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ResNet Feature Pyramid with Pytorch
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## This file is a modified version of [Pytorch's ResNet](https://github.com/pytorch/vision/blob/master/torchvision/models/resnet.py) and inherits it's licence. | |
## Author: Zeeshan Khan Suri | |
import torch | |
from torch import Tensor | |
import torch.nn as nn | |
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 | |
from typing import Type, Any, Callable, Union, List, Optional | |
__all__ = ['ResNet', 'ResNetFeatures', 'resnet18', 'resnet34', 'resnet50', 'resnet101', | |
'resnet152', 'resnext50_32x4d', 'resnext101_32x8d', | |
'wide_resnet50_2', 'wide_resnet101_2'] | |
model_urls = { | |
'resnet18': 'https://download.pytorch.org/models/resnet18-5c106cde.pth', | |
'resnet34': 'https://download.pytorch.org/models/resnet34-333f7ec4.pth', | |
'resnet50': 'https://download.pytorch.org/models/resnet50-19c8e357.pth', | |
'resnet101': 'https://download.pytorch.org/models/resnet101-5d3b4d8f.pth', | |
'resnet152': 'https://download.pytorch.org/models/resnet152-b121ed2d.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], | |
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 | |
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) | |
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) | |
x = self.relu(x) | |
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) | |
class ResNetFeatures(ResNet): | |
def __init__(self,**kwargs): | |
super(ResNetFeatures,self).__init__(**kwargs) | |
def _forward_impl(self, x: torch.Tensor) -> torch.Tensor: | |
# See note [TorchScript super()] | |
x = self.conv1(x) | |
x = self.bn1(x) | |
x0 = self.relu(x) | |
x = self.maxpool(x0) | |
x1 = self.layer1(x) | |
x2 = self.layer2(x1) | |
x3 = self.layer3(x2) | |
x4 = self.layer4(x3) | |
return [x0,x1,x2,x3,x4] # returns features with channels [64,64,128,256,512] | |
def _resnet( | |
arch: str, | |
block: Type[Union[BasicBlock, Bottleneck]], | |
layers: List[int], | |
pretrained: bool, | |
progress: bool, | |
features_only = False, | |
**kwargs: Any | |
): | |
if features_only: | |
model = ResNetFeatures(block=block, layers=layers, **kwargs) | |
else: | |
model = ResNet(block, layers, **kwargs) | |
if pretrained: | |
state_dict = load_state_dict_from_url(model_urls[arch], | |
progress=progress) | |
model.load_state_dict(state_dict) | |
return model | |
def resnet18(pretrained: bool = False, progress: bool = True, **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, | |
**kwargs) | |
def resnet34(pretrained: bool = False, progress: bool = True, **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, | |
**kwargs) | |
def resnet50(pretrained: bool = False, progress: bool = True, **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, | |
**kwargs) | |
def resnet101(pretrained: bool = False, progress: bool = True, **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, | |
**kwargs) | |
def resnet152(pretrained: bool = False, progress: bool = True, **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, | |
**kwargs) | |
def resnext50_32x4d(pretrained: bool = False, progress: bool = True, **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, **kwargs) | |
def resnext101_32x8d(pretrained: bool = False, progress: bool = True, **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, **kwargs) | |
def wide_resnet50_2(pretrained: bool = False, progress: bool = True, **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, **kwargs) | |
def wide_resnet101_2(pretrained: bool = False, progress: bool = True, **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, **kwargs) |
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Thank you! Really helpful.