Hi,
I’ve encountered error in title:
Traceback (most recent call last):
  File "train.py", line 171, in <module>
    main()
  File "train.py", line 122, in main
    training_losses, validation_losses, lr_rates = trainer.run_trainer()
  File "/content/gsn/utils/trainer.py", line 67, in run_trainer
    self._train()
  File "/content/gsn/utils/trainer.py", line 141, in _train
    loss.backward(retain_graph=True)
  File "/usr/local/lib/python3.7/dist-packages/torch/tensor.py", line 245, in backward
    torch.autograd.backward(self, gradient, retain_graph, create_graph, inputs=inputs)
  File "/usr/local/lib/python3.7/dist-packages/torch/autograd/__init__.py", line 147, in backward
    allow_unreachable=True, accumulate_grad=True)  # allow_unreachable flag
RuntimeError: one of the variables needed for gradient computation has been modified by an inplace operation: [torch.cuda.FloatTensor [1024, 512, 3, 3]] is at version 2; expected version 1 instead.

Anomaly mode suggests that probelmatic is line:
out, states = self.model(input, self.last_states_for_each_channel)

Rest of training function for context:
    def _train(self):

        if self.notebook:
            from tqdm.notebook import tqdm, trange
        else:
            from tqdm import tqdm, trange

        self.model.train()  # train mode
        train_losses = []  # accumulate the losses here
        batch_iter = tqdm(enumerate(self.training_DataLoader), 'Training', total=len(self.training_DataLoader),
                          leave=False)
        iter = 0
        torch.autograd.set_detect_anomaly(True)
        for i, (x, y) in batch_iter:

            #train_losses, loss_value = self.sample_learn(x, y)
            y = y.unsqueeze(dim=0)
            input, target = x.to(self.device), y.to(self.device)  # send to device (GPU or CPU)

            input = self.event_preprocessor(input)
            input = self.crop.pad(input)
            target = self.crop.pad(target)

            self.optimizer.zero_grad()  # zerograd the parameters
            with CudaTimer('Inference'):
                out, states = self.model(input, self.last_states_for_each_channel)  # one forward pass 
                if self.no_recurrent:
                    self.last_states_for_each_channel = None
                else:
                    self.last_states_for_each_channel = states
            # target = target.type(torch.long)
            loss = self.criterion(out, target)  # calculate loss
            loss_value = loss.item()
            train_losses.append(loss_value)
            loss.backward(retain_graph=True)
            self.optimizer.step()
            # if iter == 0:
            #     self.optimizer.step()
            #     loss.backward(retain_graph=True)  # one backward pass
            #     iter+=1
            # else:
            #     loss.backward(retain_graph=True)
            #     self.optimizer.step()
              # update the parameters

            batch_iter.set_description(f'Training: (loss {loss_value:.4f})')  # update progressbar

        self.training_loss.append(np.mean(train_losses))
        self.learning_rate.append(self.optimizer.param_groups[0]['lr'])

        batch_iter.close()