基于DRIVE数据集的视网膜UNet分割

1 数据集介绍

这是一个非常小的数据集，非常适合用于视觉分割任务练手。数据集的文件夹如图所示：

图1-1文件夹结构

test中存放的是测试图片，training中存放的是20张用于训练的图片。imges文件夹中存放的是20张原始图片，mask中存放的是掩码，用于获取感兴趣的区域。manual中存放的是人工标注的groundtruth。

图1-2 原始图片

图1-3 groundtruth?

2 数据集的加载

获取数据集中image和对应的mask

class DriveDataset(Dataset):
    def __init__(self, root: str, train: bool, transforms=None):
        super(DriveDataset, self).__init__()
        self.flag = "training" if train else "test"
        data_root = os.path.join(root, "DRIVE", self.flag)
        # 使用断言，目录不存在，则发出警告
        assert os.path.exists(data_root), f"path '{data_root}' does not exists."
        # 在transform 中对图像预处理
        self.transforms = transforms
        """
        (os.path.join(data_root, "images") 获得目录
        os.listdir(os.path.join(data_root, "images")) 获取目录下的文件名，返回列表
        [i for i in ...] 使用for循环形成新的列表
        """
        img_names = [i for i in os.listdir(os.path.join(data_root, "images")) if i.endswith(".tif")]
        # 获取图片与GT的完整路径
        self.img_list = [os.path.join(data_root, "images", i) for i in img_names]
        self.manual = [os.path.join(data_root, "1st_manual", i.split("_")[0] + "_manual1.gif")
                       for i in img_names]
        # check files
        for i in self.manual:
            if os.path.exists(i) is False:
                raise FileNotFoundError(f"file {i} does not exists.")

        self.roi_mask = [os.path.join(data_root, "mask", i.split("_")[0] + f"_{self.flag}_mask.gif")
                         for i in img_names]
        # check files
        for i in self.roi_mask:
            if os.path.exists(i) is False:
                raise FileNotFoundError(f"file {i} does not exists.")

    def __getitem__(self, idx):
        img = Image.open(self.img_list[idx]).convert('RGB')
        manual = Image.open(self.manual[idx]).convert('L')
        # 0:背景，1：前景，而此时的mask中的前景像素值是255，所以÷255，令其为1
        manual = np.array(manual) / 255
        roi_mask = Image.open(self.roi_mask[idx]).convert('L')
        # 将不感兴趣区域的no_roi区域的像素值设置成255（不参与计算LOSS）
        roi_mask = 255 - np.array(roi_mask)
        # 使用np.clip()方法，为叠加了manual(GT)与roi_mask后的像素设置像素的上下限
        mask = np.clip(manual + roi_mask, a_min=0, a_max=255)
        # 这里转回PIL的原因是，transforms中是对PIL数据进行处理
        mask = Image.fromarray(mask)

        if self.transforms is not None:
            img, mask = self.transforms(img, mask)

        return img, mask

    def __len__(self):
        return len(self.img_list)

    @staticmethod
    def collate_fn(batch):
        images, targets = list(zip(*batch))
        batched_imgs = cat_list(images, fill_value=0)
        batched_targets = cat_list(targets, fill_value=255)
        return batched_imgs, batched_targets

3. 建立UNet模型

from typing import Dict
import torch
import torch.nn as nn
import torch.nn.functional as F
import netron

class DoubleConv(nn.Sequential):
    def __init__(self, in_channels, out_channels, mid_channels=None):
        if mid_channels is None:
            mid_channels = out_channels
        super(DoubleConv, self).__init__(
            nn.Conv2d(in_channels, mid_channels, kernel_size=3, padding=1, bias=False),
            nn.BatchNorm2d(mid_channels),
            nn.ReLU(inplace=True),
            nn.Conv2d(mid_channels, out_channels, kernel_size=3, padding=1, bias=False),
            nn.BatchNorm2d(out_channels),
            nn.ReLU(inplace=True)
        )

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


class Up(nn.Module):
    def __init__(self, in_channels, out_channels, bilinear=True):
        super(Up, self).__init__()
        if bilinear:
            self.up = nn.Upsample(scale_factor=2, mode='bilinear', align_corners=True)
            self.conv = DoubleConv(in_channels, out_channels, in_channels // 2)
        else:
            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: torch.Tensor, x2: torch.Tensor) -> torch.Tensor:
        x1 = self.up(x1)
        # [N, C, H, W]
        diff_y = x2.size()[2] - x1.size()[2]
        diff_x = x2.size()[3] - x1.size()[3]

        # padding_left, padding_right, padding_top, padding_bottom
        x1 = F.pad(x1, [diff_x // 2, diff_x - diff_x // 2,
                        diff_y // 2, diff_y - diff_y // 2])

        x = torch.cat([x2, x1], dim=1)
        x = self.conv(x)
        return x

class OutConv(nn.Sequential):
    def __init__(self, in_channels, num_classes):
        super(OutConv, self).__init__(
            nn.Conv2d(in_channels, num_classes, kernel_size=1)
        )


class UNet(nn.Module):
    def __init__(self,
                 in_channels: int = 1,
                 num_classes: int = 2,
                 bilinear: bool = True,
                 base_c: int = 64):
        super(UNet, self).__init__()
        self.in_channels = in_channels
        self.num_classes = num_classes
        self.bilinear = bilinear

        self.in_conv = DoubleConv(in_channels, base_c)
        self.down1 = Down(base_c, base_c * 2)
        self.down2 = Down(base_c * 2, base_c * 4)
        self.down3 = Down(base_c * 4, base_c * 8)
        factor = 2 if bilinear else 1
        self.down4 = Down(base_c * 8, base_c * 16 // factor)
        self.up1 = Up(base_c * 16, base_c * 8 // factor, bilinear)
        self.up2 = Up(base_c * 8, base_c * 4 // factor, bilinear)
        self.up3 = Up(base_c * 4, base_c * 2 // factor, bilinear)
        self.up4 = Up(base_c * 2, base_c, bilinear)
        self.out_conv = OutConv(base_c, num_classes)

    def forward(self, x: torch.Tensor) -> Dict[str, torch.Tensor]:
        x1 = self.in_conv(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.out_conv(x)

        return {"out": logits}

4 编写损失函数和评价函数

import torch
import torch.nn as nn

def build_target(target: torch.Tensor, num_classes: int = 2, ignore_index: int = -100):
    """build target for dice coefficient"""
    dice_target = target.clone()
    if ignore_index >= 0:
        ignore_mask = torch.eq(target, ignore_index)
        dice_target[ignore_mask] = 0
        # [N, H, W] -> [N, H, W, C]
        dice_target = nn.functional.one_hot(dice_target, num_classes).float()
        dice_target[ignore_mask] = ignore_index
    else:
        dice_target = nn.functional.one_hot(dice_target, num_classes).float()

    return dice_target.permute(0, 3, 1, 2)


def dice_coeff(x: torch.Tensor, target: torch.Tensor, ignore_index: int = -100, epsilon=1e-6):
    # Average of Dice coefficient for all batches, or for a single mask
    # 计算一个batch中所有图片某个类别的dice_coefficient
    d = 0.
    batch_size = x.shape[0]
    for i in range(batch_size):
        x_i = x[i].reshape(-1)
        t_i = target[i].reshape(-1)
        if ignore_index >= 0:
            # 找出mask中不为ignore_index的区域
            roi_mask = torch.ne(t_i, ignore_index)
            x_i = x_i[roi_mask]
            t_i = t_i[roi_mask]
        inter = torch.dot(x_i, t_i)
        sets_sum = torch.sum(x_i) + torch.sum(t_i)
        if sets_sum == 0:
            sets_sum = 2 * inter

        d += (2 * inter + epsilon) / (sets_sum + epsilon)

    return d / batch_size


def multiclass_dice_coeff(x: torch.Tensor, target: torch.Tensor, ignore_index: int = -100, epsilon=1e-6):
    """Average of Dice coefficient for all classes"""
    dice = 0.
    for channel in range(x.shape[1]):
        dice += dice_coeff(x[:, channel, ...], target[:, channel, ...], ignore_index, epsilon)

    return dice / x.shape[1]


def dice_loss(x: torch.Tensor, target: torch.Tensor, multiclass: bool = False, ignore_index: int = -100):
    # Dice loss (objective to minimize) between 0 and 1
    x = nn.functional.softmax(x, dim=1)
    fn = multiclass_dice_coeff if multiclass else dice_coeff
    return 1 - fn(x, target, ignore_index=ignore_index)

5.开始训练

def main(args):
    device = torch.device(args.device if torch.cuda.is_available() else "cpu")
    batch_size = args.batch_size
    # segmentation nun_classes + background
    num_classes = args.num_classes + 1

    # using compute_mean_std.py
    mean = (0.709, 0.381, 0.224)
    std = (0.127, 0.079, 0.043)
    # 用来保存训练以及验证过程中信息
    results_file = "results{}.txt".format(datetime.datetime.now().strftime("%Y%m%d-%H%M%S"))
    train_dataset = DriveDataset(args.data_path,
                                 train=True,
                                 transforms=get_transform(train=True, mean=mean, std=std))
    val_dataset = DriveDataset(args.data_path,
                               train=False,
                               transforms=get_transform(train=False, mean=mean, std=std))
    num_workers = 0
    train_loader = torch.utils.data.DataLoader(train_dataset,
                                               batch_size=batch_size,
                                               num_workers=num_workers,
                                               shuffle=True,
                                               pin_memory=True,
                                               collate_fn=train_dataset.collate_fn)
    val_loader = torch.utils.data.DataLoader(val_dataset,
                                             batch_size=1,
                                             num_workers=num_workers,
                                             pin_memory=True,
                                             collate_fn=val_dataset.collate_fn)
    for img,lab in train_loader:
        print(img.shape)
        print(lab.shape)

    model = create_model(num_classes=num_classes)
    model.to(device)

    params_to_optimize = [p for p in model.parameters() if p.requires_grad]
    optimizer = torch.optim.SGD(
        params_to_optimize,
        lr=args.lr, momentum=args.momentum, weight_decay=args.weight_decay
    )

    scaler = torch.cuda.amp.GradScaler() if args.amp else None

    # 创建学习率更新策略，这里是每个step更新一次(不是每个epoch)
    lr_scheduler = create_lr_scheduler(optimizer, len(train_loader), args.epochs, warmup=True)
    if args.resume:
        checkpoint = torch.load(args.resume, map_location='cpu')  # load模型
        model.load_state_dict(checkpoint['model'])
        optimizer.load_state_dict(checkpoint['optimizer'])
        lr_scheduler.load_state_dict(checkpoint['lr_scheduler'])
        args.start_epoch = checkpoint['epoch'] + 1
        if args.amp:
            scaler.load_state_dict(checkpoint["scaler"])
    best_dice = 0.0
    start_time = time.time()
    for epoch in range(args.start_epoch, args.epochs):
        mean_loss, lr = train_one_epoch(model, optimizer, train_loader, device, epoch, num_classes,
                                        lr_scheduler=lr_scheduler, print_freq=args.print_freq, scaler=scaler)

        confmat, dice = evaluate(model, val_loader, device=device, num_classes=num_classes)
        val_info = str(confmat)
        print(val_info)
        print(f"dice coefficient: {dice:.3f}")
        # write into txt
        with open(results_file, "a") as f:
            # 记录每个epoch对应的train_loss、lr以及验证集各指标
            train_info = f"[epoch: {epoch}]\n" \
                         f"train_loss: {mean_loss:.4f}\n" \
                         f"lr: {lr:.6f}\n" \
                         f"dice coefficient: {dice:.3f}\n"
            f.write(train_info + val_info + "\n\n")
        if args.save_best is True:
            if best_dice < dice:
                best_dice = dice
            else:
                continue

        save_file = {"model": model.state_dict(),
                     "optimizer": optimizer.state_dict(),
                     "lr_scheduler": lr_scheduler.state_dict(),
                     "epoch": epoch,
                     "args": args}
        if args.amp:
            save_file["scaler"] = scaler.state_dict()

        if args.save_best is True:
            torch.save(save_file, "save_weights/best_model.pth")
        else:
            torch.save(save_file, "save_weights/model_{}.pth".format(epoch))

    total_time = time.time() - start_time
    total_time_str = str(datetime.timedelta(seconds=int(total_time)))
    print("training time {}".format(total_time_str))