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

class ModifiedBasicBlock(nn.Module):
    expansion = 1

    def __init__(self, in_channels, out_channels):
        super(ModifiedBasicBlock, self).__init__()
        self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1, padding=1, bias=False)
        self.bn1 = nn.BatchNorm2d(out_channels)
        self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=1, padding=1, bias=False)
        self.bn2 = nn.BatchNorm2d(out_channels)

        # 确保shortcut路径的维度匹配，如果不匹配则通过1x1卷积进行调整
        self.shortcut = nn.Sequential()
        if in_channels != out_channels:
            self.shortcut = nn.Sequential(
                nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=1, bias=False),
                nn.BatchNorm2d(out_channels)
            )

    def forward(self, x):
        out = F.relu(self.bn1(self.conv1(x)))
        out = self.bn2(self.conv2(out))
        out += self.shortcut(x)  # 添加shortcut连接
        out = F.relu(out)
        return out
class ModifiedResNet(nn.Module):
    def __init__(self, block, layers, num_classes=1000):
        super(ModifiedResNet, self).__init__()
        self.in_channels = 512  # 假设起始通道数为512
        self.conv1 = nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1, bias=False)
        self.bn1 = nn.BatchNorm2d(512)
        self.layer1 = self._make_layer(block, 512, layers[0])
        self.layer2 = self._make_layer(block, 512, layers[1])
        self.layer3 = self._make_layer(block, 512, layers[2])
        self.layer4 = self._make_layer(block, 512, layers[3])
        self.linear = nn.Linear(512*block.expansion, num_classes)

    def _make_layer(self, block, out_channels, blocks):
        layers = []
        for _ in range(blocks):
            layers.append(block(self.in_channels, out_channels))
            self.in_channels = out_channels * block.expansion
        return nn.Sequential(*layers)

    def forward(self, x):
        out = F.relu(self.bn1(self.conv1(x)))
        out = self.layer1(out)
        out = self.layer2(out)
        out = self.layer3(out)
        out = self.layer4(out)
        # 假设你有一个特定的方式来处理最终的特征图，以得到256x512维的输出
        # 例如，可以使用自适应池化层调整尺寸或者直接reshape（根据实际需求）
        out = F.adaptive_avg_pool2d(out, (x.size(0), x.size(1)))
        out = out.view(out.size(0), -1)
        out = self.linear(out)
        return out
