请画出这段代码的结构图
# 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!") 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
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！{:10_254:}{:10_254:}{:10_254:}
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 │
│ │
└───────────────────────┘
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。 +-------------------+
| |
| 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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