"""Resnet Module."""
from typing import Any, Callable, List, Optional, Type, Union
import torch
import torch.nn as nn
from torch import Tensor
__all__ = ["ResNet", "resnet18", "resnet34", "resnet50", "resnet101", "resnet152"]
def conv3x3(
in_planes: int,
out_planes: int,
stride: int = 1,
groups: int = 1,
dilation: int = 1,
padding_mode: str = "zeros",
) -> 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,
padding_mode=padding_mode,
)
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,
padding_mode: str = "zeros",
norm_layer: Optional[Callable[..., nn.Module]] = None,
) -> None:
super().__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, padding_mode=padding_mode)
self.bn1 = norm_layer(planes)
self.relu = nn.ReLU(inplace=True)
self.conv2 = conv3x3(planes, planes, padding_mode=padding_mode)
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,
padding_mode: str = "zeros",
norm_layer: Optional[Callable[..., nn.Module]] = None,
) -> None:
super().__init__()
if norm_layer is None:
norm_layer = nn.BatchNorm2d
width = int(planes * (base_width / 64.0)) * 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, padding_mode=padding_mode)
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
[docs]
class ResNet(nn.Module):
"""ResNet model from :footcite:t:`he2016deep`.
Args:
block (Type[Union[BasicBlock, Bottleneck]]): Block type.
layers (List[int]): Number of layers.
channels_in (int): Number of input channels.
num_features (int): Number of features.
zero_init_residual (bool): Zero initialization of residual.
groups (int): Number of groups.
width_per_group (int): Width per group.
replace_stride_with_dilation (Optional[List[bool]]): Replace stride with dilation.
padding_mode (str): Padding mode for the convolutional layers.
norm_layer (Optional[Callable[..., nn.Module]]): Normalization layer.
"""
def __init__(
self,
block: Type[Union[BasicBlock, Bottleneck]],
layers: List[int],
channels_in: int = 3,
num_features: int = 1024,
zero_init_residual: bool = False,
groups: int = 1,
width_per_group: int = 64,
replace_stride_with_dilation: Optional[List[bool]] = None,
padding_mode: str = "zeros",
norm_layer: Optional[Callable[..., nn.Module]] = None,
) -> None:
super().__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 "
f"or a 3-element tuple, got {replace_stride_with_dilation}"
)
self.groups = groups
self.base_width = width_per_group
self.conv1 = nn.Conv2d(
channels_in,
self.inplanes,
kernel_size=7,
stride=2,
padding=3,
bias=False,
padding_mode=padding_mode,
)
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], padding_mode=padding_mode)
self.layer2 = self._make_layer(
block,
128,
layers[1],
stride=2,
dilate=replace_stride_with_dilation[0],
padding_mode=padding_mode,
)
self.layer3 = self._make_layer(
block,
256,
layers[2],
stride=2,
dilate=replace_stride_with_dilation[1],
padding_mode=padding_mode,
)
self.layer4 = self._make_layer(
block,
512,
layers[3],
stride=2,
dilate=replace_stride_with_dilation[2],
padding_mode=padding_mode,
)
self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
self.fc = nn.Linear(512 * block.expansion, num_features, bias=False)
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) and m.bn3.weight is not None:
nn.init.constant_(m.bn3.weight, 0) # type: ignore[arg-type]
elif isinstance(m, BasicBlock) and m.bn2.weight is not None:
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,
padding_mode: str = "zeros",
) -> 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,
padding_mode,
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,
padding_mode=padding_mode,
)
)
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:
"""Forward pass of the ResNet model."""
return self._forward_impl(x)
def _resnet(
block: Type[Union[BasicBlock, Bottleneck]],
layers: List[int],
**kwargs: Any,
) -> ResNet:
model = ResNet(block, layers, **kwargs)
return model
def resnet18(*args, **kwargs: Any) -> ResNet:
"""ResNet-18 from `Deep Residual Learning for Image Recognition <https://arxiv.org/abs/1512.03385>`__."""
return _resnet(BasicBlock, [2, 2, 2, 2], **kwargs)
def resnet34(*args, **kwargs: Any) -> ResNet:
"""ResNet-34 from `Deep Residual Learning for Image Recognition <https://arxiv.org/abs/1512.03385>`__."""
return _resnet(BasicBlock, [3, 4, 6, 3], **kwargs)
def resnet50(*args, **kwargs: Any) -> ResNet:
"""ResNet-50 from `Deep Residual Learning for Image Recognition <https://arxiv.org/abs/1512.03385>`__."""
return _resnet(Bottleneck, [3, 4, 6, 3], **kwargs)
def resnet101(*args, **kwargs: Any) -> ResNet:
"""ResNet-101 from `Deep Residual Learning for Image Recognition <https://arxiv.org/abs/1512.03385>`__."""
return _resnet(Bottleneck, [3, 4, 23, 3], **kwargs)
def resnet152(*args, **kwargs: Any) -> ResNet:
"""ResNet-101 from `Deep Residual Learning for Image Recognition <https://arxiv.org/abs/1512.03385>`__."""
return _resnet(Bottleneck, [3, 8, 36, 3], **kwargs)