[已解决]请画出这段代码的结构图

Justheartyoung · 发表于 2023-8-16 21:36:14

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# 模型、损失和优化器
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = DeeperAutoencoderWithMoreAttention().to(device)
criterion = nn.MSELoss()
optimizer = optim.Adam(model.parameters(), lr=0.001)
best_psnr = 0
best_model_save_path = './save_root/best_autoencoder.pth'
model_save_path = './save_root/epoch{}_model.pth'
scheduler = optim.lr_scheduler.StepLR(optimizer, step_size=3000, gamma=0.1)

num_epochs = 5000
if __name__ == '__main__':
print(device)
print("开始运行ing")

for epoch in range(num_epochs):
      start_time = time()

      for (clean_images, _), (noisy_images, _) in zip(clean_loader, noisy_loader):
         clean_images = clean_images.to(device)
         noisy_images = noisy_images.to(device)

         outputs = model(noisy_images)
         loss = criterion(outputs, clean_images)
         optimizer.zero_grad()
         loss.backward()
         optimizer.step()

      scheduler.step()

      save_combined_images(model, noisy_loader, clean_loader, output_path=f"./save_image/index{epoch}epoch.png")

      # 计算平均PSNR
      model.eval()
      total_psnr = 0
      with torch.no_grad():
         for (clean_images, _), (noisy_images, _) in zip(clean_loader, noisy_loader):
            clean_images = clean_images.to(device)
            noisy_images = noisy_images.to(device)
            outputs = model(noisy_images)
            psnr = compute_psnr(clean_images, outputs)
            total_psnr += psnr

      avg_psnr = total_psnr / len(clean_loader)
      end_time = time()

      print(
         f"Epoch [{epoch + 1}/{num_epochs}], Loss: {loss.item():.4f}, PSNR: {avg_psnr:.4f}, Time:{end_time - start_time:.2f}")

      if avg_psnr > best_psnr:
         best_psnr = avg_psnr
         torch.save(model.state_dict(), best_model_save_path)
         print(f"在第 {epoch + 1} 个epoch保存了最佳模型，PSNR为: {avg_psnr:.4f}")

      if (epoch + 1) % 10 == 0:
         torch.save(model.state_dict(), model_save_path.format(epoch))
         print(f"在第 {epoch + 1} 个epoch保存模型，PSNR为: {avg_psnr:.4f}")

      model.train()

print("Training completed!")

最佳答案

月排行榜 / 总排行榜

琅琊王朝

2023-8-16 21:54:43

+-------------------+
| |
| DeeperAuto- |
| encoderWith |
| MoreAttention |
| |
+---------+---------+
|
|
+-----------------------------+------------^--------------+
| | |
| | |
+----v----+ +------v------+ +-------v--------+
| | | | | |
| device | | criterion | | optimizer |
| | | | | |
+----^----+ +-------------+ +-------+--------+
| |
| |
+----v----------------------------------------------------------v------+
| Model Training Loop |
| |
| +---------------------------------------------------+ |
| | | |
| | for epoch in range(num_epochs): | |
| | start_time = time() | |
| | for (clean_images, _), (noisy_images, _) in | |
| | zip(clean_loader, noisy_loader): | |
| | clean_images = clean_images.to(device) | |
| | noisy_images = noisy_images.to(device) | |
| | output = model(noisy_images) | |
| | loss = criterion(output, clean_images) | |
| | optimizer.zero_grad() | |
| | loss.backward() | |
| | optimizer.step() | |
| | scheduler.step() | |
| | save_combined_images(model, noisy_loader, | |
| | clean_loader, output_path=f"./save_image/index{epoch}epoch.png") | |
| | | |
| +---------------------------------------------------+ |
| |
| +---------------------------------------------------+ |
| | | |
| | model.eval() | |
| | total_psnr = 0 | |
| | with torch.no_grad(): | |
| | for (clean_images, _), (noisy_images, _) in | |
| | zip(clean_loader, noisy_loader): | |
| | clean_images = clean_images.to(device) | |
| | noisy_images = noisy_images.to(device) | |
| | output = model(noisy_images) | |
| | psnr = compute_psnr(clean_images, output) | |
| | total_psnr += psnr | |
| | | |
| +---------------------------------------------------+ |
| |
| +---------------------------------------------------+ |
| | | |
| | avg_psnr = total_psnr / len(clean_loader) | |
| | end_time = time() | |
| | | |
| | print( | |
| | f"Epoch [{epoch+1}/{num_epochs}], Loss: {loss.item():.4f}, | |
| | PSNR: {avg_psnr:.4f}, Time: {end_time-start_time:.2f}") | |
| | | |
| | if avg_psnr > best_psnr: | |
| | best_psnr = avg_psnr | |
| | torch.save(model.state_dict(), best_model_save_path) | |
| | print(f"Saved the best model at epoch {epoch+1}, PSNR: {avg_psnr:.4f}") | |
| | | |
| | if (epoch+1) % 10 == 0: | |
| | torch.save(model.state_dict(), model_save_path.format(epoch)) | |
| | print(f"Saved model at epoch {epoch+1}, PSNR: {avg_psnr:.4f}") | |
| | | |
| | model.train() | |
| | | |
| +---------------------------------------------------+ |
| |
+------------------------------------------------------------------------+

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学习编程中的Ben · 发表于 2023-8-16 21:36:38

抱歉，作为一个文本AI助手，我无法直接绘制图形。但是，我可以为您提供代码的层次结构说明。

- 模型、损失和优化器的定义
- 设备选择
- 主循环开始
- 对每个 epoch 进行迭代
- 记录开始时间
- 对于每个数据批次
- 将干净图像和噪声图像放入设备中
- 运行模型得到输出
- 计算损失
- 清除梯度
- 反向传播并更新参数
- 更新学习率
- 保存组合图像
- 计算平均PSNR
- 对于每个数据批次
- 将干净图像和噪声图像放入设备中
- 运行模型得到输出
- 计算PSNR
- 累计PSNR值
- 计算平均PSNR
- 记录结束时间
- 打印当前epoch的损失、PSNR和运行时间
- 如果当前PSNR超过最佳PSNR，则保存模型
- 每10个epoch保存一次模型
- 将模型设置为训练模式
- 输出训练完成的提示信息

复制代码

希望这能帮到您！如果您还有其他问题，请随时提问。

如果回答对你有帮助，请给我一个最佳答案！

isdkz · 发表于 2023-8-16 21:38:51

这段代码是一个训练深度自编码器的程序。下面是代码的结构图：

  ┌───────────────────────┐

  │                      │

  │ 模型、损失和优化器 │

  │                      │

  └───────────────────────┘

            │

            ▼

  ┌───────────────────────┐

  │                      │

  │    数据加载       │

  │                      │

  └───────────────────────┘

            │

            ▼

  ┌───────────────────────┐

  │                      │

  │    模型训练循环    │

  │                      │

  └───────────────────────┘

            │

            ▼

  ┌───────────────────────┐

  │                      │

  │ 保存最佳模型和中间模型  │

  │                      │

  └───────────────────────┘

            │

            ▼

  ┌───────────────────────┐

  │                      │

  │ 训练完成，输出信息 │

  │                      │

  └───────────────────────┘

复制代码

在代码的主要部分，有一个循环来进行模型的训练。每个epoch内部，代码会遍历clean_loader和noisy_loader，将数据加载到设备上，然后通过模型生成输出，计算损失并进行优化器的反向传播。然后，代码会进行学习率的调整，并保存生成的合成图像。接下来，代码会计算平均PSNR，并保存最佳模型和中间模型。最后，代码会输出每个epoch的训练信息。

琅琊王朝 · 发表于 2023-8-16 21:54:43

+-------------------+
| |
| DeeperAuto- |
| encoderWith |
| MoreAttention |
| |
+---------+---------+
|
|
+-----------------------------+------------^--------------+
| | |
| | |
+----v----+ +------v------+ +-------v--------+
| | | | | |
| device | | criterion | | optimizer |
| | | | | |
+----^----+ +-------------+ +-------+--------+
| |
| |
+----v----------------------------------------------------------v------+
| Model Training Loop |
| |
| +---------------------------------------------------+ |
| | | |
| | for epoch in range(num_epochs): | |
| | start_time = time() | |
| | for (clean_images, _), (noisy_images, _) in | |
| | zip(clean_loader, noisy_loader): | |
| | clean_images = clean_images.to(device) | |
| | noisy_images = noisy_images.to(device) | |
| | output = model(noisy_images) | |
| | loss = criterion(output, clean_images) | |
| | optimizer.zero_grad() | |
| | loss.backward() | |
| | optimizer.step() | |
| | scheduler.step() | |
| | save_combined_images(model, noisy_loader, | |
| | clean_loader, output_path=f"./save_image/index{epoch}epoch.png") | |
| | | |
| +---------------------------------------------------+ |
| |
| +---------------------------------------------------+ |
| | | |
| | model.eval() | |
| | total_psnr = 0 | |
| | with torch.no_grad(): | |
| | for (clean_images, _), (noisy_images, _) in | |
| | zip(clean_loader, noisy_loader): | |
| | clean_images = clean_images.to(device) | |
| | noisy_images = noisy_images.to(device) | |
| | output = model(noisy_images) | |
| | psnr = compute_psnr(clean_images, output) | |
| | total_psnr += psnr | |
| | | |
| +---------------------------------------------------+ |
| |
| +---------------------------------------------------+ |
| | | |
| | avg_psnr = total_psnr / len(clean_loader) | |
| | end_time = time() | |
| | | |
| | print( | |
| | f"Epoch [{epoch+1}/{num_epochs}], Loss: {loss.item():.4f}, | |
| | PSNR: {avg_psnr:.4f}, Time: {end_time-start_time:.2f}") | |
| | | |
| | if avg_psnr > best_psnr: | |
| | best_psnr = avg_psnr | |
| | torch.save(model.state_dict(), best_model_save_path) | |
| | print(f"Saved the best model at epoch {epoch+1}, PSNR: {avg_psnr:.4f}") | |
| | | |
| | if (epoch+1) % 10 == 0: | |
| | torch.save(model.state_dict(), model_save_path.format(epoch)) | |
| | print(f"Saved model at epoch {epoch+1}, PSNR: {avg_psnr:.4f}") | |
| | | |
| | model.train() | |
| | | |
| +---------------------------------------------------+ |
| |
+------------------------------------------------------------------------+

复制代码

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[已解决]请画出这段代码的结构图

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