详细解释每一句代码if i % 20 == 0 or i == total_step: print('{} Epoch [{:03d}/{:03d}], Step [{:04d}/{:04d}], ' '[lateral-2: {:.4f}, lateral-3: {:0.4f}, lateral-4: {:0.4f}]'. format(datetime.now(), epoch, opt.epoch, i, total_step, loss_record2.show(), loss_record3.show(), loss_record4.show())) print('lr: ', optimizer.param_groups[0]['lr']) save_path = 'snapshots/{}/'.format(opt.train_save) os.makedirs(save_path, exist_ok=True) if (epoch+1) % 1 == 0: meanloss = test(model, opt.test_path) if meanloss < best_loss: print('new best loss: ', meanloss) best_loss = meanloss torch.save(model.state_dict(), save_path + 'TransFuse-%d.pth' % epoch) print('[Saving Snapshot:]', save_path + 'TransFuse-%d.pth'% epoch) return best_loss

Sure, here's a detailed explanation of each line of code:

if i % 20 == 0 or i == total_step:

This line checks if the current step `i` is a multiple of 20 or if it is the last step `total_step`. If either condition is true, the following code block will be executed.

print('{} Epoch [{:03d}/{:03d}], Step [{:04d}/{:04d}], '
      '[lateral-2: {:.4f}, lateral-3: {:0.4f}, lateral-4: {:0.4f}]'.
      format(datetime.now(), epoch, opt.epoch, i, total_step, 
             loss_record2.show(), loss_record3.show(), loss_record4.show()))

This line prints the current date and time, epoch number, step number, and loss values for three different lateral connections (lateral-2, lateral-3, lateral-4) in a specific format. The `datetime.now()` function gets the current date and time, while the other variables such as `epoch`, `opt.epoch`, `i`, `total_step`, `loss_record2`, `loss_record3`, and `loss_record4` are defined elsewhere in the code.

print('lr: ', optimizer.param_groups[0]['lr'])

This line prints the current learning rate of the optimizer, which is stored in the optimizer's `param_groups` attribute.

save_path = 'snapshots/{}/'.format(opt.train_save)
os.makedirs(save_path, exist_ok=True)

These lines create a directory to save the model snapshots. The `opt.train_save` variable specifies the name of the directory, and the `os.makedirs()` function creates the directory if it doesn't already exist.

if (epoch+1) % 1 == 0:

This line checks if the current epoch plus one is a multiple of one (which it always will be), and if so, executes the following code block. This code block is executed every epoch.

meanloss = test(model, opt.test_path)

This line calls the `test()` function with the trained model and the specified test dataset path `opt.test_path`, and calculates the mean loss value over the test dataset.

if meanloss < best_loss:
    print('new best loss: ', meanloss)
    best_loss = meanloss
    torch.save(model.state_dict(), save_path + 'TransFuse-%d.pth' % epoch)
    print('[Saving Snapshot:]', save_path + 'TransFuse-%d.pth'% epoch)

This code block checks if the mean loss value is lower than the previous best loss value. If so, it updates the best loss value, saves the current model state dictionary to a file in the specified directory, and prints a message indicating that a new snapshot has been saved.

return best_loss

This line returns the best loss value after the training loop is complete.