resnet50.py

import torch
import torch.nn as nn
import torch.nn.functional as F

class Bottleneck(nn.Module):
    expansion = 4

    def __init__(self, in_channels, out_channels, stride=1, downsample=None):
        super(Bottleneck, self).__init__()
        self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=1, bias=False)
        self.bn1 = nn.BatchNorm2d(out_channels)
        self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=stride, padding=1, bias=False)
        self.bn2 = nn.BatchNorm2d(out_channels)
        self.conv3 = nn.Conv2d(out_channels, out_channels * self.expansion, kernel_size=1, bias=False)
        self.bn3 = nn.BatchNorm2d(out_channels * self.expansion)
        self.relu = nn.ReLU(inplace=True)
        self.downsample = downsample

    def forward(self, x):
        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 ResNet50(nn.Module):
    def __init__(self, num_classes=100):
        super(ResNet50, self).__init__()
        self.in_channels = 64
        self.conv1 = nn.Conv2d(3, self.in_channels, kernel_size=3, stride=1, padding=1, bias=False)
        self.bn1 = nn.BatchNorm2d(self.in_channels)
        self.relu = nn.ReLU(inplace=True)
        self.layer1 = self._make_layer(64, 6, stride=1)
        self.layer2 = self._make_layer(128, 8, stride=2)
        self.layer3 = self._make_layer(256, 12, stride=2)
        self.layer4 = self._make_layer(512, 6, stride=2)
        self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
        self.fc = nn.Linear(512 * Bottleneck.expansion, num_classes)

    def _make_layer(self, channels, blocks, stride):
        downsample = None
        if stride != 1 or self.in_channels != channels * Bottleneck.expansion:
            downsample = nn.Sequential(
                nn.Conv2d(self.in_channels, channels * Bottleneck.expansion, kernel_size=1, stride=stride, bias=False),
                nn.BatchNorm2d(channels * Bottleneck.expansion)
            )
        layers = []
        layers.append(Bottleneck(self.in_channels, channels, stride, downsample))
        self.in_channels = channels * Bottleneck.expansion
        for _ in range(1, blocks):
            layers.append(Bottleneck(self.in_channels, channels))
        return nn.Sequential(*layers)

    def forward(self, x):
        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


if __name__ == "__main__":
    device = "cuda"
    model = ResNet50(num_classes=100).to(device)
    total_params = sum(p.numel() for p in model.parameters() if p.requires_grad)
    print(f"Total trainable parameters in ResNet-50: {total_params}")