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
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|---|---|---|---|---|---|---|
VLC-BERT | VLC-BERT-master/data/conceptual-captions/utils/gen_val4download.py | import os
captions = []
urls = []
with open('Validation_GCC-1.1.0-Validation.tsv') as fp:
for cnt, line in enumerate(fp):
s = line.split('\t')
captions.append(s[0].split(' '))
urls.append(s[1][:-1])
with open('val4download.txt', 'w') as fp:
for cnt, url in enumerate(urls):
... | 459 | 26.058824 | 70 | py |
VLC-BERT | VLC-BERT-master/data/conceptual-captions/utils/check_valid.py | import sys
from PIL import Image
import warnings
warnings.filterwarnings("ignore", "(Possibly )?corrupt EXIF data", UserWarning)
try:
im = Image.open(sys.argv[1]).convert('RGB')
# remove images with too small or too large size
if (im.size[0] < 10 or im.size[1] < 10 or im.size[0] > 10000 or im.size[1] > 1... | 386 | 24.8 | 88 | py |
HVAE | HVAE-master/setup.py | from distutils.core import setup
from setuptools import dist
dist.Distribution().fetch_build_eggs(['Cython', 'numpy<=1.19'])
import numpy
from Cython.Build import cythonize
required = [
"cython",
"numpy",
"torch",
"editdistance",
"scikit-learn",
"tqdm",
"pymoo"
]
setup(name='HVAE',
... | 606 | 20.678571 | 63 | py |
HVAE | HVAE-master/src/symbolic_regression.py | import argparse
import json
import random
import time
import numpy as np
import torch
from pymoo.algorithms.soo.nonconvex.ga import GA
from pymoo.optimize import minimize
from pymoo.core.problem import ElementwiseProblem
from pymoo.core.sampling import Sampling
from pymoo.core.crossover import Crossover
from pymoo.cor... | 6,382 | 37.920732 | 125 | py |
HVAE | HVAE-master/src/batch_model.py | import torch
from torch.autograd import Variable
import torch.nn.functional as F
import torch.nn as nn
from tree import Node, BatchedNode
from symbol_library import SymType
class HVAE(nn.Module):
_symbols = None
def __init__(self, input_size, output_size, hidden_size=None):
super(HVAE, self).__init_... | 8,343 | 38.545024 | 111 | py |
HVAE | HVAE-master/src/batch_train.py | from argparse import ArgumentParser
import numpy as np
import torch
from torch.utils.data import Sampler, Dataset, DataLoader
from tqdm import tqdm
# from utils import tokens_to_tree, read_expressions
from utils import read_expressions_json
from batch_model import HVAE
from symbol_library import generate_symbol_libra... | 4,883 | 31.778523 | 112 | py |
HVAE | HVAE-master/src/tree.py | import torch
from torch.autograd import Variable
class Node:
_symbols = None
_s2c = None
def __init__(self, symbol=None, right=None, left=None):
self.symbol = symbol
self.right = right
self.left = left
self.target = None
self.prediction = None
def __str__(self... | 9,097 | 34.263566 | 117 | py |
HVAE | HVAE-master/src/utils.py | import json
from symbol_library import SymType
from tree import Node
def read_expressions(filepath):
expressions = []
with open(filepath, "r") as file:
for line in file:
expressions.append(line.strip().split(" "))
return expressions
def read_expressions_json(filepath):
with open... | 2,630 | 41.435484 | 109 | py |
HVAE | HVAE-master/src/model.py | import torch
from torch.autograd import Variable
import torch.nn.functional as F
import torch.nn as nn
from tree import Node
from symbol_library import SymType
class HVAE(nn.Module):
_symbols = None
def __init__(self, input_size, output_size, hidden_size=None):
super(HVAE, self).__init__()
... | 7,496 | 39.090909 | 111 | py |
HVAE | HVAE-master/src/reconstruction_accuracy.py | from argparse import ArgumentParser
import numpy as np
import torch
from sklearn.model_selection import KFold
import editdistance
from utils import read_expressions, tokens_to_tree
from symbol_library import generate_symbol_library
from model import HVAE
from train import train_hvae
def one_fold(model, train, test,... | 3,214 | 38.691358 | 119 | py |
HVAE | HVAE-master/src/__init__.py | 0 | 0 | 0 | py | |
HVAE | HVAE-master/src/evaluation.py | from rusteval import Evaluator
import numpy as np
from pymoo.core.problem import ElementwiseProblem
from pymoo.algorithms.soo.nonconvex.de import DE
from pymoo.operators.sampling.lhs import LHS
from pymoo.optimize import minimize
from pymoo.termination.default import DefaultSingleObjectiveTermination
def read_eq_dat... | 3,639 | 29.847458 | 112 | py |
HVAE | HVAE-master/src/linear_interpolation.py | import torch
from model import HVAE
from utils import tokens_to_tree
from symbol_library import generate_symbol_library
def interpolateAB(model, exprA, exprB, steps=5):
tokensA = exprA.split(" ")
tokensB = exprB.split(" ")
treeA = tokens_to_tree(tokensA, s2t)
treeB = tokens_to_tree(tokensB, s2t)
... | 1,089 | 28.459459 | 89 | py |
HVAE | HVAE-master/src/train.py | from argparse import ArgumentParser
import numpy as np
import torch
from torch.utils.data import Sampler, Dataset, DataLoader
from tqdm import tqdm
from utils import tokens_to_tree, read_expressions, read_json
from model import HVAE
from symbol_library import generate_symbol_library
def collate_fn(batch):
retur... | 4,523 | 34.904762 | 112 | py |
HVAE | HVAE-master/src/symbol_library.py | from enum import Enum
class SymType(Enum):
Var = 1
Const = 2
Operator = 3
Fun = 4
Literal = 5
def generate_symbol_library(num_vars, symbol_list, has_constant=True):
all_symbols = {
"+": {"symbol": '+', "type": SymType.Operator, "precedence": 0, "psymbol": "add"},
"-": {"symbo... | 2,430 | 48.612245 | 125 | py |
RL4SRD | RL4SRD-master/RL4SRD.py | """
Created on 2016-12-11
class: RL4SRD
@author: Long Xia
@contact: [email protected]
"""
# !/usr/bin/python
# -*- coding:utf-8 -*-
import sys
import json
import yaml
import copy
import math
import random
import numpy as np
import os
class RL4SRD(object):
"""docstring for RL4SRD"""
def __init__(self, ... | 13,959 | 41.953846 | 168 | py |
User-Object-Attention-Level | User-Object-Attention-Level-master/data_generate/util.py | import numpy as np
def ade_classes():
"""ADE20K class names for external use."""
return [
'wall', 'building', 'sky', 'floor', 'tree', 'ceiling', 'road', 'bed ',
'windowpane', 'grass', 'cabinet', 'sidewalk', 'person', 'earth',
'door', 'table', 'mountain', 'plant', 'curtain', 'chair', 'c... | 3,390 | 43.038961 | 102 | py |
User-Object-Attention-Level | User-Object-Attention-Level-master/data_generate/convert.py | import os
import shutil
import numpy as np
import json
import matplotlib.pyplot as plt
import cv2
from util import ade_classes
from tqdm import tqdm
import numpy as np
import pandas as pd
save = '../UOAL/Labels'
raw_image = '../UOAL/Images'
fix = '../UOAL/Attention'
# save = '/Users/liujiazhen/Downloads/pas_save_new... | 12,772 | 36.239067 | 108 | py |
lolo | lolo-main/python/setup.py | from setuptools import setup
from glob import glob
import shutil
import os
# single source of truth for package version
version_ns = {}
with open(os.path.join("lolopy", "version.py")) as f:
exec(f.read(), version_ns)
version = version_ns['__version__']
# Find the lolo jar
JAR_FILE = glob(os.path.join('..', 'targe... | 1,522 | 31.404255 | 102 | py |
lolo | lolo-main/python/lolopy/utils.py | import sys
import numpy as np
def send_feature_array(gateway, X):
"""Send a feature array to the JVM
Args:
gateway (JavaGateway): Connection the JVM
X ([[double]]): 2D array of features
Returns:
(Seq[Vector[Double]]) Reference to feature array in JVM
"""
# Convert X t... | 1,266 | 29.902439 | 105 | py |
lolo | lolo-main/python/lolopy/loloserver.py | """Methods related to starting and stopping the Java Gateway"""
from py4j.java_gateway import JavaGateway
import sys
import os
# Directory where the lolo project root should be
_lolo_root = os.path.join(os.path.dirname(__file__), '..', '..')
# Used for allowing multiple objects to use the same gateway
_lolopy_gateway... | 2,571 | 35.225352 | 106 | py |
lolo | lolo-main/python/lolopy/version.py | # single source of truth for package version,
# see https://packaging.python.org/en/latest/single_source_version/
__version__ = "3.0.0"
| 136 | 33.25 | 67 | py |
lolo | lolo-main/python/lolopy/metrics.py | """Functions to call lolo Merit classes, which describe the performance of a machine learning model
These are very similar to the "metrics" from scikit-learn, which is the reason for the name of this module.
"""
from lolopy.loloserver import get_java_gateway
from lolopy.utils import send_1D_array
import numpy as np
... | 3,556 | 33.201923 | 123 | py |
lolo | lolo-main/python/lolopy/__init__.py | 0 | 0 | 0 | py | |
lolo | lolo-main/python/lolopy/learners.py | from abc import abstractmethod, ABCMeta
import numpy as np
from lolopy.loloserver import get_java_gateway
from lolopy.utils import send_feature_array, send_1D_array
from sklearn.base import BaseEstimator, RegressorMixin, ClassifierMixin, is_regressor
from sklearn.exceptions import NotFittedError
__all__ = [
'Rand... | 31,442 | 45.651335 | 140 | py |
lolo | lolo-main/python/lolopy/tests/test_learners.py | from lolopy.learners import (
RandomForestRegressor,
RandomForestClassifier,
MultiTaskRandomForest,
RegressionTreeLearner,
LinearRegression,
ExtraRandomTreesRegressor,
ExtraRandomTreesClassifier
)
from sklearn.exceptions import NotFittedError
from sklearn.metrics import r2_score, accuracy_sc... | 11,873 | 33.923529 | 121 | py |
lolo | lolo-main/python/lolopy/tests/test_server.py | from lolopy.loloserver import get_java_gateway
from py4j.java_gateway import java_import, JavaClass
from unittest import TestCase
import os
class TestLoloGateway(TestCase):
def test_launch(self):
# Launch the gateway
gate = get_java_gateway()
# Make sure it runs by making a random number... | 1,512 | 32.622222 | 71 | py |
lolo | lolo-main/python/lolopy/tests/test_metrics.py | from lolopy.loloserver import get_java_gateway
from lolopy.metrics import (root_mean_squared_error, standard_confidence, standard_error, uncertainty_correlation)
from numpy.random import multivariate_normal, uniform, normal, seed
from unittest import TestCase
class TestMetrics(TestCase):
def test_rmse(self):
... | 1,752 | 42.825 | 114 | py |
pyasn | pyasn-master/setup.py | from __future__ import print_function
import codecs
import sys
import platform
import glob
from setuptools import setup, find_packages, Extension
from os.path import abspath, dirname, join
here = abspath(dirname(__file__))
with codecs.open(join(here, 'README.md'), encoding='utf-8') as f:
README = f.read()
libs ... | 1,966 | 29.261538 | 89 | py |
pyasn | pyasn-master/pyasn/mrtx.py | # Copyright (c) 2009-2017 Hadi Asghari
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, d... | 28,342 | 44.640902 | 99 | py |
pyasn | pyasn-master/pyasn/_version.py | __version__ = '1.6.2'
| 22 | 10.5 | 21 | py |
pyasn | pyasn-master/pyasn/__init__.py | # Copyright (c) 2014-2017 Hadi Asghari
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, d... | 10,702 | 42.864754 | 105 | py |
pyasn | pyasn-master/tests/test_simple.py | # Copyright (c) 2014-2017 Hadi Asghari
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, d... | 12,259 | 43.42029 | 99 | py |
pyasn | pyasn-master/tests/test_PyASN_1_2_aggressive.py | # Copyright (c) 2009-2014 Hadi Asghari
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, d... | 2,810 | 37.506849 | 97 | py |
pyasn | pyasn-master/tests/test_comparative.py | # Copyright (c) 2014-2017 Hadi Asghari
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, d... | 4,457 | 44.030303 | 99 | py |
pyasn | pyasn-master/tests/test_mrtx.py | # Copyright (c) 2014-2017 Hadi Asghari
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, d... | 23,833 | 47.443089 | 99 | py |
pyasn | pyasn-master/tests/utilities/gen_pyasn_v1_2_mapping.py | # Copyright (c) 2009-2014 Hadi Asghari
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, d... | 2,652 | 36.9 | 101 | py |
pyasn | pyasn-master/tests/utilities/gen_cymru_mapping.py | # Copyright (c) 2009-2014 Hadi Asghari
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, d... | 2,712 | 39.492537 | 119 | py |
pyasn | pyasn-master/pyasn-utils/pyasn_util_convert.py | #!/usr/bin/python
# Copyright (c) 2009-2017 Hadi Asghari
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify... | 5,474 | 45.794872 | 99 | py |
pyasn | pyasn-master/pyasn-utils/pyasn_util_download.py | #!/usr/bin/python
# Copyright (c) 2009-2017 Hadi Asghari
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify... | 7,426 | 39.807692 | 104 | py |
pyasn | pyasn-master/pyasn-utils/pyasn_util_asnames.py | #!/usr/bin/python
# Copyright (c) 2016 Italo Maia
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge... | 3,814 | 27.901515 | 83 | py |
AutoPruner | AutoPruner-master/ResNet50/50/fine_tune_compressed_model.py | import argparse
import os
import shutil
import time
import sys
import torch
import torch.nn as nn
import torch.nn.parallel
import torch.backends.cudnn as cudnn
import torch.distributed as dist
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
from torchvision import datasets, transforms
fro... | 10,872 | 35.733108 | 106 | py |
AutoPruner | AutoPruner-master/ResNet50/50/main.py | # ************************************************************
# Author : Bumsoo Kim, 2017
# Github : https://github.com/meliketoy/fine-tuning.pytorch
#
# Korea University, Data-Mining Lab
# Deep Convolutional Network Fine tuning Implementation
#
# Description : main.py
# The main code for training classification netwo... | 14,783 | 42.354839 | 146 | py |
AutoPruner | AutoPruner-master/ResNet50/50/evaluate_network.py | import torch
import torch.backends.cudnn as cudnn
import os
import sys
import argparse
import time
from src_code.lmdbdataset import lmdbDataset
from src_code import Network_FT
parser = argparse.ArgumentParser(description='PyTorch Digital Mammography Training')
parser.add_argument('--batch_size', default=100, type=int... | 4,037 | 31.564516 | 106 | py |
AutoPruner | AutoPruner-master/ResNet50/50/fine_tune_again.py | import argparse
import os
import shutil
import time
import sys
import torch
import torch.nn as nn
import torch.nn.parallel
import torch.backends.cudnn as cudnn
import torch.distributed as dist
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
from torchvision import datasets, transforms
fro... | 10,815 | 35.789116 | 106 | py |
AutoPruner | AutoPruner-master/ResNet50/50/tools/create_lmdb.py | # loads imagenet and writes it into one massive binary file
import os
import numpy as np
from tensorpack.dataflow import *
import sys
if __name__ == '__main__':
if len(sys.argv) < 4 :
print("Usage: python create_lmdb.py gt_file.txt root_folder target_lmdb_name")
print("gt_file.txt split by \"\t\"")... | 1,514 | 35.95122 | 86 | py |
AutoPruner | AutoPruner-master/ResNet50/50/tools/organize_dataset.py | """
this script is used for organize CUB200 dataset:
-- images
-- train
-- [:class 0]
-- [:class 1]
...
-- [:class n]
-- val
-- [:class 0]
-- [:class 1]
...
-- [:class n]
"""
import os
import PIL.Image
import numpy as np
import shutil
ori... | 2,295 | 31.8 | 82 | py |
AutoPruner | AutoPruner-master/ResNet50/50/src_code/my_op_fc.py | import torch
from torch.autograd import Variable
import torch.nn as nn
from torch.autograd import gradcheck
import numpy as np
class MyGAP_fc(torch.autograd.Function):
'''
Global Average Pooling with batchsize: N*4096 -> 1*4096
'''
@staticmethod
def forward(ctx, input):
ctx.save_for_backw... | 2,729 | 31.117647 | 76 | py |
AutoPruner | AutoPruner-master/ResNet50/50/src_code/my_op.py | import torch
from torch.autograd import Variable
import torch.nn as nn
from torch.autograd import gradcheck
import numpy as np
import math
class MyGAP(torch.autograd.Function):
'''
Global Average Pooling with batchsize: N*512*14*14 -> 1*512*14*14
'''
@staticmethod
def forward(ctx, input):
... | 3,182 | 33.225806 | 93 | py |
AutoPruner | AutoPruner-master/ResNet50/50/src_code/Network_FT.py | import torch.nn as nn
import math
import torch
from . import my_op
class Bottleneck(nn.Module):
expansion = 4
def __init__(self, number_list, stride=1, downsample=None):
super(Bottleneck, self).__init__()
self.conv1 = nn.Conv2d(number_list[1], number_list[0], kernel_size=1, bias=False)
... | 12,489 | 37.549383 | 111 | py |
AutoPruner | AutoPruner-master/ResNet50/50/src_code/lmdbdataset.py | import cv2
import numpy as np
import torchvision.transforms as transforms
import lmdb
import msgpack
from torch.utils.data import Dataset
from PIL import Image
class lmdbDataset(Dataset):
def __init__(self, location, is_train):
self.env = lmdb.open(location, subdir=False, max_readers=1, readonly=True, loc... | 2,431 | 34.246377 | 111 | py |
AutoPruner | AutoPruner-master/ResNet50/50/compress_model/new_model.py | import torch.nn as nn
import torch
import numpy as np
class Bottleneck(nn.Module):
expansion = 4
def __init__(self, number_list, stride=1, downsample=None):
super(Bottleneck, self).__init__()
self.conv1 = nn.Conv2d(number_list[1], number_list[0], kernel_size=1, bias=False)
self.bn1 = ... | 8,768 | 35.235537 | 95 | py |
AutoPruner | AutoPruner-master/ResNet50/50/compress_model/compress_model.py | import torch
from new_model import NetworkNew
import argparse
import torch.backends.cudnn as cudnn
parser = argparse.ArgumentParser(description='PyTorch Digital Mammography Training')
parser.add_argument('--group_id', default=0, type=int, help='the id of compressed layer, starting from 0')
args = parser.parse_args()
... | 786 | 28.148148 | 106 | py |
AutoPruner | AutoPruner-master/ResNet50/50/compress_model/evaluate_net.py | import torch
from new_model import NetworkNew_test
import argparse
import torch.backends.cudnn as cudnn
import os
import sys
import time
sys.path.append('../')
from src_code.lmdbdataset import lmdbDataset
parser = argparse.ArgumentParser(description='PyTorch Digital Mammography Training')
parser.add_argument('--batch_... | 3,966 | 31.516393 | 106 | py |
AutoPruner | AutoPruner-master/ResNet50/30/fine_tune_compressed_model.py | import argparse
import os
import shutil
import time
import sys
import torch
import torch.nn as nn
import torch.nn.parallel
import torch.backends.cudnn as cudnn
import torch.distributed as dist
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
from torchvision import datasets, transforms
fro... | 10,872 | 35.733108 | 106 | py |
AutoPruner | AutoPruner-master/ResNet50/30/main.py | # ************************************************************
# Author : Bumsoo Kim, 2017
# Github : https://github.com/meliketoy/fine-tuning.pytorch
#
# Korea University, Data-Mining Lab
# Deep Convolutional Network Fine tuning Implementation
#
# Description : main.py
# The main code for training classification netwo... | 14,760 | 42.160819 | 146 | py |
AutoPruner | AutoPruner-master/ResNet50/30/evaluate_network.py | import torch
import torch.backends.cudnn as cudnn
import os
import sys
import argparse
import time
from src_code.lmdbdataset import lmdbDataset
from src_code import Network_FT
parser = argparse.ArgumentParser(description='PyTorch Digital Mammography Training')
parser.add_argument('--batch_size', default=100, type=int... | 4,037 | 31.564516 | 106 | py |
AutoPruner | AutoPruner-master/ResNet50/30/fine_tune_again.py | import argparse
import os
import shutil
import time
import sys
import torch
import torch.nn as nn
import torch.nn.parallel
import torch.backends.cudnn as cudnn
import torch.distributed as dist
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
from torchvision import datasets, transforms
fro... | 10,814 | 35.785714 | 106 | py |
AutoPruner | AutoPruner-master/ResNet50/30/tools/create_lmdb.py | # loads imagenet and writes it into one massive binary file
import os
import numpy as np
from tensorpack.dataflow import *
import sys
if __name__ == '__main__':
if len(sys.argv) < 4 :
print("Usage: python create_lmdb.py gt_file.txt root_folder target_lmdb_name")
print("gt_file.txt split by \"\t\"")... | 1,514 | 35.95122 | 86 | py |
AutoPruner | AutoPruner-master/ResNet50/30/tools/organize_dataset.py | """
this script is used for organize CUB200 dataset:
-- images
-- train
-- [:class 0]
-- [:class 1]
...
-- [:class n]
-- val
-- [:class 0]
-- [:class 1]
...
-- [:class n]
"""
import os
import PIL.Image
import numpy as np
import shutil
ori... | 2,295 | 31.8 | 82 | py |
AutoPruner | AutoPruner-master/ResNet50/30/src_code/my_op_fc.py | import torch
from torch.autograd import Variable
import torch.nn as nn
from torch.autograd import gradcheck
import numpy as np
class MyGAP_fc(torch.autograd.Function):
'''
Global Average Pooling with batchsize: N*4096 -> 1*4096
'''
@staticmethod
def forward(ctx, input):
ctx.save_for_backw... | 2,729 | 31.117647 | 76 | py |
AutoPruner | AutoPruner-master/ResNet50/30/src_code/my_op.py | import torch
from torch.autograd import Variable
import torch.nn as nn
from torch.autograd import gradcheck
import numpy as np
import math
class MyGAP(torch.autograd.Function):
'''
Global Average Pooling with batchsize: N*512*14*14 -> 1*512*14*14
'''
@staticmethod
def forward(ctx, input):
... | 3,182 | 33.225806 | 93 | py |
AutoPruner | AutoPruner-master/ResNet50/30/src_code/Network_FT.py | import torch.nn as nn
import math
import torch
from . import my_op
class Bottleneck(nn.Module):
expansion = 4
def __init__(self, number_list, stride=1, downsample=None):
super(Bottleneck, self).__init__()
self.conv1 = nn.Conv2d(number_list[1], number_list[0], kernel_size=1, bias=False)
... | 12,489 | 37.549383 | 111 | py |
AutoPruner | AutoPruner-master/ResNet50/30/src_code/lmdbdataset.py | import cv2
import numpy as np
import torchvision.transforms as transforms
import lmdb
import msgpack
from torch.utils.data import Dataset
from PIL import Image
class lmdbDataset(Dataset):
def __init__(self, location, is_train):
self.env = lmdb.open(location, subdir=False, max_readers=1, readonly=True, loc... | 2,431 | 34.246377 | 111 | py |
AutoPruner | AutoPruner-master/ResNet50/30/compress_model/new_model.py | import torch.nn as nn
import torch
import numpy as np
class Bottleneck(nn.Module):
expansion = 4
def __init__(self, number_list, stride=1, downsample=None):
super(Bottleneck, self).__init__()
self.conv1 = nn.Conv2d(number_list[1], number_list[0], kernel_size=1, bias=False)
self.bn1 = ... | 8,767 | 35.381743 | 95 | py |
AutoPruner | AutoPruner-master/ResNet50/30/compress_model/compress_model.py | import torch
from new_model import NetworkNew
import argparse
import torch.backends.cudnn as cudnn
parser = argparse.ArgumentParser(description='PyTorch Digital Mammography Training')
parser.add_argument('--group_id', default=3, type=int, help='the id of compressed layer, starting from 0')
args = parser.parse_args()
... | 786 | 28.148148 | 106 | py |
AutoPruner | AutoPruner-master/ResNet50/30/compress_model/evaluate_net.py | import torch
from new_model import NetworkNew_test
import argparse
import torch.backends.cudnn as cudnn
import os
import sys
import time
sys.path.append('../')
from src_code.lmdbdataset import lmdbDataset
parser = argparse.ArgumentParser(description='PyTorch Digital Mammography Training')
parser.add_argument('--batch_... | 3,968 | 31.532787 | 106 | py |
AutoPruner | AutoPruner-master/vgg16/50/fine_tune_compressed_model.py | import argparse
import os
import shutil
import time
import sys
import torch
import torch.nn as nn
import torch.nn.parallel
import torch.backends.cudnn as cudnn
import torch.distributed as dist
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
from torchvision import datasets, transforms
fro... | 10,897 | 35.693603 | 106 | py |
AutoPruner | AutoPruner-master/vgg16/50/main.py | # ************************************************************
# Author : Bumsoo Kim, 2017
# Github : https://github.com/meliketoy/fine-tuning.pytorch
#
# Korea University, Data-Mining Lab
# Deep Convolutional Network Fine tuning Implementation
#
# Description : main.py
# The main code for training classification netwo... | 12,807 | 38.409231 | 126 | py |
AutoPruner | AutoPruner-master/vgg16/50/evaluate_network.py | import torch
import torch.backends.cudnn as cudnn
import os
import sys
import argparse
import time
from src_code.lmdbdataset import lmdbDataset
from src_code import Network_FT
parser = argparse.ArgumentParser(description='PyTorch Digital Mammography Training')
parser.add_argument('--batch_size', default=100, type=int... | 4,055 | 31.709677 | 107 | py |
AutoPruner | AutoPruner-master/vgg16/50/mytest.py | # ************************************************************
# Author : Bumsoo Kim, 2017
# Github : https://github.com/meliketoy/fine-tuning.pytorch
#
# Korea University, Data-Mining Lab
# Deep Convolutional Network Fine tuning Implementation
#
# Description : main.py
# The main code for training classification netwo... | 11,382 | 37.849829 | 120 | py |
AutoPruner | AutoPruner-master/vgg16/50/fine_tune_vgg16.py | import argparse
import os
import shutil
import time
import sys
import torch
import torch.nn as nn
import torch.nn.parallel
import torch.backends.cudnn as cudnn
import torch.distributed as dist
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
from torchvision import datasets, models, transf... | 11,358 | 35.760518 | 106 | py |
AutoPruner | AutoPruner-master/vgg16/50/fine_tune_GAP.py | import argparse
import os
import shutil
import time
import sys
import torch
import torch.nn as nn
import torch.nn.parallel
import torch.backends.cudnn as cudnn
import torch.distributed as dist
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
from torchvision import datasets, transforms
fro... | 10,791 | 35.707483 | 106 | py |
AutoPruner | AutoPruner-master/vgg16/50/tools/create_lmdb.py | # loads imagenet and writes it into one massive binary file
import os
import numpy as np
from tensorpack.dataflow import *
import sys
if __name__ == '__main__':
if len(sys.argv) < 4 :
print("Usage: python create_lmdb.py gt_file.txt root_folder target_lmdb_name")
print("gt_file.txt split by \"\t\"")... | 1,514 | 35.95122 | 86 | py |
AutoPruner | AutoPruner-master/vgg16/50/tools/organize_dataset.py | """
this script is used for organize CUB200 dataset:
-- images
-- train
-- [:class 0]
-- [:class 1]
...
-- [:class n]
-- val
-- [:class 0]
-- [:class 1]
...
-- [:class n]
"""
import os
import PIL.Image
import numpy as np
import shutil
ori... | 2,295 | 31.8 | 82 | py |
AutoPruner | AutoPruner-master/vgg16/50/src_code/my_op_fc.py | import torch
from torch.autograd import Variable
import torch.nn as nn
from torch.autograd import gradcheck
import numpy as np
class MyGAP_fc(torch.autograd.Function):
'''
Global Average Pooling with batchsize: N*4096 -> 1*4096
'''
@staticmethod
def forward(ctx, input):
ctx.save_for_backw... | 2,729 | 31.117647 | 76 | py |
AutoPruner | AutoPruner-master/vgg16/50/src_code/my_op.py | import torch
from torch.autograd import Variable
import torch.nn as nn
from torch.autograd import gradcheck
import math
class MyGAP(torch.autograd.Function):
'''
Global Average Pooling with batchsize: N*512*14*14 -> 1*512*14*14
'''
@staticmethod
def forward(ctx, input):
ctx.save_for_backw... | 3,318 | 33.572917 | 93 | py |
AutoPruner | AutoPruner-master/vgg16/50/src_code/Network_FT.py | import torch
from . import my_op
from torch import nn
class NetworkNew(torch.nn.Module):
def __init__(self, layer_id=0):
torch.nn.Module.__init__(self)
model_weight = torch.load('checkpoint/model.pth')
channel_length = list()
channel_length.append(3)
for k, v in model_weigh... | 8,144 | 49.590062 | 119 | py |
AutoPruner | AutoPruner-master/vgg16/50/src_code/lmdbdataset.py | import cv2
import numpy as np
import torchvision.transforms as transforms
import lmdb
import msgpack
from torch.utils.data import Dataset
from PIL import Image
class lmdbDataset(Dataset):
def __init__(self, location, is_train):
self.env = lmdb.open(location, subdir=False, max_readers=1, readonly=True, loc... | 2,431 | 34.246377 | 111 | py |
AutoPruner | AutoPruner-master/vgg16/50/compress_model/new_model.py | import torch
from torch import nn
import numpy as np
import os
import torch.nn.init as init
class vgg16_compressed(torch.nn.Module):
def __init__(self, layer_id=0, model_path=None):
torch.nn.Module.__init__(self)
model_weight = torch.load(model_path + 'model.pth')
channel_index = torch.loa... | 10,696 | 43.570833 | 119 | py |
AutoPruner | AutoPruner-master/vgg16/50/compress_model/compress_model.py | import torch
from new_model import vgg16_compressed
import argparse
import torch.backends.cudnn as cudnn
parser = argparse.ArgumentParser(description='PyTorch Digital Mammography Training')
parser.add_argument('--layer_id', default=2, type=int, help='the id of compressed layer, starting from 0')
args = parser.parse_a... | 908 | 31.464286 | 106 | py |
AutoPruner | AutoPruner-master/vgg16/50/compress_model/evaluate_net.py | import torch
from new_model import vgg16_compressed, vgg16_test
import argparse
import torch.backends.cudnn as cudnn
import os
import sys
import time
sys.path.append('../')
from src_code.lmdbdataset import lmdbDataset
parser = argparse.ArgumentParser(description='PyTorch Digital Mammography Training')
parser.add_argum... | 4,451 | 31.977778 | 106 | py |
AutoPruner | AutoPruner-master/MobileNetv2/released_model/evaluate.py | import torch
import torch.nn as nn
import torch.backends.cudnn as cudnn
import time
import os
import sys
import argparse
from torchvision import datasets, transforms
import mobilenetv2
from torchsummaryX import summary
parser = argparse.ArgumentParser(description='PyTorch Digital Mammography Training')
parser.add_argu... | 5,806 | 32.761628 | 116 | py |
AutoPruner | AutoPruner-master/MobileNetv2/released_model/mobilenetv2.py | """
Creates a MobileNetV2 Model as defined in:
Mark Sandler, Andrew Howard, Menglong Zhu, Andrey Zhmoginov, Liang-Chieh Chen. (2018).
MobileNetV2: Inverted Residuals and Linear Bottlenecks
arXiv preprint arXiv:1801.04381.
import from https://github.com/tonylins/pytorch-mobilenet-v2
"""
import torch.nn as nn
import mat... | 5,774 | 32.77193 | 120 | py |
AutoPruner | AutoPruner-master/MobileNetv2/2_fine_tune/main.py | import torch
import torch.nn as nn
import torch.backends.cudnn as cudnn
import time
import os
import sys
import argparse
from torchvision import datasets, transforms
from src_code import mobilenetv2
from torchsummaryX import summary
from math import cos, pi
parser = argparse.ArgumentParser(description='PyTorch Digital... | 9,434 | 33.944444 | 119 | py |
AutoPruner | AutoPruner-master/MobileNetv2/2_fine_tune/src_code/mobilenetv2.py | """
Creates a MobileNetV2 Model as defined in:
Mark Sandler, Andrew Howard, Menglong Zhu, Andrey Zhmoginov, Liang-Chieh Chen. (2018).
MobileNetV2: Inverted Residuals and Linear Bottlenecks
arXiv preprint arXiv:1801.04381.
import from https://github.com/tonylins/pytorch-mobilenet-v2
"""
import torch.nn as nn
import to... | 6,370 | 32.356021 | 120 | py |
AutoPruner | AutoPruner-master/MobileNetv2/2_fine_tune/src_code/Network_FT.py | import torch
from torch import nn
import numpy as np
class VGG16(torch.nn.Module):
def __init__(self, model_path):
torch.nn.Module.__init__(self)
self.maxpool = nn.MaxPool2d(kernel_size=2, stride=2)
self.ReLU = nn.ReLU(inplace=True)
# load channel index
f = open('../1_prun... | 4,591 | 38.247863 | 108 | py |
AutoPruner | AutoPruner-master/MobileNetv2/1_pruning/main.py | import torch
import torch.nn as nn
import torch.backends.cudnn as cudnn
import time
import os
import sys
import argparse
import numpy as np
import shutil
from torchvision import datasets, transforms
from src_code import mobilenetv2
parser = argparse.ArgumentParser(description='PyTorch Digital Mammography Training')
pa... | 11,003 | 35.68 | 144 | py |
AutoPruner | AutoPruner-master/MobileNetv2/1_pruning/evaluate.py | import torch
import torch.nn as nn
import torch.backends.cudnn as cudnn
import time
import os
import sys
import argparse
from torchvision import datasets, transforms
from src_code import mobilenetv2
from torchsummaryX import summary
parser = argparse.ArgumentParser(description='PyTorch Digital Mammography Training')
p... | 8,623 | 34.489712 | 144 | py |
AutoPruner | AutoPruner-master/MobileNetv2/1_pruning/src_code/mobilenetv2.py | """
Creates a MobileNetV2 Model as defined in:
Mark Sandler, Andrew Howard, Menglong Zhu, Andrey Zhmoginov, Liang-Chieh Chen. (2018).
MobileNetV2: Inverted Residuals and Linear Bottlenecks
arXiv preprint arXiv:1801.04381.
import from https://github.com/tonylins/pytorch-mobilenet-v2
"""
import torch.nn as nn
import ma... | 7,872 | 34.949772 | 122 | py |
AutoPruner | AutoPruner-master/MobileNetv2/1_pruning/src_code/my_op.py | import torch
from torch.autograd import Variable
import torch.nn as nn
from torch.autograd import gradcheck
import math
class MyGAP(torch.autograd.Function):
'''
Global Average Pooling with batchsize: N*512*14*14 -> 1*512*14*14
'''
@staticmethod
def forward(ctx, input):
ctx.save_for_backw... | 3,128 | 34.965517 | 93 | py |
AutoPruner | AutoPruner-master/MobileNetv2/1_pruning/src_code/Network_FT.py | import torch
from . import my_op
from torch import nn
class VGG16(torch.nn.Module):
def __init__(self, model_path):
torch.nn.Module.__init__(self)
self.maxpool = nn.MaxPool2d(kernel_size=2, stride=2)
self.ReLU = nn.ReLU(inplace=True)
# add feature layers
self.conv1_1 = nn.... | 4,519 | 50.363636 | 98 | py |
AutoPruner | AutoPruner-master/MobileNetv2/1_pruning/compress_model/new_model.py | import torch
from torch import nn
import numpy as np
import os
import torch.nn.init as init
class vgg16_compressed(torch.nn.Module):
def __init__(self, layer_id=0, model_path=None):
torch.nn.Module.__init__(self)
model_weight = torch.load(model_path + 'model.pth')
channel_index = torch.loa... | 10,696 | 43.570833 | 119 | py |
AutoPruner | AutoPruner-master/MobileNetv2/1_pruning/compress_model/compress_model.py | import torch
from new_model import vgg16_compressed
import argparse
import torch.backends.cudnn as cudnn
parser = argparse.ArgumentParser(description='PyTorch Digital Mammography Training')
parser.add_argument('--layer_id', default=2, type=int, help='the id of compressed layer, starting from 0')
args = parser.parse_a... | 908 | 31.464286 | 106 | py |
AutoPruner | AutoPruner-master/MobileNetv2/1_pruning/compress_model/evaluate_net.py | import torch
from new_model import vgg16_compressed, vgg16_test
import argparse
import torch.backends.cudnn as cudnn
import os
import sys
import time
sys.path.append('../')
from src_code.lmdbdataset import lmdbDataset
parser = argparse.ArgumentParser(description='PyTorch Digital Mammography Training')
parser.add_argum... | 4,451 | 31.977778 | 106 | py |
noise2noise | noise2noise-master/train_mri.py | # Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
#
# This work is licensed under the Creative Commons Attribution-NonCommercial
# 4.0 International License. To view a copy of this license, visit
# http://creativecommons.org/licenses/by-nc/4.0/ or send a letter to
# Creative Commons, PO Box 1866, Mountain ... | 19,442 | 39.422037 | 282 | py |
noise2noise | noise2noise-master/config_mri.py | # Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
#
# This work is licensed under the Creative Commons Attribution-NonCommercial
# 4.0 International License. To view a copy of this license, visit
# http://creativecommons.org/licenses/by-nc/4.0/ or send a letter to
# Creative Commons, PO Box 1866, Mountain ... | 3,947 | 40.125 | 134 | py |
noise2noise | noise2noise-master/validation.py | # Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
#
# This work is licensed under the Creative Commons Attribution-NonCommercial
# 4.0 International License. To view a copy of this license, visit
# http://creativecommons.org/licenses/by-nc/4.0/ or send a letter to
# Creative Commons, PO Box 1866, Mountain ... | 3,678 | 36.540816 | 114 | py |
noise2noise | noise2noise-master/network.py | # Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
#
# This work is licensed under the Creative Commons Attribution-NonCommercial
# 4.0 International License. To view a copy of this license, visit
# http://creativecommons.org/licenses/by-nc/4.0/ or send a letter to
# Creative Commons, PO Box 1866, Mountain... | 3,609 | 30.391304 | 97 | py |
noise2noise | noise2noise-master/dataset.py | # Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
#
# This work is licensed under the Creative Commons Attribution-NonCommercial
# 4.0 International License. To view a copy of this license, visit
# http://creativecommons.org/licenses/by-nc/4.0/ or send a letter to
# Creative Commons, PO Box 1866, Mountain ... | 2,009 | 35.545455 | 128 | py |
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