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from typing import Optional
import torch.nn as nn
import torch
class BasicBlock(nn.Module):
"""ResNet Basic Block.
Parameters
----------
in_channels : int
Number of input channels
out_channels : int
Number of output channels
stride : int, optional
Convolution stride size, by default 1
identity_downsample : Optional[torch.nn.Module], optional
Downsampling layer, by default None
"""
def __init__(self,
in_channels: int,
out_channels: int,
stride: int = 1,
identity_downsample: Optional[torch.nn.Module] = None):
super(BasicBlock, self).__init__()
self.conv1 = nn.Conv2d(in_channels,
out_channels,
kernel_size = 3,
stride = stride,
padding = 1)
self.bn1 = nn.BatchNorm2d(out_channels)
self.relu = nn.ReLU()
self.conv2 = nn.Conv2d(out_channels,
out_channels,
kernel_size = 3,
stride = 1,
padding = 1)
self.bn2 = nn.BatchNorm2d(out_channels)
self.identity_downsample = identity_downsample
def forward(self, x: torch.Tensor) -> torch.Tensor:
"""Apply forward computation."""
identity = x
x = self.conv1(x)
x = self.bn1(x)
x = self.relu(x)
x = self.conv2(x)
x = self.bn2(x)
# Apply an operation to the identity output.
# Useful to reduce the layer size and match from conv2 output
if self.identity_downsample is not None:
identity = self.identity_downsample(identity)
x += identity
x = self.relu(x)
return x
class ResNet18(nn.Module):
"""Construct ResNet-18 Model.
Parameters
----------
input_channels : int
Number of input channels
num_classes : int
Number of class outputs
"""
def __init__(self, input_channels, num_classes):
super(ResNet18, self).__init__()
self.conv1 = nn.Conv2d(input_channels,
64, kernel_size = 7,
stride = 2, padding=3)
self.bn1 = nn.BatchNorm2d(64)
self.relu = nn.ReLU()
self.maxpool = nn.MaxPool2d(kernel_size = 3,
stride = 2,
padding = 1)
self.layer1 = self._make_layer(64, 64, stride = 1)
self.layer2 = self._make_layer(64, 128, stride = 2)
self.layer3 = self._make_layer(128, 256, stride = 2)
self.layer4 = self._make_layer(256, 512, stride = 2)
# Last layers
self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
self.fc = nn.Linear(512, num_classes)
def identity_downsample(self, in_channels: int, out_channels: int) -> nn.Module:
"""Downsampling block to reduce the feature sizes."""
return nn.Sequential(
nn.Conv2d(in_channels,
out_channels,
kernel_size = 3,
stride = 2,
padding = 1),
nn.BatchNorm2d(out_channels)
)
def _make_layer(self, in_channels: int, out_channels: int, stride: int) -> nn.Module:
"""Create sequential basic block."""
identity_downsample = None
# Add downsampling function
if stride != 1:
identity_downsample = self.identity_downsample(in_channels, out_channels)
return nn.Sequential(
BasicBlock(in_channels, out_channels, identity_downsample=identity_downsample, stride=stride),
BasicBlock(out_channels, out_channels)
)
def forward(self, x: torch.Tensor) -> torch.Tensor:
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 = x.view(x.shape[0], -1)
x = self.fc(x)
return x |