博主您好，在跑您的《一文掌握图像超分辨率重建（算法原理、Pytorch实现）——含完整代码和数据》srresnet网络出现问题

博主您好，在跑您的《一文掌握图像超分辨率重建（算法原理、Pytorch实现）——含完整代码和数据》srresnet网络出现问题，来向您请教
原文
https://blog.csdn.net/qianbin3200896/article/details/104181552?ops_request_misc=%257B%2522request%255Fid%2522%253A%2522163089521416780265410543%2522%252C%2522scm%2522%253A%252220140713.130102334..%2522%257D&request_id=163089521416780265410543&biz_id=0&utm_medium=distribute.pc_search_result.none-task-blog-2~all~top_positive~default-1-104181552.first_rank_v2_pc_rank_v29&utm_term=%E5%9B%BE%E5%83%8F%E8%B6%85%E5%88%86%E8%BE%A8%E7%8E%87&spm=1018.2226.3001.4187

问题相关代码，请勿粘贴截图

在train_ssresnet.py的试运行中

由于GPU限制，我将其中两个参数进行了改动，batch_size=128， ngpu=1

# #!/usr/bin/env python
# # -*- encoding: utf-8 -*-
# '''
# @文件 :train_srresnet.py
# @说明 :训练SRResNet
# @时间 :2020/02/11 13:30:57
# @作者 :钱彬
# @版本 :1.0
# '''


import torch.backends.cudnn as cudnn
import torch
from torch import nn
from torchvision.utils import make_grid
from torch.utils.tensorboard import SummaryWriter
from models import SRResNet
from datasets import SRDataset
from utils import *


# 数据集参数
data_folder = './data/'          # 数据存放路径
crop_size = 96      # 高分辨率图像裁剪尺寸
scaling_factor = 4  # 放大比例

# 模型参数
large_kernel_size = 9   # 第一层卷积和最后一层卷积的核大小
small_kernel_size = 3   # 中间层卷积的核大小
n_channels = 64         # 中间层通道数
n_blocks = 16           # 残差模块数量

# 学习参数
checkpoint = None   # 预训练模型路径，如果不存在则为None   可以迁移学习
batch_size = 128    # 批大小
start_epoch = 1     # 轮数起始位置
epochs = 130        # 迭代轮数
workers = 1         # 工作线程数
lr = 1e-2           # 学习率

# 设备参数
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
ngpu = 1           # 用来运行的gpu数量

cudnn.benchmark = True # 对卷积进行加速

writer = SummaryWriter() # 实时监控     使用命令 tensorboard --logdir runs  进行查看

def main():
    """
    训练.
    """
    global checkpoint,start_epoch,writer   # 全局参数可以调用

    # 初始化
    model = SRResNet(large_kernel_size=large_kernel_size,
                        small_kernel_size=small_kernel_size,
                        n_channels=n_channels,
                        n_blocks=n_blocks,
                        scaling_factor=scaling_factor)
    # 初始化优化器   Adam优化算法
    optimizer = torch.optim.Adam(params=filter(lambda p: p.requires_grad, model.parameters()),lr=lr)

    # 迁移至默认设备进行训练
    model = model.to(device)
    # 损失函数迁移至gpu
    criterion = nn.MSELoss().to(device)

    # 加载预训练模型
    if checkpoint is not None:
        checkpoint = torch.load(checkpoint)
        start_epoch = checkpoint['epoch'] + 1
        model.load_state_dict(checkpoint['model'])
        optimizer.load_state_dict(checkpoint['optimizer'])
    
    if torch.cuda.is_available() and ngpu > 1:
        model = nn.DataParallel(model, device_ids=list(range(ngpu)))

    # 定制化的dataloaders
    train_dataset = SRDataset(data_folder,split='train',
                              crop_size=crop_size,
                              scaling_factor=scaling_factor,
                              lr_img_type='imagenet-norm',
                              hr_img_type='[-1, 1]')
    train_loader = torch.utils.data.DataLoader(train_dataset,
        batch_size=batch_size,
        shuffle=True,
        num_workers=workers,
        pin_memory=True) 

    # 开始逐轮训练
    for epoch in range(start_epoch, epochs+1):

        model.train()  # 训练模式：允许使用批样本归一化

        loss_epoch = AverageMeter()  # 统计损失函数

        n_iter = len(train_loader)

        # 按批处理
        for i, (lr_imgs, hr_imgs) in enumerate(train_loader):

            # 数据移至默认设备进行训练
            lr_imgs = lr_imgs.to(device)  # (batch_size (N), 3, 24, 24), imagenet-normed 格式
            hr_imgs = hr_imgs.to(device)  # (batch_size (N), 3, 96, 96),  [-1, 1]格式

            # 前向传播
            sr_imgs = model(lr_imgs)

            # 计算损失
            loss = criterion(sr_imgs, hr_imgs)  

            # 后向传播
            optimizer.zero_grad()
            loss.backward()

            # 更新模型
            optimizer.step()

            # 记录损失值
            loss_epoch.update(loss.item(), lr_imgs.size(0))

            # 监控图像变化
            if i==(n_iter-2):
                writer.add_image('SRResNet/epoch_'+str(epoch)+'_1', make_grid(lr_imgs[:4,:3,:,:].cpu(), nrow=4, normalize=True),epoch)
                writer.add_image('SRResNet/epoch_'+str(epoch)+'_2', make_grid(sr_imgs[:4,:3,:,:].cpu(), nrow=4, normalize=True),epoch)
                writer.add_image('SRResNet/epoch_'+str(epoch)+'_3', make_grid(hr_imgs[:4,:3,:,:].cpu(), nrow=4, normalize=True),epoch)

            # 打印结果
            print("第 "+str(i)+ " 个batch训练结束")
 
        # 手动释放内存              
        del lr_imgs, hr_imgs, sr_imgs

        # 监控损失值变化
        writer.add_scalar('SRResNet/MSE_Loss', loss_epoch.val, epoch)    

        # 保存预训练模型
        torch.save({
            'epoch': epoch,
            'model': model.module.state_dict(),
            'optimizer': optimizer.state_dict()
        }, 'results/checkpoint_srresnet.pth')
    
    # 训练结束关闭监控
    writer.close()


if __name__ == '__main__':
    main()

运行结果及报错内容

第 957 个batch训练结束
第 958 个batch训练结束
第 959 个batch训练结束
第 960 个batch训练结束
第 961 个batch训练结束
第 962 个batch训练结束
第 963 个batch训练结束
Traceback (most recent call last):
  File "E:/BaiduNetdiskDownload/SRGAN/train_srresnet.py", line 150, in <module>
    main()
  File "E:/BaiduNetdiskDownload/SRGAN/train_srresnet.py", line 141, in main
    'model': model.module.state_dict(),
  File "D:\ann\lib\site-packages\torch\nn\modules\module.py", line 1130, in __getattr__
    raise AttributeError("'{}' object has no attribute '{}'".format(
AttributeError: 'SRResNet' object has no attribute 'module'

我的解答思路和尝试过的方法

我想要达到的结果

能顺利跑通 train_ssresnet.py