) | [
"noreply@github.com"
] | noreply@github.com |
dca84f844680918ece78d15a17c804d7d4f4dc67 | b7683c108e68ee2d28573edf55923eb34cc2f5ee | /3_Image_Processing/9_Contours/1_Intro/1_Contours_on_binary.py | 9be454d9d82d0531c5524d093ed11ff8b9fa6b0f | [] | no_license | aCuissot/openVC_win_py_tutorial | cc42ab1a1fb6eaefe5a91c7e1bb1926a776b0e01 | 7186b629747cb16f2bf42a03d2339d3dc3ea77bd | refs/heads/master | 2020-05-18T12:17:04.619047 | 2019-07-10T13:45:00 | 2019-07-10T13:45:00 | 184,403,715 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 525 | py | import numpy as np
import cv2 as cv
im = cv.imread('../../../Data/in/a.jpg')
imgray = cv.cvtColor(im, cv.COLOR_BGR2GRAY)
_, thresh = cv.threshold(imgray, 127, 255, 0)
contours, hierarchy = cv.findContours(thresh, cv.RETR_TREE, cv.CHAIN_APPROX_SIMPLE)
cv.drawContours(im, contours, -1, (0, 255, 0), 3)
"""
pour ne dessiner qu'un contour, le 3e par ex:
cv.drawContours(im, contours, 2, (0, 255, 0), 3)
ou
cnt = contours[3]
cv.drawContours(img, [cnt], 0, (0,255,0), 3)
"""
cv.imshow('', im)
cv.waitKey(0)
cv.destroyAllWindows() | [
"harrypotter9752@gmail.com"
] | harrypotter9752@gmail.com |
7ee9aa6ee96971123bb69bee59b0a2cc28d60822 | c4ed52e5e9d0b059915bbf84c05b3af15ada4c59 | /test1.py | 0bf4d04c902597be641ec2d3c411cc4d8fe1c954 | [] | no_license | KI0KA/eop-by-Python | a70ae68d8399218d8ceed40032850fb15bdfc113 | 2ce8f1e4055dd6f3953f00c0f06f3b4adc1c8a7d | refs/heads/master | 2022-04-24T13:25:59.266896 | 2020-04-16T23:01:52 | 2020-04-16T23:01:52 | 258,407,300 | 0 | 0 | null | 2020-04-24T04:40:31 | 2020-04-24T04:40:30 | null | UTF-8 | Python | false | false | 103 | py | # test program
def main():
print("my first album".split())
if __name__ == "__main__":
main()
| [
"p9u78sxh@s.okayama-u.ac.jp"
] | p9u78sxh@s.okayama-u.ac.jp |
2508cb82f30d9ebd8cad74771e679948dda148d0 | 0a5618aba3e801cbd986a3c8a02c8bfbaafddb4d | /data_utils/utils.py | 36cd93312e8cabda8dbeb6b8b3a25c5a7a9bcf00 | [] | no_license | hmcck27/ps-helper-nlp | 2f899c2ae22524e5531c78b2a257ac0a0b043c7b | 6a99aa6a91477ac35f94b1548c9e8e9b441b9b8b | refs/heads/master | 2023-08-27T22:59:05.846434 | 2021-10-28T07:14:05 | 2021-10-28T07:14:05 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,718 | py | import json
import torch
from pathlib import Path
class Config:
def __init__(self, json_path):
with open(json_path, mode='r') as io:
params = json.loads(io.read())
self.__dict__.update(params)
def save(self, json_path):
with open(json_path, mode='w') as io:
json.dump(self.__dict__, io, indent=4)
def update(self, json_path):
with open(json_path, mode='r') as io:
params = json.loads(io.read())
self.__dict__.update(params)
@property
def dict(self):
return self.__dict__
class CheckpointManager:
def __init__(self, model_dir):
if not isinstance(model_dir, Path):
model_dir = Path(model_dir)
self._model_dir = model_dir
def save_checkpoint(self, state, filename):
torch.save(state, self._model_dir / filename)
def load_checkpoint(self, filename):
state = torch.load(self._model_dir / filename, map_location=torch.device('cpu'))
return state
class SummaryManager:
def __init__(self, model_dir):
if not isinstance(model_dir, Path):
model_dir = Path(model_dir)
self._model_dir = model_dir
self._summary = {}
def save(self, filename):
with open(self._model_dir / filename, mode='w') as io:
json.dump(self._summary, io, indent=4)
def load(self, filename):
with open(self._model_dir / filename, mode='r') as io:
metric = json.loads(io.read())
self.update(metric)
def update(self, summary):
self._summary.update(summary)
def reset(self):
self._summary = {}
@property
def summary(self):
return self._summary | [
"backend@JK.local"
] | backend@JK.local |
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"
] | noreply@github.com |
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 |
3da46091f694239b44ce3c59e315748ab9fcae39 | 09efb7c148e82c22ce6cc7a17b5140aa03aa6e55 | /env/lib/python3.6/site-packages/pandas/tests/groupby/test_filters.py | 2ce04fc77408301e12a3a44d30366dafee4d3aad | [
"MIT"
] | permissive | harryturr/harryturr_garmin_dashboard | 53071a23b267116e1945ae93d36e2a978c411261 | 734e04f8257f9f84f2553efeb7e73920e35aadc9 | refs/heads/master | 2023-01-19T22:10:57.374029 | 2020-01-29T10:47:56 | 2020-01-29T10:47:56 | 235,609,069 | 4 | 0 | MIT | 2023-01-05T05:51:27 | 2020-01-22T16:00:13 | Python | UTF-8 | Python | false | false | 20,388 | py | import numpy as np
import pytest
import pandas as pd
from pandas import DataFrame, Series, Timestamp
import pandas.util.testing as tm
def test_filter_series():
s = pd.Series([1, 3, 20, 5, 22, 24, 7])
expected_odd = pd.Series([1, 3, 5, 7], index=[0, 1, 3, 6])
expected_even = pd.Series([20, 22, 24], index=[2, 4, 5])
grouper = s.apply(lambda x: x % 2)
grouped = s.groupby(grouper)
tm.assert_series_equal(grouped.filter(lambda x: x.mean() < 10), expected_odd)
tm.assert_series_equal(grouped.filter(lambda x: x.mean() > 10), expected_even)
# Test dropna=False.
tm.assert_series_equal(
grouped.filter(lambda x: x.mean() < 10, dropna=False),
expected_odd.reindex(s.index),
)
tm.assert_series_equal(
grouped.filter(lambda x: x.mean() > 10, dropna=False),
expected_even.reindex(s.index),
)
def test_filter_single_column_df():
df = pd.DataFrame([1, 3, 20, 5, 22, 24, 7])
expected_odd = pd.DataFrame([1, 3, 5, 7], index=[0, 1, 3, 6])
expected_even = pd.DataFrame([20, 22, 24], index=[2, 4, 5])
grouper = df[0].apply(lambda x: x % 2)
grouped = df.groupby(grouper)
tm.assert_frame_equal(grouped.filter(lambda x: x.mean() < 10), expected_odd)
tm.assert_frame_equal(grouped.filter(lambda x: x.mean() > 10), expected_even)
# Test dropna=False.
tm.assert_frame_equal(
grouped.filter(lambda x: x.mean() < 10, dropna=False),
expected_odd.reindex(df.index),
)
tm.assert_frame_equal(
grouped.filter(lambda x: x.mean() > 10, dropna=False),
expected_even.reindex(df.index),
)
def test_filter_multi_column_df():
df = pd.DataFrame({"A": [1, 12, 12, 1], "B": [1, 1, 1, 1]})
grouper = df["A"].apply(lambda x: x % 2)
grouped = df.groupby(grouper)
expected = pd.DataFrame({"A": [12, 12], "B": [1, 1]}, index=[1, 2])
tm.assert_frame_equal(
grouped.filter(lambda x: x["A"].sum() - x["B"].sum() > 10), expected
)
def test_filter_mixed_df():
df = pd.DataFrame({"A": [1, 12, 12, 1], "B": "a b c d".split()})
grouper = df["A"].apply(lambda x: x % 2)
grouped = df.groupby(grouper)
expected = pd.DataFrame({"A": [12, 12], "B": ["b", "c"]}, index=[1, 2])
tm.assert_frame_equal(grouped.filter(lambda x: x["A"].sum() > 10), expected)
def test_filter_out_all_groups():
s = pd.Series([1, 3, 20, 5, 22, 24, 7])
grouper = s.apply(lambda x: x % 2)
grouped = s.groupby(grouper)
tm.assert_series_equal(grouped.filter(lambda x: x.mean() > 1000), s[[]])
df = pd.DataFrame({"A": [1, 12, 12, 1], "B": "a b c d".split()})
grouper = df["A"].apply(lambda x: x % 2)
grouped = df.groupby(grouper)
tm.assert_frame_equal(grouped.filter(lambda x: x["A"].sum() > 1000), df.loc[[]])
def test_filter_out_no_groups():
s = pd.Series([1, 3, 20, 5, 22, 24, 7])
grouper = s.apply(lambda x: x % 2)
grouped = s.groupby(grouper)
filtered = grouped.filter(lambda x: x.mean() > 0)
tm.assert_series_equal(filtered, s)
df = pd.DataFrame({"A": [1, 12, 12, 1], "B": "a b c d".split()})
grouper = df["A"].apply(lambda x: x % 2)
grouped = df.groupby(grouper)
filtered = grouped.filter(lambda x: x["A"].mean() > 0)
tm.assert_frame_equal(filtered, df)
def test_filter_out_all_groups_in_df():
# GH12768
df = pd.DataFrame({"a": [1, 1, 2], "b": [1, 2, 0]})
res = df.groupby("a")
res = res.filter(lambda x: x["b"].sum() > 5, dropna=False)
expected = pd.DataFrame({"a": [np.nan] * 3, "b": [np.nan] * 3})
tm.assert_frame_equal(expected, res)
df = pd.DataFrame({"a": [1, 1, 2], "b": [1, 2, 0]})
res = df.groupby("a")
res = res.filter(lambda x: x["b"].sum() > 5, dropna=True)
expected = pd.DataFrame({"a": [], "b": []}, dtype="int64")
tm.assert_frame_equal(expected, res)
def test_filter_condition_raises():
def raise_if_sum_is_zero(x):
if x.sum() == 0:
raise ValueError
else:
return x.sum() > 0
s = pd.Series([-1, 0, 1, 2])
grouper = s.apply(lambda x: x % 2)
grouped = s.groupby(grouper)
msg = "the filter must return a boolean result"
with pytest.raises(TypeError, match=msg):
grouped.filter(raise_if_sum_is_zero)
def test_filter_with_axis_in_groupby():
# issue 11041
index = pd.MultiIndex.from_product([range(10), [0, 1]])
data = pd.DataFrame(np.arange(100).reshape(-1, 20), columns=index, dtype="int64")
result = data.groupby(level=0, axis=1).filter(lambda x: x.iloc[0, 0] > 10)
expected = data.iloc[:, 12:20]
tm.assert_frame_equal(result, expected)
def test_filter_bad_shapes():
df = DataFrame({"A": np.arange(8), "B": list("aabbbbcc"), "C": np.arange(8)})
s = df["B"]
g_df = df.groupby("B")
g_s = s.groupby(s)
f = lambda x: x
msg = "filter function returned a DataFrame, but expected a scalar bool"
with pytest.raises(TypeError, match=msg):
g_df.filter(f)
msg = "the filter must return a boolean result"
with pytest.raises(TypeError, match=msg):
g_s.filter(f)
f = lambda x: x == 1
msg = "filter function returned a DataFrame, but expected a scalar bool"
with pytest.raises(TypeError, match=msg):
g_df.filter(f)
msg = "the filter must return a boolean result"
with pytest.raises(TypeError, match=msg):
g_s.filter(f)
f = lambda x: np.outer(x, x)
msg = "can't multiply sequence by non-int of type 'str'"
with pytest.raises(TypeError, match=msg):
g_df.filter(f)
msg = "the filter must return a boolean result"
with pytest.raises(TypeError, match=msg):
g_s.filter(f)
def test_filter_nan_is_false():
df = DataFrame({"A": np.arange(8), "B": list("aabbbbcc"), "C": np.arange(8)})
s = df["B"]
g_df = df.groupby(df["B"])
g_s = s.groupby(s)
f = lambda x: np.nan
tm.assert_frame_equal(g_df.filter(f), df.loc[[]])
tm.assert_series_equal(g_s.filter(f), s[[]])
def test_filter_against_workaround():
np.random.seed(0)
# Series of ints
s = Series(np.random.randint(0, 100, 1000))
grouper = s.apply(lambda x: np.round(x, -1))
grouped = s.groupby(grouper)
f = lambda x: x.mean() > 10
old_way = s[grouped.transform(f).astype("bool")]
new_way = grouped.filter(f)
tm.assert_series_equal(new_way.sort_values(), old_way.sort_values())
# Series of floats
s = 100 * Series(np.random.random(1000))
grouper = s.apply(lambda x: np.round(x, -1))
grouped = s.groupby(grouper)
f = lambda x: x.mean() > 10
old_way = s[grouped.transform(f).astype("bool")]
new_way = grouped.filter(f)
tm.assert_series_equal(new_way.sort_values(), old_way.sort_values())
# Set up DataFrame of ints, floats, strings.
from string import ascii_lowercase
letters = np.array(list(ascii_lowercase))
N = 1000
random_letters = letters.take(np.random.randint(0, 26, N))
df = DataFrame(
{
"ints": Series(np.random.randint(0, 100, N)),
"floats": N / 10 * Series(np.random.random(N)),
"letters": Series(random_letters),
}
)
# Group by ints; filter on floats.
grouped = df.groupby("ints")
old_way = df[grouped.floats.transform(lambda x: x.mean() > N / 20).astype("bool")]
new_way = grouped.filter(lambda x: x["floats"].mean() > N / 20)
tm.assert_frame_equal(new_way, old_way)
# Group by floats (rounded); filter on strings.
grouper = df.floats.apply(lambda x: np.round(x, -1))
grouped = df.groupby(grouper)
old_way = df[grouped.letters.transform(lambda x: len(x) < N / 10).astype("bool")]
new_way = grouped.filter(lambda x: len(x.letters) < N / 10)
tm.assert_frame_equal(new_way, old_way)
# Group by strings; filter on ints.
grouped = df.groupby("letters")
old_way = df[grouped.ints.transform(lambda x: x.mean() > N / 20).astype("bool")]
new_way = grouped.filter(lambda x: x["ints"].mean() > N / 20)
tm.assert_frame_equal(new_way, old_way)
def test_filter_using_len():
# BUG GH4447
df = DataFrame({"A": np.arange(8), "B": list("aabbbbcc"), "C": np.arange(8)})
grouped = df.groupby("B")
actual = grouped.filter(lambda x: len(x) > 2)
expected = DataFrame(
{"A": np.arange(2, 6), "B": list("bbbb"), "C": np.arange(2, 6)},
index=np.arange(2, 6),
)
tm.assert_frame_equal(actual, expected)
actual = grouped.filter(lambda x: len(x) > 4)
expected = df.loc[[]]
tm.assert_frame_equal(actual, expected)
# Series have always worked properly, but we'll test anyway.
s = df["B"]
grouped = s.groupby(s)
actual = grouped.filter(lambda x: len(x) > 2)
expected = Series(4 * ["b"], index=np.arange(2, 6), name="B")
tm.assert_series_equal(actual, expected)
actual = grouped.filter(lambda x: len(x) > 4)
expected = s[[]]
tm.assert_series_equal(actual, expected)
def test_filter_maintains_ordering():
# Simple case: index is sequential. #4621
df = DataFrame(
{"pid": [1, 1, 1, 2, 2, 3, 3, 3], "tag": [23, 45, 62, 24, 45, 34, 25, 62]}
)
s = df["pid"]
grouped = df.groupby("tag")
actual = grouped.filter(lambda x: len(x) > 1)
expected = df.iloc[[1, 2, 4, 7]]
tm.assert_frame_equal(actual, expected)
grouped = s.groupby(df["tag"])
actual = grouped.filter(lambda x: len(x) > 1)
expected = s.iloc[[1, 2, 4, 7]]
tm.assert_series_equal(actual, expected)
# Now index is sequentially decreasing.
df.index = np.arange(len(df) - 1, -1, -1)
s = df["pid"]
grouped = df.groupby("tag")
actual = grouped.filter(lambda x: len(x) > 1)
expected = df.iloc[[1, 2, 4, 7]]
tm.assert_frame_equal(actual, expected)
grouped = s.groupby(df["tag"])
actual = grouped.filter(lambda x: len(x) > 1)
expected = s.iloc[[1, 2, 4, 7]]
tm.assert_series_equal(actual, expected)
# Index is shuffled.
SHUFFLED = [4, 6, 7, 2, 1, 0, 5, 3]
df.index = df.index[SHUFFLED]
s = df["pid"]
grouped = df.groupby("tag")
actual = grouped.filter(lambda x: len(x) > 1)
expected = df.iloc[[1, 2, 4, 7]]
tm.assert_frame_equal(actual, expected)
grouped = s.groupby(df["tag"])
actual = grouped.filter(lambda x: len(x) > 1)
expected = s.iloc[[1, 2, 4, 7]]
tm.assert_series_equal(actual, expected)
def test_filter_multiple_timestamp():
# GH 10114
df = DataFrame(
{
"A": np.arange(5, dtype="int64"),
"B": ["foo", "bar", "foo", "bar", "bar"],
"C": Timestamp("20130101"),
}
)
grouped = df.groupby(["B", "C"])
result = grouped["A"].filter(lambda x: True)
tm.assert_series_equal(df["A"], result)
result = grouped["A"].transform(len)
expected = Series([2, 3, 2, 3, 3], name="A")
tm.assert_series_equal(result, expected)
result = grouped.filter(lambda x: True)
tm.assert_frame_equal(df, result)
result = grouped.transform("sum")
expected = DataFrame({"A": [2, 8, 2, 8, 8]})
tm.assert_frame_equal(result, expected)
result = grouped.transform(len)
expected = DataFrame({"A": [2, 3, 2, 3, 3]})
tm.assert_frame_equal(result, expected)
def test_filter_and_transform_with_non_unique_int_index():
# GH4620
index = [1, 1, 1, 2, 1, 1, 0, 1]
df = DataFrame(
{"pid": [1, 1, 1, 2, 2, 3, 3, 3], "tag": [23, 45, 62, 24, 45, 34, 25, 62]},
index=index,
)
grouped_df = df.groupby("tag")
ser = df["pid"]
grouped_ser = ser.groupby(df["tag"])
expected_indexes = [1, 2, 4, 7]
# Filter DataFrame
actual = grouped_df.filter(lambda x: len(x) > 1)
expected = df.iloc[expected_indexes]
tm.assert_frame_equal(actual, expected)
actual = grouped_df.filter(lambda x: len(x) > 1, dropna=False)
expected = df.copy()
expected.iloc[[0, 3, 5, 6]] = np.nan
tm.assert_frame_equal(actual, expected)
# Filter Series
actual = grouped_ser.filter(lambda x: len(x) > 1)
expected = ser.take(expected_indexes)
tm.assert_series_equal(actual, expected)
actual = grouped_ser.filter(lambda x: len(x) > 1, dropna=False)
NA = np.nan
expected = Series([NA, 1, 1, NA, 2, NA, NA, 3], index, name="pid")
# ^ made manually because this can get confusing!
tm.assert_series_equal(actual, expected)
# Transform Series
actual = grouped_ser.transform(len)
expected = Series([1, 2, 2, 1, 2, 1, 1, 2], index, name="pid")
tm.assert_series_equal(actual, expected)
# Transform (a column from) DataFrameGroupBy
actual = grouped_df.pid.transform(len)
tm.assert_series_equal(actual, expected)
def test_filter_and_transform_with_multiple_non_unique_int_index():
# GH4620
index = [1, 1, 1, 2, 0, 0, 0, 1]
df = DataFrame(
{"pid": [1, 1, 1, 2, 2, 3, 3, 3], "tag": [23, 45, 62, 24, 45, 34, 25, 62]},
index=index,
)
grouped_df = df.groupby("tag")
ser = df["pid"]
grouped_ser = ser.groupby(df["tag"])
expected_indexes = [1, 2, 4, 7]
# Filter DataFrame
actual = grouped_df.filter(lambda x: len(x) > 1)
expected = df.iloc[expected_indexes]
tm.assert_frame_equal(actual, expected)
actual = grouped_df.filter(lambda x: len(x) > 1, dropna=False)
expected = df.copy()
expected.iloc[[0, 3, 5, 6]] = np.nan
tm.assert_frame_equal(actual, expected)
# Filter Series
actual = grouped_ser.filter(lambda x: len(x) > 1)
expected = ser.take(expected_indexes)
tm.assert_series_equal(actual, expected)
actual = grouped_ser.filter(lambda x: len(x) > 1, dropna=False)
NA = np.nan
expected = Series([NA, 1, 1, NA, 2, NA, NA, 3], index, name="pid")
# ^ made manually because this can get confusing!
tm.assert_series_equal(actual, expected)
# Transform Series
actual = grouped_ser.transform(len)
expected = Series([1, 2, 2, 1, 2, 1, 1, 2], index, name="pid")
tm.assert_series_equal(actual, expected)
# Transform (a column from) DataFrameGroupBy
actual = grouped_df.pid.transform(len)
tm.assert_series_equal(actual, expected)
def test_filter_and_transform_with_non_unique_float_index():
# GH4620
index = np.array([1, 1, 1, 2, 1, 1, 0, 1], dtype=float)
df = DataFrame(
{"pid": [1, 1, 1, 2, 2, 3, 3, 3], "tag": [23, 45, 62, 24, 45, 34, 25, 62]},
index=index,
)
grouped_df = df.groupby("tag")
ser = df["pid"]
grouped_ser = ser.groupby(df["tag"])
expected_indexes = [1, 2, 4, 7]
# Filter DataFrame
actual = grouped_df.filter(lambda x: len(x) > 1)
expected = df.iloc[expected_indexes]
tm.assert_frame_equal(actual, expected)
actual = grouped_df.filter(lambda x: len(x) > 1, dropna=False)
expected = df.copy()
expected.iloc[[0, 3, 5, 6]] = np.nan
tm.assert_frame_equal(actual, expected)
# Filter Series
actual = grouped_ser.filter(lambda x: len(x) > 1)
expected = ser.take(expected_indexes)
tm.assert_series_equal(actual, expected)
actual = grouped_ser.filter(lambda x: len(x) > 1, dropna=False)
NA = np.nan
expected = Series([NA, 1, 1, NA, 2, NA, NA, 3], index, name="pid")
# ^ made manually because this can get confusing!
tm.assert_series_equal(actual, expected)
# Transform Series
actual = grouped_ser.transform(len)
expected = Series([1, 2, 2, 1, 2, 1, 1, 2], index, name="pid")
tm.assert_series_equal(actual, expected)
# Transform (a column from) DataFrameGroupBy
actual = grouped_df.pid.transform(len)
tm.assert_series_equal(actual, expected)
def test_filter_and_transform_with_non_unique_timestamp_index():
# GH4620
t0 = Timestamp("2013-09-30 00:05:00")
t1 = Timestamp("2013-10-30 00:05:00")
t2 = Timestamp("2013-11-30 00:05:00")
index = [t1, t1, t1, t2, t1, t1, t0, t1]
df = DataFrame(
{"pid": [1, 1, 1, 2, 2, 3, 3, 3], "tag": [23, 45, 62, 24, 45, 34, 25, 62]},
index=index,
)
grouped_df = df.groupby("tag")
ser = df["pid"]
grouped_ser = ser.groupby(df["tag"])
expected_indexes = [1, 2, 4, 7]
# Filter DataFrame
actual = grouped_df.filter(lambda x: len(x) > 1)
expected = df.iloc[expected_indexes]
tm.assert_frame_equal(actual, expected)
actual = grouped_df.filter(lambda x: len(x) > 1, dropna=False)
expected = df.copy()
expected.iloc[[0, 3, 5, 6]] = np.nan
tm.assert_frame_equal(actual, expected)
# Filter Series
actual = grouped_ser.filter(lambda x: len(x) > 1)
expected = ser.take(expected_indexes)
tm.assert_series_equal(actual, expected)
actual = grouped_ser.filter(lambda x: len(x) > 1, dropna=False)
NA = np.nan
expected = Series([NA, 1, 1, NA, 2, NA, NA, 3], index, name="pid")
# ^ made manually because this can get confusing!
tm.assert_series_equal(actual, expected)
# Transform Series
actual = grouped_ser.transform(len)
expected = Series([1, 2, 2, 1, 2, 1, 1, 2], index, name="pid")
tm.assert_series_equal(actual, expected)
# Transform (a column from) DataFrameGroupBy
actual = grouped_df.pid.transform(len)
tm.assert_series_equal(actual, expected)
def test_filter_and_transform_with_non_unique_string_index():
# GH4620
index = list("bbbcbbab")
df = DataFrame(
{"pid": [1, 1, 1, 2, 2, 3, 3, 3], "tag": [23, 45, 62, 24, 45, 34, 25, 62]},
index=index,
)
grouped_df = df.groupby("tag")
ser = df["pid"]
grouped_ser = ser.groupby(df["tag"])
expected_indexes = [1, 2, 4, 7]
# Filter DataFrame
actual = grouped_df.filter(lambda x: len(x) > 1)
expected = df.iloc[expected_indexes]
tm.assert_frame_equal(actual, expected)
actual = grouped_df.filter(lambda x: len(x) > 1, dropna=False)
expected = df.copy()
expected.iloc[[0, 3, 5, 6]] = np.nan
tm.assert_frame_equal(actual, expected)
# Filter Series
actual = grouped_ser.filter(lambda x: len(x) > 1)
expected = ser.take(expected_indexes)
tm.assert_series_equal(actual, expected)
actual = grouped_ser.filter(lambda x: len(x) > 1, dropna=False)
NA = np.nan
expected = Series([NA, 1, 1, NA, 2, NA, NA, 3], index, name="pid")
# ^ made manually because this can get confusing!
tm.assert_series_equal(actual, expected)
# Transform Series
actual = grouped_ser.transform(len)
expected = Series([1, 2, 2, 1, 2, 1, 1, 2], index, name="pid")
tm.assert_series_equal(actual, expected)
# Transform (a column from) DataFrameGroupBy
actual = grouped_df.pid.transform(len)
tm.assert_series_equal(actual, expected)
def test_filter_has_access_to_grouped_cols():
df = DataFrame([[1, 2], [1, 3], [5, 6]], columns=["A", "B"])
g = df.groupby("A")
# previously didn't have access to col A #????
filt = g.filter(lambda x: x["A"].sum() == 2)
tm.assert_frame_equal(filt, df.iloc[[0, 1]])
def test_filter_enforces_scalarness():
df = pd.DataFrame(
[
["best", "a", "x"],
["worst", "b", "y"],
["best", "c", "x"],
["best", "d", "y"],
["worst", "d", "y"],
["worst", "d", "y"],
["best", "d", "z"],
],
columns=["a", "b", "c"],
)
with pytest.raises(TypeError, match="filter function returned a.*"):
df.groupby("c").filter(lambda g: g["a"] == "best")
def test_filter_non_bool_raises():
df = pd.DataFrame(
[
["best", "a", 1],
["worst", "b", 1],
["best", "c", 1],
["best", "d", 1],
["worst", "d", 1],
["worst", "d", 1],
["best", "d", 1],
],
columns=["a", "b", "c"],
)
with pytest.raises(TypeError, match="filter function returned a.*"):
df.groupby("a").filter(lambda g: g.c.mean())
def test_filter_dropna_with_empty_groups():
# GH 10780
data = pd.Series(np.random.rand(9), index=np.repeat([1, 2, 3], 3))
groupped = data.groupby(level=0)
result_false = groupped.filter(lambda x: x.mean() > 1, dropna=False)
expected_false = pd.Series([np.nan] * 9, index=np.repeat([1, 2, 3], 3))
tm.assert_series_equal(result_false, expected_false)
result_true = groupped.filter(lambda x: x.mean() > 1, dropna=True)
expected_true = pd.Series(index=pd.Index([], dtype=int))
tm.assert_series_equal(result_true, expected_true)
| [
"griffin.harrisonn@gmail.com"
] | griffin.harrisonn@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"
] | noreply@github.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"
] | 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 |
eea41913ddcd22156013f964a4b6b70d017450aa | 8ea896e975fdb967f013e2b69d453df8881eb8f9 | /spark-2.x/src/main/python/ml/logistic_regression_summary_example.py | 26882a8dc7de5dbc1caf79a70959595925d295f6 | [
"Apache-2.0"
] | permissive | lhfei/spark-in-action | 1d0df5a22230e458be2583066537c09d20a1b98b | 0bf915588f4aa36b17b89a2a8f4a055b342e2295 | refs/heads/master | 2022-07-15T05:43:02.805743 | 2020-08-25T07:33:59 | 2020-08-25T07:33:59 | 33,342,589 | 6 | 3 | Apache-2.0 | 2022-06-27T16:13:02 | 2015-04-03T02:36:44 | Scala | UTF-8 | Python | false | false | 2,508 | py | #
# Licensed to the Apache Software Foundation (ASF) under one or more
# contributor license agreements. See the NOTICE file distributed with
# this work for additional information regarding copyright ownership.
# The ASF licenses this file to You 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.
#
"""
An example demonstrating Logistic Regression Summary.
Run with:
bin/spark-submit examples/src/main/python/ml/logistic_regression_summary_example.py
"""
from __future__ import print_function
# $example on$
from pyspark.ml.classification import LogisticRegression
# $example off$
from pyspark.sql import SparkSession
if __name__ == "__main__":
spark = SparkSession \
.builder \
.appName("LogisticRegressionSummary") \
.getOrCreate()
# Load training data
training = spark.read.format("libsvm").load("data/mllib/sample_libsvm_data.txt")
lr = LogisticRegression(maxIter=10, regParam=0.3, elasticNetParam=0.8)
# Fit the model
lrModel = lr.fit(training)
# $example on$
# Extract the summary from the returned LogisticRegressionModel instance trained
# in the earlier example
trainingSummary = lrModel.summary
# Obtain the objective per iteration
objectiveHistory = trainingSummary.objectiveHistory
print("objectiveHistory:")
for objective in objectiveHistory:
print(objective)
# Obtain the receiver-operating characteristic as a dataframe and areaUnderROC.
trainingSummary.roc.show()
print("areaUnderROC: " + str(trainingSummary.areaUnderROC))
# Set the model threshold to maximize F-Measure
fMeasure = trainingSummary.fMeasureByThreshold
maxFMeasure = fMeasure.groupBy().max('F-Measure').select('max(F-Measure)').head()
bestThreshold = fMeasure.where(fMeasure['F-Measure'] == maxFMeasure['max(F-Measure)']) \
.select('threshold').head()['threshold']
lr.setThreshold(bestThreshold)
# $example off$
spark.stop()
| [
"lhfeilaile@gmail.com"
] | lhfeilaile@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 |
a2c28551321a031321b269385b8a40dee9d39c56 | f4434c85e3814b6347f8f8099c081ed4af5678a5 | /sdk/tables/azure-data-tables/azure/data/tables/_generated/aio/operations/_service_operations.py | 4ef1391d9b929f9750111cbb76a4882ccc33b059 | [
"LicenseRef-scancode-generic-cla",
"MIT",
"LGPL-2.1-or-later"
] | permissive | yunhaoling/azure-sdk-for-python | 5da12a174a37672ac6ed8e3c1f863cb77010a506 | c4eb0ca1aadb76ad892114230473034830116362 | refs/heads/master | 2022-06-11T01:17:39.636461 | 2020-12-08T17:42:08 | 2020-12-08T17:42:08 | 177,675,796 | 1 | 0 | MIT | 2020-03-31T20:35:17 | 2019-03-25T22:43:40 | Python | UTF-8 | Python | false | false | 13,225 | py | # coding=utf-8
# --------------------------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License. See License.txt in the project root for license information.
# Code generated by Microsoft (R) AutoRest Code Generator.
# Changes may cause incorrect behavior and will be lost if the code is regenerated.
# --------------------------------------------------------------------------
from typing import Any, Callable, Dict, Generic, Optional, TypeVar
import warnings
from azure.core.exceptions import ClientAuthenticationError, HttpResponseError, ResourceExistsError, ResourceNotFoundError, map_error
from azure.core.pipeline import PipelineResponse
from azure.core.pipeline.transport import AsyncHttpResponse, HttpRequest
from ... import models
T = TypeVar('T')
ClsType = Optional[Callable[[PipelineResponse[HttpRequest, AsyncHttpResponse], T, Dict[str, Any]], Any]]
class ServiceOperations:
"""ServiceOperations async operations.
You should not instantiate this class directly. Instead, you should create a Client instance that
instantiates it for you and attaches it as an attribute.
:ivar models: Alias to model classes used in this operation group.
:type models: ~azure.data.tables.models
:param client: Client for service requests.
:param config: Configuration of service client.
:param serializer: An object model serializer.
:param deserializer: An object model deserializer.
"""
models = models
def __init__(self, client, config, serializer, deserializer) -> None:
self._client = client
self._serialize = serializer
self._deserialize = deserializer
self._config = config
async def set_properties(
self,
table_service_properties: "models.TableServiceProperties",
timeout: Optional[int] = None,
request_id_parameter: Optional[str] = None,
**kwargs
) -> None:
"""Sets properties for an account's Table service endpoint, including properties for Analytics and
CORS (Cross-Origin Resource Sharing) rules.
:param table_service_properties: The Table Service properties.
:type table_service_properties: ~azure.data.tables.models.TableServiceProperties
:param timeout: The timeout parameter is expressed in seconds.
:type timeout: int
:param request_id_parameter: Provides a client-generated, opaque value with a 1 KB character
limit that is recorded in the analytics logs when analytics logging is enabled.
:type request_id_parameter: str
:keyword callable cls: A custom type or function that will be passed the direct response
:return: None, or the result of cls(response)
:rtype: None
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType[None]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
restype = "service"
comp = "properties"
content_type = kwargs.pop("content_type", "application/xml")
accept = "application/xml"
# Construct URL
url = self.set_properties.metadata['url'] # type: ignore
path_format_arguments = {
'url': self._serialize.url("self._config.url", self._config.url, 'str', skip_quote=True),
}
url = self._client.format_url(url, **path_format_arguments)
# Construct parameters
query_parameters = {} # type: Dict[str, Any]
query_parameters['restype'] = self._serialize.query("restype", restype, 'str')
query_parameters['comp'] = self._serialize.query("comp", comp, 'str')
if timeout is not None:
query_parameters['timeout'] = self._serialize.query("timeout", timeout, 'int', minimum=0)
# Construct headers
header_parameters = {} # type: Dict[str, Any]
header_parameters['x-ms-version'] = self._serialize.header("self._config.version", self._config.version, 'str')
if request_id_parameter is not None:
header_parameters['x-ms-client-request-id'] = self._serialize.header("request_id_parameter", request_id_parameter, 'str')
header_parameters['Content-Type'] = self._serialize.header("content_type", content_type, 'str')
header_parameters['Accept'] = self._serialize.header("accept", accept, 'str')
body_content_kwargs = {} # type: Dict[str, Any]
body_content = self._serialize.body(table_service_properties, 'TableServiceProperties', is_xml=True)
body_content_kwargs['content'] = body_content
request = self._client.put(url, query_parameters, header_parameters, **body_content_kwargs)
pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [202]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize(models.TableServiceError, response)
raise HttpResponseError(response=response, model=error)
response_headers = {}
response_headers['x-ms-client-request-id']=self._deserialize('str', response.headers.get('x-ms-client-request-id'))
response_headers['x-ms-request-id']=self._deserialize('str', response.headers.get('x-ms-request-id'))
response_headers['x-ms-version']=self._deserialize('str', response.headers.get('x-ms-version'))
if cls:
return cls(pipeline_response, None, response_headers)
set_properties.metadata = {'url': '/'} # type: ignore
async def get_properties(
self,
timeout: Optional[int] = None,
request_id_parameter: Optional[str] = None,
**kwargs
) -> "models.TableServiceProperties":
"""Gets the properties of an account's Table service, including properties for Analytics and CORS
(Cross-Origin Resource Sharing) rules.
:param timeout: The timeout parameter is expressed in seconds.
:type timeout: int
:param request_id_parameter: Provides a client-generated, opaque value with a 1 KB character
limit that is recorded in the analytics logs when analytics logging is enabled.
:type request_id_parameter: str
:keyword callable cls: A custom type or function that will be passed the direct response
:return: TableServiceProperties, or the result of cls(response)
:rtype: ~azure.data.tables.models.TableServiceProperties
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType["models.TableServiceProperties"]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
restype = "service"
comp = "properties"
accept = "application/xml"
# Construct URL
url = self.get_properties.metadata['url'] # type: ignore
path_format_arguments = {
'url': self._serialize.url("self._config.url", self._config.url, 'str', skip_quote=True),
}
url = self._client.format_url(url, **path_format_arguments)
# Construct parameters
query_parameters = {} # type: Dict[str, Any]
query_parameters['restype'] = self._serialize.query("restype", restype, 'str')
query_parameters['comp'] = self._serialize.query("comp", comp, 'str')
if timeout is not None:
query_parameters['timeout'] = self._serialize.query("timeout", timeout, 'int', minimum=0)
# Construct headers
header_parameters = {} # type: Dict[str, Any]
header_parameters['x-ms-version'] = self._serialize.header("self._config.version", self._config.version, 'str')
if request_id_parameter is not None:
header_parameters['x-ms-client-request-id'] = self._serialize.header("request_id_parameter", request_id_parameter, 'str')
header_parameters['Accept'] = self._serialize.header("accept", accept, 'str')
request = self._client.get(url, query_parameters, header_parameters)
pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize(models.TableServiceError, response)
raise HttpResponseError(response=response, model=error)
response_headers = {}
response_headers['x-ms-client-request-id']=self._deserialize('str', response.headers.get('x-ms-client-request-id'))
response_headers['x-ms-request-id']=self._deserialize('str', response.headers.get('x-ms-request-id'))
response_headers['x-ms-version']=self._deserialize('str', response.headers.get('x-ms-version'))
deserialized = self._deserialize('TableServiceProperties', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, response_headers)
return deserialized
get_properties.metadata = {'url': '/'} # type: ignore
async def get_statistics(
self,
timeout: Optional[int] = None,
request_id_parameter: Optional[str] = None,
**kwargs
) -> "models.TableServiceStats":
"""Retrieves statistics related to replication for the Table service. It is only available on the
secondary location endpoint when read-access geo-redundant replication is enabled for the
account.
:param timeout: The timeout parameter is expressed in seconds.
:type timeout: int
:param request_id_parameter: Provides a client-generated, opaque value with a 1 KB character
limit that is recorded in the analytics logs when analytics logging is enabled.
:type request_id_parameter: str
:keyword callable cls: A custom type or function that will be passed the direct response
:return: TableServiceStats, or the result of cls(response)
:rtype: ~azure.data.tables.models.TableServiceStats
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType["models.TableServiceStats"]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
restype = "service"
comp = "stats"
accept = "application/xml"
# Construct URL
url = self.get_statistics.metadata['url'] # type: ignore
path_format_arguments = {
'url': self._serialize.url("self._config.url", self._config.url, 'str', skip_quote=True),
}
url = self._client.format_url(url, **path_format_arguments)
# Construct parameters
query_parameters = {} # type: Dict[str, Any]
query_parameters['restype'] = self._serialize.query("restype", restype, 'str')
query_parameters['comp'] = self._serialize.query("comp", comp, 'str')
if timeout is not None:
query_parameters['timeout'] = self._serialize.query("timeout", timeout, 'int', minimum=0)
# Construct headers
header_parameters = {} # type: Dict[str, Any]
header_parameters['x-ms-version'] = self._serialize.header("self._config.version", self._config.version, 'str')
if request_id_parameter is not None:
header_parameters['x-ms-client-request-id'] = self._serialize.header("request_id_parameter", request_id_parameter, 'str')
header_parameters['Accept'] = self._serialize.header("accept", accept, 'str')
request = self._client.get(url, query_parameters, header_parameters)
pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize(models.TableServiceError, response)
raise HttpResponseError(response=response, model=error)
response_headers = {}
response_headers['x-ms-client-request-id']=self._deserialize('str', response.headers.get('x-ms-client-request-id'))
response_headers['x-ms-request-id']=self._deserialize('str', response.headers.get('x-ms-request-id'))
response_headers['x-ms-version']=self._deserialize('str', response.headers.get('x-ms-version'))
response_headers['Date']=self._deserialize('rfc-1123', response.headers.get('Date'))
deserialized = self._deserialize('TableServiceStats', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, response_headers)
return deserialized
get_statistics.metadata = {'url': '/'} # type: ignore
| [
"noreply@github.com"
] | noreply@github.com |
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 |
5cd7b233417a40dd0a9ef71afaa6969d9ba3b514 | 0fc891df6703ce3f91fe6005a6c582e573ed6c13 | /CWMT/user_login/migrations/0015_auto_20170811_1319.py | a97c1ba8d678eadfeaa3a8c5c6947edbde7ec3a2 | [] | no_license | Zeco-01/CWMT2017-REG | ce155343575d3b8b49eda584b40bdd1716368e38 | 5e71ddc38f3020d1582d0b38f2f8d0eace615b88 | refs/heads/master | 2021-01-02T22:19:25.372629 | 2017-09-18T06:46:11 | 2017-09-18T06:46:11 | 99,318,036 | 1 | 1 | null | 2017-08-11T15:17:24 | 2017-08-04T07:47:43 | Python | UTF-8 | Python | false | false | 521 | py | # -*- coding: utf-8 -*-
# Generated by Django 1.11.3 on 2017-08-11 13:19
from __future__ import unicode_literals
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('user_login', '0014_auto_20170811_1310'),
]
operations = [
migrations.AlterField(
model_name='user',
name='register_number',
field=models.CharField(blank=True, max_length=20, primary_key=True, serialize=False, unique=True),
),
]
| [
"leezehua@outlook.com"
] | leezehua@outlook.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 |
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 |
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 |
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 |
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 |
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 |
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"
] | noreply@github.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 |
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 |
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 |
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