repo stringlengths 2 99 | file stringlengths 13 225 | code stringlengths 0 18.3M | file_length int64 0 18.3M | avg_line_length float64 0 1.36M | max_line_length int64 0 4.26M | extension_type stringclasses 1
value |
|---|---|---|---|---|---|---|
3DTrans | 3DTrans-master/pcdet/ops/pointnet2/pointnet2_batch/__init__.py | 0 | 0 | 0 | py | |
3DTrans | 3DTrans-master/pcdet/ops/pointnet2/pointnet2_batch/pointnet2_modules.py | from typing import List
import torch
import torch.nn as nn
import torch.nn.functional as F
from . import pointnet2_utils
class _PointnetSAModuleBase(nn.Module):
def __init__(self):
super().__init__()
self.npoint = None
self.groupers = None
self.mlps = None
self.pool_meth... | 22,835 | 43.341748 | 196 | py |
3DTrans | 3DTrans-master/pcdet/ops/iou3d_nms/iou3d_nms_utils.py | """
3D IoU Calculation and Rotated NMS
Written by Shaoshuai Shi
All Rights Reserved 2019-2020.
"""
import torch
from ...utils import common_utils
from . import iou3d_nms_cuda
def boxes_bev_iou_cpu(boxes_a, boxes_b):
"""
Args:
boxes_a: (N, 7) [x, y, z, dx, dy, dz, heading]
boxes_b: (M, 7) [x, ... | 3,673 | 30.401709 | 109 | py |
3DTrans | 3DTrans-master/pcdet/ops/iou3d_nms/__init__.py | 0 | 0 | 0 | py | |
3DTrans | 3DTrans-master/pcdet/ops/roiaware_pool3d/__init__.py | 0 | 0 | 0 | py | |
3DTrans | 3DTrans-master/pcdet/ops/roiaware_pool3d/roiaware_pool3d_utils.py | import torch
import torch.nn as nn
from torch.autograd import Function
from ...utils import common_utils
from . import roiaware_pool3d_cuda
def points_in_boxes_cpu(points, boxes):
"""
Args:
points: (num_points, 3)
boxes: [x, y, z, dx, dy, dz, heading], (x, y, z) is the box center, each box DO... | 4,075 | 35.392857 | 120 | py |
caringcaribou | caringcaribou-master/setup.py | #!/usr/bin/env python
"""
Caring Caribou
==============
- A friendly automotive security exploration tool, initiated as part of the research project HEAVENS (HEAling Vulnerabilities to ENhance Software Security and Safety), now a stand-alone project.
- A zero-knowledge tool that can be dropped onto an automotive networ... | 2,202 | 35.716667 | 194 | py |
caringcaribou | caringcaribou-master/caringcaribou/caringcaribou.py | #!/usr/bin/env python
# Released under GNU General Public License v3
# https://github.com/CaringCaribou/caringcaribou
import argparse
import can
import errno
from .utils import can_actions
import traceback
import pkg_resources
VERSION = "0.4"
def show_script_header():
"""Show script header"""
print(r"""
{0}... | 3,864 | 27.007246 | 118 | py |
caringcaribou | caringcaribou-master/caringcaribou/__init__.py | 0 | 0 | 0 | py | |
caringcaribou | caringcaribou-master/caringcaribou/modules/test.py | from __future__ import print_function
from caringcaribou.utils.can_actions import DEFAULT_INTERFACE
import caringcaribou.tests
import unittest
def print_interface_header():
"""Prints a header showing which interface is used"""
interface_str = DEFAULT_INTERFACE if DEFAULT_INTERFACE is not None else "default"
... | 667 | 32.4 | 85 | py |
caringcaribou | caringcaribou-master/caringcaribou/modules/dump.py | from __future__ import print_function
from caringcaribou.utils.can_actions import CanActions
from caringcaribou.utils.common import msg_to_candump_format, parse_int_dec_or_hex
from caringcaribou.modules.send import FILE_LINE_COMMENT_PREFIX
from sys import argv, stdout
import argparse
import datetime
def initiate_dump... | 4,952 | 36.522727 | 114 | py |
caringcaribou | caringcaribou-master/caringcaribou/modules/dcm.py | from __future__ import print_function
from caringcaribou.utils.can_actions import CanActions
from caringcaribou.utils.common import parse_int_dec_or_hex
from sys import stdout
import argparse
import time
DCM_SERVICE_NAMES = {
0x10: "DIAGNOSTIC_SESSION_CONTROL",
0x11: "ECU_RESET",
0x12: "GMLAN_READ_FAILURE_... | 19,234 | 38.016227 | 120 | py |
caringcaribou | caringcaribou-master/caringcaribou/modules/send.py | from caringcaribou.utils.can_actions import CanActions
from caringcaribou.utils.common import list_to_hex_str, parse_int_dec_or_hex, str_to_int_list
from caringcaribou.utils.constants import ARBITRATION_ID_MAX, ARBITRATION_ID_MAX_EXTENDED
from time import sleep
from sys import exit
import argparse
import re
FILE_LINE... | 10,880 | 37.72242 | 119 | py |
caringcaribou | caringcaribou-master/caringcaribou/modules/module_template.py | '''
module_template.py
This file contains a template for a simple CaringCaribou module.
The module's entry point is the 'module_main' function.
Steps to add this module to CaringCaribou and run it:
1. Copy this template into the `caringcaribou/modules` directory:
$ cp module_template.py my_module.py
2. In `set... | 4,005 | 34.767857 | 95 | py |
caringcaribou | caringcaribou-master/caringcaribou/modules/listener.py | from __future__ import print_function
from caringcaribou.utils.can_actions import CanActions
from sys import stdout
from collections import Counter
import argparse
def start_listener(falling_sort):
"""
Counts messages per arbitration ID. Prints a list of IDs afterwards, sorted by number of hits.
:param f... | 2,252 | 34.203125 | 98 | py |
caringcaribou | caringcaribou-master/caringcaribou/modules/fuzzer.py | from __future__ import print_function
from sys import version_info, stdout
import argparse
import random
from itertools import product
from caringcaribou.utils.can_actions import CanActions
from caringcaribou.utils.common import hex_str_to_nibble_list, int_from_byte_list, list_to_hex_str, parse_int_dec_or_hex
from cari... | 31,850 | 39.064151 | 120 | py |
caringcaribou | caringcaribou-master/caringcaribou/modules/uds.py | from __future__ import print_function
from caringcaribou.utils.can_actions import auto_blacklist
from caringcaribou.utils.common import list_to_hex_str, parse_int_dec_or_hex
from caringcaribou.utils.constants import ARBITRATION_ID_MAX, ARBITRATION_ID_MAX_EXTENDED
from caringcaribou.utils.constants import ARBITRATION_ID... | 58,081 | 42.637866 | 159 | py |
caringcaribou | caringcaribou-master/caringcaribou/modules/uds_fuzz.py | from __future__ import print_function
from caringcaribou.utils.common import list_to_hex_str, parse_int_dec_or_hex, str_to_int_list
from caringcaribou.utils.iso14229_1 import Iso14229_1
from caringcaribou.modules.uds import ecu_reset, print_negative_response, request_seed, extended_session
from sys import stdout
import... | 15,686 | 47.119632 | 118 | py |
caringcaribou | caringcaribou-master/caringcaribou/modules/__init__.py | 0 | 0 | 0 | py | |
caringcaribou | caringcaribou-master/caringcaribou/modules/xcp.py | from __future__ import print_function
from caringcaribou.utils.can_actions import CanActions, auto_blacklist
from caringcaribou.utils.common import list_to_hex_str, parse_int_dec_or_hex
from datetime import datetime, timedelta
from sys import stdout
import argparse
import time
# Dictionary of XCP error codes
XCP_ERROR... | 22,119 | 39.438757 | 120 | py |
caringcaribou | caringcaribou-master/caringcaribou/modules/doip.py | from __future__ import print_function
from caringcaribou.utils.common import list_to_hex_str, parse_int_dec_or_hex
from caringcaribou.utils.constants import ARBITRATION_ID_MAX, ARBITRATION_ID_MAX_EXTENDED
from caringcaribou.utils.constants import ARBITRATION_ID_MIN
from caringcaribou.utils.iso14229_1 import Constants, ... | 39,207 | 40.93369 | 120 | py |
caringcaribou | caringcaribou-master/caringcaribou/tests/test_module_uds.py | from __future__ import print_function
from caringcaribou.utils.iso14229_1 import Constants, Iso14229_1, NegativeResponseCodes, ServiceID, Services
from caringcaribou.tests.mock.mock_ecu_uds import MockEcuIso14229
from caringcaribou.modules import uds
import unittest
class UdsModuleTestCase(unittest.TestCase):
ARB... | 9,568 | 46.137931 | 114 | py |
caringcaribou | caringcaribou-master/caringcaribou/tests/test_iso_14229_1.py | from __future__ import print_function
from caringcaribou.utils.can_actions import DEFAULT_INTERFACE
from caringcaribou.tests.mock.mock_ecu_uds import MockEcuIso14229
from caringcaribou.utils import iso14229_1
from caringcaribou.utils import iso15765_2
import can
import unittest
class DiagnosticsOverIsoTpTestCase(unit... | 8,713 | 53.4625 | 118 | py |
caringcaribou | caringcaribou-master/caringcaribou/tests/test_iso_15765_2.py | from __future__ import print_function
from caringcaribou.tests.mock.mock_ecu_iso_tp import MockEcuIsoTp
from caringcaribou.utils import iso15765_2
from caringcaribou.utils.can_actions import DEFAULT_INTERFACE
import can
import unittest
class IsoTpTestCase(unittest.TestCase):
ARB_ID_REQUEST = 0x100A
ARB_ID_RE... | 2,511 | 38.25 | 90 | py |
caringcaribou | caringcaribou-master/caringcaribou/tests/__init__.py | import os
def load_tests(loader, standard_tests, pattern):
this_dir = os.path.dirname(__file__)
package_tests = loader.discover(start_dir=this_dir, pattern='test_*')
standard_tests.addTests(package_tests)
return standard_tests
| 244 | 29.625 | 73 | py |
caringcaribou | caringcaribou-master/caringcaribou/tests/test_send.py | from caringcaribou.modules import send
import unittest
class SendFileParserTestCase(unittest.TestCase):
RESULT_DATA_C0FFEE = [0xc0, 0xff, 0xee]
RESULT_DATA_DEAD_CAFE = [0xde, 0xad, 0xca, 0xfe]
def test_parse_candump_line(self):
line = "(1499197954.029156) can0 123#c0ffee"
message, timest... | 2,222 | 48.4 | 102 | py |
caringcaribou | caringcaribou-master/caringcaribou/tests/mock/mock_ecu_iso_tp.py | import multiprocessing
import time
from caringcaribou.utils.iso15765_2 import IsoTp
from caringcaribou.tests.mock.mock_ecu import MockEcu
class MockEcuIsoTp(MockEcu):
"""ISO-15765-2 (ISO-TP) mock ECU handler"""
MOCK_SINGLE_FRAME_REQUEST = [0x01, 0xAA, 0xAB, 0xAC, 0xAD, 0xAE, 0xAF]
MOCK_SINGLE_FRAME_RESP... | 3,462 | 32.95098 | 106 | py |
caringcaribou | caringcaribou-master/caringcaribou/tests/mock/mock_ecu.py | from __future__ import print_function
from caringcaribou.utils.can_actions import DEFAULT_INTERFACE
import can
class MockEcu:
"""Mock ECU base class, used for running tests over a virtual CAN bus"""
DELAY_BEFORE_RESPONSE = 0.01
def __init__(self, bus=None):
self.message_process = None
if... | 551 | 23 | 76 | py |
caringcaribou | caringcaribou-master/caringcaribou/tests/mock/mock_ecu_uds.py | from caringcaribou.utils.iso15765_2 import IsoTp
from caringcaribou.utils.iso14229_1 import *
from caringcaribou.utils.common import int_from_byte_list
from caringcaribou.tests.mock.mock_ecu import MockEcu
from caringcaribou.tests.mock.mock_ecu_iso_tp import MockEcuIsoTp
class MockEcuIso14229(MockEcuIsoTp, MockEcu):
... | 12,194 | 43.025271 | 116 | py |
caringcaribou | caringcaribou-master/caringcaribou/tests/mock/__init__.py | 0 | 0 | 0 | py | |
caringcaribou | caringcaribou-master/caringcaribou/utils/iso15765_2.py | from caringcaribou.utils.can_actions import DEFAULT_INTERFACE
from caringcaribou.utils.constants import ARBITRATION_ID_MAX_EXTENDED, ARBITRATION_ID_MAX
import can
import datetime
import time
class IsoTp:
"""
Implementation of ISO-15765-2, also known as ISO-TP. This is a multi-frame messaging protocol
over... | 16,493 | 40.756962 | 120 | py |
caringcaribou | caringcaribou-master/caringcaribou/utils/constants.py |
ARBITRATION_ID_MIN = 0x0
ARBITRATION_ID_MAX = 0x7FF
ARBITRATION_ID_MAX_EXTENDED = 0x1FFFFFFF
BYTE_MIN = 0x00
BYTE_MAX = 0xFF
| 127 | 15 | 40 | py |
caringcaribou | caringcaribou-master/caringcaribou/utils/can_actions.py | from __future__ import print_function
from caringcaribou.utils.constants import ARBITRATION_ID_MAX, ARBITRATION_ID_MAX_EXTENDED, ARBITRATION_ID_MIN, BYTE_MAX, BYTE_MIN
from sys import stdout, version_info
import can
import time
# Handle large ranges efficiently in both python 2 and 3
if version_info[0] == 2:
range... | 8,331 | 37.574074 | 129 | py |
caringcaribou | caringcaribou-master/caringcaribou/utils/iso14229_1.py | import time
# Fix for backward compatibility with Python versions older than 3.3,
# where time.process_time is not available
try:
time.process_time
except AttributeError:
time.process_time = time.clock
class DynamicallyDefinedIdentifierArg(object):
def __init__(self, source_data_identifier,
... | 19,688 | 32.484694 | 76 | py |
caringcaribou | caringcaribou-master/caringcaribou/utils/common.py |
def parse_int_dec_or_hex(value):
"""Parses an integer on base 10 (decimal) or 16 (hex with "0x" prefix)
Examples:
parse_int_dec_or_hex("1234") -> 1234
parse_int_dec_or_hex("0xa7") -> 167
:param value: the value to parse
:type value: str
:rtype int
"""
return int(value, 0)
def st... | 3,067 | 27.407407 | 88 | py |
caringcaribou | caringcaribou-master/caringcaribou/utils/__init__.py | 0 | 0 | 0 | py | |
resmap | resmap-master/sec_resonance_map.py | import numpy as np
import math
import sys
from numpy import linalg
## MASS OF THE STAR ###
# Solar units
MC=0.0
#######################
## USED FOR INTEGRATION CONTROL IN CALC OF BINT ##
NSIMP=100
##################################################
## NOT EVERTHING BELOW IS USED. EXPANSION FOR LATER ##
def load_ic(... | 12,048 | 26.015695 | 133 | py |
GPTScore | GPTScore-main/utils.py | import os
import pickle
import sys
import nltk
from mosestokenizer import *
from nltk import word_tokenize
from nltk.tokenize import sent_tokenize
import json
nltk.download('stopwords')
detokenizer = MosesDetokenizer('en')
def read_demos(json_path):
asp_demos = json.load(open(json_path))
asp_dfs, demos = asp... | 1,354 | 20.854839 | 67 | py |
GPTScore | GPTScore-main/score_d2t.py | import argparse
import os
import time
import numpy as np
from utils import *
from gpt3_score import gpt3score
from transformers import GPT2Tokenizer
import json
class Scorer:
""" Support GPT3-based (davinci, curie, babbage, ada), OPT-based, GPT2-based, FLAN-T5-based (19 models) """
def __init__(self, args=Non... | 24,959 | 53.857143 | 198 | py |
GPTScore | GPTScore-main/gpt3_score.py | from gpt_inference import GPT3Model
def gpt3score(input, output,gpt3model=None,api_key=None):
gpt3model_name = ''
if gpt3model == 'ada':
gpt3model_name = "text-ada-001"
elif gpt3model == 'babbage':
gpt3model_name = "text-babbage-001"
elif gpt3model == 'curie':
gpt3model_name = "... | 793 | 36.809524 | 99 | py |
GPTScore | GPTScore-main/gpt_inference.py | import time
import sys
from transformers import GPT2Tokenizer
import openai
class GPT3Model(object):
def __init__(self, model_name, api_key, logger=None):
self.model_name = model_name
try:
openai.api_key = api_key
except Exception:
pass
self.tokenizer = GPT2... | 2,311 | 34.569231 | 89 | py |
GPTScore | GPTScore-main/opt_score.py | # %%
import torch
import torch.nn as nn
import traceback
from transformers import BartTokenizer, BartForConditionalGeneration
from transformers import GPT2Tokenizer, OPTForCausalLM, GPT2LMHeadModel, GPTJForCausalLM
class OPTScorer:
def __init__(self, device='cuda:0', max_length=1024, checkpoint=None):
# Se... | 3,445 | 41.54321 | 94 | py |
GPTScore | GPTScore-main/flan_score.py | # %%
import torch
import torch.nn as nn
import traceback
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
class FLANScorer:
def __init__(self, device='cuda:0', max_length=1024, checkpoint='google/flan-t5-base'):
# Set up model
self.device = device
self.max_length = max_length
... | 2,981 | 39.849315 | 97 | py |
pytorch-boat | pytorch-boat-main/BOAT-CSWin/main.py | # ------------------------------------------
# CSWin Transformer
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
# written By Xiaoyi Dong
# ------------------------------------------
import argparse
import time
import yaml
import os
import logging
from collections import OrderedDict
from conte... | 36,210 | 46.272846 | 133 | py |
pytorch-boat | pytorch-boat-main/BOAT-CSWin/labeled_memcached_dataset.py | # ------------------------------------------
# CSWin Transformer
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
# written By Xiaoyi Dong
# ------------------------------------------
from torch.utils.data import Dataset
import numpy as np
import io
from PIL import Image
import os
import json
i... | 1,700 | 29.375 | 80 | py |
pytorch-boat | pytorch-boat-main/BOAT-CSWin/finetune.py | # ------------------------------------------
# CSWin Transformer
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
# written By Xiaoyi Dong
# ------------------------------------------
import argparse
import time
import yaml
import os
import logging
from collections import OrderedDict
from conte... | 43,476 | 45.900755 | 134 | py |
pytorch-boat | pytorch-boat-main/BOAT-CSWin/checkpoint_saver.py | # ------------------------------------------
# CSWin Transformer
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
# written By Xiaoyi Dong
# ------------------------------------------
import glob
import operator
import os
import logging
import torch
from timm.utils.model import unwrap_model, ... | 6,397 | 39.751592 | 104 | py |
pytorch-boat | pytorch-boat-main/BOAT-CSWin/segmentation/backbone/cswin_transformer.py | # ------------------------------------------
# CSWin Transformer
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
# written By Xiaoyi Dong
# ------------------------------------------
import torch
import torch.nn as nn
import torch.nn.functional as F
from functools import partial
from timm.da... | 14,811 | 35.126829 | 145 | py |
pytorch-boat | pytorch-boat-main/BOAT-CSWin/segmentation/mmcv_custom/checkpoint.py | # Copyright (c) Open-MMLab. All rights reserved.
import io
import os
import os.path as osp
import pkgutil
import time
import warnings
from collections import OrderedDict
from importlib import import_module
from tempfile import TemporaryDirectory
import torch
import torchvision
from torch.optim import Optimizer
from to... | 19,055 | 36.884692 | 110 | py |
pytorch-boat | pytorch-boat-main/BOAT-CSWin/segmentation/configs/_base/upernet_cswin.py | # model settings
norm_cfg = dict(type='SyncBN', requires_grad=True)
model = dict(
type='EncoderDecoder',
pretrained=None,
backbone=dict(
type='CSWin',
embed_dim=64,
patch_size=4,
depth=[1, 2, 21, 1],
num_heads=[2,4,8,16],
split_size=[1,2,7,7],
mlp_rati... | 1,303 | 26.744681 | 74 | py |
pytorch-boat | pytorch-boat-main/BOAT-CSWin/segmentation/configs/cswin/upernet_cswin_base.py | _base_ = [
'../_base_/models/upernet_cswin.py', '../_base_/datasets/ade20k.py',
'../_base_/default_runtime.py', '../_base_/schedules/schedule_160k.py'
]
model = dict(
backbone=dict(
type='CSWin',
embed_dim=96,
depth=[2,4,32,2],
num_heads=[4,8,16,32],
split_size=[1,2,7... | 1,251 | 31.947368 | 101 | py |
pytorch-boat | pytorch-boat-main/BOAT-CSWin/segmentation/configs/cswin/upernet_cswin_tiny.py | _base_ = [
'../_base_/models/upernet_cswin.py', '../_base_/datasets/ade20k.py',
'../_base_/default_runtime.py', '../_base_/schedules/schedule_160k.py'
]
model = dict(
backbone=dict(
type='CSWin',
embed_dim=64,
depth=[1,2,21,1],
num_heads=[2,4,8,16],
split_size=[1,2,7,... | 1,250 | 31.921053 | 101 | py |
pytorch-boat | pytorch-boat-main/BOAT-CSWin/segmentation/configs/cswin/upernet_cswin_small.py | _base_ = [
'../_base_/models/upernet_cswin.py', '../_base_/datasets/ade20k.py',
'../_base_/default_runtime.py', '../_base_/schedules/schedule_160k.py'
]
model = dict(
backbone=dict(
type='CSWin',
embed_dim=64,
depth=[2,4,32,2],
num_heads=[2,4,8,16],
split_size=[1,2,7,... | 1,249 | 32.783784 | 101 | py |
pytorch-boat | pytorch-boat-main/BOAT-CSWin/models/cswin_boat.py | # ------------------------------------------
# CSWin Transformer
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
# written By Xiaoyi Dong
# ------------------------------------------
import torch
import torch.nn as nn
import torch.nn.functional as F
from functools import partial
from timm.da... | 20,463 | 38.353846 | 183 | py |
pytorch-boat | pytorch-boat-main/BOAT-CSWin/models/__init__.py | # ------------------------------------------
# CSWin Transformer
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
# written By Xiaoyi Dong
# ------------------------------------------
from .cswin_boat import *
| 235 | 25.222222 | 44 | py |
pytorch-boat | pytorch-boat-main/BOAT-Swin/main.py | # --------------------------------------------------------
# Swin Transformer
# Copyright (c) 2021 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# Written by Ze Liu
# --------------------------------------------------------
import os
import time
import random
import argparse
import datetime
impo... | 14,809 | 40.368715 | 117 | py |
pytorch-boat | pytorch-boat-main/BOAT-Swin/lr_scheduler.py | # --------------------------------------------------------
# Swin Transformer
# Copyright (c) 2021 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# Written by Ze Liu
# --------------------------------------------------------
import torch
from timm.scheduler.cosine_lr import CosineLRScheduler
from... | 3,547 | 33.446602 | 105 | py |
pytorch-boat | pytorch-boat-main/BOAT-Swin/utils.py | # --------------------------------------------------------
# Swin Transformer
# Copyright (c) 2021 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# Written by Ze Liu
# --------------------------------------------------------
import os
import torch
import torch.distributed as dist
try:
# noin... | 8,012 | 42.786885 | 117 | py |
pytorch-boat | pytorch-boat-main/BOAT-Swin/logger.py | # --------------------------------------------------------
# Swin Transformer
# Copyright (c) 2021 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# Written by Ze Liu
# --------------------------------------------------------
import os
import sys
import logging
import functools
from termcolor impo... | 1,451 | 33.571429 | 102 | py |
pytorch-boat | pytorch-boat-main/BOAT-Swin/config.py | # --------------------------------------------------------
# Swin Transformer
# Copyright (c) 2021 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# Written by Ze Liu
# --------------------------------------------------------'
import os
import yaml
from yacs.config import CfgNode as CN
_C = CN()
... | 8,169 | 30.914063 | 79 | py |
pytorch-boat | pytorch-boat-main/BOAT-Swin/optimizer.py | # --------------------------------------------------------
# Swin Transformer
# Copyright (c) 2021 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# Written by Ze Liu
# --------------------------------------------------------
from torch import optim as optim
def build_optimizer(config, model):
... | 2,013 | 33.724138 | 111 | py |
pytorch-boat | pytorch-boat-main/BOAT-Swin/models/boat_swin_transformer.py | # --------------------------------------------------------
# Swin Transformer
# Copyright (c) 2021 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# Written by Ze Liu
# --------------------------------------------------------
import torch
import torch.nn as nn
import torch.utils.checkpoint as chec... | 30,671 | 41.074074 | 161 | py |
pytorch-boat | pytorch-boat-main/BOAT-Swin/models/swin_mlp.py | # --------------------------------------------------------
# Swin Transformer
# Copyright (c) 2021 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# Written by Ze Liu
# --------------------------------------------------------
import torch
import torch.nn as nn
import torch.nn.functional as F
impor... | 18,508 | 38.464819 | 118 | py |
pytorch-boat | pytorch-boat-main/BOAT-Swin/models/swin_transformer.py | # --------------------------------------------------------
# Swin Transformer
# Copyright (c) 2021 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# Written by Ze Liu
# --------------------------------------------------------
import torch
import torch.nn as nn
import torch.utils.checkpoint as chec... | 24,234 | 40.356655 | 119 | py |
pytorch-boat | pytorch-boat-main/BOAT-Swin/models/__init__.py | from .build import build_model | 30 | 30 | 30 | py |
pytorch-boat | pytorch-boat-main/BOAT-Swin/models/build.py | # --------------------------------------------------------
# Swin Transformer
# Copyright (c) 2021 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# Written by Ze Liu
# --------------------------------------------------------
from .boat_swin_transformer import SwinTransformer
from .swin_mlp import... | 2,566 | 50.34 | 75 | py |
pytorch-boat | pytorch-boat-main/BOAT-Swin/data/__init__.py | from .build import build_loader | 31 | 31 | 31 | py |
pytorch-boat | pytorch-boat-main/BOAT-Swin/data/samplers.py | # --------------------------------------------------------
# Swin Transformer
# Copyright (c) 2021 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# Written by Ze Liu
# --------------------------------------------------------
import torch
class SubsetRandomSampler(torch.utils.data.Sampler):
... | 781 | 25.066667 | 84 | py |
pytorch-boat | pytorch-boat-main/BOAT-Swin/data/zipreader.py | # --------------------------------------------------------
# Swin Transformer
# Copyright (c) 2021 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# Written by Ze Liu
# --------------------------------------------------------
import os
import zipfile
import io
import numpy as np
from PIL import Im... | 3,333 | 31.057692 | 89 | py |
pytorch-boat | pytorch-boat-main/BOAT-Swin/data/build.py | # --------------------------------------------------------
# Swin Transformer
# Copyright (c) 2021 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# Written by Ze Liu
# --------------------------------------------------------
import os
import torch
import numpy as np
import torch.distributed as di... | 5,877 | 37.927152 | 113 | py |
pytorch-boat | pytorch-boat-main/BOAT-Swin/data/cached_image_folder.py | # --------------------------------------------------------
# Swin Transformer
# Copyright (c) 2021 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# Written by Ze Liu
# --------------------------------------------------------
import io
import os
import time
import torch.distributed as dist
import ... | 9,026 | 34.679842 | 115 | py |
ehrdiff | ehrdiff-main/main.py |
import logging
import argparse
from train_util import set_seed, train_diff
def main():
parser = argparse.ArgumentParser()
parser.add_argument("--seed", type=int, default=2023,
help="random seed for initialization")
parser.add_argument(
"--data_file",
default='p... | 3,833 | 40.673913 | 95 | py |
ehrdiff | ehrdiff-main/diffusion_util.py | # -----------------------------------
# Code adapted from:
# https://github.com/lucidrains/denoising-diffusion-pytorch
# -----------------------------------
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from einops import rearrange, reduce
def exists(val):
return val is not None... | 6,942 | 27.809129 | 132 | py |
ehrdiff | ehrdiff-main/train_util.py | import os
import time
import random
import logging
import numpy as np
from scipy.stats import pearsonr
import matplotlib.pyplot as plt
import torch
from torch.utils.data import DataLoader
from transformers import get_cosine_schedule_with_warmup
from diffusion_util import LinearModel, Diffusion
def set_seed(seed=34... | 6,141 | 36.680982 | 119 | py |
ehrdiff | ehrdiff-main/gen_dat.py | from tqdm import tqdm
import torch
import numpy as np
from diffusion_util import LinearModel, Diffusion
device = torch.device('cuda:0')
dm = LinearModel(z_dim=1782, time_dim=384, unit_dims=[1024, 384, 384, 384, 1024])
dm.load_state_dict(torch.load("weight/model.pt"))
dm.to(device)
diffusion = Diffusion(
... | 1,016 | 26.486486 | 81 | py |
Text-Classification-Models-Pytorch | Text-Classification-Models-Pytorch-master/Model_TextCNN/utils.py | # utils.py
import torch
from torchtext import data
from torchtext.vocab import Vectors
import spacy
import pandas as pd
import numpy as np
from sklearn.metrics import accuracy_score
class Dataset(object):
def __init__(self, config):
self.config = config
self.train_iterator = None
self.test... | 4,498 | 37.452991 | 110 | py |
Text-Classification-Models-Pytorch | Text-Classification-Models-Pytorch-master/Model_TextCNN/model.py | # model.py
import torch
from torch import nn
import numpy as np
from utils import *
class TextCNN(nn.Module):
def __init__(self, config, vocab_size, word_embeddings):
super(TextCNN, self).__init__()
self.config = config
# Embedding Layer
self.embeddings = nn.Embedding(voca... | 4,215 | 39.932039 | 137 | py |
Text-Classification-Models-Pytorch | Text-Classification-Models-Pytorch-master/Model_TextCNN/config.py | # config.py
class Config(object):
embed_size = 300
num_channels = 100
kernel_size = [3,4,5]
output_size = 4
max_epochs = 15
lr = 0.3
batch_size = 64
max_sen_len = 30
dropout_keep = 0.8 | 221 | 17.5 | 25 | py |
Text-Classification-Models-Pytorch | Text-Classification-Models-Pytorch-master/Model_TextCNN/train.py | # train.py
from utils import *
from model import *
from config import Config
import sys
import torch.optim as optim
from torch import nn
import torch
if __name__=='__main__':
config = Config()
train_file = '../data/ag_news.train'
if len(sys.argv) > 2:
train_file = sys.argv[1]
test_file = '../d... | 1,720 | 32.096154 | 98 | py |
Text-Classification-Models-Pytorch | Text-Classification-Models-Pytorch-master/Model_TextCNN/old_code/utils.py | # utils.py
from nltk import word_tokenize
from tqdm import tqdm
from gensim.models import KeyedVectors
import numpy as np
import math
class Vocab(object):
def __init__(self):
self.word_to_index = {}
self.index_to_word = {}
self.unknown = '<unk>'
self.add_word(self.unknown)
... | 5,381 | 33.722581 | 117 | py |
Text-Classification-Models-Pytorch | Text-Classification-Models-Pytorch-master/Model_TextCNN/old_code/model.py | # model.py
import torch
from torch import nn
from torch import Tensor
from torch.autograd import Variable
import numpy as np
from sklearn.metrics import accuracy_score
class CNNText(nn.Module):
def __init__(self, config):
super(CNNText, self).__init__()
self.config = config
# Conv... | 4,642 | 40.088496 | 107 | py |
Text-Classification-Models-Pytorch | Text-Classification-Models-Pytorch-master/Model_TextCNN/old_code/config.py | # config.py
class Config(object):
embed_size = 300
in_channels = 1
num_channels = 100
kernel_size = [3,4,5]
output_size = 4
max_epochs = 10
lr = 0.25
batch_size = 64
max_sen_len = 20
dropout_keep = 0.6 | 242 | 17.692308 | 25 | py |
Text-Classification-Models-Pytorch | Text-Classification-Models-Pytorch-master/Model_TextCNN/old_code/train.py | # train.py
from utils import *
from config import Config
from sklearn.model_selection import train_test_split
import numpy as np
from tqdm import tqdm
import sys
import torch.optim as optim
from torch import nn, Tensor
from torch.autograd import Variable
import torch
from sklearn.metrics import accuracy_score
def get... | 3,445 | 36.868132 | 150 | py |
Text-Classification-Models-Pytorch | Text-Classification-Models-Pytorch-master/Model_Seq2Seq_Attention/utils.py | # utils.py
import torch
from torchtext import data
from torchtext.vocab import Vectors
import spacy
import pandas as pd
import numpy as np
from sklearn.metrics import accuracy_score
class Dataset(object):
def __init__(self, config):
self.config = config
self.train_iterator = None
self.test... | 4,492 | 37.076271 | 110 | py |
Text-Classification-Models-Pytorch | Text-Classification-Models-Pytorch-master/Model_Seq2Seq_Attention/model.py | # model.py
import torch
from torch import nn
import numpy as np
from torch.nn import functional as F
from utils import *
class Seq2SeqAttention(nn.Module):
def __init__(self, config, vocab_size, word_embeddings):
super(Seq2SeqAttention, self).__init__()
self.config = config
# Embe... | 5,529 | 42.203125 | 139 | py |
Text-Classification-Models-Pytorch | Text-Classification-Models-Pytorch-master/Model_Seq2Seq_Attention/config.py | # config.py
class Config(object):
embed_size = 300
hidden_layers = 1
hidden_size = 32
bidirectional = True
output_size = 4
max_epochs = 15
lr = 0.5
batch_size = 128
dropout_keep = 0.8
max_sen_len = None # Sequence length for RNN | 269 | 19.769231 | 48 | py |
Text-Classification-Models-Pytorch | Text-Classification-Models-Pytorch-master/Model_Seq2Seq_Attention/train.py | # train.py
from utils import *
from model import *
from config import Config
import sys
import torch.optim as optim
from torch import nn
import torch
if __name__=='__main__':
config = Config()
train_file = '../data/ag_news.train'
if len(sys.argv) > 2:
train_file = sys.argv[1]
test_file = '../d... | 1,729 | 32.269231 | 98 | py |
Text-Classification-Models-Pytorch | Text-Classification-Models-Pytorch-master/Model_CharCNN/utils.py | # utils.py
import torch
from torchtext import data
import pandas as pd
import numpy as np
from sklearn.metrics import accuracy_score
def get_embedding_matrix(vocab_chars):
# one hot embedding plus all-zero vector
vocabulary_size = len(vocab_chars)
onehot_matrix = np.eye(vocabulary_size, vocabulary_size)
... | 4,545 | 37.525424 | 103 | py |
Text-Classification-Models-Pytorch | Text-Classification-Models-Pytorch-master/Model_CharCNN/model.py | # model.py
import torch
from torch import nn
import numpy as np
from utils import *
class CharCNN(nn.Module):
def __init__(self, config, vocab_size, embeddings):
super(CharCNN, self).__init__()
self.config = config
embed_size = vocab_size
# Embedding Layer
self.emb... | 5,019 | 40.147541 | 117 | py |
Text-Classification-Models-Pytorch | Text-Classification-Models-Pytorch-master/Model_CharCNN/config.py | # config.py
class Config(object):
num_channels = 256
linear_size = 256
output_size = 4
max_epochs = 10
lr = 0.001
batch_size = 128
seq_len = 300 # 1014 in original paper
dropout_keep = 0.5 | 221 | 19.181818 | 42 | py |
Text-Classification-Models-Pytorch | Text-Classification-Models-Pytorch-master/Model_CharCNN/train.py | # train.py
from utils import *
from model import *
from config import Config
import sys
import torch
import torch.optim as optim
from torch import nn
if __name__=='__main__':
config = Config()
train_file = '../data/ag_news.train'
if len(sys.argv) > 2:
train_file = sys.argv[1]
test_file = '../d... | 1,665 | 32.32 | 98 | py |
Text-Classification-Models-Pytorch | Text-Classification-Models-Pytorch-master/Model_CharCNN/without_torchtext/utils.py | # utils.py
import pandas as pd
import numpy as np
import torch
from torch.utils.data import Dataset
from torch.utils import data
from torch.utils.data import DataLoader
from torch.autograd import Variable
from sklearn.metrics import accuracy_score
# Used part of code to read the dataset from: https://github.com/1991v... | 3,657 | 37.505263 | 128 | py |
Text-Classification-Models-Pytorch | Text-Classification-Models-Pytorch-master/Model_CharCNN/without_torchtext/model.py | # model.py
import torch
from torch import nn
import numpy as np
from torch.autograd import Variable
from utils import *
class CharCNN(nn.Module):
def __init__(self, config):
super(CharCNN, self).__init__()
self.config = config
# This stackoverflow thread explains how conv1d works
... | 5,218 | 39.773438 | 117 | py |
Text-Classification-Models-Pytorch | Text-Classification-Models-Pytorch-master/Model_CharCNN/without_torchtext/config.py | # config.py
class Config(object):
num_channels = 256
linear_size = 256
output_size = 4
max_epochs = 10
lr = 0.001
batch_size = 128
vocab_size = 68
max_len = 300 # 1014 in original paper
dropout_keep = 0.5 | 241 | 19.166667 | 42 | py |
Text-Classification-Models-Pytorch | Text-Classification-Models-Pytorch-master/Model_CharCNN/without_torchtext/train.py | # train.py
from utils import *
from model import *
from config import Config
import sys
import torch
import torch.optim as optim
from torch import nn
if __name__=='__main__':
config = Config()
train_file = '../data/ag_news.train'
if len(sys.argv) > 2:
train_file = sys.argv[1]
test_file = '../d... | 1,605 | 31.77551 | 94 | py |
Text-Classification-Models-Pytorch | Text-Classification-Models-Pytorch-master/Model_fastText/utils.py | # utils.py
import torch
from torchtext import data
from torchtext.vocab import Vectors
import spacy
import pandas as pd
import numpy as np
from sklearn.metrics import accuracy_score
class Dataset(object):
def __init__(self, config):
self.config = config
self.train_iterator = None
self.test... | 4,462 | 37.145299 | 97 | py |
Text-Classification-Models-Pytorch | Text-Classification-Models-Pytorch-master/Model_fastText/model.py | # model.py
import torch
from torch import nn
import numpy as np
from utils import *
class fastText(nn.Module):
def __init__(self, config, vocab_size, word_embeddings):
super(fastText, self).__init__()
self.config = config
# Embedding Layer
self.embeddings = nn.Embedding(vo... | 2,709 | 33.74359 | 98 | py |
Text-Classification-Models-Pytorch | Text-Classification-Models-Pytorch-master/Model_fastText/config.py | # config.py
class Config(object):
embed_size = 300
hidden_size = 10
output_size = 4
max_epochs = 30
lr = 0.5
batch_size = 128 | 150 | 15.777778 | 21 | py |
Text-Classification-Models-Pytorch | Text-Classification-Models-Pytorch-master/Model_fastText/train.py | # train.py
from utils import *
from model import *
from config import Config
import numpy as np
import sys
import torch.optim as optim
from torch import nn
import torch
if __name__=='__main__':
config = Config()
train_file = '../data/ag_news.train'
if len(sys.argv) > 2:
train_file = sys.argv[1]
... | 1,740 | 31.849057 | 98 | py |
Text-Classification-Models-Pytorch | Text-Classification-Models-Pytorch-master/Model_fastText/old_code/utils.py | # utils.py
from nltk import word_tokenize
from tqdm import tqdm
from gensim.models import KeyedVectors
import numpy as np
import math
class Vocab(object):
def __init__(self):
self.word_to_index = {}
self.index_to_word = {}
self.unknown = '<unk>'
self.add_word(self.unknown)
... | 5,339 | 32.797468 | 117 | py |
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