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import torch.nn as nn

class BasicBlock(nn.Module):
    def __init__(self, in_channels, out_channels, stride=1):
        super(BasicBlock, self).__init__()
        self.conv1 = nn.Conv2d(in_channels, out_channels, 3, stride, 1, bias=False)
        self.bn1 = nn.BatchNorm2d(out_channels)
        self.relu = nn.ReLU(inplace=True)
        self.conv2 = nn.Conv2d(out_channels, out_channels, 3, 1, 1, bias=False)
        self.bn2 = nn.BatchNorm2d(out_channels)

        self.shortcut = nn.Sequential()
        if stride != 1 or in_channels != out_channels:
            self.shortcut = nn.Sequential(
                nn.Conv2d(in_channels, out_channels, 1, stride, bias=False),
                nn.BatchNorm2d(out_channels)
            )

    def forward(self, x):
        out = self.relu(self.bn1(self.conv1(x)))
        out = self.bn2(self.conv2(out))
        out += self.shortcut(x)
        return self.relu(out)

class ResNet(nn.Module):
    def __init__(self, num_classes=1000):
        super(ResNet, self).__init__()
        self.conv1 = nn.Conv2d(3, 64, 7, 2, 3, bias=False)
        self.bn1 = nn.BatchNorm2d(64)
        self.relu = nn.ReLU(inplace=True)
        self.maxpool = nn.MaxPool2d(3, 2, 1)
        self.layer1 = self._make_layer(64, 64, 2)
        self.layer2 = self._make_layer(64, 128, 2, 2)
        self.layer3 = self._make_layer(128, 256, 2, 2)
        self.layer4 = self._make_layer(256, 512, 2, 2)
        self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
        self.fc = nn.Linear(512, num_classes)

    def _make_layer(self, in_ch, out_ch, blocks, stride=1):
        layers = [BasicBlock(in_ch, out_ch, stride)]
        for _ in range(1, blocks):
            layers.append(BasicBlock(out_ch, out_ch))
        return nn.Sequential(*layers)

    def forward(self, x):
        x = self.maxpool(self.relu(self.bn1(self.conv1(x))))
        x = self.layer4(self.layer3(self.layer2(self.layer1(x))))
        x = self.fc(torch.flatten(self.avgpool(x), 1))
        return x