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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light-moco | light-moco-main/my_models/hrnet.py | """ HRNet
Copied from https://github.com/HRNet/HRNet-Image-Classification
Original header:
Copyright (c) Microsoft
Licensed under the MIT License.
Written by Bin Xiao (Bin.Xiao@microsoft.com)
Modified by Ke Sun (sunk@mail.ustc.edu.cn)
"""
import logging
from typing import List
import torch
import torch.nn as... | 29,304 | 34.222356 | 126 | py |
light-moco | light-moco-main/my_models/selecsls.py | """PyTorch SelecSLS Net example for ImageNet Classification
License: CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/legalcode)
Author: Dushyant Mehta (@mehtadushy)
SelecSLS (core) Network Architecture as proposed in "XNect: Real-time Multi-person 3D
Human Pose Estimation with a Single RGB Camera, Mehta et al."... | 13,103 | 35.4 | 121 | py |
light-moco | light-moco-main/my_models/regnet.py | """RegNet
Paper: `Designing Network Design Spaces` - https://arxiv.org/abs/2003.13678
Original Impl: https://github.com/facebookresearch/pycls/blob/master/pycls/models/regnet.py
Based on original PyTorch impl linked above, but re-wrote to use my own blocks (adapted from ResNet here)
and cleaned up with more descripti... | 20,532 | 41.956067 | 137 | py |
light-moco | light-moco-main/my_models/efficientnet_builder.py | """ EfficientNet, MobileNetV3, etc Builder
Assembles EfficieNet and related network feature blocks from string definitions.
Handles stride, dilation calculations, and selects feature extraction points.
Hacked together by / Copyright 2020 Ross Wightman
"""
import logging
import math
import re
from copy import deepcop... | 17,482 | 41.127711 | 124 | py |
light-moco | light-moco-main/my_models/features.py | """ PyTorch Feature Extraction Helpers
A collection of classes, functions, modules to help extract features from models
and provide a common interface for describing them.
The return_layers, module re-writing idea inspired by torchvision IntermediateLayerGetter
https://github.com/pytorch/vision/blob/d88d8961ae51507d0... | 12,155 | 41.652632 | 111 | py |
light-moco | light-moco-main/my_models/inflate_from_2d_model.py | import torch
from collections import OrderedDict
def inflate_from_2d_model(state_dict_2d, state_dict_3d, skipped_keys=None, inflated_dim=2):
if skipped_keys is None:
skipped_keys = []
missed_keys = []
new_keys = []
for old_key in state_dict_2d.keys():
if old_key not in state_dict_3d.... | 1,899 | 35.538462 | 91 | py |
light-moco | light-moco-main/my_models/efficientnet.py | """ PyTorch EfficientNet Family
An implementation of EfficienNet that covers variety of related models with efficient architectures:
* EfficientNet (B0-B8, L2 + Tensorflow pretrained AutoAug/RandAug/AdvProp/NoisyStudent weight ports)
- EfficientNet: Rethinking Model Scaling for CNNs - https://arxiv.org/abs/1905.119... | 71,363 | 40.203233 | 139 | py |
light-moco | light-moco-main/my_models/pnasnet.py | """
pnasnet5large implementation grabbed from Cadene's pretrained models
Additional credit to https://github.com/creafz
https://github.com/Cadene/pretrained-models.pytorch/blob/master/pretrainedmodels/models/pnasnet.py
"""
from collections import OrderedDict
import torch
import torch.nn as nn
import torch.nn.func... | 14,839 | 41.643678 | 124 | py |
light-moco | light-moco-main/my_models/resnet.py | """PyTorch ResNet
This started as a copy of https://github.com/pytorch/vision 'resnet.py' (BSD-3-Clause) with
additional dropout and dynamic global avg/max pool.
ResNeXt, SE-ResNeXt, SENet, and MXNet Gluon stem/downsample variants, tiered stems added by Ross Wightman
Copyright 2020 Ross Wightman
"""
import math
impor... | 58,771 | 43.02397 | 135 | py |
light-moco | light-moco-main/my_models/xception_aligned.py | """Pytorch impl of Aligned Xception 41, 65, 71
This is a correct, from scratch impl of Aligned Xception (Deeplab) models compatible with TF weights at
https://github.com/tensorflow/models/blob/master/research/deeplab/g3doc/model_zoo.md
Hacked together by / Copyright 2020 Ross Wightman
"""
from collections import Orde... | 9,269 | 37.46473 | 124 | py |
light-moco | light-moco-main/my_models/rexnet.py | """ ReXNet
A PyTorch impl of `ReXNet: Diminishing Representational Bottleneck on Convolutional Neural Network` -
https://arxiv.org/abs/2007.00992
Adapted from original impl at https://github.com/clovaai/rexnet
Copyright (c) 2020-present NAVER Corp. MIT license
Changes for timm, feature extraction, and rounded channe... | 10,135 | 37.539924 | 121 | py |
light-moco | light-moco-main/my_models/densenet.py | """Pytorch Densenet implementation w/ tweaks
This file is a copy of https://github.com/pytorch/vision 'densenet.py' (BSD-3-Clause) with
fixed kwargs passthrough and addition of dynamic global avg/max pool.
"""
import re
from collections import OrderedDict
from functools import partial
import torch
import torch.nn as n... | 15,598 | 39.411917 | 129 | py |
light-moco | light-moco-main/my_models/swav_resnet50.py | # Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
#
import torch
import torch.nn as nn
def conv3x3(in_planes, out_planes, stride=1, groups=1, dilation=1):
"""3x3 convo... | 11,064 | 30.169014 | 106 | py |
light-moco | light-moco-main/my_models/resnetv2.py | """Pre-Activation ResNet v2 with GroupNorm and Weight Standardization.
A PyTorch implementation of ResNetV2 adapted from the Google Big-Transfoer (BiT) source code
at https://github.com/google-research/big_transfer to match timm interfaces. The BiT weights have
been included here as pretrained models from their origin... | 25,337 | 41.656566 | 117 | py |
light-moco | light-moco-main/my_models/mobilenetv3.py |
""" MobileNet V3
A PyTorch impl of MobileNet-V3, compatible with TF weights from official impl.
Paper: Searching for MobileNetV3 - https://arxiv.org/abs/1905.02244
Hacked together by / Copyright 2020 Ross Wightman
"""
import torch
import torch.nn as nn
import torch.nn.functional as F
from typing import List
from ... | 17,610 | 38.575281 | 141 | py |
light-moco | light-moco-main/my_models/senet.py | """
SEResNet implementation from Cadene's pretrained models
https://github.com/Cadene/pretrained-models.pytorch/blob/master/pretrainedmodels/models/senet.py
Additional credit to https://github.com/creafz
Original model: https://github.com/hujie-frank/SENet
ResNet code gently borrowed from
https://github.com/pytorch/v... | 17,640 | 36.856223 | 127 | py |
light-moco | light-moco-main/my_models/vovnet.py | """ VoVNet (V1 & V2)
Papers:
* `An Energy and GPU-Computation Efficient Backbone Network` - https://arxiv.org/abs/1904.09730
* `CenterMask : Real-Time Anchor-Free Instance Segmentation` - https://arxiv.org/abs/1911.06667
Looked at https://github.com/youngwanLEE/vovnet-detectron2 &
https://github.com/stigma0617/VoVNe... | 13,824 | 33.220297 | 126 | py |
light-moco | light-moco-main/my_models/vision_transformer.py | """ Vision Transformer (ViT) in PyTorch
A PyTorch implement of Vision Transformers as described in
'An Image Is Worth 16 x 16 Words: Transformers for Image Recognition at Scale' - https://arxiv.org/abs/2010.11929
The official jax code is released and available at https://github.com/google-research/vision_transformer
... | 36,684 | 45.554569 | 137 | py |
light-moco | light-moco-main/my_models/inception_v4.py | """ Pytorch Inception-V4 implementation
Sourced from https://github.com/Cadene/tensorflow-model-zoo.torch (MIT License) which is
based upon Google's Tensorflow implementation and pretrained weights (Apache 2.0 License)
"""
import torch
import torch.nn as nn
import torch.nn.functional as F
from .data_config import IMAG... | 10,726 | 33.16242 | 122 | py |
light-moco | light-moco-main/my_models/inception_v3.py | """ Inception-V3
Originally from torchvision Inception3 model
Licensed BSD-Clause 3 https://github.com/pytorch/vision/blob/master/LICENSE
"""
import torch
import torch.nn as nn
import torch.nn.functional as F
from .data_config import IMAGENET_DEFAULT_STD, IMAGENET_DEFAULT_MEAN, IMAGENET_INCEPTION_MEAN, IMAGENET_INCEP... | 17,434 | 36.17484 | 127 | py |
light-moco | light-moco-main/my_models/gluon_xception.py | """Pytorch impl of Gluon Xception
This is a port of the Gluon Xception code and weights, itself ported from a PyTorch DeepLab impl.
Gluon model: (https://gluon-cv.mxnet.io/_modules/gluoncv/model_zoo/xception.html)
Original PyTorch DeepLab impl: https://github.com/jfzhang95/pytorch-deeplab-xception
Hacked together by ... | 9,533 | 35.528736 | 126 | py |
light-moco | light-moco-main/my_models/tresnet.py | """
TResNet: High Performance GPU-Dedicated Architecture
https://arxiv.org/pdf/2003.13630.pdf
Original model: https://github.com/mrT23/TResNet
"""
import copy
from collections import OrderedDict
from functools import partial
import torch
import torch.nn as nn
import torch.nn.functional as F
from .helpers import bui... | 11,433 | 37.891156 | 125 | py |
light-moco | light-moco-main/my_models/resnest.py | """ ResNeSt Models
Paper: `ResNeSt: Split-Attention Networks` - https://arxiv.org/abs/2004.08955
Adapted from original PyTorch impl w/ weights at https://github.com/zhanghang1989/ResNeSt by Hang Zhang
Modified for torchscript compat, and consistency with timm by Ross Wightman
"""
import torch
from torch import nn
f... | 10,197 | 42.029536 | 131 | py |
light-moco | light-moco-main/my_models/nasnet.py | """
"""
import torch
import torch.nn as nn
import torch.nn.functional as F
from .helpers import build_model_with_cfg
from .layers import ConvBnAct, create_conv2d, create_pool2d, create_classifier
from .registry import register_model
__all__ = ['NASNetALarge']
default_cfgs = {
'nasnetalarge': {
'url': 'h... | 25,683 | 44.619893 | 116 | py |
light-moco | light-moco-main/my_models/gluon_resnet.py | """Pytorch impl of MxNet Gluon ResNet/(SE)ResNeXt variants
This file evolved from https://github.com/pytorch/vision 'resnet.py' with (SE)-ResNeXt additions
and ports of Gluon variations (https://github.com/dmlc/gluon-cv/blob/master/gluoncv/model_zoo/resnet.py)
by Ross Wightman
"""
from .data_config import IMAGENET_DE... | 11,351 | 45.146341 | 165 | py |
light-moco | light-moco-main/my_models/xception.py | """
Ported to pytorch thanks to [tstandley](https://github.com/tstandley/Xception-PyTorch)
@author: tstandley
Adapted by cadene
Creates an Xception Model as defined in:
Francois Chollet
Xception: Deep Learning with Depthwise Separable Convolutions
https://arxiv.org/pdf/1610.02357.pdf
This weights ported from the Ke... | 7,372 | 30.917749 | 120 | py |
light-moco | light-moco-main/my_models/inception_resnet_v2.py | """ Pytorch Inception-Resnet-V2 implementation
Sourced from https://github.com/Cadene/tensorflow-model-zoo.torch (MIT License) which is
based upon Google's Tensorflow implementation and pretrained weights (Apache 2.0 License)
"""
import torch
import torch.nn as nn
import torch.nn.functional as F
from .data_config impo... | 12,321 | 33.709859 | 139 | py |
light-moco | light-moco-main/my_models/dpn.py | """ PyTorch implementation of DualPathNetworks
Based on original MXNet implementation https://github.com/cypw/DPNs with
many ideas from another PyTorch implementation https://github.com/oyam/pytorch-DPNs.
This implementation is compatible with the pretrained weights from cypw's MXNet implementation.
Hacked together b... | 12,331 | 38.399361 | 118 | py |
light-moco | light-moco-main/my_models/sknet.py | """ Selective Kernel Networks (ResNet base)
Paper: Selective Kernel Networks (https://arxiv.org/abs/1903.06586)
This was inspired by reading 'Compounding the Performance Improvements...' (https://arxiv.org/abs/2001.06268)
and a streamlined impl at https://github.com/clovaai/assembled-cnn but I ended up building somet... | 8,712 | 38.785388 | 124 | py |
light-moco | light-moco-main/my_models/helpers.py | """ Model creation / weight loading / state_dict helpers
Hacked together by / Copyright 2020 Ross Wightman
"""
import logging
import os
import math
from collections import OrderedDict
from copy import deepcopy
from typing import Callable
import torch
import torch.nn as nn
from torch.hub import load_state_dict_from_ur... | 15,643 | 40.717333 | 115 | py |
light-moco | light-moco-main/my_models/res2net.py | """ Res2Net and Res2NeXt
Adapted from Official Pytorch impl at: https://github.com/gasvn/Res2Net/
Paper: `Res2Net: A New Multi-scale Backbone Architecture` - https://arxiv.org/abs/1904.01169
"""
import math
import torch
import torch.nn as nn
from .data_config import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
from .h... | 7,852 | 35.525581 | 127 | py |
light-moco | light-moco-main/my_models/cspnet.py | """PyTorch CspNet
A PyTorch implementation of Cross Stage Partial Networks including:
* CSPResNet50
* CSPResNeXt50
* CSPDarkNet53
* and DarkNet53 for good measure
Based on paper `CSPNet: A New Backbone that can Enhance Learning Capability of CNN` - https://arxiv.org/abs/1911.11929
Reference impl via darknet cfg file... | 17,907 | 38.444934 | 129 | py |
light-moco | light-moco-main/my_models/twod_models/resnet.py | from functools import partial
from inspect import signature
import numpy as np
import torch
import torch.nn as nn
import torch.utils.model_zoo as model_zoo
from models.twod_models.common import TemporalPooling
from models.twod_models.temporal_modeling import temporal_modeling_module
__all__ = ['resnet']
model_urls ... | 10,032 | 36.02214 | 106 | py |
light-moco | light-moco-main/my_models/twod_models/temporal_modeling.py |
import torch
import torch.nn.functional as F
import torch.nn as nn
class SEModule(nn.Module):
def __init__(self, channels, dw_conv):
super().__init__()
ks = 1
pad = (ks - 1) // 2
self.fc1 = nn.Conv2d(channels, channels, kernel_size=ks,
padding=pad, gr... | 3,561 | 33.921569 | 111 | py |
light-moco | light-moco-main/my_models/twod_models/common.py | import torch.nn as nn
class TemporalPooling(nn.Module):
def __init__(self, frames, kernel_size=3, stride=2, mode='avg'):
"""
Parameters
----------
frames (int): number of input frames
kernel_size
stride
mode
"""
super().__init__()
s... | 1,052 | 29.970588 | 92 | py |
light-moco | light-moco-main/my_models/twod_models/inception_v1.py | from functools import partial
from inspect import signature
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.model_zoo as model_zoo
import numpy as np
from models.twod_models.common import TemporalPooling
from models.twod_models.temporal_modeling import temporal_modeling_module
... | 10,783 | 38.072464 | 100 | py |
light-moco | light-moco-main/my_models/layers/split_batchnorm.py | """ Split BatchNorm
A PyTorch BatchNorm layer that splits input batch into N equal parts and passes each through
a separate BN layer. The first split is passed through the parent BN layers with weight/bias
keys the same as the original BN. All other splits pass through BN sub-layers under the '.aux_bn'
namespace.
Thi... | 3,441 | 44.289474 | 118 | py |
light-moco | light-moco-main/my_models/layers/blur_pool.py | """
BlurPool layer inspired by
- Kornia's Max_BlurPool2d
- Making Convolutional Networks Shift-Invariant Again :cite:`zhang2019shiftinvar`
FIXME merge this impl with those in `anti_aliasing.py`
Hacked together by Chris Ha and Ross Wightman
"""
import torch
import torch.nn as nn
import torch.nn.functional as F
impo... | 2,180 | 35.966102 | 117 | py |
light-moco | light-moco-main/my_models/layers/separable_conv.py | """ Depthwise Separable Conv Modules
Basic DWS convs. Other variations of DWS exist with batch norm or activations between the
DW and PW convs such as the Depthwise modules in MobileNetV2 / EfficientNet and Xception.
Hacked together by / Copyright 2020 Ross Wightman
"""
from torch import nn as nn
from .create_conv2d... | 2,641 | 34.226667 | 110 | py |
light-moco | light-moco-main/my_models/layers/mixed_conv2d.py | """ PyTorch Mixed Convolution
Paper: MixConv: Mixed Depthwise Convolutional Kernels (https://arxiv.org/abs/1907.09595)
Hacked together by / Copyright 2020 Ross Wightman
"""
import torch
from torch import nn as nn
from .conv2d_same import create_conv2d_pad
def _split_channels(num_chan, num_groups):
split = [nu... | 1,844 | 34.480769 | 99 | py |
light-moco | light-moco-main/my_models/layers/anti_aliasing.py | import torch
import torch.nn.parallel
import torch.nn as nn
import torch.nn.functional as F
class AntiAliasDownsampleLayer(nn.Module):
def __init__(self, channels: int = 0, filt_size: int = 3, stride: int = 2, no_jit: bool = False):
super(AntiAliasDownsampleLayer, self).__init__()
if no_jit:
... | 2,293 | 36.606557 | 118 | py |
light-moco | light-moco-main/my_models/layers/weight_init.py | import torch
import math
import warnings
def _no_grad_trunc_normal_(tensor, mean, std, a, b):
# Cut & paste from PyTorch official master until it's in a few official releases - RW
# Method based on https://people.sc.fsu.edu/~jburkardt/presentations/truncated_normal.pdf
def norm_cdf(x):
# Computes ... | 2,359 | 37.688525 | 93 | py |
light-moco | light-moco-main/my_models/layers/evo_norm.py | """EvoNormB0 (Batched) and EvoNormS0 (Sample) in PyTorch
An attempt at getting decent performing EvoNorms running in PyTorch.
While currently faster than other impl, still quite a ways off the built-in BN
in terms of memory usage and throughput (roughly 5x mem, 1/2 - 1/3x speed).
Still very much a WIP, fiddling with ... | 3,328 | 38.630952 | 111 | py |
light-moco | light-moco-main/my_models/layers/pool2d_same.py | """ AvgPool2d w/ Same Padding
Hacked together by / Copyright 2020 Ross Wightman
"""
import torch
import torch.nn as nn
import torch.nn.functional as F
from typing import List, Tuple, Optional
from .helpers import to_2tuple
from .padding import pad_same, get_padding_value
def avg_pool2d_same(x, kernel_size: List[int... | 2,969 | 40.25 | 118 | py |
light-moco | light-moco-main/my_models/layers/create_act.py | """ Activation Factory
Hacked together by / Copyright 2020 Ross Wightman
"""
from .activations import *
from .activations_jit import *
from .activations_me import *
from .config import is_exportable, is_scriptable, is_no_jit
# PyTorch has an optimized, native 'silu' (aka 'swish') operator as of PyTorch 1.7. This code
... | 3,904 | 28.141791 | 97 | py |
light-moco | light-moco-main/my_models/layers/classifier.py | """ Classifier head and layer factory
Hacked together by / Copyright 2020 Ross Wightman
"""
from torch import nn as nn
from torch.nn import functional as F
from .adaptive_avgmax_pool import SelectAdaptivePool2d
from .linear import Linear
def _create_pool(num_features, num_classes, pool_type='avg', use_conv=False):
... | 2,300 | 40.089286 | 111 | py |
light-moco | light-moco-main/my_models/layers/cond_conv2d.py | """ PyTorch Conditionally Parameterized Convolution (CondConv)
Paper: CondConv: Conditionally Parameterized Convolutions for Efficient Inference
(https://arxiv.org/abs/1904.04971)
Hacked together by / Copyright 2020 Ross Wightman
"""
import math
from functools import partial
import numpy as np
import torch
from torc... | 5,129 | 40.707317 | 119 | py |
light-moco | light-moco-main/my_models/layers/conv2d_same.py | """ Conv2d w/ Same Padding
Hacked together by / Copyright 2020 Ross Wightman
"""
import torch
import torch.nn as nn
import torch.nn.functional as F
from typing import Tuple, Optional
from .padding import pad_same, get_padding_value
def conv2d_same(
x, weight: torch.Tensor, bias: Optional[torch.Tensor] = Non... | 1,490 | 33.674419 | 108 | py |
light-moco | light-moco-main/my_models/layers/adaptive_avgmax_pool.py | """ PyTorch selectable adaptive pooling
Adaptive pooling with the ability to select the type of pooling from:
* 'avg' - Average pooling
* 'max' - Max pooling
* 'avgmax' - Sum of average and max pooling re-scaled by 0.5
* 'avgmaxc' - Concatenation of average and max pooling along feature dim, doubles fea... | 3,903 | 31.533333 | 111 | py |
light-moco | light-moco-main/my_models/layers/conv_bn_act.py | """ Conv2d + BN + Act
Hacked together by / Copyright 2020 Ross Wightman
"""
from torch import nn as nn
from .create_conv2d import create_conv2d
from .create_norm_act import convert_norm_act_type
class ConvBnAct(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size=1, stride=1, padding='', dilati... | 1,466 | 34.780488 | 108 | py |
light-moco | light-moco-main/my_models/layers/linear.py | """ Linear layer (alternate definition)
"""
import torch
import torch.nn.functional as F
from torch import nn as nn
class Linear(nn.Linear):
r"""Applies a linear transformation to the incoming data: :math:`y = xA^T + b`
Wraps torch.nn.Linear to support AMP + torchscript usage by manually casting
weight &... | 743 | 36.2 | 89 | py |
light-moco | light-moco-main/my_models/layers/config.py | """ Model / Layer Config singleton state
"""
from typing import Any, Optional
__all__ = [
'is_exportable', 'is_scriptable', 'is_no_jit',
'set_exportable', 'set_scriptable', 'set_no_jit', 'set_layer_config'
]
# Set to True if prefer to have layers with no jit optimization (includes activations)
_NO_JIT = False... | 3,069 | 25.465517 | 102 | py |
light-moco | light-moco-main/my_models/layers/cbam.py | """ CBAM (sort-of) Attention
Experimental impl of CBAM: Convolutional Block Attention Module: https://arxiv.org/abs/1807.06521
WARNING: Results with these attention layers have been mixed. They can significantly reduce performance on
some tasks, especially fine-grained it seems. I may end up removing this impl.
Hack... | 3,337 | 32.38 | 106 | py |
light-moco | light-moco-main/my_models/layers/activations_jit.py | """ Activations
A collection of jit-scripted activations fn and modules with a common interface so that they can
easily be swapped. All have an `inplace` arg even if not used.
All jit scripted activations are lacking in-place variations on purpose, scripted kernel fusion does not
currently work across in-place op bou... | 2,529 | 26.802198 | 107 | py |
light-moco | light-moco-main/my_models/layers/activations_me.py | """ Activations (memory-efficient w/ custom autograd)
A collection of activations fn and modules with a common interface so that they can
easily be swapped. All have an `inplace` arg even if not used.
These activations are not compatible with jit scripting or ONNX export of the model, please use either
the JIT or bas... | 5,339 | 24.550239 | 108 | py |
light-moco | light-moco-main/my_models/layers/split_attn.py | """ Split Attention Conv2d (for ResNeSt Models)
Paper: `ResNeSt: Split-Attention Networks` - /https://arxiv.org/abs/2004.08955
Adapted from original PyTorch impl at https://github.com/zhanghang1989/ResNeSt
Modified for torchscript compat, performance, and consistency with timm by Ross Wightman
"""
import torch
impor... | 3,013 | 32.865169 | 90 | py |
light-moco | light-moco-main/my_models/layers/activations.py | """ Activations
A collection of activations fn and modules with a common interface so that they can
easily be swapped. All have an `inplace` arg even if not used.
Hacked together by / Copyright 2020 Ross Wightman
"""
import torch
from torch import nn as nn
from torch.nn import functional as F
def swish(x, inplace:... | 4,040 | 26.678082 | 107 | py |
light-moco | light-moco-main/my_models/layers/eca.py | """
ECA module from ECAnet
paper: ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks
https://arxiv.org/abs/1910.03151
Original ECA model borrowed from https://github.com/BangguWu/ECANet
Modified circular ECA implementation and adaption for use in timm package
by Chris Ha https://github.com/V... | 4,701 | 42.537037 | 104 | py |
light-moco | light-moco-main/my_models/layers/space_to_depth.py | import torch
import torch.nn as nn
class SpaceToDepth(nn.Module):
def __init__(self, block_size=4):
super().__init__()
assert block_size == 4
self.bs = block_size
def forward(self, x):
N, C, H, W = x.size()
x = x.view(N, C, H // self.bs, self.bs, W // self.bs, self.bs)... | 1,750 | 31.425926 | 102 | py |
light-moco | light-moco-main/my_models/layers/create_attn.py | """ Select AttentionFactory Method
Hacked together by / Copyright 2020 Ross Wightman
"""
import torch
from .se import SEModule, EffectiveSEModule
from .eca import EcaModule, CecaModule
from .cbam import CbamModule, LightCbamModule
def create_attn(attn_type, channels, **kwargs):
module_cls = None
if attn_type... | 1,222 | 31.184211 | 68 | py |
light-moco | light-moco-main/my_models/layers/median_pool.py | """ Median Pool
Hacked together by / Copyright 2020 Ross Wightman
"""
import torch.nn as nn
import torch.nn.functional as F
from .helpers import to_2tuple, to_4tuple
class MedianPool2d(nn.Module):
""" Median pool (usable as median filter when stride=1) module.
Args:
kernel_size: size of pooling kern... | 1,737 | 33.76 | 87 | py |
light-moco | light-moco-main/my_models/layers/test_time_pool.py | """ Test Time Pooling (Average-Max Pool)
Hacked together by / Copyright 2020 Ross Wightman
"""
import logging
from torch import nn
import torch.nn.functional as F
from .adaptive_avgmax_pool import adaptive_avgmax_pool2d
_logger = logging.getLogger(__name__)
class TestTimePoolHead(nn.Module):
def __init__(sel... | 1,851 | 36.04 | 97 | py |
light-moco | light-moco-main/my_models/layers/selective_kernel.py | """ Selective Kernel Convolution/Attention
Paper: Selective Kernel Networks (https://arxiv.org/abs/1903.06586)
Hacked together by / Copyright 2020 Ross Wightman
"""
import torch
from torch import nn as nn
from .conv_bn_act import ConvBnAct
def _kernel_valid(k):
if isinstance(k, (list, tuple)):
for ki i... | 5,282 | 43.394958 | 116 | py |
light-moco | light-moco-main/my_models/layers/norm_act.py | """ Normalization + Activation Layers
"""
import torch
from torch import nn as nn
from torch.nn import functional as F
from .create_act import get_act_layer
class BatchNormAct2d(nn.BatchNorm2d):
"""BatchNorm + Activation
This module performs BatchNorm + Activation in a manner that will remain backwards
... | 3,542 | 39.724138 | 109 | py |
light-moco | light-moco-main/my_models/layers/create_conv2d.py | """ Create Conv2d Factory Method
Hacked together by / Copyright 2020 Ross Wightman
"""
from .mixed_conv2d import MixedConv2d
from .cond_conv2d import CondConv2d
from .conv2d_same import create_conv2d_pad
def create_conv2d(in_channels, out_channels, kernel_size, **kwargs):
""" Select a 2d convolution implementat... | 1,399 | 44.16129 | 98 | py |
light-moco | light-moco-main/my_models/layers/helpers.py | """ Layer/Module Helpers
Hacked together by / Copyright 2020 Ross Wightman
"""
from itertools import repeat
# from torch._six import container_abcs
import collections.abc as container_abcs
# From PyTorch internals
def _ntuple(n):
def parse(x):
if isinstance(x, container_abcs.Iterable):
return... | 494 | 16.068966 | 50 | py |
light-moco | light-moco-main/my_models/layers/create_norm_act.py | """ NormAct (Normalizaiton + Activation Layer) Factory
Create norm + act combo modules that attempt to be backwards compatible with separate norm + act
isntances in models. Where these are used it will be possible to swap separate BN + act layers with
combined modules like IABN or EvoNorms.
Hacked together by / Copyr... | 3,327 | 43.373333 | 118 | py |
light-moco | light-moco-main/my_models/layers/padding.py | """ Padding Helpers
Hacked together by / Copyright 2020 Ross Wightman
"""
import math
from typing import List, Tuple
import torch.nn.functional as F
# Calculate symmetric padding for a convolution
def get_padding(kernel_size: int, stride: int = 1, dilation: int = 1, **_) -> int:
padding = ((stride - 1) + dilati... | 2,167 | 37.035088 | 99 | py |
light-moco | light-moco-main/my_models/layers/se.py | from torch import nn as nn
from .create_act import create_act_layer
class SEModule(nn.Module):
def __init__(self, channels, reduction=16, act_layer=nn.ReLU, min_channels=8, reduction_channels=None,
gate_layer='sigmoid'):
super(SEModule, self).__init__()
reduction_channels = reduc... | 1,406 | 37.027027 | 106 | py |
light-moco | light-moco-main/my_models/layers/drop.py | """ DropBlock, DropPath
PyTorch implementations of DropBlock and DropPath (Stochastic Depth) regularization layers.
Papers:
DropBlock: A regularization method for convolutional networks (https://arxiv.org/abs/1810.12890)
Deep Networks with Stochastic Depth (https://arxiv.org/abs/1603.09382)
Code:
DropBlock impl ins... | 6,938 | 40.059172 | 118 | py |
light-moco | light-moco-main/my_models/layers/inplace_abn.py | import torch
from torch import nn as nn
try:
from inplace_abn.functions import inplace_abn, inplace_abn_sync
has_iabn = True
except ImportError:
has_iabn = False
def inplace_abn(x, weight, bias, running_mean, running_var,
training=True, momentum=0.1, eps=1e-05, activation="leaky_re... | 3,353 | 37.113636 | 111 | py |
light-moco | light-moco-main/my_models/threed_models/c3d.py | import torch
import torch.nn as nn
'''
https://github.com/jfzhang95/pytorch-video-recognition/blob/master/network/C3D_model.py
'''
__all__ = ['c3d']
defaultcfg = {
'11': [64, 'M', 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512],
'11pruned_aa' : [64, 'M', 128, 'M', 256, 256, 'M', 256, 256, 'M', 256, 256]
... | 7,850 | 35.013761 | 113 | py |
light-moco | light-moco-main/my_models/threed_models/s3d_resnet.py | import numpy as np
import torch
import torch.nn as nn
import torch.utils.model_zoo as model_zoo
import torch.nn.functional as F
from models.inflate_from_2d_model import inflate_from_2d_model
__all__ = ['s3d_resnet']
model_urls = {
'resnet18': 'https://download.pytorch.org/models/resnet18-5c106cde.pth',
'resn... | 10,015 | 37.972763 | 107 | py |
light-moco | light-moco-main/my_models/threed_models/i3d.py | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.model_zoo as model_zoo
from models.inflate_from_2d_model import inflate_from_2d_model
__all__ = ['i3d']
model_urls = {
'googlenet': 'https://download.pytorch.org/models/googlenet-1378be20.pth',
}
class I3D(nn.Module):
def... | 6,808 | 37.039106 | 99 | py |
light-moco | light-moco-main/my_models/threed_models/s3d.py | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.model_zoo as model_zoo
from models.inflate_from_2d_model import inflate_from_2d_model
__all__ = ['s3d']
model_urls = {
'googlenet': 'https://download.pytorch.org/models/googlenet-1378be20.pth',
}
'''
A pytorch implementation ... | 8,403 | 37.728111 | 102 | py |
light-moco | light-moco-main/my_models/threed_models/i3d_resnet.py | import numpy as np
import torch
import torch.nn as nn
import torch.utils.model_zoo as model_zoo
import torch.nn.functional as F
from models.inflate_from_2d_model import inflate_from_2d_model
__all__ = ['i3d_resnet']
model_urls = {
'resnet18': 'https://download.pytorch.org/models/resnet18-5c106cde.pth',
'resn... | 8,498 | 35.792208 | 107 | py |
light-moco | light-moco-main/detection/convert-pretrain-to-detectron2.py | #!/usr/bin/env python
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
import pickle as pkl
import sys
import torch
if __name__ == "__main__":
input = sys.argv[1]
obj = torch.load(input, map_location="cpu")
obj = obj["state_dict"]
newmodel = {}
for k, v in obj.items():
... | 1,021 | 28.2 | 80 | py |
segmentation_models | segmentation_models-master/segmentation_models/utils.py | """ Utility functions for segmentation models """
from keras_applications import get_submodules_from_kwargs
from . import inject_global_submodules
def set_trainable(model, recompile=True, **kwargs):
"""Set all layers of model trainable and recompile it
Note:
Model is recompiled using same optimizer,... | 3,002 | 32.366667 | 87 | py |
segmentation_models | segmentation_models-master/segmentation_models/__init__.py | import os
import functools
from .__version__ import __version__
from . import base
_KERAS_FRAMEWORK_NAME = 'keras'
_TF_KERAS_FRAMEWORK_NAME = 'tf.keras'
_DEFAULT_KERAS_FRAMEWORK = _KERAS_FRAMEWORK_NAME
_KERAS_FRAMEWORK = None
_KERAS_BACKEND = None
_KERAS_LAYERS = None
_KERAS_MODELS = None
_KERAS_UTILS = None
_KERAS_L... | 4,041 | 27.464789 | 103 | py |
segmentation_models | segmentation_models-master/segmentation_models/models/pspnet.py | from keras_applications import get_submodules_from_kwargs
from ._common_blocks import Conv2dBn
from ._utils import freeze_model, filter_keras_submodules
from ..backbones.backbones_factory import Backbones
backend = None
layers = None
models = None
keras_utils = None
# -----------------------------------------------... | 8,499 | 33.552846 | 113 | py |
segmentation_models | segmentation_models-master/segmentation_models/models/_utils.py | from keras_applications import get_submodules_from_kwargs
def freeze_model(model, **kwargs):
"""Set all layers non trainable, excluding BatchNormalization layers"""
_, layers, _, _ = get_submodules_from_kwargs(kwargs)
for layer in model.layers:
if not isinstance(layer, layers.BatchNormalization):
... | 616 | 35.294118 | 77 | py |
segmentation_models | segmentation_models-master/segmentation_models/models/linknet.py | from keras_applications import get_submodules_from_kwargs
from ._common_blocks import Conv2dBn
from ._utils import freeze_model, filter_keras_submodules
from ..backbones.backbones_factory import Backbones
backend = None
layers = None
models = None
keras_utils = None
# -----------------------------------------------... | 9,762 | 34.118705 | 125 | py |
segmentation_models | segmentation_models-master/segmentation_models/models/unet.py | from keras_applications import get_submodules_from_kwargs
from ._common_blocks import Conv2dBn
from ._utils import freeze_model, filter_keras_submodules
from ..backbones.backbones_factory import Backbones
backend = None
layers = None
models = None
keras_utils = None
# -----------------------------------------------... | 8,414 | 32.26087 | 121 | py |
segmentation_models | segmentation_models-master/segmentation_models/models/fpn.py | from keras_applications import get_submodules_from_kwargs
from ._common_blocks import Conv2dBn
from ._utils import freeze_model, filter_keras_submodules
from ..backbones.backbones_factory import Backbones
backend = None
layers = None
models = None
keras_utils = None
# -----------------------------------------------... | 9,094 | 34.666667 | 125 | py |
segmentation_models | segmentation_models-master/segmentation_models/models/_common_blocks.py | from keras_applications import get_submodules_from_kwargs
def Conv2dBn(
filters,
kernel_size,
strides=(1, 1),
padding='valid',
data_format=None,
dilation_rate=(1, 1),
activation=None,
kernel_initializer='glorot_uniform',
bias_initializer='zeros',... | 2,133 | 29.485714 | 82 | py |
segmentation_models | segmentation_models-master/segmentation_models/base/functional.py | SMOOTH = 1e-5
# ----------------------------------------------------------------
# Helpers
# ----------------------------------------------------------------
def _gather_channels(x, indexes, **kwargs):
"""Slice tensor along channels axis by given indexes"""
backend = kwargs['backend']
if backend.image_... | 11,668 | 36.763754 | 118 | py |
segmentation_models | segmentation_models-master/segmentation_models/backbones/inception_v3.py | """Inception V3 model for Keras.
Note that the input image format for this model is different than for
the VGG16 and ResNet models (299x299 instead of 224x224),
and that the input preprocessing function is also different (same as Xception).
# Reference
- [Rethinking the Inception Architecture for Computer Vision](
... | 14,611 | 36.180662 | 80 | py |
segmentation_models | segmentation_models-master/segmentation_models/backbones/inception_resnet_v2.py | """Inception-ResNet V2 model for Keras.
Model naming and structure follows TF-slim implementation
(which has some additional layers and different number of
filters from the original arXiv paper):
https://github.com/tensorflow/models/blob/master/research/slim/nets/inception_resnet_v2.py
Pre-trained ImageNet weights are ... | 14,925 | 41.645714 | 90 | py |
segmentation_models | segmentation_models-master/tests/test_models.py | import os
import pytest
import random
import six
import numpy as np
import segmentation_models as sm
from segmentation_models import Unet
from segmentation_models import Linknet
from segmentation_models import PSPNet
from segmentation_models import FPN
from segmentation_models import get_available_backbone_names
if s... | 3,550 | 24.919708 | 80 | py |
segmentation_models | segmentation_models-master/tests/test_metrics.py | import pytest
import numpy as np
import segmentation_models as sm
from segmentation_models.metrics import IOUScore, FScore
from segmentation_models.losses import JaccardLoss, DiceLoss
if sm.framework() == sm._TF_KERAS_FRAMEWORK_NAME:
from tensorflow import keras
elif sm.framework() == sm._KERAS_FRAMEWORK_NAME:
... | 4,481 | 19.280543 | 95 | py |
segmentation_models | segmentation_models-master/tests/test_utils.py | import pytest
import numpy as np
import segmentation_models as sm
from segmentation_models.utils import set_regularization
from segmentation_models import Unet
if sm.framework() == sm._TF_KERAS_FRAMEWORK_NAME:
from tensorflow import keras
elif sm.framework() == sm._KERAS_FRAMEWORK_NAME:
import keras
else:
... | 3,143 | 26.823009 | 91 | py |
segmentation_models | segmentation_models-master/docs/conf.py | # -*- coding: utf-8 -*-
#
# Configuration file for the Sphinx documentation builder.
#
# This file does only contain a selection of the most common options. For a
# full list see the documentation:
# http://www.sphinx-doc.org/en/master/config
# -- Path setup ------------------------------------------------------------... | 5,775 | 28.620513 | 80 | py |
PySyft | PySyft-master/packages/syft/examples/duet/dcgan/original/main.py | # future
from __future__ import print_function
# stdlib
import argparse
import os
import random
# third party
import torch
import torch.backends.cudnn as cudnn
import torch.nn as nn
import torch.nn.parallel
import torch.optim as optim
import torch.utils.data
import torchvision.datasets as dset
import torchvision.tran... | 11,189 | 30.432584 | 88 | py |
PySyft | PySyft-master/packages/syft/examples/duet/snli/original/model.py | # third party
import torch
import torch.nn as nn
class Bottle(nn.Module):
def forward(self, input):
if len(input.size()) <= 2:
return super(Bottle, self).forward(input)
size = input.size()[:2]
out = super(Bottle, self).forward(input.view(size[0] * size[1], -1))
return o... | 2,753 | 31.785714 | 76 | py |
PySyft | PySyft-master/packages/syft/examples/duet/snli/original/util.py | # stdlib
from argparse import ArgumentParser
import os
def makedirs(name):
"""helper function for python 2 and 3 to call os.makedirs()
avoiding an error if the directory to be created already exists"""
# stdlib
import errno
import os
try:
os.makedirs(name)
except OSError as ex:
... | 4,348 | 28.385135 | 102 | py |
PySyft | PySyft-master/packages/syft/examples/duet/snli/original/train.py | # stdlib
import glob
import os
import time
# third party
from model import SNLIClassifier
import torch
import torch.nn as nn
import torch.optim as O
from torchtext import data
from torchtext import datasets
from util import get_args
from util import makedirs
args = get_args()
if torch.cuda.is_available():
torch.c... | 6,231 | 30.16 | 102 | py |
PySyft | PySyft-master/packages/syft/examples/duet/time_sequence_prediction/original/generate_sine_wave.py | # third party
import numpy as np
import torch
np.random.seed(2)
T = 20
L = 1000
N = 100
x = np.empty((N, L), "int64")
x[:] = np.array(range(L)) + np.random.randint(-4 * T, 4 * T, N).reshape(N, 1)
data = np.sin(x / 1.0 / T).astype("float64")
torch.save(data, open("traindata.pt", "wb"))
| 289 | 18.333333 | 77 | py |
PySyft | PySyft-master/packages/syft/examples/duet/time_sequence_prediction/original/train.py | # future
from __future__ import print_function
# stdlib
import argparse
# third party
import matplotlib
import numpy as np
import torch
import torch.nn as nn
import torch.optim as optim
matplotlib.use("Agg")
# third party
import matplotlib.pyplot as plt
class Sequence(nn.Module):
def __init__(self):
su... | 3,566 | 28.237705 | 89 | py |
PySyft | PySyft-master/packages/syft/examples/duet/reinforcement_learning/original/reinforce.py | # stdlib
import argparse
from itertools import count
# third party
import gym
import numpy as np
import torch
from torch.distributions import Categorical
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
parser = argparse.ArgumentParser(description="PyTorch REINFORCE example")
parser.a... | 3,344 | 25.975806 | 83 | py |
PySyft | PySyft-master/packages/syft/examples/duet/reinforcement_learning/original/actor_critic.py | # stdlib
import argparse
from collections import namedtuple
from itertools import count
# third party
import gym
import numpy as np
import torch
from torch.distributions import Categorical
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
# Cart Pole
parser = argparse.ArgumentParser(de... | 5,421 | 25.841584 | 83 | py |
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