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import torch
import torch.nn as nn
from typing import Type, Union, List, Optional
class BasicBlock(nn.Module):
expansion: int = 1
def __init__(
self,
in_channels: int,
out_channels: int,
stride: int = 1,
downsample: Optional[nn.Module] = None,
) -> None:
super().__init__()
self.conv1 = nn.Conv2d(
in_channels, out_channels, kernel_size=3, stride=stride, padding=1, bias=False)
self.bn1 = nn.BatchNorm2d(out_channels)
self.relu = nn.ReLU(inplace=True)
self.conv2 = nn.Conv2d(out_channels, out_channels,
kernel_size=3, stride=1, padding=1, bias=False)
self.bn2 = nn.BatchNorm2d(out_channels)
self.downsample = downsample
self.stride = stride
def forward(self, x: torch.Tensor) -> torch.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):
expansion: int = 4
def __init__(
self,
in_channels: int,
out_channels: int,
stride: int = 1,
downsample: Optional[nn.Module] = None,
) -> None:
super().__init__()
width = out_channels
self.conv1 = nn.Conv2d(
in_channels, width, kernel_size=1, stride=1, bias=False)
self.bn1 = nn.BatchNorm2d(width)
self.conv2 = nn.Conv2d(width, width, kernel_size=3,
stride=stride, padding=1, bias=False)
self.bn2 = nn.BatchNorm2d(width)
self.conv3 = nn.Conv2d(
width, out_channels * self.expansion, kernel_size=1, stride=1, bias=False)
self.bn3 = nn.BatchNorm2d(out_channels * self.expansion)
self.relu = nn.ReLU(inplace=True)
self.downsample = downsample
self.stride = stride
def forward(self, x: torch.Tensor) -> torch.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 = 100,
) -> None:
super().__init__()
self.in_channels = 64
# Modified for CIFAR-100 (32x32 images)
self.conv1 = nn.Conv2d(3, 64, kernel_size=3,
stride=1, padding=1, bias=False)
self.bn1 = nn.BatchNorm2d(64)
self.relu = nn.ReLU(inplace=True)
self.layer1 = self._make_layer(block, 64, layers[0])
self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
self.layer3 = self._make_layer(block, 256, layers[2], stride=2)
self.layer4 = self._make_layer(block, 512, layers[3], stride=2)
self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
self.fc = nn.Linear(512 * block.expansion, num_classes)
def _make_layer(
self,
block: Type[Union[BasicBlock, Bottleneck]],
out_channels: int,
blocks: int,
stride: int = 1,
) -> nn.Sequential:
downsample = None
if stride != 1 or self.in_channels != out_channels * block.expansion:
downsample = nn.Sequential(
nn.Conv2d(self.in_channels, out_channels * block.expansion,
kernel_size=1, stride=stride, bias=False),
nn.BatchNorm2d(out_channels * block.expansion),
)
layers = []
layers.append(
block(self.in_channels, out_channels, stride, downsample))
self.in_channels = out_channels * block.expansion
for _ in range(1, blocks):
layers.append(block(self.in_channels, out_channels))
return nn.Sequential(*layers)
def forward(self, x: torch.Tensor) -> torch.Tensor:
x = self.conv1(x)
x = self.bn1(x)
x = self.relu(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 resnet18(num_classes: int = 100) -> ResNet:
return ResNet(BasicBlock, [2, 2, 2, 2], num_classes=num_classes)
def resnet34(num_classes: int = 100) -> ResNet:
return ResNet(BasicBlock, [3, 4, 6, 3], num_classes=num_classes)
def resnet50(num_classes: int = 100) -> ResNet:
return ResNet(Bottleneck, [3, 4, 6, 3], num_classes=num_classes)
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