Errors and exceptions for any step of the `DataLoader` process
/mnt/d/lib/python3.7/site-packages/torch/cuda/__init__.py:52: UserWarning: CUDA initialization: Found no NVIDIA driver on your system. Please check that you have an NVIDIA GPU and installed a driver from http://www.nvidia.com/Download/index.aspx (Triggered internally at /pytorch/c10/cuda/CUDAFunctions.cpp:100.) return torch._C._cuda_getDeviceCount() > 0
This includes after_item, after_batch, and collating. Anything in relation to the Datasets or anything before the DataLoader process can be found in fastdebug.fastai.dataset
collate_error[source]
collate_error(e:Exception,batch)
Raises an explicit error when the batch could not collate, stating what items in the batch are different sizes and their types
collate_error is @patch'd into DataLoader's create_batch function through importing this module, so if there is any possible reason why the data cannot be collated into the batch, it is presented to the user.
An example is below, where we forgot to include an item transform that resizes all our images to the same size:
from fastai.vision.all import *
path = untar_data(URLs.PETS)/'images'
dls = ImageDataLoaders.from_name_func(
path, get_image_files(path), valid_pct=0.2,
label_func=lambda x: x[0].isupper())
x,y = dls.train.one_batch()
--------------------------------------------------------------------------- RuntimeError Traceback (most recent call last) <ipython-input-8-c493bee87237> in <module>() 6 label_func=lambda x: x[0].isupper()) 7 ----> 8 x,y = dls.train.one_batch() /usr/local/lib/python3.7/dist-packages/fastai/data/load.py in one_batch(self) 148 def one_batch(self): 149 if self.n is not None and len(self)==0: raise ValueError(f'This DataLoader does not contain any batches') --> 150 with self.fake_l.no_multiproc(): res = first(self) 151 if hasattr(self, 'it'): delattr(self, 'it') 152 return res /usr/local/lib/python3.7/dist-packages/fastcore/basics.py in first(x, f, negate, **kwargs) 545 x = iter(x) 546 if f: x = filter_ex(x, f=f, negate=negate, gen=True, **kwargs) --> 547 return next(x, None) 548 549 # Cell /usr/local/lib/python3.7/dist-packages/fastai/data/load.py in __iter__(self) 107 self.before_iter() 108 self.__idxs=self.get_idxs() # called in context of main process (not workers/subprocesses) --> 109 for b in _loaders[self.fake_l.num_workers==0](self.fake_l): 110 # fix issue 2899. If the process start method isn't fork, the data will be copied to cuda in learner one_batch. 111 if self.device is not None and multiprocessing.get_start_method().lower() == "fork": /usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py in __next__(self) 433 if self._sampler_iter is None: 434 self._reset() --> 435 data = self._next_data() 436 self._num_yielded += 1 437 if self._dataset_kind == _DatasetKind.Iterable and \ /usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py in _next_data(self) 473 def _next_data(self): 474 index = self._next_index() # may raise StopIteration --> 475 data = self._dataset_fetcher.fetch(index) # may raise StopIteration 476 if self._pin_memory: 477 data = _utils.pin_memory.pin_memory(data) /usr/local/lib/python3.7/dist-packages/torch/utils/data/_utils/fetch.py in fetch(self, possibly_batched_index) 32 raise StopIteration 33 else: ---> 34 data = next(self.dataset_iter) 35 return self.collate_fn(data) 36 /usr/local/lib/python3.7/dist-packages/fastai/data/load.py in create_batches(self, samps) 118 if self.dataset is not None: self.it = iter(self.dataset) 119 res = filter(lambda o:o is not None, map(self.do_item, samps)) --> 120 yield from map(self.do_batch, self.chunkify(res)) 121 122 def new(self, dataset=None, cls=None, **kwargs): /usr/local/lib/python3.7/dist-packages/fastai/data/load.py in do_batch(self, b) 144 else: raise IndexError("Cannot index an iterable dataset numerically - must use `None`.") 145 def create_batch(self, b): return (fa_collate,fa_convert)[self.prebatched](b) --> 146 def do_batch(self, b): return self.retain(self.create_batch(self.before_batch(b)), b) 147 def to(self, device): self.device = device 148 def one_batch(self): <ipython-input-7-a9809be51294> in create_batch(self, b) 8 except Exception as e: 9 if not self.prebatched: ---> 10 collate_error(e, b) 11 else: raise e <ipython-input-6-f0b390dbe89c> in collate_error(e, batch) 23 err += f'Please include a transform in `after_item` that ensures all data of type {type_a} is the same size' 24 e.args = [err] ---> 25 raise e <ipython-input-7-a9809be51294> in create_batch(self, b) 5 func = (fa_collate,fa_convert)[self.prebatched] 6 try: ----> 7 return func(b) 8 except Exception as e: 9 if not self.prebatched: /usr/local/lib/python3.7/dist-packages/fastai/data/load.py in fa_collate(t) 48 b = t[0] 49 return (default_collate(t) if isinstance(b, _collate_types) ---> 50 else type(t[0])([fa_collate(s) for s in zip(*t)]) if isinstance(b, Sequence) 51 else default_collate(t)) 52 /usr/local/lib/python3.7/dist-packages/fastai/data/load.py in <listcomp>(.0) 48 b = t[0] 49 return (default_collate(t) if isinstance(b, _collate_types) ---> 50 else type(t[0])([fa_collate(s) for s in zip(*t)]) if isinstance(b, Sequence) 51 else default_collate(t)) 52 /usr/local/lib/python3.7/dist-packages/fastai/data/load.py in fa_collate(t) 47 "A replacement for PyTorch `default_collate` which maintains types and handles `Sequence`s" 48 b = t[0] ---> 49 return (default_collate(t) if isinstance(b, _collate_types) 50 else type(t[0])([fa_collate(s) for s in zip(*t)]) if isinstance(b, Sequence) 51 else default_collate(t)) /usr/local/lib/python3.7/dist-packages/torch/utils/data/_utils/collate.py in default_collate(batch) 53 storage = elem.storage()._new_shared(numel) 54 out = elem.new(storage) ---> 55 return torch.stack(batch, 0, out=out) 56 elif elem_type.__module__ == 'numpy' and elem_type.__name__ != 'str_' \ 57 and elem_type.__name__ != 'string_': /usr/local/lib/python3.7/dist-packages/fastai/torch_core.py in __torch_function__(self, func, types, args, kwargs) 327 convert=False 328 if _torch_handled(args, self._opt, func): convert,types = type(self),(torch.Tensor,) --> 329 res = super().__torch_function__(func, types, args=args, kwargs=kwargs) 330 if convert: res = convert(res) 331 if isinstance(res, TensorBase): res.set_meta(self, as_copy=True) /usr/local/lib/python3.7/dist-packages/torch/tensor.py in __torch_function__(cls, func, types, args, kwargs) 993 994 with _C.DisableTorchFunction(): --> 995 ret = func(*args, **kwargs) 996 return _convert(ret, cls) 997 RuntimeError: Error when trying to collate the data into batches with fa_collate, at least two tensors in the batch are not the same size. Mismatch found within the 0th axis of the batch and is of type TensorImage: The first item has shape: torch.Size([3, 500, 333]) The second item has shape: torch.Size([3, 333, 500]) Please include a transform in `after_item` that ensures all data of type TensorImage is the same size
TfmdDL.new[source]
TfmdDL.new(dataset=None)
Create a new version of self with a few changed attributes