import torch
import torch.nn as nn

class DoubleConv(nn.Module):
    def __init__(self, in_channels, out_channels):
        super().__init__()
        self.double_conv = nn.Sequential(
            nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=1),
            nn.BatchNorm2d(out_channels),
            nn.ReLU(inplace=True),
            nn.Conv2d(out_channels, out_channels, kernel_size=3, padding=1),
            nn.BatchNorm2d(out_channels),
            nn.ReLU(inplace=True)
        )

    def forward(self, x):
        return self.double_conv(x)

class Down(nn.Module):
    def __init__(self, in_channels, out_channels):
        super().__init__()
        self.maxpool_conv = nn.Sequential(
            nn.MaxPool2d(2),
            DoubleConv(in_channels, out_channels)
        )

    def forward(self, x):
        return self.maxpool_conv(x)

class Up(nn.Module):
    def __init__(self, in_channels, out_channels):
        super().__init__()
        self.up = nn.ConvTranspose2d(in_channels, in_channels // 2, kernel_size=2, stride=2)
        self.conv = DoubleConv(in_channels, out_channels)

    def forward(self, x1, x2):
        x1 = self.up(x1)
        # 调整x2大小以匹配x1
        diffY = x2.size()[2] - x1.size()[2]
        diffX = x2.size()[3] - x1.size()[3]
        x1 = nn.functional.pad(x1, [diffX // 2, diffX - diffX // 2,
                                    diffY // 2, diffY - diffY // 2])
        x = torch.cat([x2, x1], dim=1)
        return self.conv(x)

class OutConv(nn.Module):
    def __init__(self, in_channels, out_channels):
        super(OutConv, self).__init__()
        self.conv = nn.Conv2d(in_channels, out_channels, kernel_size=1)

    def forward(self, x):
        return self.conv(x)

class UNet(nn.Module):
    def __init__(self, n_channels, n_classes):
        super(UNet, self).__init__()
        self.n_channels = n_channels
        self.n_classes = n_classes

        self.inc = DoubleConv(n_channels, 64)
        self.down1 = Down(64, 128)
        self.down2 = Down(128, 256)
        self.down3 = Down(256, 512)
        self.up1 = Up(512, 256)
        self.up2 = Up(256, 128)
        self.up3 = Up(128, 64)
        self.outc = OutConv(64, n_classes)

    def forward(self, x):
        # 合并B和T维度（如果存在）
        is_5d = x.dim() == 5
        if is_5d:
            B, T, C, H, W = x.size()
            x = x.view(B * T, C, H, W)
        else:
            B, C, H, W = x.size()
            T = 1  # 无时间维度时设为1

        # 原UNet处理流程
        x1 = self.inc(x)
        x2 = self.down1(x1)
        x3 = self.down2(x2)
        x4 = self.down3(x3)
        x = self.up1(x4, x3)
        x = self.up2(x, x2)
        x = self.up3(x, x1)
        logits = self.outc(x)

        # 恢复原始维度（如果是5维输入）
        if is_5d:
            logits = logits.view(B, T, self.n_classes, H, W)

        return logits

# # 测试四维输入
# model = UNet(n_channels=2, n_classes=2)
# input_4d = torch.randn(1, 2, 128, 128)
# output_4d = model(input_4d)
# print(f"4D Output shape: {output_4d.shape}")  # 应为 [1, 2, 128, 128]

# # 测试五维输入
# input_5d = torch.randn(2, 10, 2, 128, 128)
# output_5d = model(input_5d)
# print(f"5D Output shape: {output_5d.shape}")  # 应为 [2, 3, 2, 128, 128]