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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CorgiPile-PyTorch | CorgiPile-PyTorch-main/nlpformat/in_mem_sliding_window/__init__.py | from nlpformat.in_mem_sliding_window import dataset
from nlpformat.in_mem_sliding_window.dataset import InMemSlidingWindowDocDataset
from nlpformat.in_mem_sliding_window.dataset import InMemSlidingWindowSentDataset
| 217 | 35.333333 | 81 | py |
CorgiPile-PyTorch | CorgiPile-PyTorch-main/nlpformat/in_mem_block/block_iterator_utils.py | """Iterator utils."""
from __future__ import division
import typing
import warnings
import random
import datetime
import numpy as np
def cycle(iterator_fn: typing.Callable) -> typing.Iterable[typing.Any]:
"""Create a repeating iterator from an iterator generator."""
while True:
for element in itera... | 2,101 | 24.02381 | 78 | py |
CorgiPile-PyTorch | CorgiPile-PyTorch-main/nlpformat/in_mem_block/__init__.py | from nlpformat.in_mem_block import block_dataset
from nlpformat.in_mem_block import block_iterator_utils
from nlpformat.in_mem_block.block_dataset import InMemBlockDocDataset
from nlpformat.in_mem_block.block_dataset import InMemBlockSentDataset
| 247 | 40.333333 | 70 | py |
CorgiPile-PyTorch | CorgiPile-PyTorch-main/nlpformat/in_mem_block/block_dataset.py | """Load tfrecord files into torch datasets."""
import typing
import numpy as np
import datetime
import random
import time
import os
import torch.utils.data
from nlpformat.loader import nlp_format_dataloader
from nlpformat.in_mem_block import block_iterator_utils
class InMemBlockDocDataset(torch.utils.data.Iterable... | 7,594 | 33.522727 | 159 | py |
CorgiPile-PyTorch | CorgiPile-PyTorch-main/nlpformat/in_mem_bismarck/dataset.py | """Load tfrecord files into torch datasets."""
import numpy as np
import os
import pickle
import time
import torch.utils.data
from nlpformat.loader import nlp_format_dataloader
from nlpformat.in_mem_bismarck import iterator_utils
class InMemBismarckDocDataset(torch.utils.data.IterableDataset):
def __init__(sel... | 9,240 | 36.718367 | 159 | py |
CorgiPile-PyTorch | CorgiPile-PyTorch-main/nlpformat/in_mem_bismarck/iterator_utils.py | """Iterator utils."""
from __future__ import division
import typing
import warnings
import random
import io
import pickle
import numpy as np
def cycle(iterator_fn: typing.Callable) -> typing.Iterable[typing.Any]:
"""Create a repeating iterator from an iterator generator."""
while True:
for element ... | 16,662 | 37.841492 | 120 | py |
CorgiPile-PyTorch | CorgiPile-PyTorch-main/nlpformat/in_mem_bismarck/__init__.py | from nlpformat.in_mem_bismarck import dataset
from nlpformat.in_mem_bismarck import iterator_utils
from nlpformat.in_mem_bismarck.dataset import InMemBismarckDocDataset
from nlpformat.in_mem_bismarck.dataset import InMemBismarckSentDataset
| 241 | 39.333333 | 70 | py |
CorgiPile-PyTorch | CorgiPile-PyTorch-main/nlpformat/in_mem_once_fully_shuffle/dataset.py | import numpy as np
import random
import time
import os
import torch.utils.data
from nlpformat.loader import nlp_format_dataloader
class InMemOnceFullyShuffleDocDataset(torch.utils.data.Dataset):
def __init__(self,
data_folder: str, split: str,
use_clustered_data: bool
... | 3,641 | 33.685714 | 157 | py |
CorgiPile-PyTorch | CorgiPile-PyTorch-main/nlpformat/in_mem_once_fully_shuffle/__init__.py | from nlpformat.in_mem_once_fully_shuffle import dataset
from nlpformat.in_mem_once_fully_shuffle.dataset import InMemOnceFullyShuffleDocDataset
from nlpformat.in_mem_once_fully_shuffle.dataset import InMemOnceFullyShuffleSentDataset
| 234 | 46 | 88 | py |
CorgiPile-PyTorch | CorgiPile-PyTorch-main/nlpformat/loader/nlp_format_dataloader.py | """Reader utils"""
import time
def get_current_time() :
return time.strftime("%Y-%m-%d %H:%M:%S", time.localtime())
| 123 | 14.5 | 63 | py |
CorgiPile-PyTorch | CorgiPile-PyTorch-main/nlpformat/loader/__init__.py | from nlpformat.loader import nlp_format_dataloader | 50 | 50 | 50 | py |
CorgiPile-PyTorch | CorgiPile-PyTorch-main/nlpformat/in_mem_block_only/block_only_dataset.py | """Load tfrecord files into torch datasets."""
import numpy as np
import random
import time
import os
import torch.utils.data
from nlpformat.loader import nlp_format_dataloader
from nlpformat.in_mem_block_only import block_only_iterator_utils
class InMemBlockOnlyDocDataset(torch.utils.data.IterableDataset):
... | 7,336 | 34.965686 | 159 | py |
CorgiPile-PyTorch | CorgiPile-PyTorch-main/nlpformat/in_mem_block_only/__init__.py | from nlpformat.in_mem_block_only import block_only_dataset
from nlpformat.in_mem_block_only import block_only_iterator_utils
from nlpformat.in_mem_block_only.block_only_dataset import InMemBlockOnlyDocDataset
from nlpformat.in_mem_block_only.block_only_dataset import InMemBlockOnlySentDataset | 294 | 58 | 84 | py |
CorgiPile-PyTorch | CorgiPile-PyTorch-main/nlpformat/in_mem_block_only/block_only_iterator_utils.py | """Iterator utils."""
from __future__ import division
import typing
import warnings
import random
import datetime
import numpy as np
def cycle(iterator_fn: typing.Callable) -> typing.Iterable[typing.Any]:
"""Create a repeating iterator from an iterator generator."""
while True:
for element in itera... | 2,067 | 23.915663 | 78 | py |
CorgiPile-PyTorch | CorgiPile-PyTorch-main/nlpformat/in_mem_no_shuffle/dataset.py | import numpy as np
import random
import time
import torch.utils.data
from nlpformat.loader import nlp_format_dataloader
"""
Load data from manually preprocessed data (see ``datasets/prepocess/``).
"""
import os
from typing import Tuple
import torch
from torch.utils.data import Dataset
class InMemNoShuffleDocDat... | 4,597 | 32.562044 | 168 | py |
CorgiPile-PyTorch | CorgiPile-PyTorch-main/nlpformat/in_mem_no_shuffle/__init__.py | from nlpformat.in_mem_no_shuffle.dataset import InMemNoShuffleDocDataset
from nlpformat.in_mem_no_shuffle.dataset import InMemNoShuffleSentDataset
| 148 | 36.25 | 73 | py |
CorgiPile-PyTorch | CorgiPile-PyTorch-main/cifarformat/__init__.py | from cifarformat import in_mem_block
from cifarformat import in_mem_block_only
from cifarformat import in_mem_sliding_window
from cifarformat import in_mem_bismarck
from cifarformat import in_mem_once_fully_shuffle
from cifarformat import in_mem_always_fully_shuffle
from cifarformat import in_mem_no_shuffle
from cifarf... | 339 | 41.5 | 51 | py |
CorgiPile-PyTorch | CorgiPile-PyTorch-main/cifarformat/in_mem_sliding_window/dataset.py | import numpy as np
import warnings
import random
import time
import torch.utils.data
from cifarformat.loader import cifar_format_dataloader
class InMemSlidingWindowCifarDataset(torch.utils.data.IterableDataset):
def __init__(self,
base_dir: str,
use_clustered_data: bool,
... | 2,903 | 30.565217 | 126 | py |
CorgiPile-PyTorch | CorgiPile-PyTorch-main/cifarformat/in_mem_sliding_window/__init__.py | from cifarformat.in_mem_sliding_window import dataset
from cifarformat.in_mem_sliding_window.dataset import InMemSlidingWindowCifarDataset
| 141 | 27.4 | 84 | py |
CorgiPile-PyTorch | CorgiPile-PyTorch-main/cifarformat/in_mem_block/block_iterator_utils.py | """Iterator utils."""
from __future__ import division
import typing
import warnings
import random
import datetime
import numpy as np
def cycle(iterator_fn: typing.Callable) -> typing.Iterable[typing.Any]:
"""Create a repeating iterator from an iterator generator."""
while True:
for element in itera... | 2,122 | 24.27381 | 78 | py |
CorgiPile-PyTorch | CorgiPile-PyTorch-main/cifarformat/in_mem_block/__init__.py | from cifarformat.in_mem_block import block_dataset
from cifarformat.in_mem_block import block_iterator_utils
from cifarformat.in_mem_block.block_dataset import InMemBlockCifarDataset
| 184 | 36 | 73 | py |
CorgiPile-PyTorch | CorgiPile-PyTorch-main/cifarformat/in_mem_block/block_dataset.py | """Load tfrecord files into torch datasets."""
import typing
import numpy as np
import datetime
import random
import time
import torch.utils.data
from cifarformat.loader import cifar_format_dataloader
from cifarformat.in_mem_block import block_iterator_utils
class InMemBlockCifarDataset(torch.utils.data.IterableDa... | 3,726 | 32.276786 | 126 | py |
CorgiPile-PyTorch | CorgiPile-PyTorch-main/cifarformat/in_mem_bismarck/dataset.py | """Load tfrecord files into torch datasets."""
import numpy as np
import os
import pickle
import time
import torch.utils.data
from cifarformat.loader import cifar_format_dataloader
from cifarformat.in_mem_bismarck import iterator_utils
class InMemBismarckCifarDataset(torch.utils.data.IterableDataset):
def __in... | 4,460 | 35.867769 | 126 | py |
CorgiPile-PyTorch | CorgiPile-PyTorch-main/cifarformat/in_mem_bismarck/iterator_utils.py | """Iterator utils."""
from __future__ import division
import typing
import warnings
import random
import io
import pickle
import numpy as np
def cycle(iterator_fn: typing.Callable) -> typing.Iterable[typing.Any]:
"""Create a repeating iterator from an iterator generator."""
while True:
for element ... | 3,930 | 33.182609 | 120 | py |
CorgiPile-PyTorch | CorgiPile-PyTorch-main/cifarformat/in_mem_bismarck/__init__.py | from cifarformat.in_mem_bismarck import dataset
from cifarformat.in_mem_bismarck import iterator_utils
from cifarformat.in_mem_bismarck.dataset import InMemBismarckCifarDataset
| 179 | 29 | 73 | py |
CorgiPile-PyTorch | CorgiPile-PyTorch-main/cifarformat/in_mem_once_fully_shuffle/dataset.py | import numpy as np
import random
import time
import torch.utils.data
from cifarformat.loader import cifar_format_dataloader
class InMemOnceFullyShuffleCifarDataset(torch.utils.data.Dataset):
def __init__(self,
base_dir: str,
use_clustered_data: bool,
train... | 2,060 | 29.761194 | 126 | py |
CorgiPile-PyTorch | CorgiPile-PyTorch-main/cifarformat/in_mem_once_fully_shuffle/__init__.py | from cifarformat.in_mem_once_fully_shuffle import dataset
from cifarformat.in_mem_once_fully_shuffle.dataset import InMemOnceFullyShuffleCifarDataset
| 150 | 49.333333 | 91 | py |
CorgiPile-PyTorch | CorgiPile-PyTorch-main/cifarformat/in_mem_always_fully_shuffle/dataset.py | import numpy as np
import random
import time
import torch.utils.data
from cifarformat.loader import cifar_format_dataloader
class InMemAlwaysFullyShuffleCifarDataset(torch.utils.data.Dataset):
def __init__(self,
base_dir: str,
use_clustered_data: bool,
tra... | 2,096 | 29.838235 | 126 | py |
CorgiPile-PyTorch | CorgiPile-PyTorch-main/cifarformat/in_mem_always_fully_shuffle/__init__.py | from cifarformat.in_mem_always_fully_shuffle import dataset
from cifarformat.in_mem_always_fully_shuffle.dataset import InMemAlwaysFullyShuffleCifarDataset
| 156 | 51.333333 | 95 | py |
CorgiPile-PyTorch | CorgiPile-PyTorch-main/cifarformat/loader/cifar_format_dataloader.py | """Reader utils"""
import os
import time
import numpy as np
import torchvision
import random
class MY_CIFAR10(torchvision.datasets.CIFAR10):
def __init__(self, root, train=True, use_clustered_data=True):
super(MY_CIFAR10, self).__init__(root, train=train, transform=None,
... | 1,059 | 29.285714 | 96 | py |
CorgiPile-PyTorch | CorgiPile-PyTorch-main/cifarformat/loader/__init__.py | from cifarformat.loader import cifar_format_dataloader | 54 | 54 | 54 | py |
CorgiPile-PyTorch | CorgiPile-PyTorch-main/cifarformat/in_mem_block_only/block_only_dataset.py | """Load tfrecord files into torch datasets."""
import typing
import numpy as np
import datetime
import random
import time
import torch.utils.data
from cifarformat.loader import cifar_format_dataloader
from cifarformat.in_mem_block_only import block_only_iterator_utils
class InMemBlockOnlyCifarDataset(torch.utils.d... | 3,748 | 32.473214 | 126 | py |
CorgiPile-PyTorch | CorgiPile-PyTorch-main/cifarformat/in_mem_block_only/__init__.py | from cifarformat.in_mem_block_only import block_only_dataset
from cifarformat.in_mem_block_only import block_only_iterator_utils
from cifarformat.in_mem_block_only.block_only_dataset import InMemBlockOnlyCifarDataset
| 218 | 42.8 | 87 | py |
CorgiPile-PyTorch | CorgiPile-PyTorch-main/cifarformat/in_mem_block_only/block_only_iterator_utils.py | """Iterator utils."""
from __future__ import division
import typing
import warnings
import random
import datetime
import numpy as np
def cycle(iterator_fn: typing.Callable) -> typing.Iterable[typing.Any]:
"""Create a repeating iterator from an iterator generator."""
while True:
for element in itera... | 2,057 | 24.097561 | 78 | py |
CorgiPile-PyTorch | CorgiPile-PyTorch-main/cifarformat/in_mem_no_shuffle/dataset.py | import numpy as np
import random
import time
import torch.utils.data
from cifarformat.loader import cifar_format_dataloader
class InMemNoShuffleCifarDataset(torch.utils.data.Dataset):
def __init__(self,
base_dir: str,
use_clustered_data: bool,
train=True, transf... | 1,802 | 28.557377 | 126 | py |
CorgiPile-PyTorch | CorgiPile-PyTorch-main/cifarformat/in_mem_no_shuffle/__init__.py | from cifarformat.in_mem_no_shuffle import dataset
from cifarformat.in_mem_no_shuffle.dataset import InMemNoShuffleCifarDataset
| 127 | 41.666667 | 76 | py |
CorgiPile-PyTorch | CorgiPile-PyTorch-main/cifar_dl_bench/cifar_dl_bench_train.py | '''Train CIFAR10 with PyTorch.'''
import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
import torch.backends.cudnn as cudnn
import torchvision
import torchvision.transforms as transforms
import os
import argparse
import sys
import time
import random
sys.path.append("../cifa... | 14,325 | 30.906459 | 170 | py |
CorgiPile-PyTorch | CorgiPile-PyTorch-main/cifar_dl_bench/models/dla.py | '''DLA in PyTorch.
Reference:
Deep Layer Aggregation. https://arxiv.org/abs/1707.06484
'''
import torch
import torch.nn as nn
import torch.nn.functional as F
class BasicBlock(nn.Module):
expansion = 1
def __init__(self, in_planes, planes, stride=1):
super(BasicBlock, self).__init__()
sel... | 4,425 | 31.544118 | 83 | py |
CorgiPile-PyTorch | CorgiPile-PyTorch-main/cifar_dl_bench/models/shufflenetv2.py | '''ShuffleNetV2 in PyTorch.
See the paper "ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design" for more details.
'''
import torch
import torch.nn as nn
import torch.nn.functional as F
class ShuffleBlock(nn.Module):
def __init__(self, groups=2):
super(ShuffleBlock, self).__init__()
... | 5,530 | 32.932515 | 107 | py |
CorgiPile-PyTorch | CorgiPile-PyTorch-main/cifar_dl_bench/models/regnet.py | '''RegNet in PyTorch.
Paper: "Designing Network Design Spaces".
Reference: https://github.com/keras-team/keras-applications/blob/master/keras_applications/efficientnet.py
'''
import torch
import torch.nn as nn
import torch.nn.functional as F
class SE(nn.Module):
'''Squeeze-and-Excitation block.'''
def __in... | 4,548 | 28.160256 | 106 | py |
CorgiPile-PyTorch | CorgiPile-PyTorch-main/cifar_dl_bench/models/efficientnet.py | '''EfficientNet in PyTorch.
Paper: "EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks".
Reference: https://github.com/keras-team/keras-applications/blob/master/keras_applications/efficientnet.py
'''
import torch
import torch.nn as nn
import torch.nn.functional as F
def swish(x):
return x ... | 5,719 | 31.5 | 106 | py |
CorgiPile-PyTorch | CorgiPile-PyTorch-main/cifar_dl_bench/models/pnasnet.py | '''PNASNet in PyTorch.
Paper: Progressive Neural Architecture Search
'''
import torch
import torch.nn as nn
import torch.nn.functional as F
class SepConv(nn.Module):
'''Separable Convolution.'''
def __init__(self, in_planes, out_planes, kernel_size, stride):
super(SepConv, self).__init__()
se... | 4,258 | 32.801587 | 105 | py |
CorgiPile-PyTorch | CorgiPile-PyTorch-main/cifar_dl_bench/models/resnet.py | '''ResNet in PyTorch.
For Pre-activation ResNet, see 'preact_resnet.py'.
Reference:
[1] Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun
Deep Residual Learning for Image Recognition. arXiv:1512.03385
'''
import torch
import torch.nn as nn
import torch.nn.functional as F
class BasicBlock(nn.Module):
expansi... | 4,218 | 30.721805 | 83 | py |
CorgiPile-PyTorch | CorgiPile-PyTorch-main/cifar_dl_bench/models/dla_simple.py | '''Simplified version of DLA in PyTorch.
Note this implementation is not identical to the original paper version.
But it seems works fine.
See dla.py for the original paper version.
Reference:
Deep Layer Aggregation. https://arxiv.org/abs/1707.06484
'''
import torch
import torch.nn as nn
import torch.nn.function... | 4,084 | 30.666667 | 83 | py |
CorgiPile-PyTorch | CorgiPile-PyTorch-main/cifar_dl_bench/models/mobilenetv2.py | '''MobileNetV2 in PyTorch.
See the paper "Inverted Residuals and Linear Bottlenecks:
Mobile Networks for Classification, Detection and Segmentation" for more details.
'''
import torch
import torch.nn as nn
import torch.nn.functional as F
class Block(nn.Module):
'''expand + depthwise + pointwise'''
def __init... | 3,092 | 34.551724 | 114 | py |
CorgiPile-PyTorch | CorgiPile-PyTorch-main/cifar_dl_bench/models/vgg.py | '''VGG11/13/16/19 in Pytorch.'''
import torch
import torch.nn as nn
cfg = {
'VGG11': [64, 'M', 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512, 'M'],
'VGG13': [64, 64, 'M', 128, 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512, 'M'],
'VGG16': [64, 64, 'M', 128, 128, 'M', 256, 256, 256, 'M', 512, 512, 512... | 1,442 | 29.0625 | 117 | py |
CorgiPile-PyTorch | CorgiPile-PyTorch-main/cifar_dl_bench/models/densenet.py | '''DenseNet in PyTorch.'''
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
class Bottleneck(nn.Module):
def __init__(self, in_planes, growth_rate):
super(Bottleneck, self).__init__()
self.bn1 = nn.BatchNorm2d(in_planes)
self.conv1 = nn.Conv2d(in_planes, 4*gr... | 3,542 | 31.805556 | 96 | py |
CorgiPile-PyTorch | CorgiPile-PyTorch-main/cifar_dl_bench/models/preact_resnet.py | '''Pre-activation ResNet in PyTorch.
Reference:
[1] Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun
Identity Mappings in Deep Residual Networks. arXiv:1603.05027
'''
import torch
import torch.nn as nn
import torch.nn.functional as F
class PreActBlock(nn.Module):
'''Pre-activation version of the BasicBlock.... | 4,078 | 33.277311 | 102 | py |
CorgiPile-PyTorch | CorgiPile-PyTorch-main/cifar_dl_bench/models/googlenet.py | '''GoogLeNet with PyTorch.'''
import torch
import torch.nn as nn
import torch.nn.functional as F
class Inception(nn.Module):
def __init__(self, in_planes, n1x1, n3x3red, n3x3, n5x5red, n5x5, pool_planes):
super(Inception, self).__init__()
# 1x1 conv branch
self.b1 = nn.Sequential(
... | 3,221 | 28.833333 | 83 | py |
CorgiPile-PyTorch | CorgiPile-PyTorch-main/cifar_dl_bench/models/resnext.py | '''ResNeXt in PyTorch.
See the paper "Aggregated Residual Transformations for Deep Neural Networks" for more details.
'''
import torch
import torch.nn as nn
import torch.nn.functional as F
class Block(nn.Module):
'''Grouped convolution block.'''
expansion = 2
def __init__(self, in_planes, cardinality=32... | 3,478 | 35.239583 | 129 | py |
CorgiPile-PyTorch | CorgiPile-PyTorch-main/cifar_dl_bench/models/senet.py | '''SENet in PyTorch.
SENet is the winner of ImageNet-2017. The paper is not released yet.
'''
import torch
import torch.nn as nn
import torch.nn.functional as F
class BasicBlock(nn.Module):
def __init__(self, in_planes, planes, stride=1):
super(BasicBlock, self).__init__()
self.conv1 = nn.Conv2d(... | 4,027 | 32.016393 | 102 | py |
CorgiPile-PyTorch | CorgiPile-PyTorch-main/cifar_dl_bench/models/shufflenet.py | '''ShuffleNet in PyTorch.
See the paper "ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices" for more details.
'''
import torch
import torch.nn as nn
import torch.nn.functional as F
class ShuffleBlock(nn.Module):
def __init__(self, groups):
super(ShuffleBlock, self).__init... | 3,542 | 31.209091 | 126 | py |
CorgiPile-PyTorch | CorgiPile-PyTorch-main/cifar_dl_bench/models/lenet.py | '''LeNet in PyTorch.'''
import torch.nn as nn
import torch.nn.functional as F
class LeNet(nn.Module):
def __init__(self):
super(LeNet, self).__init__()
self.conv1 = nn.Conv2d(3, 6, 5)
self.conv2 = nn.Conv2d(6, 16, 5)
self.fc1 = nn.Linear(16*5*5, 120)
self.fc2 = nn.Linear... | 699 | 28.166667 | 43 | py |
CorgiPile-PyTorch | CorgiPile-PyTorch-main/cifar_dl_bench/models/__init__.py | from .vgg import *
from .dpn import *
from .lenet import *
from .senet import *
from .pnasnet import *
from .densenet import *
from .googlenet import *
from .shufflenet import *
from .shufflenetv2 import *
from .resnet import *
from .resnext import *
from .preact_resnet import *
from .mobilenet import *
from .mobilenet... | 428 | 20.45 | 28 | py |
CorgiPile-PyTorch | CorgiPile-PyTorch-main/cifar_dl_bench/models/mobilenet.py | '''MobileNet in PyTorch.
See the paper "MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications"
for more details.
'''
import torch
import torch.nn as nn
import torch.nn.functional as F
class Block(nn.Module):
'''Depthwise conv + Pointwise conv'''
def __init__(self, in_planes, out_... | 2,025 | 31.677419 | 123 | py |
CorgiPile-PyTorch | CorgiPile-PyTorch-main/cifar_dl_bench/models/dpn.py | '''Dual Path Networks in PyTorch.'''
import torch
import torch.nn as nn
import torch.nn.functional as F
class Bottleneck(nn.Module):
def __init__(self, last_planes, in_planes, out_planes, dense_depth, stride, first_layer):
super(Bottleneck, self).__init__()
self.out_planes = out_planes
sel... | 3,562 | 34.989899 | 116 | py |
Network-Analysis-Tool | Network-Analysis-Tool-main/Analysis Tool/Edge_Analysis.py | # -*- coding: utf-8 -*-
"""
Created on Thu May 26 12:29:37 2022
@author: Owner
"""
import math
import numpy as np
import matplotlib.pyplot as plt
from tqdm import tqdm
import pandas as pd
import networkx as nx
node_path = 'C:/Python_Programs/Fai_ER_Project/Example Networks/Neuromuscular Junction/nodes.xls'#excel f... | 24,246 | 53.122768 | 351 | py |
Network-Analysis-Tool | Network-Analysis-Tool-main/Analysis Tool/Node_Analysis.py | # -*- coding: utf-8 -*-
"""
Created on Wed Jul 6 12:27:50 2022
@author: Owner
"""
import math
import numpy as np
import matplotlib.pyplot as plt
from tqdm import tqdm
import pandas as pd
node_path = 'C:/Python_Programs/Fai_ER_Project/Example Networks/Neuromuscular Junction/nodes.xls'#excel file conatining x y coo... | 7,948 | 53.82069 | 203 | py |
Network-Analysis-Tool | Network-Analysis-Tool-main/Peer Revision Code/Edge_Analysis_Unweighted.py | # -*- coding: utf-8 -*-
"""
Created on Thu May 26 12:29:37 2022
@author: Owner
"""
import math
import numpy as np
import matplotlib.pyplot as plt
from tqdm import tqdm
import pandas as pd
import networkx as nx
save_data = True #if true, program will run analysis on network and save MFPT data as excel file to save_d... | 24,719 | 53.32967 | 351 | py |
Network-Analysis-Tool | Network-Analysis-Tool-main/Peer Revision Code/Node_Analysis_Weighted_GMFPT.py | # -*- coding: utf-8 -*-
"""
Created on Wed Jul 6 12:27:50 2022
@author: Owner
"""
import math
import numpy as np
import matplotlib.pyplot as plt
from tqdm import tqdm
import pandas as pd
from scipy.linalg import eig
save_data = True #if true, program will run analysis on network and save MFPT data as excel file to... | 8,841 | 56.79085 | 351 | py |
Network-Analysis-Tool | Network-Analysis-Tool-main/Peer Revision Code/Edge_Analysis_Weighted_GMFPT.py | # -*- coding: utf-8 -*-
"""
Created on Thu May 26 12:29:37 2022
@author: Owner
"""
import math
import numpy as np
import matplotlib.pyplot as plt
from tqdm import tqdm
import pandas as pd
import networkx as nx
from scipy.linalg import eig
save_data = True #if true, program will run analysis on network and save MFP... | 26,225 | 53.410788 | 351 | py |
Network-Analysis-Tool | Network-Analysis-Tool-main/Peer Revision Code/Edge_Time_Series_Generator.py | # -*- coding: utf-8 -*-
"""
Created on Thu May 26 12:29:37 2022
@author: Owner
"""
import math
import numpy as np
import matplotlib.pyplot as plt
from tqdm import tqdm
import pandas as pd
import networkx as nx
import os
save_data = False #if true, program will run analysis on network and save MFPT data as excel fil... | 25,329 | 50.48374 | 352 | py |
Network-Analysis-Tool | Network-Analysis-Tool-main/Peer Revision Code/Node_Analysis_original.py | # -*- coding: utf-8 -*-
"""
Created on Wed Jul 6 12:27:50 2022
@author: Owner
"""
import math
import numpy as np
import matplotlib.pyplot as plt
from tqdm import tqdm
import pandas as pd
save_data = True #if true, program will run analysis on network and save MFPT data as excel file to save_data_path. if False, pro... | 8,423 | 56.69863 | 351 | py |
Network-Analysis-Tool | Network-Analysis-Tool-main/Peer Revision Code/Node_Stationary_Dist.py | # -*- coding: utf-8 -*-
"""
Created on Wed Jul 6 12:27:50 2022
@author: Owner
"""
import math
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
from scipy.linalg import eig
save_data = False #if true, program will run analysis on network and save MFPT data as excel file to save_data_path. if F... | 8,165 | 57.748201 | 352 | py |
Network-Analysis-Tool | Network-Analysis-Tool-main/Peer Revision Code/Critical_Edge_Map.py | # -*- coding: utf-8 -*-
"""
Created on Thu May 26 12:29:37 2022
@author: Owner
"""
import math
import numpy as np
import matplotlib.pyplot as plt
from tqdm import tqdm
import pandas as pd
import networkx as nx
save_data = False #if true, program will run analysis on network and save MFPT data as excel file to save... | 23,925 | 53.253968 | 352 | py |
linux-sgx | linux-sgx-master/external/CppMicroServices/fixcoveragefilepaths.py | # The Cobertura report generated by OpenCooCpverage tool has file paths relative
# to the drive and in case-insensitive format. This script converts the file
# paths to be relative to the build directory and case-sensitive, which is
# required for uploading reports to codecov.io
import xml.etree.ElementTree as xml
imp... | 2,984 | 39.890411 | 98 | py |
linux-sgx | linux-sgx-master/external/CppMicroServices/conf.py | # -*- coding: utf-8 -*-
#
# CppMicroServices documentation build configuration file, created by
# sphinx-quickstart on Mon Nov 28 18:59:28 2016.
#
# This file is execfile()d with the current directory set to its
# containing dir.
#
# Note that not all possible configuration values are present in this
# autogenerated fi... | 13,068 | 30.567633 | 133 | py |
linux-sgx | linux-sgx-master/external/CppMicroServices/doc/cmake.py | # Distributed under the OSI-approved BSD 3-Clause License. See accompanying
# file Copyright.txt or https://cmake.org/licensing for details.
import os
import re
# Monkey patch for pygments reporting an error when generator expressions are
# used.
# https://bitbucket.org/birkenfeld/pygments-main/issue/942/cmake-gener... | 15,086 | 38.807388 | 98 | py |
linux-sgx | linux-sgx-master/linux/installer/common/gen_source/copy_source.py | #!/usr/bin/env python
#
# Copyright (C) 2011-2021 Intel Corporation. All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions
# are met:
#
# * Redistributions of source code must retain the above copyright
# no... | 4,358 | 36.25641 | 117 | py |
linux-sgx | linux-sgx-master/linux/installer/common/gen_source/gen_source.py | #!/usr/bin/env python
#
# Copyright (C) 2011-2021 Intel Corporation. All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions
# are met:
#
# * Redistributions of source code must retain the above copyright
# no... | 7,691 | 33.80543 | 158 | py |
linux-sgx | linux-sgx-master/build-scripts/sgx-asm-pp.py | #!/usr/bin/env python
#
# Copyright (C) 2011-2021 Intel Corporation. All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions
# are met:
#
# * Redistributions of source code must retain the above copyright
# no... | 15,081 | 43.621302 | 183 | py |
linux-sgx | linux-sgx-master/sdk/debugger_interface/linux/gdb-sgx-plugin/printers.py | #===----------------------------------------------------------------------===##
#
# Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
# See https://llvm.org/LICENSE.txt for license information.
# SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
#
#===----------------------------------... | 34,881 | 32.508165 | 86 | py |
linux-sgx | linux-sgx-master/sdk/debugger_interface/linux/gdb-sgx-plugin/load_symbol_cmd.py | #
# Copyright (C) 2011-2021 Intel Corporation. All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions
# are met:
#
# * Redistributions of source code must retain the above copyright
# notice, this list of con... | 5,954 | 43.440299 | 129 | py |
linux-sgx | linux-sgx-master/sdk/debugger_interface/linux/gdb-sgx-plugin/sgx_emmt.py | #
# Copyright (C) 2011-2021 Intel Corporation. All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions
# are met:
#
# * Redistributions of source code must retain the above copyright
# notice, this list of con... | 3,243 | 33.510638 | 76 | py |
linux-sgx | linux-sgx-master/sdk/debugger_interface/linux/gdb-sgx-plugin/readelf.py | #
# Copyright (C) 2011-2021 Intel Corporation. All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions
# are met:
#
# * Redistributions of source code must retain the above copyright
# notice, this list of con... | 2,183 | 42.68 | 71 | py |
linux-sgx | linux-sgx-master/sdk/debugger_interface/linux/gdb-sgx-plugin/gdb_sgx_plugin.py | #
# Copyright (C) 2011-2021 Intel Corporation. All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions
# are met:
#
# * Redistributions of source code must retain the above copyright
# notice, this list of con... | 28,864 | 38.758953 | 192 | py |
SBM-Transformer | SBM-Transformer-main/datasets/retrieval.py | import sys
sys.path.append("./long-range-arena/lra_benchmarks/matching/")
import input_pipeline
import numpy as np
import pickle
train_ds, eval_ds, test_ds, encoder = input_pipeline.get_matching_datasets(
n_devices = 1, task_name = None, data_dir = "./lra_release/lra_release/tsv_data/",
batch_size = 1, fixed_v... | 1,023 | 41.666667 | 103 | py |
SBM-Transformer | SBM-Transformer-main/datasets/pathfinder.py |
import numpy as np
import os
import pickle
import tensorflow as tf
import random
root_dir = "./lra_release/lra_release/"
subdir = "pathfinder32"
for diff_level in ["curv_baseline", "curv_contour_length_9", "curv_contour_length_14"]:
data_dir = os.path.join(root_dir, subdir, diff_level)
metadata_list = [
... | 1,684 | 34.851064 | 97 | py |
SBM-Transformer | SBM-Transformer-main/datasets/text.py | import sys
sys.path.append("./long-range-arena/lra_benchmarks/text_classification/")
import input_pipeline
import numpy as np
import pickle
train_ds, eval_ds, test_ds, encoder = input_pipeline.get_tc_datasets(
n_devices = 1, task_name = "imdb_reviews", data_dir = None,
batch_size = 1, fixed_vocab = None, max_l... | 846 | 37.5 | 102 | py |
SBM-Transformer | SBM-Transformer-main/datasets/cifar10.py | import sys
sys.path.append("./long-range-arena/lra_benchmarks/image/")
import input_pipeline
import numpy as np
import pickle
train_ds, eval_ds, test_ds, num_classes, vocab_size, input_shape = input_pipeline.get_cifar10_datasets(
n_devices = 1, batch_size = 1, normalize = False)
mapping = {"train":train_ds, "dev"... | 818 | 33.125 | 103 | py |
SBM-Transformer | SBM-Transformer-main/datasets/listops.py | import sys
sys.path.append("./long-range-arena/lra_benchmarks/listops/")
import input_pipeline
import numpy as np
import pickle
train_ds, eval_ds, test_ds, encoder = input_pipeline.get_datasets(
n_devices = 1, task_name = "basic", data_dir = "./lra_release/lra_release/listops-1000/",
batch_size = 1, max_length... | 837 | 37.090909 | 102 | py |
SBM-Transformer | SBM-Transformer-main/code/model_wrapper.py | import torch
import torch.nn as nn
import math
from model import Model
def pooling(inp, mode):
if mode == "CLS":
pooled = inp[:, 0, :]
elif mode == "MEAN":
pooled = inp.mean(dim = 1)
else:
raise Exception()
return pooled
def append_cls(inp, mask, vocab_size):
batch_size = i... | 4,761 | 35.630769 | 119 | py |
SBM-Transformer | SBM-Transformer-main/code/attention_sbm.py | import torch
import torch.nn as nn
import math
import json
import torch.nn.functional as F
from torch.utils.checkpoint import checkpoint
from fastRG import fastRG
from STE import *
import time
from dgl.nn.functional import edge_softmax
import dgl.function as fn
import dgl.ops as DF
import dgl
@torch.no_grad()
def blo... | 4,000 | 33.791304 | 139 | py |
SBM-Transformer | SBM-Transformer-main/code/attention_linformer.py | import torch
import torch.nn as nn
import math
class LinformerAttention(nn.Module):
projection_matrix = None
def __init__(self, config):
super().__init__()
self.num_head = config["num_head"]
self.head_dim = config["head_dim"]
self.linformer_k = config["linformer_k"]
se... | 1,203 | 30.684211 | 124 | py |
SBM-Transformer | SBM-Transformer-main/code/attention_nystrom.py | import torch
import torch.nn as nn
import math
class NystromAttention(nn.Module):
def __init__(self, config):
super().__init__()
self.head_dim = config["head_dim"]
self.num_head = config["num_head"]
self.num_landmarks = config["num_landmarks"]
self.seq_len = config["max_se... | 2,845 | 42.784615 | 145 | py |
SBM-Transformer | SBM-Transformer-main/code/model.py | import torch
import torch.nn as nn
import numpy as np
import math
from torch.utils.checkpoint import checkpoint
from attention import Attention
class Embeddings(nn.Module):
def __init__(self, config):
super().__init__()
assert config["embedding_dim"] == config["transformer_dim"]
self.dim ... | 4,326 | 33.070866 | 122 | py |
SBM-Transformer | SBM-Transformer-main/code/dataset.py | import torch
import torch.nn as nn
import math
from torch.utils.data.dataset import Dataset
import sys
import os
import random
import json
import pickle
import numpy as np
class LRADataset(Dataset):
def __init__(self, file_path, endless):
self.endless = endless
with open(file_path, "rb") as f:
... | 1,513 | 29.28 | 89 | py |
SBM-Transformer | SBM-Transformer-main/code/attention_performer.py | import torch
import torch.nn as nn
import math
from performer_pytorch import FastAttention
class PerformerAttention(nn.Module):
def __init__(self, config):
super().__init__()
self.head_dim = config["head_dim"]
self.rp_dim = config["rp_dim"]
self.kernel_type = config["kernel_type"]
... | 1,003 | 39.16 | 133 | py |
SBM-Transformer | SBM-Transformer-main/code/lra_config.py | config = {
"listops":{
"dataset":{
"train":96000,
"dev":2000,
"test":2000,
},
"model":{
"learn_pos_emb":True,
"tied_weights":False,
"embedding_dim":64,
"transformer_dim":64,
"transformer_hidden_di... | 10,876 | 36.898955 | 109 | py |
SBM-Transformer | SBM-Transformer-main/code/fastRG.py | import torch
import numpy as np
import time
import torch.nn.functional as F
from torch import LongTensor, Tensor
from typing import Generator, Iterable, List, Optional, Tuple
@torch.no_grad()
def batched_bincount(inp: Tensor, max_num: int):
batch_shape, num_samples = inp.shape[:-1], inp.shape[-1]
num_batc... | 5,974 | 34.147059 | 145 | py |
SBM-Transformer | SBM-Transformer-main/code/attention_linear.py | import torch
import torch.nn as nn
import math
class LinearAttention(nn.Module):
def __init__(self, config):
super().__init__()
def forward(self, Q, K, V, mask):
Q = (nn.functional.elu(Q) + 1) / math.sqrt(math.sqrt(Q.size(2)))
K = (nn.functional.elu(K) + 1) * mask[:, None, :, None] / ... | 483 | 25.888889 | 97 | py |
SBM-Transformer | SBM-Transformer-main/code/STE.py | import torch
import torch.nn as nn
import torch.nn.functional as F
class SampleGraphSparseGraph(torch.autograd.Function):
@staticmethod
def forward(ctx, input):
A = torch.bernoulli(torch.clamp(input+0.01, min=0, max=1)).requires_grad_(True)
ctx.save_for_backward(A)
return A
... | 424 | 29.357143 | 87 | py |
SBM-Transformer | SBM-Transformer-main/code/attention_reformer.py | import torch
import torch.nn as nn
from reformer_pytorch import LSHSelfAttention
class LSHAttention(LSHSelfAttention):
def __init__(self, config, query, key, value):
self.num_hash = config["num_hash"]
self.attention_head_size = config["head_dim"]
self.num_attention_heads = config["num_head"... | 730 | 33.809524 | 58 | py |
SBM-Transformer | SBM-Transformer-main/code/attention.py | import torch
import torch.nn as nn
import math
import json
from torch.utils.checkpoint import checkpoint
class SoftmaxAttention(nn.Module):
def __init__(self, config):
super().__init__()
self.drop_attn = torch.nn.Dropout(p = config["attention_dropout"])
self.head_dim = config["head_dim"]
... | 4,387 | 35.87395 | 109 | py |
SBM-Transformer | SBM-Transformer-main/code/run_tasks.py | from model_wrapper import ModelForSC, ModelForSCDual
from dataset import LRADataset
from torch.utils.data import DataLoader
import torch
import torch.nn as nn
import torch.nn.functional as F
import datetime
import time
import os
import json
import requests
import pickle
import numpy as np
import argparse
import math
im... | 9,630 | 35.900383 | 212 | py |
cymetric | cymetric-main/setup.py | import setuptools
with open("README.md", "r") as fh:
LONG_DESCRIPTION = fh.read()
with open("VERSION", "r") as fh:
VERSION = fh.read()
with open("requirements.txt", "r") as fh:
REQUIREMENTS = fh.read().splitlines()
setuptools.setup(
name="cymetric",
version=VERSION,
author="Fabian Ruehle, Ro... | 1,032 | 32.322581 | 75 | py |
cymetric | cymetric-main/__init__.py | 0 | 0 | 0 | py | |
cymetric | cymetric-main/tests/test_tfmodels.py | """
Pytest for some tensorflow models.
Requires that `test_pointgen.py` has been run before.
"""
import pytest
import numpy as np
import os as os
#import pickle as pickle
import itertools as it
import tensorflow as tf
tfk = tf.keras
#TODO: Import all metrics and Measures and callbacks and ... then run them.
from cyme... | 3,581 | 33.442308 | 80 | py |
cymetric | cymetric-main/tests/test_pointgen.py | """
Pytest for some PointGenerators.
"""
import numpy as np
import os as os
from cymetric.pointgen.pointgen import PointGenerator
from cymetric.pointgen.pointgen_cicy import CICYPointGenerator
from cymetric.pointgen.nphelper import generate_monomials
import itertools as it
#TODO: Test every non private function.
class... | 4,529 | 35.532258 | 78 | py |
cymetric | cymetric-main/tests/test_pointgen_cicy.py | import numpy as np
from cymetric.pointgen.pointgen_cicy import CICYPointGenerator
def cicy_init():
monomials = np.array(list(generate_monomials(6, 3)))
monomials_per_hyper = [monomials, monomials]
coeff = [np.random.randn(len(m)) for m in monomials_per_hyper]
kmoduli = np.ones(1)
ambient = np.arr... | 416 | 28.785714 | 73 | py |
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