图像分割：Pytorch实现UNet++进行医学细胞分割

前言
相关介绍
项目结构
具体步骤
- 准备数据集
- 读取数据集
- 设置并解析相关参数
- 定义网络模型
- 定义损失函数
- 定义优化器
- 训练
- 验证
参考

前言

由于本人水平有限，难免出现错漏，敬请批评改正。
更多精彩内容，可点击进入人工智能知识点专栏、Python日常小操作专栏、OpenCV-Python小应用专栏、YOLO系列专栏、自然语言处理专栏或我的个人主页查看
基于DETR的人脸伪装检测
YOLOv7训练自己的数据集（口罩检测）
YOLOv8训练自己的数据集（足球检测）
YOLOv5：TensorRT加速YOLOv5模型推理
YOLOv5：IoU、GIoU、DIoU、CIoU、EIoU
玩转Jetson Nano（五）：TensorRT加速YOLOv5目标检测
YOLOv5：添加SE、CBAM、CoordAtt、ECA注意力机制
YOLOv5：yolov5s.yaml配置文件解读、增加小目标检测层
Python将COCO格式实例分割数据集转换为YOLO格式实例分割数据集
YOLOv5：使用7.0版本训练自己的实例分割模型（车辆、行人、路标、车道线等实例分割）
使用Kaggle GPU资源免费体验Stable Diffusion开源项目

项目结构

在这里插入图片描述

具体步骤

准备数据集

原图
mask图

在这里插入图片描述

读取数据集

    # Data loading codeimg_ids = glob(os.path.join('inputs', config['dataset'], 'images', '*' + config['img_ext']))img_ids = [os.path.splitext(os.path.basename(p))[0] for p in img_ids]train_img_ids, val_img_ids = train_test_split(img_ids, test_size=0.2, random_state=41)#　数据增强train_transform = Compose([albu.RandomRotate90(),albu.Flip(),OneOf([albu.HueSaturationValue(),albu.RandomBrightness(),albu.RandomContrast(),], p=1),#按照归一化的概率选择执行哪一个albu.Resize(config['input_h'], config['input_w']),albu.Normalize(),])val_transform = Compose([albu.Resize(config['input_h'], config['input_w']),albu.Normalize(),])train_dataset = Dataset(img_ids=train_img_ids,img_dir=os.path.join('inputs', config['dataset'], 'images'),mask_dir=os.path.join('inputs', config['dataset'], 'masks'),img_ext=config['img_ext'],mask_ext=config['mask_ext'],num_classes=config['num_classes'],transform=train_transform)val_dataset = Dataset(img_ids=val_img_ids,img_dir=os.path.join('inputs', config['dataset'], 'images'),mask_dir=os.path.join('inputs', config['dataset'], 'masks'),img_ext=config['img_ext'],mask_ext=config['mask_ext'],num_classes=config['num_classes'],transform=val_transform)train_loader = torch.utils.data.DataLoader(train_dataset,batch_size=config['batch_size'],shuffle=True,num_workers=config['num_workers'],drop_last=True)#不能整除的batch是否就不要了val_loader = torch.utils.data.DataLoader(val_dataset,batch_size=config['batch_size'],shuffle=False,num_workers=config['num_workers'],drop_last=False)

设置并解析相关参数

	config = vars(parse_args())if config['name'] is None:if config['deep_supervision']:config['name'] = '%s_%s_wDS' % (config['dataset'], config['arch'])else:config['name'] = '%s_%s_woDS' % (config['dataset'], config['arch'])os.makedirs('models/%s' % config['name'], exist_ok=True)print('-' * 20)for key in config:print('%s: %s' % (key, config[key]))print('-' * 20)with open('models/%s/config.yml' % config['name'], 'w') as f:yaml.dump(config, f)

定义网络模型

在这里插入图片描述

class VGGBlock(nn.Module):def __init__(self, in_channels, middle_channels, out_channels):super().__init__()self.relu = nn.ReLU(inplace=True)self.conv1 = nn.Conv2d(in_channels, middle_channels, 3, padding=1)self.bn1 = nn.BatchNorm2d(middle_channels)self.conv2 = nn.Conv2d(middle_channels, out_channels, 3, padding=1)self.bn2 = nn.BatchNorm2d(out_channels)def forward(self, x):out = self.conv1(x)out = self.bn1(out)out = self.relu(out)out = self.conv2(out)out = self.bn2(out)out = self.relu(out)return outclass NestedUNet(nn.Module):def __init__(self, num_classes, input_channels=3, deep_supervision=False, **kwargs):super().__init__()nb_filter = [32, 64, 128, 256, 512]self.deep_supervision = deep_supervisionself.pool = nn.MaxPool2d(2, 2)self.up = nn.Upsample(scale_factor=2, mode='bilinear', align_corners=True)self.conv0_0 = VGGBlock(input_channels, nb_filter[0], nb_filter[0])self.conv1_0 = VGGBlock(nb_filter[0], nb_filter[1], nb_filter[1])self.conv2_0 = VGGBlock(nb_filter[1], nb_filter[2], nb_filter[2])self.conv3_0 = VGGBlock(nb_filter[2], nb_filter[3], nb_filter[3])self.conv4_0 = VGGBlock(nb_filter[3], nb_filter[4], nb_filter[4])self.conv0_1 = VGGBlock(nb_filter[0]+nb_filter[1], nb_filter[0], nb_filter[0])self.conv1_1 = VGGBlock(nb_filter[1]+nb_filter[2], nb_filter[1], nb_filter[1])self.conv2_1 = VGGBlock(nb_filter[2]+nb_filter[3], nb_filter[2], nb_filter[2])self.conv3_1 = VGGBlock(nb_filter[3]+nb_filter[4], nb_filter[3], nb_filter[3])self.conv0_2 = VGGBlock(nb_filter[0]*2+nb_filter[1], nb_filter[0], nb_filter[0])self.conv1_2 = VGGBlock(nb_filter[1]*2+nb_filter[2], nb_filter[1], nb_filter[1])self.conv2_2 = VGGBlock(nb_filter[2]*2+nb_filter[3], nb_filter[2], nb_filter[2])self.conv0_3 = VGGBlock(nb_filter[0]*3+nb_filter[1], nb_filter[0], nb_filter[0])self.conv1_3 = VGGBlock(nb_filter[1]*3+nb_filter[2], nb_filter[1], nb_filter[1])self.conv0_4 = VGGBlock(nb_filter[0]*4+nb_filter[1], nb_filter[0], nb_filter[0])if self.deep_supervision:self.final1 = nn.Conv2d(nb_filter[0], num_classes, kernel_size=1)self.final2 = nn.Conv2d(nb_filter[0], num_classes, kernel_size=1)self.final3 = nn.Conv2d(nb_filter[0], num_classes, kernel_size=1)self.final4 = nn.Conv2d(nb_filter[0], num_classes, kernel_size=1)else:self.final = nn.Conv2d(nb_filter[0], num_classes, kernel_size=1)def forward(self, input):x0_0 = self.conv0_0(input)x1_0 = self.conv1_0(self.pool(x0_0))x0_1 = self.conv0_1(torch.cat([x0_0, self.up(x1_0)], 1))x2_0 = self.conv2_0(self.pool(x1_0))x1_1 = self.conv1_1(torch.cat([x1_0, self.up(x2_0)], 1))x0_2 = self.conv0_2(torch.cat([x0_0, x0_1, self.up(x1_1)], 1))x3_0 = self.conv3_0(self.pool(x2_0))x2_1 = self.conv2_1(torch.cat([x2_0, self.up(x3_0)], 1))x1_2 = self.conv1_2(torch.cat([x1_0, x1_1, self.up(x2_1)], 1))x0_3 = self.conv0_3(torch.cat([x0_0, x0_1, x0_2, self.up(x1_2)], 1))x4_0 = self.conv4_0(self.pool(x3_0))x3_1 = self.conv3_1(torch.cat([x3_0, self.up(x4_0)], 1))x2_2 = self.conv2_2(torch.cat([x2_0, x2_1, self.up(x3_1)], 1))x1_3 = self.conv1_3(torch.cat([x1_0, x1_1, x1_2, self.up(x2_2)], 1))x0_4 = self.conv0_4(torch.cat([x0_0, x0_1, x0_2, x0_3, self.up(x1_3)], 1))if self.deep_supervision:output1 = self.final1(x0_1)output2 = self.final2(x0_2)output3 = self.final3(x0_3)output4 = self.final4(x0_4)return [output1, output2, output3, output4]else:output = self.final(x0_4)return output

定义损失函数

UNet++损失函数是二元交叉熵损失（Binary Cross Entropy, BCE）与Dice损失函数（Dice Coefficient Loss ）的结合，相关介绍可查阅损失函数：BCE Loss（二元交叉熵损失函数）、Dice Loss（Dice相似系数损失函数）

class BCEDiceLoss(nn.Module):def __init__(self):super().__init__()def forward(self, input, target):bce = F.binary_cross_entropy_with_logits(input, target)smooth = 1e-5input = torch.sigmoid(input)num = target.size(0)input = input.view(num, -1)target = target.view(num, -1)intersection = (input * target)dice = (2. * intersection.sum(1) + smooth) / (input.sum(1) + target.sum(1) + smooth)dice = 1 - dice.sum() / numreturn 0.5 * bce + dice

    # define loss function (criterion)if config['loss'] == 'BCEWithLogitsLoss':criterion = nn.BCEWithLogitsLoss().cuda()#WithLogits 就是先将输出结果经过sigmoid再交叉熵else:criterion = losses.__dict__[config['loss']]().cuda()cudnn.benchmark = True

定义优化器

    params = filter(lambda p: p.requires_grad, model.parameters())if config['optimizer'] == 'Adam':optimizer = optim.Adam(params, lr=config['lr'], weight_decay=config['weight_decay'])elif config['optimizer'] == 'SGD':optimizer = optim.SGD(params, lr=config['lr'], momentum=config['momentum'],nesterov=config['nesterov'], weight_decay=config['weight_decay'])else:raise NotImplementedErrorif config['scheduler'] == 'CosineAnnealingLR':scheduler = lr_scheduler.CosineAnnealingLR(optimizer, T_max=config['epochs'], eta_min=config['min_lr'])elif config['scheduler'] == 'ReduceLROnPlateau':scheduler = lr_scheduler.ReduceLROnPlateau(optimizer, factor=config['factor'], patience=config['patience'],verbose=1, min_lr=config['min_lr'])elif config['scheduler'] == 'MultiStepLR':scheduler = lr_scheduler.MultiStepLR(optimizer, milestones=[int(e) for e in config['milestones'].split(',')], gamma=config['gamma'])elif config['scheduler'] == 'ConstantLR':scheduler = Noneelse:raise NotImplementedError

训练

def train(config, train_loader, model, criterion, optimizer):avg_meters = {'loss': AverageMeter(),'iou': AverageMeter()}model.train()pbar = tqdm(total=len(train_loader))for input, target, _ in train_loader:input = input.cuda()target = target.cuda()# compute outputif config['deep_supervision']:outputs = model(input)loss = 0for output in outputs:loss += criterion(output, target)loss /= len(outputs)iou = iou_score(outputs[-1], target)else:output = model(input)loss = criterion(output, target)iou = iou_score(output, target)# compute gradient and do optimizing stepoptimizer.zero_grad()loss.backward()optimizer.step()avg_meters['loss'].update(loss.item(), input.size(0))avg_meters['iou'].update(iou, input.size(0))postfix = OrderedDict([('loss', avg_meters['loss'].avg),('iou', avg_meters['iou'].avg),])pbar.set_postfix(postfix)pbar.update(1)pbar.close()return OrderedDict([('loss', avg_meters['loss'].avg),('iou', avg_meters['iou'].avg)])

$ python train.py --dataset dsb2018_96 --arch NestedUNet
--------------------
arch NestedUNet
--------------------
name: dsb2018_96_NestedUNet_woDS
epochs: 100
batch_size: 8
arch: NestedUNet
deep_supervision: False
input_channels: 3
num_classes: 1
input_w: 96
input_h: 96
loss: BCEDiceLoss
dataset: dsb2018_96
img_ext: .png
mask_ext: .png
optimizer: SGD
lr: 0.001
momentum: 0.9
weight_decay: 0.0001
nesterov: False
scheduler: CosineAnnealingLR
min_lr: 1e-05
factor: 0.1
patience: 2
milestones: 1,2
gamma: 0.6666666666666666
early_stopping: -1
num_workers: 0
--------------------
=> creating model NestedUNet
/home/leiwei/anaconda3/envs/mycuda/lib/python3.8/site-packages/albumentations/augmentations/transforms.py:1800: FutureWarning: This class has been deprecated. Please use RandomBrightnessContrastwarnings.warn(
/home/leiwei/anaconda3/envs/mycuda/lib/python3.8/site-packages/albumentations/augmentations/transforms.py:1826: FutureWarning: This class has been deprecated. Please use RandomBrightnessContrastwarnings.warn(
Epoch [0/100]
100%|███████████████████████████████████████████████████████████████████| 67/67 [00:04<00:00, 16.68it/s, loss=1.06, iou=0.291]
100%|███████████████████████████████████████████████████████████████████| 17/17 [00:00<00:00, 33.02it/s, loss=1.14, iou=0.163]
loss 1.0559 - iou 0.2912 - val_loss 1.1419 - val_iou 0.1633
=> saved best model
Epoch [1/100]
100%|██████████████████████████████████████████████████████████████████| 67/67 [00:03<00:00, 20.44it/s, loss=0.829, iou=0.519]
100%|██████████████████████████████████████████████████████████████████| 17/17 [00:00<00:00, 53.06it/s, loss=0.746, iou=0.609]
loss 0.8288 - iou 0.5193 - val_loss 0.7464 - val_iou 0.6088
=> saved best model
Epoch [2/100]
100%|██████████████████████████████████████████████████████████████████| 67/67 [00:03<00:00, 20.37it/s, loss=0.705, iou=0.577]
100%|███████████████████████████████████████████████████████████████████| 17/17 [00:00<00:00, 52.80it/s, loss=0.629, iou=0.66]
loss 0.7047 - iou 0.5771 - val_loss 0.6294 - val_iou 0.6601
=> saved best model
......
Epoch [97/100]
100%|██████████████████████████████████████████████████████████████████| 67/67 [00:03<00:00, 20.28it/s, loss=0.178, iou=0.813]
100%|██████████████████████████████████████████████████████████████████| 17/17 [00:00<00:00, 53.27it/s, loss=0.165, iou=0.831]
loss 0.1782 - iou 0.8127 - val_loss 0.1651 - val_iou 0.8313
Epoch [98/100]
100%|██████████████████████████████████████████████████████████████████| 67/67 [00:03<00:00, 20.16it/s, loss=0.185, iou=0.802]
100%|██████████████████████████████████████████████████████████████████| 17/17 [00:00<00:00, 53.31it/s, loss=0.166, iou=0.826]
loss 0.1848 - iou 0.8017 - val_loss 0.1656 - val_iou 0.8264
Epoch [99/100]
100%|██████████████████████████████████████████████████████████████████| 67/67 [00:03<00:00, 20.02it/s, loss=0.204, iou=0.788]
100%|██████████████████████████████████████████████████████████████████| 17/17 [00:00<00:00, 52.86it/s, loss=0.168, iou=0.833]
loss 0.2035 - iou 0.7876 - val_loss 0.1677 - val_iou 0.8328

验证

import argparse
import os
from glob import glob
import matplotlib.pyplot as plt
import numpy as np
import cv2
import torch
import torch.backends.cudnn as cudnn
import yaml
from albumentations.augmentations import transforms
from albumentations.core.composition import Compose# tfx20240410
import albumentations as albufrom sklearn.model_selection import train_test_split
from tqdm import tqdmimport archs
from dataset import Dataset
from metrics import iou_score
from utils import AverageMeter
"""
需要指定参数：--name dsb2018_96_NestedUNet_woDS
"""def parse_args():parser = argparse.ArgumentParser()parser.add_argument('--name', default=None,help='model name')args = parser.parse_args()return argsdef main():args = parse_args()with open('models/%s/config.yml' % args.name, 'r') as f:config = yaml.load(f, Loader=yaml.FullLoader)print('-'*20)for key in config.keys():print('%s: %s' % (key, str(config[key])))print('-'*20)cudnn.benchmark = True# create modelprint("=> creating model %s" % config['arch'])model = archs.__dict__[config['arch']](config['num_classes'],config['input_channels'],config['deep_supervision'])model = model.cuda()# Data loading codeimg_ids = glob(os.path.join('inputs', config['dataset'], 'images', '*' + config['img_ext']))img_ids = [os.path.splitext(os.path.basename(p))[0] for p in img_ids]_, val_img_ids = train_test_split(img_ids, test_size=0.2, random_state=41)model.load_state_dict(torch.load('models/%s/model.pth' %config['name']))model.eval()# val_transform = Compose([#     transforms.Resize(config['input_h'], config['input_w']),#     transforms.Normalize(),# ])# tfx20240410 val_transform = Compose([albu.Resize(config['input_h'], config['input_w']),albu.Normalize(),])val_dataset = Dataset(img_ids=val_img_ids,img_dir=os.path.join('inputs', config['dataset'], 'images'),mask_dir=os.path.join('inputs', config['dataset'], 'masks'),img_ext=config['img_ext'],mask_ext=config['mask_ext'],num_classes=config['num_classes'],transform=val_transform)val_loader = torch.utils.data.DataLoader(val_dataset,batch_size=config['batch_size'],shuffle=False,num_workers=config['num_workers'],drop_last=False)avg_meter = AverageMeter()for c in range(config['num_classes']):os.makedirs(os.path.join('outputs', config['name'], str(c)), exist_ok=True)with torch.no_grad():for input, target, meta in tqdm(val_loader, total=len(val_loader)):input = input.cuda()target = target.cuda()# compute outputif config['deep_supervision']:output = model(input)[-1]else:output = model(input)iou = iou_score(output, target)avg_meter.update(iou, input.size(0))output = torch.sigmoid(output).cpu().numpy()for i in range(len(output)):for c in range(config['num_classes']):cv2.imwrite(os.path.join('outputs', config['name'], str(c), meta['img_id'][i] + '.jpg'),(output[i, c] * 255).astype('uint8'))print('IoU: %.4f' % avg_meter.avg)plot_examples(input, target, model,num_examples=3)torch.cuda.empty_cache()def plot_examples(datax, datay, model,num_examples=6):fig, ax = plt.subplots(nrows=num_examples, ncols=3, figsize=(18,4*num_examples))m = datax.shape[0]for row_num in range(num_examples):image_indx = np.random.randint(m)image_arr = model(datax[image_indx:image_indx+1]).squeeze(0).detach().cpu().numpy()ax[row_num][0].imshow(np.transpose(datax[image_indx].cpu().numpy(), (1,2,0))[:,:,0])ax[row_num][0].set_title("Orignal Image")ax[row_num][1].imshow(np.squeeze((image_arr > 0.40)[0,:,:].astype(int)))ax[row_num][1].set_title("Segmented Image localization")ax[row_num][2].imshow(np.transpose(datay[image_indx].cpu().numpy(), (1,2,0))[:,:,0])ax[row_num][2].set_title("Target image")plt.show()if __name__ == '__main__':main()

$ python val.py --name dsb2018_96_NestedUNet_woDS
--------------------
arch: NestedUNet
batch_size: 8
dataset: dsb2018_96
deep_supervision: False
early_stopping: -1
epochs: 100
factor: 0.1
gamma: 0.6666666666666666
img_ext: .png
input_channels: 3
input_h: 96
input_w: 96
loss: BCEDiceLoss
lr: 0.001
mask_ext: .png
milestones: 1,2
min_lr: 1e-05
momentum: 0.9
name: dsb2018_96_NestedUNet_woDS
nesterov: False
num_classes: 1
num_workers: 0
optimizer: SGD
patience: 2
scheduler: CosineAnnealingLR
weight_decay: 0.0001
--------------------
=> creating model NestedUNet
100%|█████████████████████████████████████████████████████████████████████████████████████████| 17/17 [00:00<00:00, 21.87it/s]
IoU: 0.8358

在这里插入图片描述

参考

[1] Zongwei Zhou, Md Mahfuzur Rahman Siddiquee, Nima Tajbakhsh, and Jianming Liang. UNet++: A Nested U-Net Architecture for Medical Image Segmentation. 2020
[2] UNet++源代码地址. https://github.com/MrGiovanni/UNetPlusPlus

由于本人水平有限，难免出现错漏，敬请批评改正。
更多精彩内容，可点击进入人工智能知识点专栏、Python日常小操作专栏、OpenCV-Python小应用专栏、YOLO系列专栏、自然语言处理专栏或我的个人主页查看
基于DETR的人脸伪装检测
YOLOv7训练自己的数据集（口罩检测）
YOLOv8训练自己的数据集（足球检测）
YOLOv5：TensorRT加速YOLOv5模型推理
YOLOv5：IoU、GIoU、DIoU、CIoU、EIoU
玩转Jetson Nano（五）：TensorRT加速YOLOv5目标检测
YOLOv5：添加SE、CBAM、CoordAtt、ECA注意力机制
YOLOv5：yolov5s.yaml配置文件解读、增加小目标检测层
Python将COCO格式实例分割数据集转换为YOLO格式实例分割数据集
YOLOv5：使用7.0版本训练自己的实例分割模型（车辆、行人、路标、车道线等实例分割）
使用Kaggle GPU资源免费体验Stable Diffusion开源项目