基于U-net的自然图像分割识别

data.py 用于数据处理

import os

from torch.utils.data import Dataset
from utils import *
from torchvision import transforms
transform=transforms.Compose([
    transforms.ToTensor()
])

class MyDataset(Dataset):
    def __init__(self,path):
        self.path=path
        self.name=os.listdir(os.path.join(path,'SegmentationClass1'))

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

    def __getitem__(self, index):
        segment_name=self.name[index]  #xx.png
        segment_path=os.path.join(self.path,'SegmentationClass1',segment_name)
        image_path=os.path.join(self.path,'JPEGImages1',segment_name.replace('png','jpg'))
        segment_image=keep_image_size_open(segment_path)
        image=keep_image_size_open(image_path)
        return transform(image),transform(segment_image)

if __name__ == '__main__':
    data=MyDataset('C:\code\pythonProject1\datasets')
    print(data[0][0].shape)
    print(data[0][1].shape)

net.py   unet网络模型

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

class Conv_Block(nn.Module):
    def __init__(self,in_channel,out_channel):
        super(Conv_Block, self).__init__()
        self.layer=nn.Sequential(