数据增强

transforms.Compose([：这表示创建一个转换组合，将多个数据转换操作串联在一起
transforms.RandomHorizontalFlip()：这个操作是随机水平翻转图像，以增加数据的多样性。它以一定的概率随机地水平翻转输入的图像。
transforms.Resize(image_size)：这个操作用于将图像调整为指定的大小。image_size 是所需的输出图像大小，可以是一个整数或一个 (height, width) 元组。
transforms.CenterCrop(image_size)：这个操作用于从图像的中心裁剪出指定大小的区域。同样，image_size 可以是一个整数或一个 (height, width) 元组。
transforms.ToTensor()：这个操作将图像转换为 PyTorch 张量格式。它会将 PIL 图像或 ndarray 转换为张量，并对像素值进行归一化到 [0, 1] 的范围内。
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])：这个操作用于对图像进行标准化。它对张量的每个通道进行归一化处理，使得每个通道的均值为 0.485、0.456、0.406，标准差为 0.229、0.224、0.225。
Unet下采样：两层的卷积+relu+maxpooling
1.继承nn.model
2.初始化参数，输入channel，输出channel
nn.sequential序列 中写 卷积，relu(inplce=True节省计算资源)，卷积，Relu
最大池化层，缩减为1/2 长宽都减小一般
3.前向传播：需要有参数是否做maxpooling


Unet上采样：卷积、卷积 反卷积 不需要设置outchannel
1.继承nn.model
2.初始化参数，只需要输入通道数
nn.sequential序列中写 卷积（输入是输出的2倍（有contact操作））relu ,卷积，relu
反卷积的nn.sequential 输出通道数减半，保证图片的长宽是原来的2倍和relu函数


3.前向传播，卷积卷积 ，反卷积
Unet的整体结构：
encoder：先池化后卷积
decoder:卷积卷积反卷积
需要把前面卷积的数据进行融合

#网络
class Downsample(nn.Module):
    def __init__(self, in_channels, out_channels):
        super(Downsample, self).__init__()
        self.conv_relu = nn.Sequential(
                            nn.Conv2d(in_channels, out_channels, 
                                      kernel_size=3, padding=1),
                            nn.ReLU(inplace=True),
                            nn.Conv2d(out_channels, out_channels, 
                                      kernel_size=3, padding=1),
                            nn.ReLU(inplace=True)
            )
        self.pool = nn.MaxPool2d(kernel_size=2)
    def forward(self, x, is_pool=True):
        if is_pool:
            x = self.pool(x)
        x = self.conv_relu(x)
        return x
class Upsample(nn.Module):
    def __init__(self, channels):
        super(Upsample, self).__init__()
        self.conv_relu = nn.Sequential(
                            nn.Conv2d(2*channels, channels, 
                                      kernel_size=3, padding=1),
                            nn.ReLU(inplace=True),
                            nn.Conv2d(channels, channels,  
                                      kernel_size=3, padding=1),
                            nn.ReLU(inplace=True)
            )
        self.upconv_relu = nn.Sequential(
                               nn.ConvTranspose2d(channels, 
                                                  channels//2, 
                                                  kernel_size=3,
                                                  stride=2,
                                                  padding=1,
                                                  output_padding=1),
                               nn.ReLU(inplace=True)
            )
        
    def forward(self, x):
        x = self.conv_relu(x)
        x = self.upconv_relu(x)
        return x
class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.down1 = Downsample(3, 64)
        self.down2 = Downsample(64, 128)
        self.down3 = Downsample(128, 256)
        self.down4 = Downsample(256, 512)
        self.down5 = Downsample(512, 1024)
        
        self.up = nn.Sequential(
                               nn.ConvTranspose2d(1024, 
                                                  512, 
                                                  kernel_size=3,
                                                  stride=2,
                                                  padding=1,
                                                  output_padding=1),
                               nn.ReLU(inplace=True)
            )
        
        self.up1 = Upsample(512)
        self.up2 = Upsample(256)
        self.up3 = Upsample(128)
        
        self.conv_2 = Downsample(128, 64)
        self.last = nn.Conv2d(64, 2, kernel_size=1)

    def forward(self, x):
        x1 = self.down1(x, is_pool=False)
        x2 = self.down2(x1)
        x3 = self.down3(x2)
        x4 = self.down4(x3)
        x5 = self.down5(x4)
        
        x5 = self.up(x5)
        
        x5 = torch.cat([x4, x5], dim=1)           # 32*32*1024
        x5 = self.up1(x5)                         # 64*64*256)
        x5 = torch.cat([x3, x5], dim=1)           # 64*64*512  
        x5 = self.up2(x5)                         # 128*128*128
        x5 = torch.cat([x2, x5], dim=1)           # 128*128*256
        x5 = self.up3(x5)                         # 256*256*64
        x5 = torch.cat([x1, x5], dim=1)           # 256*256*128
        
        x5 = self.conv_2(x5, is_pool=False)       # 256*256*64
        
        x5 = self.last(x5)                        # 256*256*3

        return x5

#测试模型
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
model=Net().to(device)
# x = torch.rand([8,3,256,256])
# x=x.to(device)
# y=model(x)
# y.shape