StyleTransferData

class flash.image.style_transfer.data.StyleTransferData(train_input=None, val_input=None, test_input=None, predict_input=None, data_fetcher=None, transform=<class 'flash.core.data.io.input_transform.InputTransform'>, transform_kwargs=None, val_split=None, batch_size=None, num_workers=0, sampler=None, pin_memory=True, persistent_workers=False)

The StyleTransferData class is a DataModule with a set of classmethods for loading data for image style transfer.

classmethod from_files(train_files=None, predict_files=None, input_cls=<class 'flash.image.classification.input.ImageClassificationFilesInput'>, transform=<class 'flash.image.style_transfer.input_transform.StyleTransferInputTransform'>, transform_kwargs=None, **data_module_kwargs)

Load the StyleTransferData from lists of image files.

The supported file extensions are: .jpg, .jpeg, .png, .ppm, .bmp, .pgm, .tif, .tiff, .webp, and .npy. To learn how to customize the transforms applied for each stage, read our customizing transforms guide.

Parameters

• train_files (Optional[Sequence[str]]) – The list of image files to use when training.

• predict_files (Optional[Sequence[str]]) – The list of image files to use when predicting.

• transform (TypeVar(INPUT_TRANSFORM_TYPE, Type[flash.core.data.io.input_transform.InputTransform], Callable, Tuple[Union[StrEnum, str], Dict[str, Any]], Union[StrEnum, str], None)) – The InputTransform type to use.

• input_cls (Type[Input]) – The Input type to use for loading the data.

• transform – The InputTransform type to use.

• transform_kwargs (Optional[Dict]) – Dict of keyword arguments to be provided when instantiating the transforms.

• data_module_kwargs (Any) – Additional keyword arguments to provide to the DataModule constructor.

Return type

StyleTransferData

Returns

The constructed StyleTransferData.

Examples

>>> from flash import Trainer
>>> from flash.image import StyleTransfer, StyleTransferData
>>> datamodule = StyleTransferData.from_files(
...     train_files=["image_1.png", "image_2.png", "image_3.png"],
...     predict_files=["predict_image_1.png", "predict_image_2.png", "predict_image_3.png"],
...     transform_kwargs=dict(image_size=(128, 128)),
...     batch_size=2,
... )
>>> model = StyleTransfer()
>>> trainer = Trainer(fast_dev_run=True)
>>> trainer.fit(model, datamodule=datamodule)  
Training...
>>> trainer.predict(model, datamodule=datamodule)  
Predicting...

classmethod from_folders(train_folder=None, predict_folder=None, input_cls=<class 'flash.image.classification.input.ImageClassificationFolderInput'>, transform=<class 'flash.image.style_transfer.input_transform.StyleTransferInputTransform'>, transform_kwargs=None, **data_module_kwargs)

Load the StyleTransferData from folders containing images.

The supported file extensions are: .jpg, .jpeg, .png, .ppm, .bmp, .pgm, .tif, .tiff, .webp, and .npy. Here’s the required folder structure:

train_folder
├── image_1.png
├── image_2.png
├── image_3.png
...

To learn how to customize the transforms applied for each stage, read our customizing transforms guide.

Parameters

• train_folder (Optional[str]) – The folder containing images to use when training.

• predict_folder (Optional[str]) – The folder containing images to use when predicting.

• transform (TypeVar(INPUT_TRANSFORM_TYPE, Type[flash.core.data.io.input_transform.InputTransform], Callable, Tuple[Union[StrEnum, str], Dict[str, Any]], Union[StrEnum, str], None)) – The InputTransform type to use.

• input_cls (Type[Input]) – The Input type to use for loading the data.

• transform – The InputTransform type to use.

• transform_kwargs (Optional[Dict]) – Dict of keyword arguments to be provided when instantiating the transforms.

• data_module_kwargs (Any) – Additional keyword arguments to provide to the DataModule constructor.

Return type

StyleTransferData

Returns

The constructed StyleTransferData.

Examples

>>> from flash import Trainer
>>> from flash.image import StyleTransfer, StyleTransferData
>>> datamodule = StyleTransferData.from_folders(
...     train_folder="train_folder",
...     predict_folder="predict_folder",
...     transform_kwargs=dict(image_size=(128, 128)),
...     batch_size=2,
... )
>>> model = StyleTransfer()
>>> trainer = Trainer(fast_dev_run=True)
>>> trainer.fit(model, datamodule=datamodule)  
Training...
>>> trainer.predict(model, datamodule=datamodule)  
Predicting...

classmethod from_numpy(train_data=None, predict_data=None, input_cls=<class 'flash.image.data.ImageNumpyInput'>, transform=<class 'flash.image.style_transfer.input_transform.StyleTransferInputTransform'>, transform_kwargs=None, **data_module_kwargs)

Load the StyleTransferData from numpy arrays (or lists of arrays).

To learn how to customize the transforms applied for each stage, read our customizing transforms guide.

Parameters

• train_data (Optional[Collection[ndarray]]) – The numpy array or list of arrays to use when training.

• predict_data (Optional[Collection[ndarray]]) – The numpy array or list of arrays to use when predicting.

• transform (TypeVar(INPUT_TRANSFORM_TYPE, Type[flash.core.data.io.input_transform.InputTransform], Callable, Tuple[Union[StrEnum, str], Dict[str, Any]], Union[StrEnum, str], None)) – The InputTransform type to use.

• input_cls (Type[Input]) – The Input type to use for loading the data.

• transform – The InputTransform type to use.

• transform_kwargs (Optional[Dict]) – Dict of keyword arguments to be provided when instantiating the transforms.

• data_module_kwargs (Any) – Additional keyword arguments to provide to the DataModule constructor.

Return type

StyleTransferData

Returns

The constructed StyleTransferData.

Examples

>>> import numpy as np
>>> from flash import Trainer
>>> from flash.image import StyleTransfer, StyleTransferData
>>> datamodule = StyleTransferData.from_numpy(
...     train_data=[np.random.rand(3, 64, 64), np.random.rand(3, 64, 64), np.random.rand(3, 64, 64)],
...     predict_data=[np.random.rand(3, 64, 64)],
...     transform_kwargs=dict(image_size=(128, 128)),
...     batch_size=2,
... )
>>> model = StyleTransfer()
>>> trainer = Trainer(fast_dev_run=True)
>>> trainer.fit(model, datamodule=datamodule)  
Training...
>>> trainer.predict(model, datamodule=datamodule)  
Predicting...

classmethod from_tensors(train_data=None, predict_data=None, input_cls=<class 'flash.image.data.ImageTensorInput'>, transform=<class 'flash.image.style_transfer.input_transform.StyleTransferInputTransform'>, transform_kwargs=None, **data_module_kwargs)

Load the StyleTransferData from torch tensors (or lists of tensors).

To learn how to customize the transforms applied for each stage, read our customizing transforms guide.

Parameters

• train_data (Optional[Collection[Tensor]]) – The torch tensor or list of tensors to use when training.

• predict_data (Optional[Collection[Tensor]]) – The torch tensor or list of tensors to use when predicting.

• transform (TypeVar(INPUT_TRANSFORM_TYPE, Type[flash.core.data.io.input_transform.InputTransform], Callable, Tuple[Union[StrEnum, str], Dict[str, Any]], Union[StrEnum, str], None)) – The InputTransform type to use.

• input_cls (Type[Input]) – The Input type to use for loading the data.

• transform – The InputTransform type to use.

• transform_kwargs (Optional[Dict]) – Dict of keyword arguments to be provided when instantiating the transforms.

• data_module_kwargs (Any) – Additional keyword arguments to provide to the DataModule constructor.

Return type

StyleTransferData

Returns

The constructed StyleTransferData.

Examples

>>> import torch
>>> from flash import Trainer
>>> from flash.image import StyleTransfer, StyleTransferData
>>> datamodule = StyleTransferData.from_tensors(
...     train_data=[torch.rand(3, 64, 64), torch.rand(3, 64, 64), torch.rand(3, 64, 64)],
...     predict_data=[torch.rand(3, 64, 64)],
...     transform_kwargs=dict(image_size=(128, 128)),
...     batch_size=2,
... )
>>> model = StyleTransfer()
>>> trainer = Trainer(fast_dev_run=True)
>>> trainer.fit(model, datamodule=datamodule)  
Training...
>>> trainer.predict(model, datamodule=datamodule)  
Predicting...

input_transform_cls

alias of flash.image.style_transfer.input_transform.StyleTransferInputTransform