u-net模型model.train()无法加载

Question

在运行U-net做图像分割

对应U-net代码

import numpy as np
import torch
from torchvision import models
from torch import nn

def contracting_block(in_channels, out_channels):   #收缩路径 一个模块是 卷积+卷积+池化（池化放到大类中去）
    block = torch.nn.Sequential(     #block = 2个卷积快 一个卷积块 = 卷积+激活函数+归一化
                nn.Conv2d(kernel_size=(3,3), in_channels=in_channels, out_channels=out_channels),#无padding 默认为0
                nn.ReLU(),
                nn.BatchNorm2d(out_channels),
                nn.Conv2d(kernel_size=(3,3), in_channels=out_channels, out_channels=out_channels),
                nn.ReLU(),
                nn.BatchNorm2d(out_channels)
            )
    return block


class expansive_block(nn.Module):     #扩张路径
    def __init__(self, in_channels, mid_channels, out_channels):#mid_channel 过渡 通道减半
        super(expansive_block, self).__init__()
#上采样
        self.up = nn.ConvTranspose2d(in_channels, in_channels//2, kernel_size=(3, 3), stride=2, padding=1, 
                                     output_padding=1, dilation=1)  #in_channels//2 取向下取整数
#上采样的卷积操作
        self.block = nn.Sequential(
                    nn.Conv2d(kernel_size=(3,3), in_channels=in_channels, out_channels=mid_channels),
                    nn.ReLU(),
                    nn.BatchNorm2d(mid_channels),
                    nn.Conv2d(kernel_size=(3,3), in_channels=mid_channels, out_channels=out_channels),
                    nn.ReLU(),
                    nn.BatchNorm2d(out_channels)
                    )
        
    def forward(self, e, d):     #d扩张路径里还未被上采样的卷积块  e收缩路径里待复制与拼接的卷积块
        d = self.up(d)
        #concat                  tensor（batchsize，channel，height，weight）
        diffY = e.size()[2] - d.size()[2]   #e.size()[2] e的高度 d.size()[2] d的高度
        diffX = e.size()[3] - d.size()[3]   #宽的差距
        e = e[:,:, diffY//2:e.size()[2]-diffY//2, diffX//2:e.size()[3]-diffX//2]
        cat = torch.cat([e, d], dim=1)#dim=1 特征通道
        out = self.block(cat)
        return out


def final_block(in_channels, out_channels):#最后分类 1*1卷积
    block = nn.Sequential(
            nn.Conv2d(kernel_size=(1,1), in_channels=in_channels, out_channels=out_channels),
            nn.ReLU(),
            nn.BatchNorm2d(out_channels),
            )
    return  block


class UNet(nn.Module):
    def __init__(self, in_channel, out_channel):
        super(UNet, self).__init__()
        #Encode
        self.conv_encode1 = contracting_block(in_channels=in_channel, out_channels=64)
        self.conv_pool1 = nn.MaxPool2d(kernel_size=2, stride=2) #下采样中的最大池化放在这里了
        self.conv_encode2 = contracting_block(in_channels=64, out_channels=128)
        self.conv_pool2 = nn.MaxPool2d(kernel_size=2, stride=2)
        self.conv_encode3 = contracting_block(in_channels=128, out_channels=256)
        self.conv_pool3 = nn.MaxPool2d(kernel_size=2, stride=2)
        self.conv_encode4 = contracting_block(in_channels=256, out_channels=512)
        self.conv_pool4 = nn.MaxPool2d(kernel_size=2, stride=2)
        # Bottleneck  最下面剩余的两个卷积块 通道数1024
        self.bottleneck = torch.nn.Sequential(
                            nn.Conv2d(kernel_size=3, in_channels=512, out_channels=1024),
                            nn.ReLU(),
                            nn.BatchNorm2d(1024),
                            nn.Conv2d(kernel_size=3, in_channels=1024, out_channels=1024),
                            nn.ReLU(),
                            nn.BatchNorm2d(1024)
                            )
        # Decode
        self.conv_decode4 = expansive_block(1024, 512, 512) #输入通道 中间通道 输出通道
        self.conv_decode3 = expansive_block(512, 256, 256)
        self.conv_decode2 = expansive_block(256, 128, 128)
        self.conv_decode1 = expansive_block(128, 64, 64)
        self.final_layer = final_block(64, out_channel) #输出想要的分类数
    
    def forward(self, x):
        #set_trace()
        # Encode
        encode_block1 = self.conv_encode1(x);print('encode_block1:', encode_block1.size())
        encode_pool1 = self.conv_pool1(encode_block1);print('encode_pool1:', encode_pool1.size())
        encode_block2 = self.conv_encode2(encode_pool1);print('encode_block2:', encode_block2.size())
        encode_pool2 = self.conv_pool2(encode_block2);print('encode_pool2:', encode_pool2.size())
        encode_block3 = self.conv_encode3(encode_pool2);print('encode_block3:', encode_block3.size())
        encode_pool3 = self.conv_pool3(encode_block3);print('encode_pool3:', encode_pool3.size())
        encode_block4 = self.conv_encode4(encode_pool3);print('encode_block4:', encode_block4.size())
        encode_pool4 = self.conv_pool4(encode_block4);print('encode_pool4:', encode_pool4.size())
        
        # Bottleneck
        bottleneck = self.bottleneck(encode_pool4);print('bottleneck:', bottleneck.size())
        
        # Decode
        decode_block4 = self.conv_decode4(encode_block4, bottleneck);print('decode_block4:', decode_block4.size())
        decode_block3 = self.conv_decode3(encode_block3, decode_block4);print('decode_block3:', decode_block3.size())
        decode_block2 = self.conv_decode2(encode_block2, decode_block3);print('decode_block2:', decode_block2.size())
        decode_block1 = self.conv_decode1(encode_block1, decode_block2);print('decode_block1:', decode_block1.size())
        
        final_layer = self.final_layer(decode_block1)
        return final_layer


if __name__ == "__main__":
    import torch as t

    rgb = t.randn(1, 3, 1500, 1500)

    #net = UNet(3, 12)
    net = UNet(3, 2)
    out = net(rgb)

    print(out.shape)

对应train.py 部分代码

def train(model):
    best = [0]
    train_loss = 0
    net = model.train()
    running_metrics_val = runningScore(2)

if __name__ == "__main__":
    train(UNet)

Traceback (most recent call last):
File "C:\Users\强强强强\U-net\train.py", line 105, in
train(UNet)
File "C:\Users\强强强强\U-net\train.py", line 36, in train
net = model.train()
AttributeError: module 'Models.UNet' has no attribute 'train'