尝试在UNet添加SK模块和CBAM模块

数据集：refuge数据集

训练轮数：10

Architecture	dice coefficient	mean IOU
unet	0.946	52.6
sk-unet	0.989	66.1
cbam-unet	0.988	65.8

（1）在UNet最后的输出卷积前添加，SK模块

训练结果：

[epoch: 9]
train_loss: 0.0710
lr: 0.009592
dice coefficient: 0.989
global correct: 97.8
average row correct: ['47.3', '99.1']
IoU: ['34.4', '97.8']
mean IoU: 66.1

模型改动：

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

# sk-unet
'''------------- SK模块-----------------------------'''
class SKConv(nn.Module):
    def __init__(self, features, WH, M, G, r, stride=1, L=32):
        """ Constructor
        Args:
            features: 输入通道维度
            WH: 输入特征图的空间维度
            M: 分支的数量
            G: 卷积组的数量
            r: 计算d，向量s的压缩倍数，C/r
            stride: 步长，默认为1
            L: 矢量z的最小维度，默认为32
        """
        super(SKConv, self).__init__()
        d = max(int(features / r), L)
        self.M = M
        self.features = features
        self.convs = nn.ModuleList([])
        # 使用不同kernel size的卷积，增加不同的感受野
        for i in range(M):
            self.convs.append(nn.Sequential(
                nn.Conv2d(features, features, kernel_size=3 + i * 2, stride=stride, padding=1 + i, groups=G),
                nn.BatchNorm2d(features),
                nn.ReLU(inplace=False)
            ))
        # 全局平均池化
        self.gap = nn.AvgPool2d(int(WH / stride))
        self.fc = nn.Linear(features, d)
        self.fcs = nn.ModuleList([])
        # 全连接层
        for i in range(M):
            self.fcs.append(
                nn.Linear(d, features)
            )
        self.softmax = nn.Softmax(dim=1)

    def forward(self, x):
        ''' Split操作'''
        for i, conv in enumerate(self.convs):
            fea = conv(x).unsqueeze_(dim=1)
            if i == 0:
                feas = fea
            else:
                feas = torch.cat([feas, fea], dim=1)

        ''' Fuse操作'''
        fea_U = torch.sum(feas, dim=1)
        fea_s = self.gap(fea_U).squeeze_()
        fea_z = self.fc(fea_s)

        ''' Select操作'''
        for i, fc in enumerate(self.fcs):
            # fc-->d*c维
            vector = fc(fea_z).unsqueeze_(dim=1)
            if i == 0:
                attention_vectors = vector
            else:
                attention_vectors = torch.cat([attention_vectors, vector], dim=1)
        # 计算attention权重
        attention_vectors = self.softmax(attention_vectors)
        attention_vectors = attention_vectors.unsqueeze(-1).unsqueeze(-1)
        # 最后一步，各特征图与对应的注意力权重相乘，得到输出特征图V
        fea_v = (feas * attention_vectors).sum(dim=1)
        return fea_v


# 卷积，在uent中卷积一般成对使用
class DoubleConv(nn.Sequential):
    # 输入通道数， 输出通道数， mid_channels为成对卷积中第一个卷积层的输出通道数
    def __init__(self, in_channels, out_channels, mid_channels=None):
        if mid_channels is None:
            mid_channels = out_channels
        super(DoubleConv, self).__init__(
            # 3*3卷积，填充为1，卷积之后输入输出的特征图大小一致
            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)
        )

# 下采样
class Down(nn.Sequential):
    def __init__(self, in_channels, out_channels):
        super(Down, self).__init__(
            # 1.最大池化的窗口大小为2， 步长为2
            nn.MaxPool2d(2, stride=2),
            # 2.两个卷积
            DoubleConv(in_channels, out_channels)
        )

# 上采样
class Up(nn.Module):
    # bilinear是否采用双线性插值
    def __init__(self, in_channels, out_channels, bilinear=True):
        super(Up, self).__init__()
        if bilinear:
            # 使用双线性插值上采样
            # 上采样率为2，双线性插值模式
            self.up = nn.Upsample(scale_factor=2, mode='bilinear', align_corners=True)
            self.conv = DoubleConv(in_channels, out_channels, in_channels // 2)
        else:
            # 使用转置卷积上采样
            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]
        # 上采样之后的特征图与要拼接的特征图，高度方向的差值
        diff_y = x2.size()[2] - x1.size()[2]
        # 上采样之后的特征图与要拼接的特征图，宽度方向的差值
        diff_x = x2.size()[3] - x1.size()[3]

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

        # 2.拼接
        x = torch.cat([x2, x1], dim=1)
        # 3.卷积，两次卷积
        x = self.conv(x)
        return x

# 最后的1*1输出卷积
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):
    # 参数: 输入通道数， 分割任务个数， 是否使用双线插值， 网络中第一个卷积通道个数
    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)

        # 下采样，参数:输入通道,输出通道
        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)


        # 如果采用双线插值上采样为 2，采用转置矩阵上采样为 1
        factor = 2 if bilinear else 1
        # 最后一个下采样，如果是双线插值则输出通道为512，否则为1024
        self.down4 = Down(base_c * 8, base_c * 16 // factor)

        # 上采样，参数:输入通道,输出通道
        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)
        # 最后的1*1输出卷积
        self.out_conv = OutConv(base_c, num_classes)

        # # sk模块
        self.sk = SKConv(base_c, 480, 2, 1, 2)

    # 正向传播过程
    def forward(self, x: torch.Tensor) -> Dict[str, torch.Tensor]:
        # 1. 定义最开始的两个卷积层
        x1 = self.in_conv(x)

        # 2. contracting path（收缩路径）
        x2 = self.down1(x1)
        x3 = self.down2(x2)
        x4 = self.down3(x3)
        x5 = self.down4(x4)
        # 3. expanding path（扩展路径）
        x = self.up1(x5, x4)
        x = self.up2(x, x3)
        x = self.up3(x, x2)
        x = self.up4(x, x1)
        # sk模块
        x = self.sk(x)

        # 4. 最后1*1输出卷积
        logits = self.out_conv(x)


        return {"out": logits}

（2）在UNet最后的输出卷积后添加，CBAM模块

训练结果：

[epoch: 9]
train_loss: 0.2040
lr: 0.000000
dice coefficient: 0.988
global correct: 97.7
average row correct: ['48.6', '98.9']
IoU: ['33.8', '97.7']
mean IoU: 65.8

模型改动：

import os
import time
import datetime

import torch

from src import UNet
from train_utils import train_one_epoch, evaluate, create_lr_scheduler
from my_dataset import DriveDataset
import transforms as T


class SegmentationPresetTrain:
    def __init__(self, base_size, crop_size, hflip_prob=0.5, vflip_prob=0.5,
                 mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)):
        min_size = int(0.5 * base_size)
        max_size = int(1.2 * base_size)

        trans = [T.RandomResize(min_size, max_size)]
        if hflip_prob > 0:
            trans.append(T.RandomHorizontalFlip(hflip_prob))
        if vflip_prob > 0:
            trans.append(T.RandomVerticalFlip(vflip_prob))
        trans.extend([
            T.RandomCrop(crop_size),
            T.ToTensor(),
            T.Normalize(mean=mean, std=std),
        ])
        self.transforms = T.Compose(trans)

    def __call__(self, img, target):
        return self.transforms(img, target)


class SegmentationPresetEval:
    def __init__(self, mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)):
        self.transforms = T.Compose([
            T.ToTensor(),
            T.Normalize(mean=mean, std=std),
        ])

    def __call__(self, img, target):
        return self.transforms(img, target)


def get_transform(train, mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)):
    base_size = 565
    crop_size = 480

    if train:
        return SegmentationPresetTrain(base_size, crop_size, mean=mean, std=std)
    else:
        return SegmentationPresetEval(mean=mean, std=std)

# 传入参数，创建模型
def create_model(num_classes):
    model = UNet(in_channels=3, num_classes=num_classes, base_c=32)
    return model


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 = min([os.cpu_count(), batch_size if batch_size > 1 else 0, 8])
    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)

    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')
        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.
    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))


def parse_args():
    import argparse
    parser = argparse.ArgumentParser(description="pytorch unet training")

    parser.add_argument("--data-path", default="./", help="DRIVE2 root")
    # exclude background
    parser.add_argument("--num-classes", default=1, type=int)
    parser.add_argument("--device", default="cuda", help="training device")
    parser.add_argument("-b", "--batch-size", default=4, type=int)
    parser.add_argument("--epochs", default=10, type=int, metavar="N",
                        help="number of total epochs to train")

    parser.add_argument('--lr', default=0.01, type=float, help='initial learning rate')
    parser.add_argument('--momentum', default=0.9, type=float, metavar='M',
                        help='momentum')
    parser.add_argument('--wd', '--weight-decay', default=1e-4, type=float,
                        metavar='W', help='weight decay (default: 1e-4)',
                        dest='weight_decay')
    parser.add_argument('--print-freq', default=1, type=int, help='print frequency')
    parser.add_argument('--resume', default='', help='resume from checkpoint')
    parser.add_argument('--start-epoch', default=0, type=int, metavar='N',
                        help='start epoch')
    parser.add_argument('--save-best', default=True, type=bool, help='only save best dice weights')
    # Mixed precision training parameters
    parser.add_argument("--amp", default=False, type=bool,
                        help="Use torch.cuda.amp for mixed precision training")

    args = parser.parse_args()

    return args


if __name__ == '__main__':
    args = parse_args()

    if not os.path.exists("./save_weights"):
        os.mkdir("./save_weights")

    main(args)