Running ./modules/mednist_GAN_workflow_array.ipynb
Checking PEP8 compliance...
Running notebook...
Before:
    "max_epochs = 50\n",
After:
    "max_epochs = 1\n",
Before:
    "disc_train_steps = 5\n",
After:
    "disc_train_steps = 1\n",

Executing:   0%|          | 0/33 [00:00<?, ?cell/s]
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Executing:  30%|███       | 10/33 [00:16<00:33,  1.48s/cell]
Executing:  52%|█████▏    | 17/33 [00:25<00:22,  1.42s/cell]
Executing:  61%|██████    | 20/33 [00:27<00:15,  1.18s/cell]
Executing:  79%|███████▉  | 26/33 [00:30<00:06,  1.02cell/s]
Executing:  79%|███████▉  | 26/33 [00:32<00:08,  1.24s/cell]
Traceback (most recent call last):
  File "/opt/conda/bin/papermill", line 8, in <module>
    sys.exit(papermill())
  File "/opt/conda/lib/python3.8/site-packages/click/core.py", line 829, in __call__
    return self.main(*args, **kwargs)
  File "/opt/conda/lib/python3.8/site-packages/click/core.py", line 782, in main
    rv = self.invoke(ctx)
  File "/opt/conda/lib/python3.8/site-packages/click/core.py", line 1066, in invoke
    return ctx.invoke(self.callback, **ctx.params)
  File "/opt/conda/lib/python3.8/site-packages/click/core.py", line 610, in invoke
    return callback(*args, **kwargs)
  File "/opt/conda/lib/python3.8/site-packages/click/decorators.py", line 21, in new_func
    return f(get_current_context(), *args, **kwargs)
  File "/opt/conda/lib/python3.8/site-packages/papermill/cli.py", line 250, in papermill
    execute_notebook(
  File "/opt/conda/lib/python3.8/site-packages/papermill/execute.py", line 122, in execute_notebook
    raise_for_execution_errors(nb, output_path)
  File "/opt/conda/lib/python3.8/site-packages/papermill/execute.py", line 234, in raise_for_execution_errors
    raise error
papermill.exceptions.PapermillExecutionError: 
---------------------------------------------------------------------------
Exception encountered at "In [14]":
---------------------------------------------------------------------------
RuntimeError                              Traceback (most recent call last)
<ipython-input-14-041e2033e90a> in <module>
----> 1 trainer.run()

/opt/conda/lib/python3.8/site-packages/monai/engines/trainer.py in run(self)
     54         """
     55         self.scaler = torch.cuda.amp.GradScaler() if self.amp else None
---> 56         super().run()
     57 
     58     def get_train_stats(self) -> Dict[str, float]:

/opt/conda/lib/python3.8/site-packages/monai/engines/workflow.py in run(self)
    248 
    249         """
--> 250         super().run(data=self.data_loader, max_epochs=self.state.max_epochs)
    251 
    252     def _iteration(self, engine: Engine, batchdata: Dict[str, torch.Tensor]):

/opt/conda/lib/python3.8/site-packages/ignite/engine/engine.py in run(self, data, max_epochs, epoch_length, seed)
    699 
    700         self.state.dataloader = data
--> 701         return self._internal_run()
    702 
    703     @staticmethod

/opt/conda/lib/python3.8/site-packages/ignite/engine/engine.py in _internal_run(self)
    772             self._dataloader_iter = None
    773             self.logger.error(f"Engine run is terminating due to exception: {e}")
--> 774             self._handle_exception(e)
    775 
    776         self._dataloader_iter = None

/opt/conda/lib/python3.8/site-packages/ignite/engine/engine.py in _handle_exception(self, e)
    465     def _handle_exception(self, e: BaseException) -> None:
    466         if Events.EXCEPTION_RAISED in self._event_handlers:
--> 467             self._fire_event(Events.EXCEPTION_RAISED, e)
    468         else:
    469             raise e

/opt/conda/lib/python3.8/site-packages/ignite/engine/engine.py in _fire_event(self, event_name, *event_args, **event_kwargs)
    422             kwargs.update(event_kwargs)
    423             first, others = ((args[0],), args[1:]) if (args and args[0] == self) else ((), args)
--> 424             func(*first, *(event_args + others), **kwargs)
    425 
    426     def fire_event(self, event_name: Any) -> None:

/opt/conda/lib/python3.8/site-packages/monai/handlers/stats_handler.py in exception_raised(self, engine, e)
    146         """
    147         self.logger.exception(f"Exception: {e}")
--> 148         raise e
    149 
    150     def _default_epoch_print(self, engine: Engine) -> None:

/opt/conda/lib/python3.8/site-packages/ignite/engine/engine.py in _internal_run(self)
    742                     self._setup_engine()
    743 
--> 744                 time_taken = self._run_once_on_dataset()
    745                 # time is available for handlers but must be update after fire
    746                 self.state.times[Events.EPOCH_COMPLETED.name] = time_taken

/opt/conda/lib/python3.8/site-packages/ignite/engine/engine.py in _run_once_on_dataset(self)
    846         except Exception as e:
    847             self.logger.error(f"Current run is terminating due to exception: {e}")
--> 848             self._handle_exception(e)
    849 
    850         return time.time() - start_time

/opt/conda/lib/python3.8/site-packages/ignite/engine/engine.py in _handle_exception(self, e)
    465     def _handle_exception(self, e: BaseException) -> None:
    466         if Events.EXCEPTION_RAISED in self._event_handlers:
--> 467             self._fire_event(Events.EXCEPTION_RAISED, e)
    468         else:
    469             raise e

/opt/conda/lib/python3.8/site-packages/ignite/engine/engine.py in _fire_event(self, event_name, *event_args, **event_kwargs)
    422             kwargs.update(event_kwargs)
    423             first, others = ((args[0],), args[1:]) if (args and args[0] == self) else ((), args)
--> 424             func(*first, *(event_args + others), **kwargs)
    425 
    426     def fire_event(self, event_name: Any) -> None:

/opt/conda/lib/python3.8/site-packages/monai/handlers/stats_handler.py in exception_raised(self, engine, e)
    146         """
    147         self.logger.exception(f"Exception: {e}")
--> 148         raise e
    149 
    150     def _default_epoch_print(self, engine: Engine) -> None:

/opt/conda/lib/python3.8/site-packages/ignite/engine/engine.py in _run_once_on_dataset(self)
    832                 self.state.iteration += 1
    833                 self._fire_event(Events.ITERATION_STARTED)
--> 834                 self.state.output = self._process_function(self, self.state.batch)
    835                 self._fire_event(Events.ITERATION_COMPLETED)
    836 

/opt/conda/lib/python3.8/site-packages/monai/engines/trainer.py in _iteration(self, engine, batchdata)
    361             else:
    362                 self.d_optimizer.zero_grad(set_to_none=self.optim_set_to_none)
--> 363             dloss = self.d_loss_function(g_output, d_input)
    364             dloss.backward()
    365             self.d_optimizer.step()

<ipython-input-11-aa4677288b64> in discriminator_loss(gen_images, real_images)
     39     gen = gen_images.new_full((gen_images.shape[0], 1), fake_label)
     40 
---> 41     realloss = disc_loss_criterion(disc_net(real_images), real)
     42     genloss = disc_loss_criterion(disc_net(gen_images.detach()), gen)
     43 

/opt/conda/lib/python3.8/site-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs)
   1013         if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks
   1014                 or _global_forward_hooks or _global_forward_pre_hooks):
-> 1015             return forward_call(*input, **kwargs)
   1016         # Do not call functions when jit is used
   1017         full_backward_hooks, non_full_backward_hooks = [], []

/opt/conda/lib/python3.8/site-packages/monai/networks/nets/regressor.py in forward(self, x)
    139 
    140     def forward(self, x: torch.Tensor) -> torch.Tensor:
--> 141         x = self.net(x)
    142         x = self.final(x)
    143         x = self.reshape(x)

/opt/conda/lib/python3.8/site-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs)
   1013         if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks
   1014                 or _global_forward_hooks or _global_forward_pre_hooks):
-> 1015             return forward_call(*input, **kwargs)
   1016         # Do not call functions when jit is used
   1017         full_backward_hooks, non_full_backward_hooks = [], []

/opt/conda/lib/python3.8/site-packages/torch/nn/modules/container.py in forward(self, input)
    116     def forward(self, input):
    117         for module in self:
--> 118             input = module(input)
    119         return input
    120 

/opt/conda/lib/python3.8/site-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs)
   1013         if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks
   1014                 or _global_forward_hooks or _global_forward_pre_hooks):
-> 1015             return forward_call(*input, **kwargs)
   1016         # Do not call functions when jit is used
   1017         full_backward_hooks, non_full_backward_hooks = [], []

/opt/conda/lib/python3.8/site-packages/monai/networks/blocks/convolutions.py in forward(self, x)
    322 
    323     def forward(self, x: torch.Tensor) -> torch.Tensor:
--> 324         res: torch.Tensor = self.residual(x)  # create the additive residual from x
    325         cx: torch.Tensor = self.conv(x)  # apply x to sequence of operations
    326         return cx + res  # add the residual to the output

/opt/conda/lib/python3.8/site-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs)
   1013         if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks
   1014                 or _global_forward_hooks or _global_forward_pre_hooks):
-> 1015             return forward_call(*input, **kwargs)
   1016         # Do not call functions when jit is used
   1017         full_backward_hooks, non_full_backward_hooks = [], []

/opt/conda/lib/python3.8/site-packages/torch/nn/modules/conv.py in forward(self, input)
    397 
    398     def forward(self, input: Tensor) -> Tensor:
--> 399         return self._conv_forward(input, self.weight, self.bias)
    400 
    401 class Conv3d(_ConvNd):

/opt/conda/lib/python3.8/site-packages/torch/nn/modules/conv.py in _conv_forward(self, input, weight, bias)
    393                             weight, bias, self.stride,
    394                             _pair(0), self.dilation, self.groups)
--> 395         return F.conv2d(input, weight, bias, self.stride,
    396                         self.padding, self.dilation, self.groups)
    397 

RuntimeError: Input type (torch.cuda.DoubleTensor) and weight type (torch.cuda.FloatTensor) should be the same