"""ResNet implementation
All of this code is copied from torchvision.models.resnet. Only the input size of the first layer of the resnet
architecture is changes to allow for a 2D input
"""
import torch
from torch import Tensor, nn
[docs]
def conv3x3(in_planes: int, out_planes: int, stride: int = 1, groups: int = 1, dilation: int = 1) -> nn.Conv2d:
"""Create a 3x3 convolutional layer with padding.
This is a standard building block for ResNet, applying convolution with
kernel size 3x3 and padding to preserve spatial dimensions when stride=1.
Parameters
----------
in_planes : int
Number of input channels
out_planes : int
Number of output channels
stride : int
Stride for the convolution (default: 1)
groups : int
Number of groups for grouped convolution (default: 1)
dilation : int
Dilation rate for dilated convolution (default: 1)
Returns
-------
nn.Conv2d
A Conv2d module configured as a 3x3 convolution
"""
return nn.Conv2d(
in_planes,
out_planes,
kernel_size=3,
stride=stride,
padding=dilation,
groups=groups,
bias=False,
dilation=dilation,
)
[docs]
def conv1x1(in_planes: int, out_planes: int, stride: int = 1) -> nn.Conv2d:
"""Create a 1x1 convolutional layer.
Used in ResNet for bottleneck layers to change the number of channels
with minimal computational cost. Can also be used for spatial downsampling
when stride > 1.
Parameters
----------
in_planes : int
Number of input channels
out_planes : int
Number of output channels
stride : int
Stride for the convolution (default: 1)
Returns
-------
nn.Conv2d
A Conv2d module configured as a 1x1 convolution
"""
return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False)
[docs]
class BasicBlock(nn.Module):
"""ResNet BasicBlock with two 3x3 convolutions.
This is the building block for ResNet-18 and ResNet-34. It consists of two
consecutive 3x3 convolutional layers with batch normalization and ReLU
activation, plus a residual connection (skip connection) around the block.
"""
expansion: int = 1
"""Factor by which the output channels are expanded relative to input (always 1 for BasicBlock)."""
def __init__(
self,
inplanes: int,
planes: int,
stride: int = 1,
downsample=None,
groups: int = 1,
base_width: int = 64,
dilation: int = 1,
norm_layer=None,
) -> None:
"""Initialize a ResNet BasicBlock.
Parameters
----------
inplanes : int
Number of input channels
planes : int
Number of output channels for the main path
stride : int
Stride for the first convolution (default: 1)
downsample : nn.Module, optional
Module applied to the input to match output channels and spatial dimensions
groups : int
Number of groups for grouped convolution (default: 1). Must be 1 for BasicBlock.
base_width : int
Base width (default: 64). Must be 64 for BasicBlock.
dilation : int
Dilation rate (default: 1). Must be 1 for BasicBlock.
norm_layer : type, optional
Normalization layer class (default: BatchNorm2d)
Raises
------
ValueError
If groups != 1 or base_width != 64
NotImplementedError
If dilation > 1
"""
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)
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
[docs]
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
[docs]
class ResNet(nn.Module):
def __init__(
self,
block,
layers,
input_channels=1,
num_classes: int = 1000,
zero_init_residual: bool = False,
groups: int = 1,
width_per_group: int = 64,
replace_stride_with_dilation=None,
norm_layer=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]
self.groups = groups
self.base_width = width_per_group
#################################################################################
self.conv1 = nn.Conv2d(input_channels, self.inplanes, kernel_size=7, stride=2, padding=3, bias=False)
# This is different from the orgiginal pytorch code, variable input channel size
#################################################################################
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, BasicBlock) and m.bn2.weight is not None:
nn.init.constant_(m.bn2.weight, 0) # type: ignore[arg-type]
def _make_layer(
self,
block,
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
[docs]
def forward(self, x: Tensor) -> Tensor:
return self._forward_impl(x)
def _resnet(
block,
layers,
**kwargs,
) -> ResNet:
model = ResNet(block, layers, **kwargs)
return model
[docs]
def resnet18(**kwargs) -> ResNet:
"""ResNet-18 from `Deep Residual Learning for Image Recognition <https://arxiv.org/pdf/1512.03385.pdf>`__.
Parameters
----------
**kwargs
Parameters passed to the `torchvision.models.resnet.ResNet` base class. Please refer to the
`source code <https://github.com/pytorch/vision/blob/main/torchvision/models/resnet.py>`_
for more details about this class.
"""
return _resnet(BasicBlock, [2, 2, 2, 2], **kwargs)