Training gets slow down by each batch slowly

reinforcement-learning
25

hey, could you please explain more about the usage .detach() a bit more in the case of accumulating of loss. Sorry if my question is too basic, I’m still a new hand to PyTorch.

epoch_loss = 0
n_train = len(train_loader)
 with tqdm(total=n_train, desc=f'Epoch {epoch + 1}/{epochs}', unit='img') as pbar:
      for batch in train_loader:
          net.train()
          imgs = batch['image']
          true_masks = batch['labels']
          imgs = imgs.to(device=device, dtype=torch.float32)
          mask_type = torch.float32 if net.n_classes == 1 else torch.long
          true_masks = true_masks.to(device=device, dtype=mask_type)
          logits,probs,masks_pred = net(imgs)
          logits = torch.squeeze(logits,1)
          loss = criterion(logits, true_masks)
          epoch_loss += loss.item()/n_train
          optimizer.zero_grad()
          loss.backward()
          optimizer.step()
          # to be continued

If I understood it correctly in this case, epoch_loss = 0, wouldn’t let the loss being saved from one iteration to the another. And, this is called .detach().
Thank u.