saturn error

saturn_error.py

/srv/conda/envs/saturn/lib/python3.7/site-packages/torch/nn/functional.py:1204: UserWarning: Output 0 of BackwardHookFunctionBackward is a view and is being modified inplace. This view was created inside a custom Function (or because an input was returned as-is) and the autograd logic to handle view+inplace would override the custom backward associated with the custom Function, leading to incorrect gradients. This behavior is deprecated and will be forbidden starting version 1.6. You can remove this warning by cloning the output of the custom Function. (Triggered internally at  /pytorch/torch/csrc/autograd/variable.cpp:547.)
  result = torch.relu_(input)
---------------------------------------------------------------------------
RuntimeError                              Traceback (most recent call last)
<ipython-input-7-8393222d813a> in <module>
----> 1 simple_train_single(**model_params)

<ipython-input-6-29dd15a1ccdf> in simple_train_single(bucket, prefix, batch_size, downsample_to, n_epochs, base_lr, pretrained_classes)
     75             # zero the parameter gradients
     76             optimizer.zero_grad()
---> 77             loss.backward()
     78             scheduler.step()
     79 

/srv/conda/envs/saturn/lib/python3.7/site-packages/torch/tensor.py in backward(self, gradient, retain_graph, create_graph, inputs)
    243                 create_graph=create_graph,
    244                 inputs=inputs)
--> 245         torch.autograd.backward(self, gradient, retain_graph, create_graph, inputs=inputs)
    246 
    247     def register_hook(self, hook):

/srv/conda/envs/saturn/lib/python3.7/site-packages/torch/autograd/__init__.py in backward(tensors, grad_tensors, retain_graph, create_graph, grad_variables, inputs)
    145     Variable._execution_engine.run_backward(
    146         tensors, grad_tensors_, retain_graph, create_graph, inputs,
--> 147         allow_unreachable=True, accumulate_grad=True)  # allow_unreachable flag
    148 
    149 

/srv/conda/envs/saturn/lib/python3.7/site-packages/torch/utils/hooks.py in hook(grad_input, _)
    101         def hook(grad_input, _):
    102             if self.grad_outputs is None:
--> 103                 raise RuntimeError("Module backward hook for grad_input is called before "
    104                                    "the grad_output one. This happens because the gradient "
    105                                    "in your nn.Module flows to the Module's input without "

RuntimeError: Module backward hook for grad_input is called before the grad_output one. This happens because the gradient in your nn.Module flows to the Module's input without passing through the Module's output. Make sure that the output depends on the input and that the loss is computed based on the output.