深度学习项目入门

我的建议是直接从相关的简单项目入手，这里以pytorch版本的U-Net分割代码为例（https://github.com/milesial/Pytorch-UNet）
我会先对项目的代码做一个全面的解析，再结合自身的经验给出一些关于网络模块自定义和调参的经验。

项目整体流程

这里先用伪代码的形式介绍下这个项目的大致流程

# 定义网络
 	net = UNet(n_channels=3, n_classes=args.classes, bilinear=args.bilinear)

# 加载数据集
    dataset = BasicDataset(dir_img, dir_mask)
    n_val = int(len(dataset) * val_percent)
    n_train = len(dataset) - n_val
    train_set, val_set = random_split(dataset, [n_train, n_val], generator=torch.Generator().manual_seed(0))
    
    loader_args = dict(batch_size=batch_size, num_workers=4, pin_memory=True)
    train_loader = DataLoader(train_set, shuffle=True, **loader_args)
    val_loader = DataLoader(val_set, shuffle=False, drop_last=True, **loader_args)

# 优化参数设置
 	optimizer = optim.RMSprop(net.parameters(), lr=learning_rate, weight_decay=1e-8, momentum=0.9)
    scheduler = optim.lr_scheduler.ReduceLROnPlateau(optimizer, 'max', patience=2)  # goal: maximize Dice score
    grad_scaler = torch.cuda.amp.GradScaler(enabled=amp)
    criterion = nn.CrossEntropyLoss()

# 训练
 	for epoch in range(1, epochs+1):
        net.train()
        epoch_loss = 0
        with tqdm(total=n_train, desc=f'Epoch {
     
     epoch}/{
     
     epochs}', unit='img') as pbar:
            for batch in train_loader:
            	optimizer.zero_grad()
                images = batch['image']
                true_masks = batch['mask']
                
                images = images.to(device=device, dtype=torch.float32)
                true_masks = true_masks.to(device=device, dtype=torch.long)
                masks_pred = net(images)
                loss = criterion(masks_pred, true_masks) \
                        + dice_loss(F.softmax(masks_pred, dim=1).float(),
                                    F.one_hot(true_masks, net.n_classes).permute(0, 3, 1, 2).float(),
                                    multiclass=True)
				optimizer.step()
                loss.backward()
	# 验证
		val_score = evaluate(net, val_loader, device)

定义网络

这里先回顾一下U-Net网络的基本结构，如下图所示

本质上就是先进行多次下采样，再进行多次上采样，途中使用双卷积提取特征，上采样时结合下采样时同尺寸的特征。

本项目中网络代码在/unet目录下，/unet/unet_model.py给出了模型的定义

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

        self.inc = DoubleConv(n_channels, 64)
        self.down1 = Down(64, 128)
        self.down2 = Down(128, 256)
        self.down3 = Down(256, 512)
        factor = 2 if bilinear else 1
        self.down4 = Down(512, 1024 // factor)
        self.up1 = Up(1024, 512 // factor, bilinear)
        self.up2 = Up(512, 256 // factor, bilinear)
        self.up3 = Up(256, 128 // factor, bilinear)
        self.up4 = Up(128, 64, bilinear)
        self.outc = OutConv(64, n_classes)

    def forward(self, x):
        x1 = self.inc(x)
        x2 = self.down1(x1)
        x3 = self.down2(x2)
        x4 = self.down3(x3)
        x5 = self.down4(x4)
        x = self.up1(x5, x4)
        x = self.up2(x, x3)
        x = self.up3(x, x2)
        x = self.up4(x, x1)
        logits = self.outc(x)
        return logits

可以看到，作者通过定义了一个U-Net的类来实现网络的定义，class中一共有两个函数：__init__与forward。

__init__函数用于对网络需要使用的参数或模块进行定义与声明，当通过下行代码构建网络时

 	net = UNet(n_channels=3, n_classes=args.classes, bilinear=args.bilinear)

网络会先唤起__init__函数将传入的n_channel等参数在网络中进行定义，令其成为class中的全局变量，这样才能让class中的其他函数对其访问。

	self.n_channels = n_channels
    self.n_classes = n_classes
    self.bilinear = bilinear

forward函数则定义了网络前向传播的过程，

 def forward(self, x):
        x1 = self.inc(x)
        x2 = self.down1(x1)
        x3 = self.down2(x2)
        x4 = self.down3(x3)
        x5