【语义分割】unet结构和代码实现

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前言

Unet是2015年诞生的模型，它几乎是当segmentation项目中应用最广的模型。
Unet已经成为大多做医疗影像语义分割任务的最基础的网络结构。也启发了大量研究者去思考U型语义分割网络。即使在自然影像理解方面，也有越来越多的语义分割和目标检测模型开始关注和使用U型结构

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Unet能从更少的训练图像中进行学习。当它在少于 40 张图的生物医学数据集上训练时，IOU 值仍能达到 92%。
其姊妹篇 linknet的学习，敬请期待

1.语义分割的UNET网络结构

1.1 结构

U-net网络非常简单，前半部分作用是特征提取，后半部分是上采样。在一些文献中也把这样的结构叫做编码器-解码器结构。由于此网络整体结构类似于大写的英文字母U，故得名U-net。
U-net与其他常见的分割网络有一点非常不同的地方：U-net采用了完全不同的特征融合方式：拼接（tf.concat）U-net采用将特征在channel维度拼接在一起，形成更厚的特征。而FCN融合时使用的对应点相加，并不形成更厚的特征。
语义分割网络在特征融合时有两种办法：

1. FCN式的对应点相加，对应于TensorFlow中的tf.add()函数；
2. U-net式的channel维度拼接融合，对应TensorFlow的tf.concat()函数，比较占显存。

1.2 特点

1、网络对图像特征的多尺度特征识别。
2、上采样部分会融合特征提取部分的输出，这样做实际上是将多尺
度特征融合在了一起，以最后一个上采样为例，它的特征既来自第一
个卷积block的输出(同尺度特征)，也来自上采样的输出(大尺度特征)，

Unet的左侧是convolution layers，
右侧则是upsamping layers，
convolutions layers中每个pooling layer前输出值
会concatenate到对应的upsamping层的输出值中。
注意是concatenate，而FCN是add

2. 代码实现

2.1 数据初探

用的是香港中文大学的数据

# 显示1张图片
res = Image.open(r'F:\hk\training\00001.png')
res
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标签：

pil_img = Image.open(r'F:\hk\training\00001_matte.png')
np_img = np.array(pil_img)
plt.imshow(np_img)
plt.show()
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查看原图像有多少像素值

np.unique(np_img)
array([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,  10,  11,  12,
        13,  14,  15,  16,  17,  18,  19,  20,  21,  22,  23,  24,  25,
        26,  27,  28,  29,  30,  31,  32,  33,  34,  35,  36,  37,  38,
        39,  40,  41,  42,  43,  44,  45,  46,  47,  48,  49,  50,  51,
        52,  53,  54,  55,  56,  57,  58,  59,  60,  61,  62,  63,  64,
        65,  66,  67,  68,  69,  70,  71,  72,  73,  74,  75,  76,  77,
        78,  79,  80,  81,  82,  83,  84,  85,  86,  87,  88,  89,  90,
        91,  92,  93,  94,  95,  96,  97,  98,  99, 100, 101, 102, 103,
       104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116,
       117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129,
       130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142,
       143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155,
       156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168,
       169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181,
       182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194,
       195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207,
       208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220,
       221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233,
       234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246,
       247, 248, 249, 250, 251, 252, 253, 254, 255], dtype=uint8)
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2.2 构建数据集

# 构建训练集， 测试集
all_pics = glob.glob(r'F:\hk\training\*.png')
all_pics[:5]
['F:\\hk\\training\\00001.png',
 'F:\\hk\\training\\00001_matte.png',
 'F:\\hk\\training\\00002.png',
 'F:\\hk\\training\\00002_matte.png',
 'F:\\hk\\training\\00003.png']
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区分出 图片和label,可以看到一共1700张

images= [p for p in all_pics if 'matte' not in p]
annotations = [p for p in all_pics if 'matte'  in p]
len(annotations)
1700
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打乱顺序，为保证可以复现，用一个固定的随机种子

# 随便取出一张来
np.random.seed(2023)
index = np.random.permutation(len(images))
index
array([ 630, 1065,   14, ...,  454, 1561,  855])
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测试集同理:

# test 和 train 是平行的
# 构建训练集， 测试集
all_test_pics = glob.glob(r'F:\hk\testing\*.png')
test_images = [p for p in all_test_pics if 'matte' not in p]
test_anno = [p for p in all_test_pics if 'matte' in p]
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resize & 转tensor， 转tensor的同时，它会把特征调到0-1范围内，并且增加c维度， 构成 c，h，w的形式

transform = transforms.Compose([
    transforms.Resize((256,256)),
    transforms.ToTensor(),
])
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构建dataset

# 这个方法很有用, 让训练集和测试集有了  切片的功能
class Portrait_dataset(data.Dataset):
    def __init__(self, img_paths, anno_paths) -> None:
        super().__init__()
        self.imgs = img_paths
        self.annos = anno_paths
    
    def __getitem__(self, index):
        img = self.imgs[index]
        anno = self.annos[index]

        pil_img = Image.open(img)
        img_tensor = transform(pil_img)

        anno_img = Image.open(anno)
        anno_tensor = transform(anno_img)
        anno_tensor = torch.squeeze(anno_tensor).type(torch.long)
        anno_tensor[anno_tensor>0] = 1
        return img_tensor, anno_tensor
    
    def __len__(self):
        return len(self.imgs)
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构建测试集训练集

train_dataset = Portrait_dataset(images, anno)
test_dataset = Portrait_dataset(test_images, test_anno)
BATCH_SIZE = 12
train_dl = data.DataLoader(
                           train_dataset,
                           batch_size=BATCH_SIZE,
                           shuffle=True)
test_dl = data.DataLoader(
                          test_dataset,
                          batch_size=BATCH_SIZE,
)
                         
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看一张长得什么样子

imgs_batch, annos_batch = next(iter(train_dl))
img = imgs_batch[0].permute(1,2,0).numpy()
anno = annos_batch[0].numpy()
plt.subplot(1,2,1)
plt.imshow(img)
plt.subplot(1,2,2)
plt.imshow(anno)
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2.3 构建网络

分三部分， 下采样，上采样 和全体网络

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().__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
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2.4 训练网络

model = Net()
if torch.cuda.is_available():
    model.to('cuda')
loss_fn = nn.CrossEntropyLoss()
from torch.optim import lr_scheduler
optimizer = torch.optim.Adam(model.parameters(), lr=0.001)
exp_lr_scheduler = lr_scheduler.StepLR(optimizer, step_size=7, gamma=0.1)

def fit(epoch, model, trainloader, testloader):
    correct = 0
    total = 0
    running_loss = 0
    
    model.train()
    for x, y in trainloader:
        if torch.cuda.is_available():
            x, y = x.to('cuda'), y.to('cuda')
        y_pred = model(x)
        loss = loss_fn(y_pred, y)
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()
        with torch.no_grad():
            y_pred = torch.argmax(y_pred, dim=1)
            correct += (y_pred == y).sum().item()
            total += y.size(0)
            running_loss += loss.item()
    exp_lr_scheduler.step()
    epoch_loss = running_loss / len(trainloader.dataset)
    epoch_acc = correct / (total*256*256)
        
        
    test_correct = 0
    test_total = 0
    test_running_loss = 0 
    
    model.eval()
    with torch.no_grad():
        for x, y in testloader:
            if torch.cuda.is_available():
                x, y = x.to('cuda'), y.to('cuda')
            y_pred = model(x)
            loss = loss_fn(y_pred, y)
            y_pred = torch.argmax(y_pred, dim=1)
            test_correct += (y_pred == y).sum().item()
            test_total += y.size(0)
            test_running_loss += loss.item()
    
    epoch_test_loss = test_running_loss / len(testloader.dataset)
    epoch_test_acc = test_correct / (test_total*256*256)
    
        
    print('epoch: ', epoch, 
          'loss： ', round(epoch_loss, 3),
          'accuracy:', round(epoch_acc, 3),
          'test_loss： ', round(epoch_test_loss, 3),
          'test_accuracy:', round(epoch_test_acc, 3)
             )
        
    return epoch_loss, epoch_acc, epoch_test_loss, epoch_test_acc

epochs = 30

train_loss = []
train_acc = []
test_loss = []
test_acc = []

for epoch in range(epochs):
    epoch_loss, epoch_acc, epoch_test_loss, epoch_test_acc = fit(epoch,
                                                                 model,
                                                                 train_dl,
                                                                 test_dl)
    train_loss.append(epoch_loss)
    train_acc.append(epoch_acc)
    test_loss.append(epoch_test_loss)
    test_acc.append(epoch_test_acc)
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后面10轮次的正确率已经不提高了，所以应该用earlier stop 策略

2.5 保存模型 & 测试

保存模型

PATH = 'unet_model.pth'
torch.save(model.state_dict(), PATH)
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测试模型

my_model = Net()
my_model.load_state_dict(torch.load(PATH))
num=3
image, mask = next(iter(test_dl))
pred_mask = my_model(image)

plt.figure(figsize=(10, 10))
for i in range(num):
    plt.subplot(num, 3, i*num+1)
    plt.imshow(image[i].permute(1,2,0).cpu().numpy())
    plt.subplot(num, 3, i*num+2)
    plt.imshow(mask[i].cpu().numpy())
    plt.subplot(num, 3, i*num+3)
    plt.imshow(torch.argmax(pred_mask[i].permute(1,2,0), axis=-1).detach().numpy())
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3. 总结

语义分割训练的过程很慢，因为对每一个像素分类的原因。
earily stop 是必须使用的。
他的结构，downsample 和 upsample的代码，以及融合代码要反复琢磨，还没完全学会。

4.补充 20230113

代码在这里：https://github.com/justinge/learn_pytorch/blob/main/UNET%E5%9B%BE%E5%83%8F%E8%AF%AD%E4%B9%89%E5%88%86%E5%89%B2.ipynb