overfit_batches flag is throwing an error when used in combination with the data module.
‘generator’ object is not callable
The same error is not repeating when I do not use the overfit_batches flag.
My Code:
trainer = pl.Trainer(gpus=1, overfit_batches=1)
trainer.fit(model, dm)
Full Stack Trace:
TypeError Traceback (most recent call last)
<ipython-input-28-5c4f8ec623ff> in <module>()
1 trainer = pl.Trainer(gpus=1, overfit_batches=1)
----> 2 trainer.fit(model, dm)
12 frames
/usr/local/lib/python3.6/dist-packages/pytorch_lightning/trainer/trainer.py in fit(self, model, train_dataloader, val_dataloaders, datamodule)
442 self.call_hook('on_fit_start')
443
--> 444 results = self.accelerator_backend.train()
445 self.accelerator_backend.teardown()
446
/usr/local/lib/python3.6/dist-packages/pytorch_lightning/accelerators/gpu_accelerator.py in train(self)
61
62 # train or test
---> 63 results = self.train_or_test()
64 return results
65
/usr/local/lib/python3.6/dist-packages/pytorch_lightning/accelerators/accelerator.py in train_or_test(self)
72 results = self.trainer.run_test()
73 else:
---> 74 results = self.trainer.train()
75 return results
76
/usr/local/lib/python3.6/dist-packages/pytorch_lightning/trainer/trainer.py in train(self)
464
465 def train(self):
--> 466 self.run_sanity_check(self.get_model())
467
468 self.checkpoint_connector.has_trained = False
/usr/local/lib/python3.6/dist-packages/pytorch_lightning/trainer/trainer.py in run_sanity_check(self, ref_model)
646 # to make sure program won't crash during val
647 if should_sanity_check:
--> 648 self.reset_val_dataloader(ref_model)
649 self.num_sanity_val_batches = [
650 min(self.num_sanity_val_steps, val_batches) for val_batches in self.num_val_batches
/usr/local/lib/python3.6/dist-packages/pytorch_lightning/trainer/data_loading.py in reset_val_dataloader(self, model)
316 has_step = is_overridden('validation_step', model)
317 if has_loader and has_step:
--> 318 self.num_val_batches, self.val_dataloaders = self._reset_eval_dataloader(model, 'val')
319
320 def reset_test_dataloader(self, model) -> None:
/usr/local/lib/python3.6/dist-packages/pytorch_lightning/trainer/data_loading.py in _reset_eval_dataloader(self, model, mode)
246 num_loaders = len(dataloaders)
247 train_dataloader = self.request_dataloader(getattr(model, 'train_dataloader'))
--> 248 dataloaders = [deepcopy(train_dataloader) for _ in range(num_loaders)]
249
250 self.dev_debugger.track_load_dataloader_call(loader_name, dataloaders=dataloaders)
/usr/local/lib/python3.6/dist-packages/pytorch_lightning/trainer/data_loading.py in <listcomp>(.0)
246 num_loaders = len(dataloaders)
247 train_dataloader = self.request_dataloader(getattr(model, 'train_dataloader'))
--> 248 dataloaders = [deepcopy(train_dataloader) for _ in range(num_loaders)]
249
250 self.dev_debugger.track_load_dataloader_call(loader_name, dataloaders=dataloaders)
/usr/lib/python3.6/copy.py in deepcopy(x, memo, _nil)
178 y = x
179 else:
--> 180 y = _reconstruct(x, memo, *rv)
181
182 # If is its own copy, don't memoize.
/usr/lib/python3.6/copy.py in _reconstruct(x, memo, func, args, state, listiter, dictiter, deepcopy)
278 if state is not None:
279 if deep:
--> 280 state = deepcopy(state, memo)
281 if hasattr(y, '__setstate__'):
282 y.__setstate__(state)
/usr/lib/python3.6/copy.py in deepcopy(x, memo, _nil)
148 copier = _deepcopy_dispatch.get(cls)
149 if copier:
--> 150 y = copier(x, memo)
151 else:
152 try:
/usr/lib/python3.6/copy.py in _deepcopy_dict(x, memo, deepcopy)
238 memo[id(x)] = y
239 for key, value in x.items():
--> 240 y[deepcopy(key, memo)] = deepcopy(value, memo)
241 return y
242 d[dict] = _deepcopy_dict
/usr/lib/python3.6/copy.py in deepcopy(x, memo, _nil)
159 copier = getattr(x, "__deepcopy__", None)
160 if copier:
--> 161 y = copier(memo)
162 else:
163 reductor = dispatch_table.get(cls)
TypeError: 'generator' object is not callable