语义分割系列7-Attention Unet（pytorch实现）

继前文Unet和Unet++之后，本文将介绍Attention Unet。

Attention Unet地址，《Attention U-Net: Learning Where to Look for the Pancreas》。

AttentionUnet

Attention Unet发布于2018年，主要应用于医学领域的图像分割，全文中主要以肝脏的分割论证。

论文中心

Attention Unet主要的中心思想就是提出来Attention gate模块，使用soft-attention替代hard-attention，将attention集成到Unet的跳跃连接和上采样模块中，实现空间上的注意力机制。通过attention机制来抑制图像中的无关信息，突出局部的重要特征。

网络架构

 Attention Unet的模型结构和Unet十分相像，只是增加了Attention Gate模块来对skip connection和upsampling层做attention机制（图2）。

在Attention Gate模块中，g和xl分别为skip connection的输出和下一层的输出，如图3。

需要注意的是，在计算Wg和Wx后，对两者进行相加。但是，此时g的维度和xl的维度并不相等，则需要对g做下采样或对xl做上采样。（我倾向于对xl做上采样，因为在原本的Unet中，在Decoder就需要对下一层做上采样，所以，直接使用这个上采样结果可以减少网络计算）。

Wg和Wx经过相加，ReLU激活，1x1x1卷积，Sigmoid激活，生成一个权重信息，将这个权重与原始输入xl相乘，得到了对xl的attention激活。这就是Attenton Gate的思想。

Attenton Gate还有一个比较重要的特点是：这个权重可以经由网络学习！因为soft-attention是可微的，可以微分的attention就可以通过神经网络算出梯度并且前向传播和后向反馈来学习得到attention的权重。以此来学习更重要的特征。

模型复现

Attention Unet代码

import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn import init
 
 
def init_weights(net, init_type='normal', gain=0.02):
 
    def init_func(m):
        classname = m.__class__.__name__
        if hasattr(m, 'weight') and (classname.find('Conv') != -1
                                     or classname.find('Linear') != -1):
            if init_type == 'normal':
                init.normal_(m.weight.data, 0.0, gain)
            elif init_type == 'xavier':
                init.xavier_normal_(m.weight.data, gain=gain)
            elif init_type == 'kaiming':
                init.kaiming_normal_(m.weight.data, a=0, mode='fan_in')
            elif init_type == 'orthogonal':
                init.orthogonal_(m.weight.data, gain=gain)
            else:
                raise NotImplementedError(
                    'initialization method [%s] is not implemented' %
                    init_type)
            if hasattr(m, 'bias') and m.bias is not None:
                init.constant_(m.bias.data, 0.0)
        elif classname.find('BatchNorm2d') != -1:
            init.normal_(m.weight.data, 1.0, gain)
            init.constant_(m.bias.data, 0.0)
 
    print('initialize network with %s' % init_type)
    net.apply(init_func)
 
 
class conv_block(nn.Module):
 
    def __init__(self, ch_in, ch_out):
        super(conv_block, self).__init__()
        self.conv = nn.Sequential(
            nn.Conv2d(ch_in,
                      ch_out,
                      kernel_size=3,
                      stride=1,
                      padding=1,
                      bias=True), 
            nn.BatchNorm2d(ch_out),
            nn.ReLU(inplace=True),
 
            nn.Conv2d(ch_out,
                      ch_out,
                      kernel_size=3,
                      stride=1,
                      padding=1,
                      bias=True), 
            nn.BatchNorm2d(ch_out),
            nn.ReLU(inplace=True))
 
    def forward(self, x):
        x = self.conv(x)
        return x
 
 
class up_conv(nn.Module):
    def __init__(self, ch_in, ch_out, convTranspose=True):
        super(up_conv, self).__init__()
        if convTranspose:
            self.up = nn.ConvTranspose2d(in_channels=ch_in, out_channels=ch_in,kernel_size=4,stride=2, padding=1)
        else:
            self.up = nn.Upsample(scale_factor=2)
 
        self.Conv = nn.Sequential(
            nn.Conv2d(ch_in,
                      ch_out,
                      kernel_size=3,
                      stride=1,
                      padding=1,
                      bias=True), 
            nn.BatchNorm2d(ch_out),
            nn.ReLU(inplace=True))
 
    def forward(self, x):
        x = self.up(x)
        x = self.Conv(x)
        return x
 
 
class single_conv(nn.Module):
    def __init__(self, ch_in, ch_out):
        super(single_conv, self).__init__()
        self.conv = nn.Sequential(
            nn.Conv2d(ch_in,
                      ch_out,
                      kernel_size=3,
                      stride=1,
                      padding=1,
                      bias=True), 
            nn.BatchNorm2d(ch_out),
            nn.ReLU(inplace=True))
 
    def forward(self, x):
        x = self.conv(x)
        return x
 
 
class Attention_block(nn.Module):
 
    def __init__(self, F_g, F_l, F_int):
        super(Attention_block, self).__init__()
        self.W_g = nn.Sequential(
            nn.Conv2d(F_g,
                      F_int,
                      kernel_size=1,
                      stride=1,
                      padding=0,
                      bias=True), 
            nn.BatchNorm2d(F_int))
 
        self.W_x = nn.Sequential(
            nn.Conv2d(F_l,
                      F_int,
                      kernel_size=1,
                      stride=1,
                      padding=0,
                      bias=True), 
            nn.BatchNorm2d(F_int))
 
        self.psi = nn.Sequential(
            nn.Conv2d(F_int, 1, kernel_size=1, stride=1, padding=0, bias=True),
            nn.BatchNorm2d(1), nn.Sigmoid())
 
        self.relu = nn.ReLU(inplace=True)
 
    def forward(self, g, x):
        g1 = self.W_g(g)
        x1 = self.W_x(x)
        psi = self.relu(g1 + x1)
        psi = self.psi(psi)
 
        return x * psi
 
 
class AttU_Net(nn.Module):
    """
    in_channel: input image channels
    num_classes: output class number 
    channel_list: a channel list for adjust the model size
    checkpoint: 是否有checkpoint  if False： call normal init
    convTranspose: 是否使用反卷积上采样。True: use nn.convTranspose  Flase: use nn.Upsample
    """
    def __init__(self,
                 in_channel=3,
                 num_classes=1,
                 channel_list=[64, 128, 256, 512, 1024],
                 checkpoint=False,
                 convTranspose=True):
        super(AttU_Net, self).__init__()
 
        self.Maxpool = nn.MaxPool2d(kernel_size=2, stride=2)
 
        self.Conv1 = conv_block(ch_in=in_channel, ch_out=channel_list[0])
        self.Conv2 = conv_block(ch_in=channel_list[0], ch_out=channel_list[1])
        self.Conv3 = conv_block(ch_in=channel_list[1], ch_out=channel_list[2])
        self.Conv4 = conv_block(ch_in=channel_list[2], ch_out=channel_list[3])
        self.Conv5 = conv_block(ch_in=channel_list[3], ch_out=channel_list[4])
 
        self.Up5 = up_conv(ch_in=channel_list[4], ch_out=channel_list[3], convTranspose=convTranspose)
        self.Att5 = Attention_block(F_g=channel_list[3],
                                    F_l=channel_list[3],
                                    F_int=channel_list[2])
        self.Up_conv5 = conv_block(ch_in=channel_list[4],
                                   ch_out=channel_list[3])
 
        self.Up4 = up_conv(ch_in=channel_list[3], ch_out=channel_list[2], convTranspose=convTranspose)
        self.Att4 = Attention_block(F_g=channel_list[2],
                                    F_l=channel_list[2],
                                    F_int=channel_list[1])
        self.Up_conv4 = conv_block(ch_in=channel_list[3],
                                   ch_out=channel_list[2])
 
        self.Up3 = up_conv(ch_in=channel_list[2], ch_out=channel_list[1], convTranspose=convTranspose)
        self.Att3 = Attention_block(F_g=channel_list[1],
                                    F_l=channel_list[1],
                                    F_int=64)
        self.Up_conv3 = conv_block(ch_in=channel_list[2],
                                   ch_out=channel_list[1])
 
        self.Up2 = up_conv(ch_in=channel_list[1], ch_out=channel_list[0], convTranspose=convTranspose)
        self.Att2 = Attention_block(F_g=channel_list[0],
                                    F_l=channel_list[0],
                                    F_int=channel_list[0] // 2)
        self.Up_conv2 = conv_block(ch_in=channel_list[1],
                                   ch_out=channel_list[0])
 
        self.Conv_1x1 = nn.Conv2d(channel_list[0],
                                  num_classes,
                                  kernel_size=1,
                                  stride=1,
                                  padding=0)
 
        if not checkpoint:
            init_weights(self)
 
    def forward(self, x):
        # encoder
        x1 = self.Conv1(x)
 
        x2 = self.Maxpool(x1)
        x2 = self.Conv2(x2)
 
        x3 = self.Maxpool(x2)
        x3 = self.Conv3(x3)
 
        x4 = self.Maxpool(x3)
        x4 = self.Conv4(x4)
 
        x5 = self.Maxpool(x4)
        x5 = self.Conv5(x5)
 
        # decoder
        d5 = self.Up5(x5)
        x4 = self.Att5(g=d5, x=x4)
        d5 = torch.cat((x4, d5), dim=1)
        d5 = self.Up_conv5(d5)
 
        d4 = self.Up4(d5)
        x3 = self.Att4(g=d4, x=x3)
        d4 = torch.cat((x3, d4), dim=1)
        d4 = self.Up_conv4(d4)
 
        d3 = self.Up3(d4)
        x2 = self.Att3(g=d3, x=x2)
        d3 = torch.cat((x2, d3), dim=1)
        d3 = self.Up_conv3(d3)
 
        d2 = self.Up2(d3)
        x1 = self.Att2(g=d2, x=x1)
        d2 = torch.cat((x1, d2), dim=1)
        d2 = self.Up_conv2(d2)
 
        d1 = self.Conv_1x1(d2)
 
        return d1

数据集

数据集依旧使用Camvid数据集，见Camvid数据集的构建和使用。

# 导入库
import os
os.environ['CUDA_VISIBLE_DEVICES'] = '0'
 
import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
from torch import optim
from torch.utils.data import Dataset, DataLoader, random_split
from tqdm import tqdm
import warnings
warnings.filterwarnings("ignore")
import os.path as osp
import matplotlib.pyplot as plt
from PIL import Image
import numpy as np
import albumentations as A
from albumentations.pytorch.transforms import ToTensorV2
 
torch.manual_seed(17)
# 自定义数据集CamVidDataset
class CamVidDataset(torch.utils.data.Dataset):
    """CamVid Dataset. Read images, apply augmentation and preprocessing transformations.
    
    Args:
        images_dir (str): path to images folder
        masks_dir (str): path to segmentation masks folder
        class_values (list): values of classes to extract from segmentation mask
        augmentation (albumentations.Compose): data transfromation pipeline 
            (e.g. flip, scale, etc.)
        preprocessing (albumentations.Compose): data preprocessing 
            (e.g. noralization, shape manipulation, etc.)
    """
    
    def __init__(self, images_dir, masks_dir):
        self.transform = A.Compose([
            A.Resize(224, 224),
            A.HorizontalFlip(),
            A.VerticalFlip(),
            A.Normalize(),
            ToTensorV2(),
        ]) 
        self.ids = os.listdir(images_dir)
        self.images_fps = [os.path.join(images_dir, image_id) for image_id in self.ids]
        self.masks_fps = [os.path.join(masks_dir, image_id) for image_id in self.ids]
 
    
    def __getitem__(self, i):
        # read data
        image = np.array(Image.open(self.images_fps[i]).convert('RGB'))
        mask = np.array( Image.open(self.masks_fps[i]).convert('RGB'))
        image = self.transform(image=image,mask=mask)
        
        return image['image'], image['mask'][:,:,0]
        
    def __len__(self):
        return len(self.ids)
    
    
# 设置数据集路径
DATA_DIR = r'dataset\camvid' # 根据自己的路径来设置
x_train_dir = os.path.join(DATA_DIR, 'train_images')
y_train_dir = os.path.join(DATA_DIR, 'train_labels')
x_valid_dir = os.path.join(DATA_DIR, 'valid_images')
y_valid_dir = os.path.join(DATA_DIR, 'valid_labels')
    
train_dataset = CamVidDataset(
    x_train_dir, 
    y_train_dir, 
)
val_dataset = CamVidDataset(
    x_valid_dir, 
    y_valid_dir, 
)
 
train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True,drop_last=True)
val_loader = DataLoader(val_dataset, batch_size=32, shuffle=True,drop_last=True)

模型训练

model = AttentionUnet(num_classes=33).cuda()
#model.load_state_dict(torch.load(r"checkpoints/Unet_100.pth"),strict=False)
 
from d2l import torch as d2l
from tqdm import tqdm
import pandas as pd
#损失函数选用多分类交叉熵损失函数
lossf = nn.CrossEntropyLoss(ignore_index=255)
#选用adam优化器来训练
optimizer = optim.SGD(model.parameters(),lr=0.1)
scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=50, gamma=0.1, last_epoch=-1)
 
#训练50轮
epochs_num = 50
def train_ch13(net, train_iter, test_iter, loss, trainer, num_epochs,scheduler,
               devices=d2l.try_all_gpus()):
    timer, num_batches = d2l.Timer(), len(train_iter)
    animator = d2l.Animator(xlabel='epoch', xlim=[1, num_epochs], ylim=[0, 1],
                            legend=['train loss', 'train acc', 'test acc'])
    net = nn.DataParallel(net, device_ids=devices).to(devices[0])
    
    loss_list = []
    train_acc_list = []
    test_acc_list = []
    epochs_list = []
    time_list = []
    for epoch in range(num_epochs):
        # Sum of training loss, sum of training accuracy, no. of examples,
        # no. of predictions
        metric = d2l.Accumulator(4)
        for i, (features, labels) in enumerate(train_iter):
            timer.start()
            l, acc = d2l.train_batch_ch13(
                net, features, labels.long(), loss, trainer, devices)
            metric.add(l, acc, labels.shape[0], labels.numel())
            timer.stop()
            if (i + 1) % (num_batches // 5) == 0 or i == num_batches - 1:
                animator.add(epoch + (i + 1) / num_batches,
                             (metric[0] / metric[2], metric[1] / metric[3],
                              None))
        test_acc = d2l.evaluate_accuracy_gpu(net, test_iter)
        animator.add(epoch + 1, (None, None, test_acc))
        scheduler.step()
#         print(f'loss {metric[0] / metric[2]:.3f}, train acc '
#               f'{metric[1] / metric[3]:.3f}, test acc {test_acc:.3f}')
#         print(f'{metric[2] * num_epochs / timer.sum():.1f} examples/sec on '
#               f'{str(devices)}')
        print(f"epoch {epoch+1} --- loss {metric[0] / metric[2]:.3f} ---  train acc {metric[1] / metric[3]:.3f} --- test acc {test_acc:.3f} --- cost time {timer.sum()}")
        
        #---------保存训练数据---------------
        df = pd.DataFrame()
        loss_list.append(metric[0] / metric[2])
        train_acc_list.append(metric[1] / metric[3])
        test_acc_list.append(test_acc)
        epochs_list.append(epoch+1)
        time_list.append(timer.sum())
        
        df['epoch'] = epochs_list
        df['loss'] = loss_list
        df['train_acc'] = train_acc_list
        df['test_acc'] = test_acc_list
        df['time'] = time_list
        df.to_excel("savefile/AttentionUnet_camvid1.xlsx")
        #----------------保存模型-------------------
        if np.mod(epoch+1, 5) == 0:
            torch.save(model.state_dict(), f'checkpoints/AttentionUnet_{epoch+1}.pth')

开始训练

train_ch13(model, train_loader, val_loader, lossf, optimizer, epochs_num,scheduler)

训练结果

插在最后。

最近很多同学找我要代码，我有时候长时间不看就容易遗漏。我把代码和数据文件传到网盘上，供大家自行下载。

链接：https://pan.baidu.com/s/1taJlov4VvN-Nwp_xoUbgOA?pwd=yumi 
提取码：yumi 
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