Running ./modules/mednist_GAN_workflow_dict.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%|          | 0/34 [00:00<?, ?cell/s]
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Executing:  29%|██▉       | 10/34 [00:22<00:45,  1.89s/cell]
Executing:  53%|█████▎    | 18/34 [00:29<00:25,  1.59s/cell]
Executing:  62%|██████▏   | 21/34 [00:31<00:17,  1.32s/cell]
Executing:  79%|███████▉  | 27/34 [00:32<00:06,  1.02cell/s]
Executing:  79%|███████▉  | 27/34 [00:33<00:08,  1.26s/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 [15]":
---------------------------------------------------------------------------
RuntimeError                              Traceback (most recent call last)
<ipython-input-15-041e2033e90a> in <module>
----> 1 trainer.run()

/__w/MONAI/MONAI/monai/engines/trainer.py in run(self)
     46         """
     47         self.scaler = torch.cuda.amp.GradScaler() if self.amp else None
---> 48         super().run()
     49 
     50     def get_train_stats(self) -> Dict[str, float]:

/__w/MONAI/MONAI/monai/engines/workflow.py in run(self)
    195 
    196         """
--> 197         super().run(data=self.data_loader, max_epochs=self.state.max_epochs)
    198 
    199     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)
    700 
    701         self.state.dataloader = data
--> 702         return self._internal_run()
    703 
    704     @staticmethod

/opt/conda/lib/python3.8/site-packages/ignite/engine/engine.py in _internal_run(self)
    773             self._dataloader_iter = None
    774             self.logger.error(f"Engine run is terminating due to exception: {e}")
--> 775             self._handle_exception(e)
    776 
    777         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:

/__w/MONAI/MONAI/monai/handlers/stats_handler.py in exception_raised(self, engine, e)
    143         """
    144         self.logger.exception(f"Exception: {e}")
--> 145         raise e
    146 
    147     def _default_epoch_print(self, engine: Engine) -> None:

/opt/conda/lib/python3.8/site-packages/ignite/engine/engine.py in _internal_run(self)
    743                     self._setup_engine()
    744 
--> 745                 time_taken = self._run_once_on_dataset()
    746                 # time is available for handlers but must be update after fire
    747                 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)
    848         except Exception as e:
    849             self.logger.error(f"Current run is terminating due to exception: {e}")
--> 850             self._handle_exception(e)
    851 
    852         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:

/__w/MONAI/MONAI/monai/handlers/stats_handler.py in exception_raised(self, engine, e)
    143         """
    144         self.logger.exception(f"Exception: {e}")
--> 145         raise e
    146 
    147     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)
    799                     if self.last_event_name != Events.DATALOADER_STOP_ITERATION:
    800                         self._fire_event(Events.GET_BATCH_STARTED)
--> 801                     self.state.batch = next(self._dataloader_iter)
    802                     self._fire_event(Events.GET_BATCH_COMPLETED)
    803                     iter_counter += 1

/opt/conda/lib/python3.8/site-packages/torch/utils/data/dataloader.py in __next__(self)
    515             if self._sampler_iter is None:
    516                 self._reset()
--> 517             data = self._next_data()
    518             self._num_yielded += 1
    519             if self._dataset_kind == _DatasetKind.Iterable and \

/opt/conda/lib/python3.8/site-packages/torch/utils/data/dataloader.py in _next_data(self)
   1197             else:
   1198                 del self._task_info[idx]
-> 1199                 return self._process_data(data)
   1200 
   1201     def _try_put_index(self):

/opt/conda/lib/python3.8/site-packages/torch/utils/data/dataloader.py in _process_data(self, data)
   1223         self._try_put_index()
   1224         if isinstance(data, ExceptionWrapper):
-> 1225             data.reraise()
   1226         return data
   1227 

/opt/conda/lib/python3.8/site-packages/torch/_utils.py in reraise(self)
    427             # have message field
    428             raise self.exc_type(message=msg)
--> 429         raise self.exc_type(msg)
    430 
    431 

RuntimeError: Caught RuntimeError in DataLoader worker process 0.
Original Traceback (most recent call last):
  File "/__w/MONAI/MONAI/monai/data/utils.py", line 257, in list_data_collate
    return default_collate(data)
  File "/opt/conda/lib/python3.8/site-packages/torch/utils/data/_utils/collate.py", line 73, in default_collate
    return {key: default_collate([d[key] for d in batch]) for key in elem}
  File "/opt/conda/lib/python3.8/site-packages/torch/utils/data/_utils/collate.py", line 73, in <dictcomp>
    return {key: default_collate([d[key] for d in batch]) for key in elem}
  File "/opt/conda/lib/python3.8/site-packages/torch/utils/data/_utils/collate.py", line 83, in default_collate
    return [default_collate(samples) for samples in transposed]
  File "/opt/conda/lib/python3.8/site-packages/torch/utils/data/_utils/collate.py", line 83, in <listcomp>
    return [default_collate(samples) for samples in transposed]
  File "/opt/conda/lib/python3.8/site-packages/torch/utils/data/_utils/collate.py", line 73, in default_collate
    return {key: default_collate([d[key] for d in batch]) for key in elem}
  File "/opt/conda/lib/python3.8/site-packages/torch/utils/data/_utils/collate.py", line 73, in <dictcomp>
    return {key: default_collate([d[key] for d in batch]) for key in elem}
  File "/opt/conda/lib/python3.8/site-packages/torch/utils/data/_utils/collate.py", line 73, in default_collate
    return {key: default_collate([d[key] for d in batch]) for key in elem}
  File "/opt/conda/lib/python3.8/site-packages/torch/utils/data/_utils/collate.py", line 73, in <dictcomp>
    return {key: default_collate([d[key] for d in batch]) for key in elem}
  File "/opt/conda/lib/python3.8/site-packages/torch/utils/data/_utils/collate.py", line 63, in default_collate
    return default_collate([torch.as_tensor(b) for b in batch])
  File "/opt/conda/lib/python3.8/site-packages/torch/utils/data/_utils/collate.py", line 55, in default_collate
    return torch.stack(batch, 0, out=out)
RuntimeError: stack expects each tensor to be equal size, but got [3, 3] at entry 0 and [4, 4] at entry 4

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "/opt/conda/lib/python3.8/site-packages/torch/utils/data/_utils/worker.py", line 202, in _worker_loop
    data = fetcher.fetch(index)
  File "/opt/conda/lib/python3.8/site-packages/torch/utils/data/_utils/fetch.py", line 47, in fetch
    return self.collate_fn(data)
  File "/__w/MONAI/MONAI/monai/data/utils.py", line 266, in list_data_collate
    raise RuntimeError(re_str)
RuntimeError: stack expects each tensor to be equal size, but got [3, 3] at entry 0 and [4, 4] at entry 4
MONAI hint: if your transforms intentionally create images of different shapes, creating your `DataLoader` with `collate_fn=pad_list_data_collate` might solve this problem (check its documentation).