573 lines
21 KiB
Python
573 lines
21 KiB
Python
# -*- coding: utf-8 -*-
|
|
# Copyright (c) Alibaba, Inc. and its affiliates.
|
|
|
|
# Modified from https://github.com/pytorch/vision/blob/main/torchvision/models/resnet.py
|
|
|
|
# BSD 3-Clause License
|
|
#
|
|
# Copyright (c) Soumith Chintala 2016,
|
|
# 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 conditions and the following disclaimer.
|
|
#
|
|
# * Redistributions in binary form must reproduce the above copyright notice,
|
|
# this list of conditions and the following disclaimer in the documentation
|
|
# and/or other materials provided with the distribution.
|
|
#
|
|
# * Neither the name of the copyright holder nor the names of its
|
|
# contributors may be used to endorse or promote products derived from
|
|
# this software without specific prior written permission.
|
|
#
|
|
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
|
|
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
|
|
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
|
|
# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
|
|
# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
|
|
# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
|
|
# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
|
|
# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
|
|
# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
|
|
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
|
|
|
|
from typing import Any, Callable, List, Optional, Type, Union
|
|
|
|
import torch.nn as nn
|
|
from torch import Tensor
|
|
|
|
try:
|
|
from torch.hub import load_state_dict_from_url
|
|
except ImportError:
|
|
from torch.utils.model_zoo import load_url as load_state_dict_from_url
|
|
|
|
__all__ = [
|
|
'ResNet', 'resnet18', 'resnet34', 'resnet50', 'resnet101', 'resnet152',
|
|
'resnext50_32x4d', 'resnext101_32x8d', 'wide_resnet50_2',
|
|
'wide_resnet101_2'
|
|
]
|
|
|
|
model_urls = {
|
|
'resnet18':
|
|
'https://download.pytorch.org/models/resnet18-f37072fd.pth',
|
|
'resnet34':
|
|
'https://download.pytorch.org/models/resnet34-b627a593.pth',
|
|
'resnet50':
|
|
'https://download.pytorch.org/models/resnet50-0676ba61.pth',
|
|
'resnet101':
|
|
'https://download.pytorch.org/models/resnet101-63fe2227.pth',
|
|
'resnet152':
|
|
'https://download.pytorch.org/models/resnet152-394f9c45.pth',
|
|
'resnext50_32x4d':
|
|
'https://download.pytorch.org/models/resnext50_32x4d-7cdf4587.pth',
|
|
'resnext101_32x8d':
|
|
'https://download.pytorch.org/models/resnext101_32x8d-8ba56ff5.pth',
|
|
'wide_resnet50_2':
|
|
'https://download.pytorch.org/models/wide_resnet50_2-95faca4d.pth',
|
|
'wide_resnet101_2':
|
|
'https://download.pytorch.org/models/wide_resnet101_2-32ee1156.pth',
|
|
}
|
|
|
|
|
|
def conv3x3(in_planes: int,
|
|
out_planes: int,
|
|
stride: int = 1,
|
|
groups: int = 1,
|
|
dilation: int = 1) -> nn.Conv2d:
|
|
"""3x3 convolution with padding"""
|
|
return nn.Conv2d(in_planes,
|
|
out_planes,
|
|
kernel_size=3,
|
|
stride=stride,
|
|
padding=dilation,
|
|
groups=groups,
|
|
bias=False,
|
|
dilation=dilation)
|
|
|
|
|
|
def conv1x1(in_planes: int, out_planes: int, stride: int = 1) -> nn.Conv2d:
|
|
"""1x1 convolution"""
|
|
return nn.Conv2d(in_planes,
|
|
out_planes,
|
|
kernel_size=1,
|
|
stride=stride,
|
|
bias=False)
|
|
|
|
|
|
class BasicBlock(nn.Module):
|
|
expansion: int = 1
|
|
|
|
def __init__(
|
|
self,
|
|
inplanes: int,
|
|
planes: int,
|
|
stride: int = 1,
|
|
downsample: Optional[nn.Module] = None,
|
|
groups: int = 1,
|
|
base_width: int = 64,
|
|
dilation: int = 1,
|
|
norm_layer: Optional[Callable[..., nn.Module]] = None) -> None:
|
|
super(BasicBlock, self).__init__()
|
|
if norm_layer is None:
|
|
norm_layer = nn.BatchNorm2d
|
|
if groups != 1 or base_width != 64:
|
|
raise ValueError(
|
|
'BasicBlock only supports groups=1 and base_width=64')
|
|
if dilation > 1:
|
|
raise NotImplementedError(
|
|
'Dilation > 1 not supported in BasicBlock')
|
|
# Both self.conv1 and self.downsample layers downsample the input when stride != 1
|
|
self.conv1 = conv3x3(inplanes, planes, stride)
|
|
self.bn1 = norm_layer(planes)
|
|
self.relu = nn.ReLU(inplace=True)
|
|
self.conv2 = conv3x3(planes, planes)
|
|
self.bn2 = norm_layer(planes)
|
|
self.downsample = downsample
|
|
self.stride = stride
|
|
|
|
def forward(self, x: Tensor) -> Tensor:
|
|
identity = x
|
|
|
|
out = self.conv1(x)
|
|
out = self.bn1(out)
|
|
out = self.relu(out)
|
|
|
|
out = self.conv2(out)
|
|
out = self.bn2(out)
|
|
|
|
if self.downsample is not None:
|
|
identity = self.downsample(x)
|
|
|
|
out += identity
|
|
out = self.relu(out)
|
|
|
|
return out
|
|
|
|
|
|
class Bottleneck(nn.Module):
|
|
# Bottleneck in torchvision places the stride for downsampling at 3x3 convolution(self.conv2)
|
|
# while original implementation places the stride at the first 1x1 convolution(self.conv1)
|
|
# according to "Deep residual learning for image recognition"https://arxiv.org/abs/1512.03385.
|
|
# This variant is also known as ResNet V1.5 and improves accuracy according to
|
|
# https://ngc.nvidia.com/catalog/model-scripts/nvidia:resnet_50_v1_5_for_pytorch.
|
|
|
|
expansion: int = 4
|
|
|
|
def __init__(
|
|
self,
|
|
inplanes: int,
|
|
planes: int,
|
|
stride: int = 1,
|
|
downsample: Optional[nn.Module] = None,
|
|
groups: int = 1,
|
|
base_width: int = 64,
|
|
dilation: int = 1,
|
|
norm_layer: Optional[Callable[..., nn.Module]] = None) -> None:
|
|
super(Bottleneck, self).__init__()
|
|
if norm_layer is None:
|
|
norm_layer = nn.BatchNorm2d
|
|
width = int(planes * (base_width / 64.)) * groups
|
|
# Both self.conv2 and self.downsample layers downsample the input when stride != 1
|
|
self.conv1 = conv1x1(inplanes, width)
|
|
self.bn1 = norm_layer(width)
|
|
self.conv2 = conv3x3(width, width, stride, groups, dilation)
|
|
self.bn2 = norm_layer(width)
|
|
self.conv3 = conv1x1(width, planes * self.expansion)
|
|
self.bn3 = norm_layer(planes * self.expansion)
|
|
self.relu = nn.ReLU(inplace=True)
|
|
self.downsample = downsample
|
|
self.stride = stride
|
|
|
|
def forward(self, x: Tensor) -> Tensor:
|
|
identity = x
|
|
|
|
out = self.conv1(x)
|
|
out = self.bn1(out)
|
|
out = self.relu(out)
|
|
|
|
out = self.conv2(out)
|
|
out = self.bn2(out)
|
|
out = self.relu(out)
|
|
|
|
out = self.conv3(out)
|
|
out = self.bn3(out)
|
|
|
|
if self.downsample is not None:
|
|
identity = self.downsample(x)
|
|
|
|
out += identity
|
|
out = self.relu(out)
|
|
|
|
return out
|
|
|
|
|
|
class ResNet(nn.Module):
|
|
def __init__(
|
|
self,
|
|
block: Type[Union[BasicBlock, Bottleneck]],
|
|
layers: List[int],
|
|
kernel_size=7,
|
|
use_relu=True,
|
|
use_maxpool=True,
|
|
first_conv_stride=1,
|
|
first_max_pool_stride=1,
|
|
num_classes: int = 1000,
|
|
zero_init_residual: bool = False,
|
|
groups: int = 1,
|
|
width_per_group: int = 64,
|
|
replace_stride_with_dilation: Optional[List[bool]] = None,
|
|
norm_layer: Optional[Callable[..., nn.Module]] = None) -> None:
|
|
super(ResNet, self).__init__()
|
|
if norm_layer is None:
|
|
norm_layer = nn.BatchNorm2d
|
|
self._norm_layer = norm_layer
|
|
|
|
self.inplanes = 64
|
|
self.dilation = 1
|
|
if replace_stride_with_dilation is None:
|
|
# each element in the tuple indicates if we should replace
|
|
# the 2x2 stride with a dilated convolution instead
|
|
replace_stride_with_dilation = [False, False, False]
|
|
if len(replace_stride_with_dilation) != 3:
|
|
raise ValueError('replace_stride_with_dilation should be None '
|
|
'or a 3-element tuple, got {}'.format(
|
|
replace_stride_with_dilation))
|
|
self.groups = groups
|
|
self.base_width = width_per_group
|
|
if kernel_size == 7:
|
|
self.conv1 = nn.Conv2d(3,
|
|
self.inplanes,
|
|
kernel_size=7,
|
|
stride=2,
|
|
padding=3,
|
|
bias=False)
|
|
self.bn1 = norm_layer(self.inplanes)
|
|
self.relu = nn.ReLU(inplace=True)
|
|
self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
|
|
elif kernel_size == 3:
|
|
self.conv1 = nn.Conv2d(3,
|
|
self.inplanes,
|
|
kernel_size=3,
|
|
stride=first_conv_stride,
|
|
padding=1,
|
|
bias=False)
|
|
self.bn1 = norm_layer(self.inplanes)
|
|
if use_relu:
|
|
self.relu = nn.ReLU(inplace=True)
|
|
if use_maxpool:
|
|
self.maxpool = nn.MaxPool2d(kernel_size=3,
|
|
stride=first_max_pool_stride,
|
|
padding=1)
|
|
self.layer1 = self._make_layer(block, 64, layers[0])
|
|
self.layer2 = self._make_layer(block,
|
|
128,
|
|
layers[1],
|
|
stride=2,
|
|
dilate=replace_stride_with_dilation[0])
|
|
self.layer3 = self._make_layer(block,
|
|
256,
|
|
layers[2],
|
|
stride=2,
|
|
dilate=replace_stride_with_dilation[1])
|
|
self.layer4 = self._make_layer(block,
|
|
512,
|
|
layers[3],
|
|
stride=2,
|
|
dilate=replace_stride_with_dilation[2])
|
|
# self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
|
|
# self.fc = nn.Linear(512 * block.expansion, num_classes)
|
|
|
|
for m in self.modules():
|
|
if isinstance(m, nn.Conv2d):
|
|
nn.init.kaiming_normal_(m.weight,
|
|
mode='fan_out',
|
|
nonlinearity='relu')
|
|
elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)):
|
|
nn.init.constant_(m.weight, 1)
|
|
nn.init.constant_(m.bias, 0)
|
|
|
|
# Zero-initialize the last BN in each residual branch,
|
|
# so that the residual branch starts with zeros, and each residual block behaves like an identity.
|
|
# This improves the model by 0.2~0.3% according to https://arxiv.org/abs/1706.02677
|
|
if zero_init_residual:
|
|
for m in self.modules():
|
|
if isinstance(m, Bottleneck):
|
|
nn.init.constant_(m.bn3.weight,
|
|
0) # type: ignore[arg-type]
|
|
elif isinstance(m, BasicBlock):
|
|
nn.init.constant_(m.bn2.weight,
|
|
0) # type: ignore[arg-type]
|
|
|
|
def _make_layer(self,
|
|
block: Type[Union[BasicBlock, Bottleneck]],
|
|
planes: int,
|
|
blocks: int,
|
|
stride: int = 1,
|
|
dilate: bool = False) -> nn.Sequential:
|
|
norm_layer = self._norm_layer
|
|
downsample = None
|
|
previous_dilation = self.dilation
|
|
if dilate:
|
|
self.dilation *= stride
|
|
stride = 1
|
|
if stride != 1 or self.inplanes != planes * block.expansion:
|
|
downsample = nn.Sequential(
|
|
conv1x1(self.inplanes, planes * block.expansion, stride),
|
|
norm_layer(planes * block.expansion),
|
|
)
|
|
|
|
layers = []
|
|
layers.append(
|
|
block(self.inplanes, planes, stride, downsample, self.groups,
|
|
self.base_width, previous_dilation, norm_layer))
|
|
self.inplanes = planes * block.expansion
|
|
for _ in range(1, blocks):
|
|
layers.append(
|
|
block(self.inplanes,
|
|
planes,
|
|
groups=self.groups,
|
|
base_width=self.base_width,
|
|
dilation=self.dilation,
|
|
norm_layer=norm_layer))
|
|
|
|
return nn.Sequential(*layers)
|
|
|
|
def _forward_impl(self, x: Tensor) -> Tensor:
|
|
# See note [TorchScript super()]
|
|
x = self.conv1(x)
|
|
x = self.bn1(x)
|
|
if hasattr(self, 'relu'):
|
|
x = self.relu(x)
|
|
if hasattr(self, 'maxpool'):
|
|
x = self.maxpool(x)
|
|
|
|
x = self.layer1(x)
|
|
x = self.layer2(x)
|
|
x = self.layer3(x)
|
|
x = self.layer4(x)
|
|
|
|
# x = self.avgpool(x)
|
|
# x = torch.flatten(x, 1)
|
|
# x = self.fc(x)
|
|
|
|
return x
|
|
|
|
def forward(self, x: Tensor) -> Tensor:
|
|
return self._forward_impl(x)
|
|
|
|
|
|
def _resnet(arch: str,
|
|
block: Type[Union[BasicBlock, Bottleneck]],
|
|
layers: List[int],
|
|
pretrained: bool,
|
|
progress: bool,
|
|
kernel_size: int,
|
|
use_relu=True,
|
|
use_maxpool=True,
|
|
first_conv_stride=1,
|
|
first_max_pool_stride=1,
|
|
**kwargs: Any) -> ResNet:
|
|
model = ResNet(block,
|
|
layers,
|
|
kernel_size=kernel_size,
|
|
use_relu=use_relu,
|
|
use_maxpool=use_maxpool,
|
|
first_conv_stride=first_conv_stride,
|
|
first_max_pool_stride=first_max_pool_stride,
|
|
**kwargs)
|
|
if pretrained:
|
|
state_dict = load_state_dict_from_url(model_urls[arch],
|
|
progress=progress)
|
|
state_dict.move_to_end('fc.weight', last=True)
|
|
state_dict.popitem(last=True)
|
|
state_dict.move_to_end('fc.bias', last=True)
|
|
state_dict.popitem(last=True)
|
|
model.load_state_dict(state_dict)
|
|
return model
|
|
|
|
|
|
def resnet18(pretrained: bool = False,
|
|
progress: bool = True,
|
|
kernel_size=7,
|
|
use_relu=True,
|
|
use_maxpool=True,
|
|
first_conv_stride=1,
|
|
first_max_pool_stride=1,
|
|
**kwargs: Any) -> ResNet:
|
|
r"""ResNet-18 model from
|
|
`"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_.
|
|
Args:
|
|
pretrained (bool): If True, returns a model pre-trained on ImageNet
|
|
progress (bool): If True, displays a progress bar of the download to stderr
|
|
"""
|
|
return _resnet('resnet18',
|
|
BasicBlock, [2, 2, 2, 2],
|
|
pretrained,
|
|
progress,
|
|
kernel_size=kernel_size,
|
|
use_relu=use_relu,
|
|
use_maxpool=use_maxpool,
|
|
first_conv_stride=first_conv_stride,
|
|
first_max_pool_stride=first_max_pool_stride,
|
|
**kwargs)
|
|
|
|
|
|
def resnet34(pretrained: bool = False,
|
|
progress: bool = True,
|
|
kernel_size=7,
|
|
**kwargs: Any) -> ResNet:
|
|
r"""ResNet-34 model from
|
|
`"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_.
|
|
Args:
|
|
pretrained (bool): If True, returns a model pre-trained on ImageNet
|
|
progress (bool): If True, displays a progress bar of the download to stderr
|
|
"""
|
|
return _resnet('resnet34',
|
|
BasicBlock, [3, 4, 6, 3],
|
|
pretrained,
|
|
progress,
|
|
kernel_size=kernel_size,
|
|
**kwargs)
|
|
|
|
|
|
def resnet50(pretrained: bool = False,
|
|
progress: bool = True,
|
|
kernel_size=7,
|
|
**kwargs: Any) -> ResNet:
|
|
r"""ResNet-50 model from
|
|
`"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_.
|
|
Args:
|
|
pretrained (bool): If True, returns a model pre-trained on ImageNet
|
|
progress (bool): If True, displays a progress bar of the download to stderr
|
|
"""
|
|
return _resnet('resnet50',
|
|
Bottleneck, [3, 4, 6, 3],
|
|
pretrained,
|
|
progress,
|
|
kernel_size=kernel_size,
|
|
**kwargs)
|
|
|
|
|
|
def resnet101(pretrained: bool = False,
|
|
progress: bool = True,
|
|
kernel_size=7,
|
|
**kwargs: Any) -> ResNet:
|
|
r"""ResNet-101 model from
|
|
`"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_.
|
|
Args:
|
|
pretrained (bool): If True, returns a model pre-trained on ImageNet
|
|
progress (bool): If True, displays a progress bar of the download to stderr
|
|
"""
|
|
return _resnet('resnet101',
|
|
Bottleneck, [3, 4, 23, 3],
|
|
pretrained,
|
|
progress,
|
|
kernel_size=kernel_size,
|
|
**kwargs)
|
|
|
|
|
|
def resnet152(pretrained: bool = False,
|
|
progress: bool = True,
|
|
kernel_size=7,
|
|
**kwargs: Any) -> ResNet:
|
|
r"""ResNet-152 model from
|
|
`"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_.
|
|
Args:
|
|
pretrained (bool): If True, returns a model pre-trained on ImageNet
|
|
progress (bool): If True, displays a progress bar of the download to stderr
|
|
"""
|
|
return _resnet('resnet152',
|
|
Bottleneck, [3, 8, 36, 3],
|
|
pretrained,
|
|
progress,
|
|
kernel_size=kernel_size,
|
|
**kwargs)
|
|
|
|
|
|
def resnext50_32x4d(pretrained: bool = False,
|
|
progress: bool = True,
|
|
kernel_size=7,
|
|
**kwargs: Any) -> ResNet:
|
|
r"""ResNeXt-50 32x4d model from
|
|
`"Aggregated Residual Transformation for Deep Neural Networks" <https://arxiv.org/pdf/1611.05431.pdf>`_.
|
|
Args:
|
|
pretrained (bool): If True, returns a model pre-trained on ImageNet
|
|
progress (bool): If True, displays a progress bar of the download to stderr
|
|
"""
|
|
kwargs['groups'] = 32
|
|
kwargs['width_per_group'] = 4
|
|
return _resnet('resnext50_32x4d',
|
|
Bottleneck, [3, 4, 6, 3],
|
|
pretrained,
|
|
progress,
|
|
kernel_size=kernel_size,
|
|
**kwargs)
|
|
|
|
|
|
def resnext101_32x8d(pretrained: bool = False,
|
|
progress: bool = True,
|
|
kernel_size=7,
|
|
**kwargs: Any) -> ResNet:
|
|
r"""ResNeXt-101 32x8d model from
|
|
`"Aggregated Residual Transformation for Deep Neural Networks" <https://arxiv.org/pdf/1611.05431.pdf>`_.
|
|
Args:
|
|
pretrained (bool): If True, returns a model pre-trained on ImageNet
|
|
progress (bool): If True, displays a progress bar of the download to stderr
|
|
"""
|
|
kwargs['groups'] = 32
|
|
kwargs['width_per_group'] = 8
|
|
return _resnet('resnext101_32x8d',
|
|
Bottleneck, [3, 4, 23, 3],
|
|
pretrained,
|
|
progress,
|
|
kernel_size=7,
|
|
**kwargs)
|
|
|
|
|
|
def wide_resnet50_2(pretrained: bool = False,
|
|
progress: bool = True,
|
|
kernel_size=7,
|
|
**kwargs: Any) -> ResNet:
|
|
r"""Wide ResNet-50-2 model from
|
|
`"Wide Residual Networks" <https://arxiv.org/pdf/1605.07146.pdf>`_.
|
|
The model is the same as ResNet except for the bottleneck number of channels
|
|
which is twice larger in every block. The number of channels in outer 1x1
|
|
convolutions is the same, e.g. last block in ResNet-50 has 2048-512-2048
|
|
channels, and in Wide ResNet-50-2 has 2048-1024-2048.
|
|
Args:
|
|
pretrained (bool): If True, returns a model pre-trained on ImageNet
|
|
progress (bool): If True, displays a progress bar of the download to stderr
|
|
"""
|
|
kwargs['width_per_group'] = 64 * 2
|
|
return _resnet('wide_resnet50_2',
|
|
Bottleneck, [3, 4, 6, 3],
|
|
pretrained,
|
|
progress,
|
|
kernel_size=kernel_size,
|
|
**kwargs)
|
|
|
|
|
|
def wide_resnet101_2(pretrained: bool = False,
|
|
progress: bool = True,
|
|
kernel_size=7,
|
|
**kwargs: Any) -> ResNet:
|
|
r"""Wide ResNet-101-2 model from
|
|
`"Wide Residual Networks" <https://arxiv.org/pdf/1605.07146.pdf>`_.
|
|
The model is the same as ResNet except for the bottleneck number of channels
|
|
which is twice larger in every block. The number of channels in outer 1x1
|
|
convolutions is the same, e.g. last block in ResNet-50 has 2048-512-2048
|
|
channels, and in Wide ResNet-50-2 has 2048-1024-2048.
|
|
Args:
|
|
pretrained (bool): If True, returns a model pre-trained on ImageNet
|
|
progress (bool): If True, displays a progress bar of the download to stderr
|
|
"""
|
|
kwargs['width_per_group'] = 64 * 2
|
|
return _resnet('wide_resnet101_2',
|
|
Bottleneck, [3, 4, 23, 3],
|
|
pretrained,
|
|
progress,
|
|
kernel_size=kernel_size,
|
|
**kwargs)
|