DataLoader

fastai DataLoader Mixin

fastxtend’s DataLoaderMixin allows adding fastai functionality to non-fastai DataLoaders. DataLoaderMixin supports batch transforms, one_batch, show_batch, and show_results, although inputs will need to be converted to fastai typed tensors for show methods to work.

For an example of using DataLoaderMixin, look at the source code for Loader.

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DataLoaderMixin.one_batch

 DataLoaderMixin.one_batch ()

Return one processed batch of input(s) and target(s)

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DataLoaderMixin.show_batch

 DataLoaderMixin.show_batch (b:Optional[Tuple[torch.Tensor,...]]=None,
                             max_n:int=9, ctxs=None, show:bool=True,
                             unique:bool=False, **kwargs)

Show max_n input(s) and target(s) from the batch.

	Type	Default	Details
b	Tuple[Tensor, …] \| None	None	Batch to show. If None calls `one_batch`
max_n	int	9	Maximum number of items to show
ctxs	NoneType	None	List of `ctx` objects to show data. Could be matplotlib axis, DataFrame etc
show	bool	True	If False, return decoded batch instead of showing
unique	bool	False	Whether to show only one
kwargs

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DataLoaderMixin.show_results

 DataLoaderMixin.show_results (b, out, max_n:int=9, ctxs=None,
                               show:bool=True, **kwargs)

Show max_n results with input(s), target(s) and prediction(s).

	Type	Default	Details
b			Batch to show results for
out			Predicted output from model for the batch
max_n	int	9	Maximum number of items to show
ctxs	NoneType	None	List of `ctx` objects to show data. Could be matplotlib axis, DataFrame etc
show	bool	True	If False, return decoded batch instead of showing
kwargs

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DataLoaderMixin.to

 DataLoaderMixin.to (device:Union[int,str,torch.device])

Sets self.device=device.

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DataLoaderMixin.n_inp

 DataLoaderMixin.n_inp ()

Number of elements in a batch for model input

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DataLoaderMixin.split_idx

 DataLoaderMixin.split_idx ()

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DataLoaderMixin.decode

 DataLoaderMixin.decode (b:Tuple[torch.Tensor,...])

Decode batch b

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DataLoaderMixin.decode_batch

 DataLoaderMixin.decode_batch (b:Tuple[torch.Tensor,...], max_n:int=9)

Decode up to max_n input(s) from batch b