repo stringlengths 1 99 | file stringlengths 13 215 | code stringlengths 12 59.2M | file_length int64 12 59.2M | avg_line_length float64 3.82 1.48M | max_line_length int64 12 2.51M | extension_type stringclasses 1
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Torch-Pruning | Torch-Pruning-master/benchmarks/engine/models/graph/dgcnn.py | #!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
https://github.com/WangYueFt/dgcnn/blob/master/pytorch/model.py
@Author: Yue Wang
@Contact: yuewangx@mit.edu
@File: model.py
@Time: 2018/10/13 6:35 PM
"""
import torch
import torch.nn as nn
import torch.nn.functional as F
def knn(x, k):
inner = -2*torch.matmul(x.... | 5,442 | 36.537931 | 181 | py |
Torch-Pruning | Torch-Pruning-master/benchmarks/engine/models/cifar/inceptionv4.py | # -*- coding: UTF-8 -*-
""" inceptionv4 in pytorch
[1] Christian Szegedy, Sergey Ioffe, Vincent Vanhoucke, Alex Alemi
Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning
https://arxiv.org/abs/1602.07261
"""
import torch
import torch.nn as nn
class BasicConv2d(nn.Module):
def... | 18,161 | 32.202925 | 109 | py |
Torch-Pruning | Torch-Pruning-master/benchmarks/engine/models/cifar/swin.py | import torch
from torch import nn, einsum
import numpy as np
from einops import rearrange, repeat
class CyclicShift(nn.Module):
def __init__(self, displacement):
super().__init__()
self.displacement = displacement
def forward(self, x):
return torch.roll(x, shifts=(self.displacement, s... | 10,305 | 41.941667 | 118 | py |
Torch-Pruning | Torch-Pruning-master/benchmarks/engine/models/cifar/resnet.py | # ResNet for CIFAR (32x32)
# 2019.07.24-Changed output of forward function
# Huawei Technologies Co., Ltd. <foss@huawei.com>
# taken from https://github.com/huawei-noah/Data-Efficient-Model-Compression/blob/master/DAFL/resnet.py
# for comparison with DAFL
import torch
import torch.nn as nn
import torch.nn.functional ... | 4,252 | 35.663793 | 103 | py |
Torch-Pruning | Torch-Pruning-master/benchmarks/engine/models/cifar/mobilenetv2.py | """mobilenetv2 in pytorch
[1] Mark Sandler, Andrew Howard, Menglong Zhu, Andrey Zhmoginov, Liang-Chieh Chen
MobileNetV2: Inverted Residuals and Linear Bottlenecks
https://arxiv.org/abs/1801.04381
"""
import torch
import torch.nn as nn
import torch.nn.functional as F
class LinearBottleNeck(nn.Module):
de... | 2,857 | 28.163265 | 109 | py |
Torch-Pruning | Torch-Pruning-master/benchmarks/engine/models/cifar/vgg.py | """https://github.com/HobbitLong/RepDistiller/blob/master/models/vgg.py
"""
import torch.nn as nn
import torch.nn.functional as F
import math
__all__ = [
'VGG', 'vgg11', 'vgg11_bn', 'vgg13', 'vgg13_bn', 'vgg16', 'vgg16_bn',
'vgg19_bn', 'vgg19',
]
model_urls = {
'vgg11': 'https://download.pytorch.org/mod... | 6,706 | 29.348416 | 98 | py |
Torch-Pruning | Torch-Pruning-master/benchmarks/engine/models/cifar/preactresnet.py | """preactresnet in pytorch
[1] Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun
Identity Mappings in Deep Residual Networks
https://arxiv.org/abs/1603.05027
"""
import torch
import torch.nn as nn
import torch.nn.functional as F
class PreActBasic(nn.Module):
expansion = 1
def __init__(self, in_channe... | 4,077 | 30.859375 | 111 | py |
Torch-Pruning | Torch-Pruning-master/benchmarks/engine/models/cifar/densenet.py | """dense net in pytorch
[1] Gao Huang, Zhuang Liu, Laurens van der Maaten, Kilian Q. Weinberger.
Densely Connected Convolutional Networks
https://arxiv.org/abs/1608.06993v5
"""
import torch
import torch.nn as nn
#"""Bottleneck layers. Although each layer only produces k
#output feature-maps, it typically has... | 5,231 | 41.193548 | 147 | py |
Torch-Pruning | Torch-Pruning-master/benchmarks/engine/models/cifar/googlenet.py |
"""google net in pytorch
[1] Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed,
Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, Andrew Rabinovich.
Going Deeper with Convolutions
https://arxiv.org/abs/1409.4842v1
"""
import torch
import torch.nn as nn
class Inception(nn.Module):
... | 4,526 | 32.533333 | 94 | py |
Torch-Pruning | Torch-Pruning-master/benchmarks/engine/models/cifar/vit.py |
import torch
from torch import nn
from einops import rearrange, repeat
from einops.layers.torch import Rearrange
# helpers
def pair(t):
return t if isinstance(t, tuple) else (t, t)
# classes
class PreNorm(nn.Module):
def __init__(self, dim, fn):
super().__init__()
self.norm = nn.LayerNorm(... | 4,362 | 32.305344 | 166 | py |
Torch-Pruning | Torch-Pruning-master/benchmarks/engine/models/cifar/resnext.py | """resnext in pytorch
[1] Saining Xie, Ross Girshick, Piotr Dollár, Zhuowen Tu, Kaiming He.
Aggregated Residual Transformations for Deep Neural Networks
https://arxiv.org/abs/1611.05431
"""
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
#only implements ResNext bottleneck c
#... | 4,302 | 33.98374 | 99 | py |
Torch-Pruning | Torch-Pruning-master/benchmarks/engine/models/cifar/senet.py | """
The script is adapted from torchvision.models.ResNet
"""
import torch.nn as nn
__all__ = ['se_resnet20', 'se_resnet32', 'se_resnet44', 'se_resnet56', 'se_resnet110', 'se_resnet164',
'resnet20', 'resnet32', 'resnet44', 'resnet56', 'resnet110', 'resnet164']
model_urls = {
'se_resnet18': None,
's... | 8,792 | 26.392523 | 114 | py |
Torch-Pruning | Torch-Pruning-master/benchmarks/engine/models/cifar/nasnet.py | """nasnet in pytorch
[1] Barret Zoph, Vijay Vasudevan, Jonathon Shlens, Quoc V. Le
Learning Transferable Architectures for Scalable Image Recognition
https://arxiv.org/abs/1707.07012
"""
import torch
import torch.nn as nn
class SeperableConv2d(nn.Module):
def __init__(self, input_channels, output_channel... | 9,626 | 28.990654 | 125 | py |
Torch-Pruning | Torch-Pruning-master/benchmarks/engine/models/cifar/xception.py |
"""xception in pytorch
[1] François Chollet
Xception: Deep Learning with Depthwise Separable Convolutions
https://arxiv.org/abs/1610.02357
"""
import torch
import torch.nn as nn
class SeperableConv2d(nn.Module):
#***Figure 4. An “extreme” version of our Inception module,
#with one spatial convolutio... | 6,142 | 26.547085 | 82 | py |
Torch-Pruning | Torch-Pruning-master/benchmarks/engine/models/cifar/resnet_tiny.py | # Tiny ResNet for CIFAR (32x32)
from __future__ import absolute_import
'''Resnet for cifar dataset.
https://github.com/HobbitLong/RepDistiller/blob/master/models/resnet.py
Ported form
https://github.com/facebook/fb.resnet.torch
and
https://github.com/pytorch/vision/blob/master/torchvision/models/resnet.py
(c) YANG, W... | 6,215 | 30.876923 | 116 | py |
Torch-Pruning | Torch-Pruning-master/benchmarks/engine/models/cifar/inceptionv3.py | import os
from collections import namedtuple
import torch
import torch.nn as nn
import torch.nn.functional as F
__all__ = ["Inception3", "inception_v3"]
_InceptionOuputs = namedtuple("InceptionOuputs", ["logits", "aux_logits"])
def inception_v3(num_classes, pretrained=False, progress=True, device="cpu", **kwargs)... | 13,148 | 38.368263 | 102 | py |
Torch-Pruning | Torch-Pruning-master/benchmarks/engine/utils/utils.py | from contextlib import contextmanager
import logging
import os, sys
from termcolor import colored
import copy
import numpy as np
import torch
class MagnitudeRecover():
def __init__(self, model, reg=1e-3):
self.rec = {}
self.reg = reg
self.cnt = 0
with torch.no_grad():
fo... | 3,306 | 32.40404 | 91 | py |
Torch-Pruning | Torch-Pruning-master/benchmarks/engine/utils/evaluator.py | from tqdm import tqdm
import torch.nn.functional as F
import torch
from . import metrics
class Evaluator(object):
def __init__(self, metric, dataloader):
self.dataloader = dataloader
self.metric = metric
def eval(self, model, device=None, progress=False):
self.metric.reset()
w... | 1,370 | 36.054054 | 97 | py |
Torch-Pruning | Torch-Pruning-master/benchmarks/engine/utils/metrics.py | import numpy as np
import torch
from typing import Callable
__all__=['Accuracy', 'TopkAccuracy']
from abc import ABC, abstractmethod
from typing import Callable, Union, Any, Mapping, Sequence
import numbers
import numpy as np
class Metric(ABC):
@abstractmethod
def update(self, pred, target):
""" Over... | 3,186 | 27.455357 | 84 | py |
Torch-Pruning | Torch-Pruning-master/benchmarks/engine/utils/datasets/modelnet40.py | #!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
https://github.com/WangYueFt/dgcnn/blob/master/pytorch/data.py
@Author: Yue Wang
@Contact: yuewangx@mit.edu
@File: data.py
@Time: 2018/10/13 6:21 PM
"""
import os
import sys
import glob
import h5py
import numpy as np
from torch.utils.data import Dataset
def downloa... | 2,734 | 29.388889 | 110 | py |
Torch-Pruning | Torch-Pruning-master/benchmarks/engine/utils/imagenet_utils/sampler.py | import math
import torch
import torch.distributed as dist
class RASampler(torch.utils.data.Sampler):
"""Sampler that restricts data loading to a subset of the dataset for distributed,
with repeated augmentation.
It ensures that different each augmented version of a sample will be visible to a
differe... | 2,393 | 38.245902 | 103 | py |
Torch-Pruning | Torch-Pruning-master/benchmarks/engine/utils/imagenet_utils/presets.py | import torch
from torchvision.transforms import autoaugment, transforms
from torchvision.transforms.functional import InterpolationMode
class ClassificationPresetTrain:
def __init__(
self,
*,
crop_size,
mean=(0.485, 0.456, 0.406),
std=(0.229, 0.224, 0.225),
interpol... | 2,502 | 34.253521 | 106 | py |
Torch-Pruning | Torch-Pruning-master/benchmarks/engine/utils/imagenet_utils/utils.py | import copy
import datetime
import errno
import hashlib
import os
import time
from collections import defaultdict, deque, OrderedDict
from typing import List, Optional, Tuple
import torch
import torch.distributed as dist
class SmoothedValue:
"""Track a series of values and provide access to smoothed values over ... | 15,753 | 33.472648 | 120 | py |
Torch-Pruning | Torch-Pruning-master/benchmarks/engine/utils/imagenet_utils/transforms.py | import math
from typing import Tuple
import torch
from torch import Tensor
from torchvision.transforms import functional as F
class RandomMixup(torch.nn.Module):
"""Randomly apply Mixup to the provided batch and targets.
The class implements the data augmentations as described in the paper
`"mixup: Beyon... | 6,774 | 36.849162 | 108 | py |
Torch-Pruning | Torch-Pruning-master/tests/test_score_normalization.py | import sys, os
sys.path.append(os.path.dirname(os.path.dirname(os.path.realpath(__file__))))
import torch
from torchvision.models import resnet18 as entry
import torch_pruning as tp
from torch import nn
import torch.nn.functional as F
def test_pruner():
# Global metrics
example_inputs = torch.randn(1, 3... | 1,923 | 32.754386 | 120 | py |
Torch-Pruning | Torch-Pruning-master/tests/test_unwrapped_parameters.py | import sys, os
sys.path.append(os.path.dirname(os.path.dirname(os.path.realpath(__file__))))
import torch
from torchvision.models import convnext_base as entry
import torch_pruning as tp
model = entry()
print(model)
# Global metrics
example_inputs = torch.randn(1, 3, 224, 224)
imp = tp.importance.MagnitudeImportance... | 1,634 | 28.196429 | 112 | py |
Torch-Pruning | Torch-Pruning-master/tests/test_customized_layer.py | import sys, os
sys.path.append(os.path.dirname(os.path.dirname(os.path.realpath(__file__))))
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch_pruning as tp
from typing import Sequence
############
# Customize your layer
#
class CustomizedLayer(nn.Module):
def __init__(self, in_dim)... | 3,179 | 31.783505 | 108 | py |
Torch-Pruning | Torch-Pruning-master/tests/test_single_channel_output.py | import torch
from torch import nn
import torch.nn.functional as F
import torch_pruning as tp
class TestModel(nn.Module):
def __init__(self):
super().__init__()
self.conv1 = nn.Conv2d(3, 16, 3, stride=1, padding=1)
self.bn1 = nn.BatchNorm2d(16)
self.conv2 = nn.Conv2d(16, 64, 2, strid... | 982 | 29.71875 | 84 | py |
Torch-Pruning | Torch-Pruning-master/tests/test_concat.py | import sys, os
sys.path.append(os.path.dirname(os.path.dirname(os.path.realpath(__file__))))
import torch
import torch_pruning as tp
import torch.nn as nn
class Net(nn.Module):
def __init__(self, in_dim):
super().__init__()
self.block1 = nn.Sequential(
nn.Conv2d(in_dim, in_dim, 1),
... | 2,401 | 28.654321 | 116 | py |
Torch-Pruning | Torch-Pruning-master/tests/test_pruner.py | import sys, os
sys.path.append(os.path.dirname(os.path.dirname(os.path.realpath(__file__))))
import torch
from torchvision.models import resnet18 as entry
import torch_pruning as tp
from torch import nn
import torch.nn.functional as F
def test_pruner():
model = entry()
print(model)
# Global metrics
e... | 4,324 | 32.527132 | 125 | py |
Torch-Pruning | Torch-Pruning-master/tests/test_taylor_importance.py | import torch
from torchvision.models import resnet18
import torch_pruning as tp
def test_taylor():
model = resnet18(pretrained=True)
# Importance criteria
example_inputs = torch.randn(1, 3, 224, 224)
imp = tp.importance.TaylorImportance()
ignored_layers = []
for m in model.modules():
... | 1,379 | 33.5 | 116 | py |
Torch-Pruning | Torch-Pruning-master/tests/test_split.py | import sys, os
sys.path.append(os.path.dirname(os.path.dirname(os.path.realpath(__file__))))
import torch
import torch_pruning as tp
import torch.nn as nn
class Net(nn.Module):
def __init__(self, in_dim):
super().__init__()
self.block1 = nn.Sequential(
nn.Conv2d(in_dim, in_di... | 2,595 | 28.5 | 116 | py |
Torch-Pruning | Torch-Pruning-master/tests/test_load.py | import sys, os
sys.path.append(os.path.dirname(os.path.dirname(os.path.realpath(__file__))))
import torch
from torchvision.models import resnet18 as entry
import torch_pruning as tp
def test_pruner():
model = entry()
print(model)
# Global metrics
example_inputs = torch.randn(1, 3, 224, 224)
imp =... | 1,961 | 31.163934 | 116 | py |
Torch-Pruning | Torch-Pruning-master/tests/test_interactive_pruner.py | import sys, os
sys.path.append(os.path.dirname(os.path.dirname(os.path.realpath(__file__))))
import torch
from torchvision.models import resnet18 as entry
import torch_pruning as tp
def test_interactive_pruner():
model = entry()
print(model)
# Global metrics
example_inputs = torch.randn(1, 3, 224, 22... | 1,715 | 30.2 | 116 | py |
Torch-Pruning | Torch-Pruning-master/tests/test_reshape.py | import torch
import torch.nn as nn
import torch.nn.functional as F
import sys, os
sys.path.append(os.path.dirname(os.path.dirname(os.path.realpath(__file__))))
import torch_pruning as tp
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.Linear = nn.Linear(in_features=512, ... | 2,073 | 32.451613 | 116 | py |
Torch-Pruning | Torch-Pruning-master/tests/test_fully_connected_layers.py | import sys, os
sys.path.append(os.path.dirname(os.path.dirname(os.path.realpath(__file__))))
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch_pruning as tp
class FullyConnectedNet(nn.Module):
"""https://github.com/VainF/Torch-Pruning/issues/21"""
def __init__(self, input_size, ... | 1,304 | 28 | 99 | py |
Torch-Pruning | Torch-Pruning-master/tests/test_dependency_graph.py | import sys, os
sys.path.append(os.path.dirname(os.path.dirname(os.path.realpath(__file__))))
import torch
from torchvision.models import resnet18
import torch_pruning as tp
def test_depgraph():
model = resnet18().eval()
# 1. build dependency graph for resnet18
DG = tp.DependencyGraph()
DG.build_depen... | 1,231 | 28.333333 | 102 | py |
Torch-Pruning | Torch-Pruning-master/tests/test_importance.py | import sys, os
sys.path.append(os.path.dirname(os.path.dirname(os.path.realpath(__file__))))
import torch
from torchvision.models import resnet18
import torch_pruning as tp
model = resnet18()
# Global metrics
def test_imp():
DG = tp.DependencyGraph()
example_inputs = torch.randn(1,3,224,224)
DG.build_depe... | 1,693 | 35.042553 | 101 | py |
Torch-Pruning | Torch-Pruning-master/tests/test_dependency_lenet.py | import sys, os
sys.path.append(os.path.dirname(os.path.dirname(os.path.realpath(__file__))))
import torch
import torch.nn as nn
import torch_pruning as tp
class Model(nn.Module):
def __init__(self):
super(Model, self).__init__()
self.conv1 = nn.Conv2d(1, 6, 5)
self.relu1 = nn.ReLU()
... | 1,749 | 29.172414 | 99 | py |
Torch-Pruning | Torch-Pruning-master/tests/graph_drawing.py | import torch
import sys, os
sys.path.append(os.path.dirname(os.path.dirname(os.path.realpath(__file__))))
import torch_pruning as tp
from torchvision.models import densenet121, resnet18, googlenet, vgg16_bn
import torch.nn as nn
from torchvision.models.vision_transformer import VisionTransformer, vit_b_16
import matplo... | 1,502 | 33.953488 | 136 | py |
Torch-Pruning | Torch-Pruning-master/tests/test_pruning_fn.py | from torchvision.models import alexnet
import sys, os
sys.path.append(os.path.dirname(os.path.dirname(os.path.realpath(__file__))))
import torch_pruning as tp
def test_pruning_fn():
model = alexnet()
print("Before pruning: ")
print(model.features[:4])
print(model.features[0].weight.shape)
print(mo... | 681 | 28.652174 | 77 | py |
Torch-Pruning | Torch-Pruning-master/tests/test_concat_split.py | import sys, os
sys.path.append(os.path.dirname(os.path.dirname(os.path.realpath(__file__))))
import torch
import torch_pruning as tp
import torch.nn as nn
class Net(nn.Module):
def __init__(self, in_dim):
super().__init__()
self.block1 = nn.Sequential(
nn.Conv2d(in_dim, in_dim, 1),
... | 2,666 | 30.376471 | 116 | py |
Torch-Pruning | Torch-Pruning-master/tests/test_serialization.py | import sys, os
sys.path.append(os.path.dirname(os.path.dirname(os.path.realpath(__file__))))
import torch
from torchvision.models import vit_b_16 as entry
import torch_pruning as tp
from torchvision.models.vision_transformer import VisionTransformer
def test_serialization():
model = entry().eval()
customized... | 1,819 | 30.929825 | 120 | py |
Torch-Pruning | Torch-Pruning-master/tests/test_backward.py | import os, sys
import torchvision
sys.path.append(os.path.dirname(os.path.dirname(os.path.dirname(os.path.dirname(os.path.realpath(__file__))))))
# torchvision==0.13.1
###########################################
# Prunable Models
############################################
try:
from torchvision.models.vision_tra... | 8,079 | 34.130435 | 136 | py |
Torch-Pruning | Torch-Pruning-master/tests/test_multiple_inputs_and_outputs.py | import sys, os
sys.path.append(os.path.dirname(os.path.dirname(os.path.realpath(__file__))))
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch_pruning as tp
class FullyConnectedNet(nn.Module):
"""https://github.com/VainF/Torch-Pruning/issues/21"""
def __init__(self, input_size... | 1,468 | 28.979592 | 100 | py |
Torch-Pruning | Torch-Pruning-master/torch_pruning/dependency.py | import typing
import warnings
from numbers import Number
from collections import namedtuple
import torch
import torch.nn as nn
from .pruner import function
from . import _helpers, utils, ops
__all__ = ["Dependency", "Group", "DependencyGraph"]
class Node(object):
""" Nodes of DepGraph
"""
def __init__... | 42,920 | 40.309913 | 284 | py |
Torch-Pruning | Torch-Pruning-master/torch_pruning/_helpers.py | import torch.nn as nn
import numpy as np
import torch
from operator import add
from numbers import Number
def is_scalar(x):
if isinstance(x, torch.Tensor):
return len(x.shape) == 0
elif isinstance(x, Number):
return True
elif isinstance(x, (list, tuple)):
return False
return Fa... | 3,356 | 26.072581 | 74 | py |
Torch-Pruning | Torch-Pruning-master/torch_pruning/importance.py | import abc
import torch
import torch.nn as nn
import typing
from .pruner import function
from ._helpers import _FlattenIndexMapping
from . import ops
import math
class Importance(abc.ABC):
""" estimate the importance of a Pruning Group, and return an 1-D per-channel importance score.
"""
@abc.abstractcla... | 19,716 | 41.863043 | 119 | py |
Torch-Pruning | Torch-Pruning-master/torch_pruning/ops.py | import torch.nn as nn
from enum import IntEnum
class DummyMHA(nn.Module):
def __init__(self):
super(DummyMHA, self).__init__()
class _CustomizedOp(nn.Module):
def __init__(self, op_class):
self.op_cls = op_class
def __repr__(self):
return "CustomizedOp({})".format(str(self.op_cl... | 6,864 | 27.367769 | 76 | py |
Torch-Pruning | Torch-Pruning-master/torch_pruning/serialization.py | import torch
from torch.serialization import DEFAULT_PROTOCOL
import pickle
load = torch.load
save = torch.save
def state_dict(model: torch.nn.Module):
full_state_dict = {}
attributions = {}
for name, module in model.named_modules():
# state dicts
full_state_dict[name] = module.__dict__.co... | 1,461 | 36.487179 | 113 | py |
Torch-Pruning | Torch-Pruning-master/torch_pruning/pruner/function.py | import torch
import torch.nn as nn
from .. import ops
from copy import deepcopy
from functools import reduce
from operator import mul
from abc import ABC, abstractclassmethod, abstractmethod, abstractstaticmethod
from typing import Callable, Sequence, Tuple, Dict
__all__=[
'BasePruningFunc',
'PrunerBox',
... | 20,280 | 39.562 | 197 | py |
Torch-Pruning | Torch-Pruning-master/torch_pruning/pruner/algorithms/batchnorm_scale_pruner.py | from numbers import Number
from typing import Callable
from .metapruner import MetaPruner
from .scheduler import linear_scheduler
import torch
import torch.nn as nn
class BNScalePruner(MetaPruner):
def __init__(
self,
model,
example_inputs,
importance,
reg=1e-5,
iter... | 1,647 | 32.632653 | 131 | py |
Torch-Pruning | Torch-Pruning-master/torch_pruning/pruner/algorithms/metapruner.py | import torch
import torch.nn as nn
import typing
from .scheduler import linear_scheduler
from ..import function
from ... import ops, dependency
class MetaPruner:
"""
Meta Pruner for structural pruning.
Args:
model (nn.Module): A to-be-pruned model
example_inputs (torch.Te... | 11,945 | 40.769231 | 126 | py |
Torch-Pruning | Torch-Pruning-master/torch_pruning/pruner/algorithms/group_norm_pruner.py | import torch
import math
from .metapruner import MetaPruner
from .scheduler import linear_scheduler
from .. import function
from ..._helpers import _FlattenIndexMapping
class GroupNormPruner(MetaPruner):
def __init__(
self,
model,
example_inputs,
importance,
reg=1e-4,
... | 8,060 | 43.783333 | 189 | py |
Torch-Pruning | Torch-Pruning-master/torch_pruning/utils/utils.py | from ..ops import TORCH_CONV, TORCH_BATCHNORM, TORCH_PRELU, TORCH_LINEAR
from ..ops import module2type
import torch
from .op_counter import count_ops_and_params
import torch.nn as nn
@torch.no_grad()
def count_params(module):
return sum([p.numel() for p in module.parameters()])
def flatten_as_list(obj):
if is... | 5,557 | 42.76378 | 152 | py |
Torch-Pruning | Torch-Pruning-master/torch_pruning/utils/op_counter.py | '''
This opcounter is adapted from https://github.com/sovrasov/flops-counter.pytorch
Copyright (C) 2021 Sovrasov V. - All Rights Reserved
* You may use, distribute and modify this code under the
* terms of the MIT license.
* You should have received a copy of the MIT license with
* this file. If not visit https://... | 15,847 | 31.879668 | 89 | py |
invertinggradients | invertinggradients-master/rec_mult.py | """Run reconstruction in a terminal prompt.
Optional arguments can be found in inversefed/options.py
This CLI can recover the baseline experiments.
"""
import torch
import torchvision
import numpy as np
import inversefed
torch.backends.cudnn.benchmark = inversefed.consts.BENCHMARK
from collections import defaultdi... | 14,673 | 44.571429 | 153 | py |
invertinggradients | invertinggradients-master/reconstruct_image.py | """Run reconstruction in a terminal prompt.
Optional arguments can be found in inversefed/options.py
"""
import torch
import torchvision
import numpy as np
from PIL import Image
import inversefed
from collections import defaultdict
import datetime
import time
import os
torch.backends.cudnn.benchmark = inversefed.... | 10,859 | 38.347826 | 130 | py |
invertinggradients | invertinggradients-master/inversefed/reconstruction_algorithms.py | """Mechanisms for image reconstruction from parameter gradients."""
import torch
from collections import defaultdict, OrderedDict
from inversefed.nn import MetaMonkey
from .metrics import total_variation as TV
from .metrics import InceptionScore
from .medianfilt import MedianPool2d
from copy import deepcopy
import ti... | 18,066 | 44.97201 | 131 | py |
invertinggradients | invertinggradients-master/inversefed/utils.py | """Various utilities."""
import os
import csv
import torch
import random
import numpy as np
import socket
import datetime
def system_startup(args=None, defs=None):
"""Print useful system information."""
# Choose GPU device and print status information:
device = torch.device('cuda:0') if torch.cuda.is_a... | 2,420 | 33.098592 | 107 | py |
invertinggradients | invertinggradients-master/inversefed/metrics.py | """This is code based on https://sudomake.ai/inception-score-explained/."""
import torch
import torchvision
from collections import defaultdict
class InceptionScore(torch.nn.Module):
"""Class that manages and returns the inception score of images."""
def __init__(self, batch_size=32, setup=dict(device=torch.... | 3,866 | 35.140187 | 107 | py |
invertinggradients | invertinggradients-master/inversefed/medianfilt.py | """This is code for median pooling from https://gist.github.com/rwightman.
https://gist.github.com/rwightman/f2d3849281624be7c0f11c85c87c1598
"""
import torch.nn as nn
import torch.nn.functional as F
from torch.nn.modules.utils import _pair, _quadruple
class MedianPool2d(nn.Module):
"""Median pool (usable as med... | 2,025 | 35.836364 | 87 | py |
invertinggradients | invertinggradients-master/inversefed/nn/modules.py | """For monkey-patching into meta-learning frameworks."""
import torch
import torch.nn.functional as F
from collections import OrderedDict
from functools import partial
import warnings
from ..consts import BENCHMARK
torch.backends.cudnn.benchmark = BENCHMARK
DEBUG = False # Emit warning messages when patching. Use th... | 4,125 | 40.676768 | 120 | py |
invertinggradients | invertinggradients-master/inversefed/nn/densenet.py | """DenseNet in PyTorch."""
"""Adaptation we did with ******."""
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().__init__()
self.bn1 = nn.BatchNorm2d(in_planes)
self.conv1 = nn.... | 3,305 | 34.548387 | 98 | py |
invertinggradients | invertinggradients-master/inversefed/nn/revnet_utils.py | """https://github.com/jhjacobsen/pytorch-i-revnet/blob/master/models/model_utils.py.
Code for "i-RevNet: Deep Invertible Networks"
https://openreview.net/pdf?id=HJsjkMb0Z
ICLR, 2018
(c) Joern-Henrik Jacobsen, 2018
"""
"""
MIT License
Copyright (c) 2018 Jörn Jacobsen
Permission is hereby granted, free of charge, t... | 4,614 | 33.699248 | 135 | py |
invertinggradients | invertinggradients-master/inversefed/nn/revnet.py | """https://github.com/jhjacobsen/pytorch-i-revnet/blob/master/models/iRevNet.py.
Code for "i-RevNet: Deep Invertible Networks"
https://openreview.net/pdf?id=HJsjkMb0Z
ICLR, 2018
(c) Joern-Henrik Jacobsen, 2018
"""
"""
MIT License
Copyright (c) 2018 Jörn Jacobsen
Permission is hereby granted, free of charge, to an... | 7,362 | 37.150259 | 80 | py |
invertinggradients | invertinggradients-master/inversefed/nn/models.py | """Define basic models and translate some torchvision stuff."""
"""Stuff from https://github.com/pytorch/vision/blob/master/torchvision/models/resnet.py."""
import torch
import torchvision
import torch.nn as nn
from torchvision.models.resnet import Bottleneck
from .revnet import iRevNet
from .densenet import _DenseNet... | 15,319 | 44.868263 | 132 | py |
invertinggradients | invertinggradients-master/inversefed/training/scheduler.py | """This file is part of https://github.com/ildoonet/pytorch-gradual-warmup-lr.
MIT License
Copyright (c) 2019 Ildoo Kim
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, inclu... | 4,218 | 42.947917 | 143 | py |
invertinggradients | invertinggradients-master/inversefed/training/training_routine.py | """Implement the .train function."""
import torch
import numpy as np
from collections import defaultdict
from .scheduler import GradualWarmupScheduler
from ..consts import BENCHMARK, NON_BLOCKING
torch.backends.cudnn.benchmark = BENCHMARK
def train(model, loss_fn, trainloader, validloader, defs, setup=dict(dtype=t... | 4,586 | 35.696 | 117 | py |
invertinggradients | invertinggradients-master/inversefed/data/loss.py | """Define various loss functions and bundle them with appropriate metrics."""
import torch
import numpy as np
class Loss:
"""Abstract class, containing necessary methods.
Abstract class to collect information about the 'higher-level' loss function, used to train an energy-based model
containing the eval... | 3,507 | 29.504348 | 117 | py |
invertinggradients | invertinggradients-master/inversefed/data/data.py | """This is data.py from pytorch-examples.
Refer to
https://github.com/pytorch/examples/blob/master/super_resolution/data.py.
"""
from os.path import exists, join, basename
from os import makedirs, remove
from six.moves import urllib
import tarfile
from torchvision.transforms import Compose, CenterCrop, ToTensor, Resi... | 3,694 | 37.092784 | 116 | py |
invertinggradients | invertinggradients-master/inversefed/data/data_processing.py | """Repeatable code parts concerning data loading."""
import torch
import torchvision
import torchvision.transforms as transforms
import os
from ..consts import *
from .data import _build_bsds_sr, _build_bsds_dn
from .loss import Classification, PSNR
def construct_dataloaders(dataset, defs, data_path='~/data', sh... | 8,575 | 39.838095 | 126 | py |
invertinggradients | invertinggradients-master/inversefed/data/datasets.py | """This is dataset.py from pytorch-examples.
Refer to
https://github.com/pytorch/examples/blob/master/super_resolution/dataset.py.
"""
import torch
import torch.utils.data as data
from os import listdir
from os.path import join
from PIL import Image
def _is_image_file(filename):
return any(filename.endswith(ex... | 1,881 | 28.873016 | 119 | py |
Set_Functions_for_Time_Series | Set_Functions_for_Time_Series-master/seft/callbacks.py | """Module containing Keras callbacks."""
import time
from itertools import chain
import numpy as np
import tensorflow as tf
import tensorflow_datasets as tfds
import tensorboard.plugins.hparams.api as hp
from tensorflow.keras.callbacks import TensorBoard
class FailsafeTensorBoard(TensorBoard):
"""Failsafe versi... | 6,605 | 33.40625 | 79 | py |
Set_Functions_for_Time_Series | Set_Functions_for_Time_Series-master/seft/training_utils.py | """Utility functions for training and evaluation."""
import math
import random
from collections.abc import Sequence
import tensorflow as tf
import tensorflow_datasets as tfds
import medical_ts_datasets
import seft.models
from tensorflow.data.experimental import AUTOTUNE
import tensorboard.plugins.hparams.api as hp
fr... | 8,761 | 32.570881 | 78 | py |
Set_Functions_for_Time_Series | Set_Functions_for_Time_Series-master/seft/training_routine.py | """Training routine for models."""
from os.path import join
import json
from itertools import chain
import numpy as np
import tensorflow as tf
from typing import Callable
from tensorflow.keras.callbacks import (
CSVLogger, EarlyStopping, ModelCheckpoint, ReduceLROnPlateau, TensorBoard)
from .callbacks import (
... | 12,606 | 36.298817 | 88 | py |
Set_Functions_for_Time_Series | Set_Functions_for_Time_Series-master/seft/cli/fit_model.py | """Fit a model in SeFT using a tensorflow dataset."""
import os
import logging
import seft.cli.silence_warnings
# import tensorflow as tf
# tf.enable_eager_execution()
# tf.config.experimental_run_functions_eagerly(True)
import tensorboard.plugins.hparams.api as hp
import seft.models
from seft.training_routine impor... | 4,834 | 30.396104 | 79 | py |
Set_Functions_for_Time_Series | Set_Functions_for_Time_Series-master/seft/models/set_utils.py | import inspect
from itertools import chain
import numpy as np
import tensorflow as tf
from tensorflow.keras.layers import Dense, Dropout
class PaddedToSegments(tf.keras.layers.Layer):
"""Convert a padded tensor with mask to a stacked tensor with segments."""
def compute_output_shape(self, input_shape):
... | 11,322 | 32.699405 | 103 | py |
Set_Functions_for_Time_Series | Set_Functions_for_Time_Series-master/seft/models/deep_set_attention.py | from collections.abc import Sequence
from itertools import chain
import numpy as np
import tensorflow as tf
from tensorflow.keras.layers import Dense
from tensorflow.python.framework.smart_cond import smart_cond
from .set_utils import (
build_dense_dropout_model, PaddedToSegments, SegmentAggregation,
cumulati... | 23,841 | 36.665087 | 107 | py |
Set_Functions_for_Time_Series | Set_Functions_for_Time_Series-master/seft/models/gru_simple.py | """GRU_simple.
RECURRENT NEURAL NETWORKS FOR MULTIVARIATE TIME SERIES WITH MISSING VALUES,
Che et al., 2016
"""
from collections.abc import Sequence
import tensorflow as tf
from .delta_t_utils import get_delta_t
class GRUSimpleModel(tf.keras.Model):
def __init__(self, output_activation, output_dims, n_units,
... | 3,269 | 33.0625 | 76 | py |
Set_Functions_for_Time_Series | Set_Functions_for_Time_Series-master/seft/models/utils.py | """Module containing utility functions specific to implementing models."""
import logging
import tensorflow as tf
K = tf.keras.backend
def build_and_compute_output_shape(layer, input_shape):
layer.build(input_shape)
return layer.compute_output_shape(input_shape)
class ResNetBlock(tf.keras.layers.Layer):
... | 6,479 | 33.468085 | 79 | py |
Set_Functions_for_Time_Series | Set_Functions_for_Time_Series-master/seft/models/gru_d.py | """Implementation of GRU-D model.
The below implementation is based on and adapted from
https://github.com/PeterChe1990/GRU-D
Which is published unter the MIT licence.
"""
from collections.abc import Sequence
from collections import namedtuple
import tensorflow as tf
from tensorflow.keras import backend as K
from ten... | 23,399 | 39.554593 | 86 | py |
Set_Functions_for_Time_Series | Set_Functions_for_Time_Series-master/seft/models/transformer.py | """Module with implementation of Transformer architecture."""
from collections.abc import Sequence
import tensorflow as tf
import numpy as np
import keras_transformer
from .set_utils import PaddedToSegments, SegmentAggregation
class PositionalEncoding(tf.keras.layers.Layer):
def __init__(self, max_time=20000, n_... | 8,048 | 35.586364 | 86 | py |
Set_Functions_for_Time_Series | Set_Functions_for_Time_Series-master/seft/models/interpolation_prediction.py | """Interpolation-Prediction Networks."""
from collections.abc import Sequence
import numpy as np
import tensorflow as tf
K = tf.keras.backend
class single_channel_interp(tf.keras.layers.Layer):
def __init__(self, **kwargs):
self.reconstruction = False
super(single_channel_interp, self).__init__(... | 14,598 | 38.671196 | 84 | py |
Set_Functions_for_Time_Series | Set_Functions_for_Time_Series-master/seft/models/phased_lstm.py | """Phased LSTM implementation based on the version in tensorflow contrib.
See: https://github.com/tensorflow/tensorflow/blob/r1.15/tensorflow/contrib/rnn/python/ops/rnn_cell.py#L1915-L2064
Due to restructurings in tensorflow some adaptions were required. This
implementation does not use global naming of variables and... | 10,675 | 36.724382 | 114 | py |
MDD-StochasticSolvers | MDD-StochasticSolvers-main/setup.py | import os
from setuptools import setup, find_packages
def src(pth):
return os.path.join(os.path.dirname(__file__), pth)
# Project description
descr = """
StochasticMDD
"""
# Setup
setup(
name='stochmdd',
description=descr,
long_description=open(src('README.md')).read(),
long_descr... | 887 | 28.6 | 83 | py |
MDD-StochasticSolvers | MDD-StochasticSolvers-main/stochmdd/stochmdd.py | import time
import numpy as np
import torch
import torch.nn as nn
import pylops_gpu
from math import ceil
from torch.utils.data import TensorDataset, DataLoader
from pylops.waveeqprocessing.mdd import MDC as MDClops
class MDC(nn.Module):
r"""Multi-dimensional convolution
Wrap PyLops :py:func:`pylops.waveeqp... | 19,896 | 37.634951 | 187 | py |
MDD-StochasticSolvers | MDD-StochasticSolvers-main/stochmdd/stochmdd_numpy.py | import numpy as np
import time
from .mdc import MDC
from .solver_numpy import *
def MDDminibatch(nt, nr, dt, dr, Gfft, d, optimizer, n_epochs, batch_size,
shuffle=True, shuffleonce=False,
twosided=True, mtrue=None, ivstrue=None, enormabsscaling=False,
seed=None, sche... | 12,597 | 34.789773 | 156 | py |
MultiEchoAI | MultiEchoAI-main/models.py | # -*- coding: utf-8 -*-
"""
Created on Sun Apr 25 15:25:31 2021
@author: degerli
"""
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Convolution1D
from tensorflow.keras.layers import MaxPooling1D
from tensorflow.keras.layers import Flatten, Input
from tensorflow.keras.layers impor... | 5,193 | 39.263566 | 174 | py |
glide-text2im | glide-text2im-main/setup.py | from setuptools import setup
setup(
name="glide-text2im",
packages=[
"glide_text2im",
"glide_text2im.clip",
"glide_text2im.tokenizer",
],
package_data={
"glide_text2im.tokenizer": [
"bpe_simple_vocab_16e6.txt.gz",
"encoder.json.gz",
"v... | 610 | 18.709677 | 46 | py |
glide-text2im | glide-text2im-main/glide_text2im/download.py | import os
from functools import lru_cache
from typing import Dict, Optional
import requests
import torch as th
from filelock import FileLock
from tqdm.auto import tqdm
MODEL_PATHS = {
"base": "https://openaipublic.blob.core.windows.net/diffusion/dec-2021/base.pt",
"upsample": "https://openaipublic.blob.core.w... | 2,609 | 35.25 | 108 | py |
glide-text2im | glide-text2im-main/glide_text2im/text2im_model.py | import torch as th
import torch.nn as nn
import torch.nn.functional as F
from .nn import timestep_embedding
from .unet import UNetModel
from .xf import LayerNorm, Transformer, convert_module_to_f16
class Text2ImUNet(UNetModel):
"""
A UNetModel that conditions on text with an encoding transformer.
Expect... | 7,803 | 32.350427 | 127 | py |
glide-text2im | glide-text2im-main/glide_text2im/nn.py | """
Various utilities for neural networks.
"""
import math
import torch as th
import torch.nn as nn
import torch.nn.functional as F
class GroupNorm32(nn.GroupNorm):
def __init__(self, num_groups, num_channels, swish, eps=1e-5):
super().__init__(num_groups=num_groups, num_channels=num_channels, eps=eps)
... | 2,859 | 25.981132 | 85 | py |
glide-text2im | glide-text2im-main/glide_text2im/fp16_util.py | """
Helpers to inference with 16-bit precision.
"""
import torch.nn as nn
def convert_module_to_f16(l):
"""
Convert primitive modules to float16.
"""
if isinstance(l, (nn.Conv1d, nn.Conv2d, nn.Conv3d)):
l.weight.data = l.weight.data.half()
if l.bias is not None:
l.bias.dat... | 646 | 23.884615 | 74 | py |
glide-text2im | glide-text2im-main/glide_text2im/unet.py | import math
from abc import abstractmethod
import torch as th
import torch.nn as nn
import torch.nn.functional as F
from .fp16_util import convert_module_to_f16, convert_module_to_f32
from .nn import avg_pool_nd, conv_nd, linear, normalization, timestep_embedding, zero_module
class TimestepBlock(nn.Module):
"""... | 23,009 | 35.179245 | 124 | py |
glide-text2im | glide-text2im-main/glide_text2im/gaussian_diffusion.py | """
Simplified from https://github.com/openai/guided-diffusion/blob/main/guided_diffusion/gaussian_diffusion.py.
"""
import math
import numpy as np
import torch as th
def _warmup_beta(beta_start, beta_end, num_diffusion_timesteps, warmup_frac):
betas = beta_end * np.ones(num_diffusion_timesteps, dtype=np.float6... | 23,741 | 36.096875 | 129 | py |
glide-text2im | glide-text2im-main/glide_text2im/respace.py | """
Utilities for changing sampling schedules of a trained model.
Simplified from: https://github.com/openai/guided-diffusion/blob/main/guided_diffusion/respace.py
"""
import numpy as np
import torch as th
from .gaussian_diffusion import GaussianDiffusion
def space_timesteps(num_timesteps, section_counts):
"""... | 4,879 | 40.355932 | 99 | py |
glide-text2im | glide-text2im-main/glide_text2im/xf.py | """
Transformer implementation adapted from CLIP ViT:
https://github.com/openai/CLIP/blob/4c0275784d6d9da97ca1f47eaaee31de1867da91/clip/model.py
"""
import math
import torch as th
import torch.nn as nn
def convert_module_to_f16(l):
"""
Convert primitive modules to float16.
"""
if isinstance(l, (nn.L... | 3,382 | 24.824427 | 90 | py |
glide-text2im | glide-text2im-main/glide_text2im/clip/model_creation.py | import os
from functools import lru_cache
from typing import Any, Callable, Dict, List, Optional, Tuple
import attr
import numpy as np
import torch
import torch.nn as nn
import yaml
from glide_text2im.tokenizer.simple_tokenizer import SimpleTokenizer
from .encoders import ImageEncoder, TextEncoder
@lru_cache()
def ... | 3,859 | 31.711864 | 92 | py |
glide-text2im | glide-text2im-main/glide_text2im/clip/utils.py | import math
from typing import Callable, Optional
import attr
import torch
import torch.nn as nn
import torch.nn.functional as F
FilterFn = Callable[[torch.Tensor], torch.Tensor]
class ZeroKeyBiasGrad(torch.autograd.Function):
@staticmethod
def forward(ctx, x):
return x
@staticmethod
def ba... | 3,354 | 33.234694 | 100 | py |
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