#
Feature Extractor

A feature extractor is in charge of preparing input features for audio or vision models. This includes feature extraction from sequences, e.g., pre-processing audio files to generate Log-Mel Spectrogram features, feature extraction from images, e.g., cropping image files, but also padding, normalization, and conversion to NumPy, PyTorch, and TensorFlow tensors.

##
FeatureExtractionMixin

###
class transformers.FeatureExtractionMixin

< source >

( **kwargs )

This is a feature extraction mixin used to provide saving/loading functionality for sequential and image feature extractors.

####
from_pretrained

< source >

( pretrained_model_name_or_path: typing.Union[str, os.PathLike] cache_dir: typing.Union[str, os.PathLike, NoneType] = None force_download: bool = False local_files_only: bool = False token: typing.Union[str, bool, NoneType] = None revision: str = 'main' **kwargs )

Parameters

- pretrained_model_name_or_path (`str` or `os.PathLike`) — This can be either:
  - a string, the model id of a pretrained feature_extractor hosted inside a model repo on huggingface.co. Valid model ids can be located at the root-level, like `bert-base-uncased`, or namespaced under a user or organization name, like `dbmdz/bert-base-german-cased`.
  - a path to a directory containing a feature extractor file saved using the save_pretrained() method, e.g., `./my_model_directory/`.
  - a path or url to a saved feature extractor JSON file, e.g., `./my_model_directory/preprocessor_config.json`.
- cache_dir (`str` or `os.PathLike`, optional) — Path to a directory in which a downloaded pretrained model feature extractor should be cached if the standard cache should not be used.
- force_download (`bool`, optional, defaults to `False`) — Whether or not to force to (re-)download the feature extractor files and override the cached versions if they exist.
- resume_download (`bool`, optional, defaults to `False`) — Whether or not to delete incompletely received file. Attempts to resume the download if such a file exists.
- proxies (`Dict[str, str]`, optional) — A dictionary of proxy servers to use by protocol or endpoint, e.g., `{'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}.` The proxies are used on each request.
- token (`str` or `bool`, optional) — The token to use as HTTP bearer authorization for remote files. If `True`, or not specified, will use the token generated when running `huggingface-cli login` (stored in `~/.huggingface`).
- revision (`str`, optional, defaults to `"main"`) — The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any identifier allowed by git.

Instantiate a type of FeatureExtractionMixin from a feature extractor, e.g. a derived class of SequenceFeatureExtractor.

Examples:

    # We can't instantiate directly the base class *FeatureExtractionMixin* nor *SequenceFeatureExtractor* so let's show the examples on a
    # derived class: *Wav2Vec2FeatureExtractor*
    feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained(
        "facebook/wav2vec2-base-960h"
    )  # Download feature_extraction_config from huggingface.co and cache.
    feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained(
        "./test/saved_model/"
    )  # E.g. feature_extractor (or model) was saved using *save_pretrained('./test/saved_model/')*
    feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained("./test/saved_model/preprocessor_config.json")
    feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained(
        "facebook/wav2vec2-base-960h", return_attention_mask=False, foo=False
    )
    assert feature_extractor.return_attention_mask is False
    feature_extractor, unused_kwargs = Wav2Vec2FeatureExtractor.from_pretrained(
        "facebook/wav2vec2-base-960h", return_attention_mask=False, foo=False, return_unused_kwargs=True
    )
    assert feature_extractor.return_attention_mask is False
    assert unused_kwargs == {"foo": False}

####
save_pretrained

< source >

( save_directory: typing.Union[str, os.PathLike] push_to_hub: bool = False **kwargs )

Parameters

- save_directory (`str` or `os.PathLike`) — Directory where the feature extractor JSON file will be saved (will be created if it does not exist).
- push_to_hub (`bool`, optional, defaults to `False`) — Whether or not to push your model to the Hugging Face model hub after saving it. You can specify the repository you want to push to with `repo_id` (will default to the name of `save_directory` in your namespace).
- kwargs (`Dict[str, Any]`, optional) — Additional key word arguments passed along to the push_to_hub() method.

Save a feature_extractor object to the directory `save_directory`, so that it can be re-loaded using the from_pretrained() class method.

##
SequenceFeatureExtractor

###
class transformers.SequenceFeatureExtractor

< source >

( feature_size: int sampling_rate: int padding_value: float **kwargs )

Parameters

- feature_size (`int`) — The feature dimension of the extracted features.
- sampling_rate (`int`) — The sampling rate at which the audio files should be digitalized expressed in hertz (Hz).
- padding_value (`float`) — The value that is used to fill the padding values / vectors.

This is a general feature extraction class for speech recognition.

####
pad

< source >

( processed_features: typing.Union[transformers.feature_extraction_utils.BatchFeature, typing.List[transformers.feature_extraction_utils.BatchFeature], typing.Dict[str, transformers.feature_extraction_utils.BatchFeature], typing.Dict[str, typing.List[transformers.feature_extraction_utils.BatchFeature]], typing.List[typing.Dict[str, transformers.feature_extraction_utils.BatchFeature]]] padding: typing.Union[bool, str, transformers.utils.generic.PaddingStrategy] = True max_length: typing.Optional[int] = None truncation: bool = False pad_to_multiple_of: typing.Optional[int] = None return_attention_mask: typing.Optional[bool] = None return_tensors: typing.Union[str, transformers.utils.generic.TensorType, NoneType] = None )

Parameters

- processed_features (BatchFeature, list of BatchFeature, `Dict[str, List[float]]`, `Dict[str, List[List[float]]` or `List[Dict[str, List[float]]]`) — Processed inputs. Can represent one input (BatchFeature or `Dict[str, List[float]]`) or a batch of input values / vectors (list of BatchFeature, Dict[str, List[List[float]]] or List[Dict[str, List[float]]]) so you can use this method during preprocessing as well as in a PyTorch Dataloader collate function.

  Instead of `List[float]` you can have tensors (numpy arrays, PyTorch tensors or TensorFlow tensors), see the note above for the return type.

- padding (`bool`, `str` or PaddingStrategy, optional, defaults to `True`) — Select a strategy to pad the returned sequences (according to the model’s padding side and padding index) among:
  - `True` or `'longest'`: Pad to the longest sequence in the batch (or no padding if only a single sequence if provided).
  - `'max_length'`: Pad to a maximum length specified with the argument `max_length` or to the maximum acceptable input length for the model if that argument is not provided.
  - `False` or `'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of different lengths).
- max_length (`int`, optional) — Maximum length of the returned list and optionally padding length (see above).
- truncation (`bool`) — Activates truncation to cut input sequences longer than `max_length` to `max_length`.
- pad_to_multiple_of (`int`, optional) — If set will pad the sequence to a multiple of the provided value.

  This is especially useful to enable the use of Tensor Cores on NVIDIA hardware with compute capability `>= 7.5` (Volta), or on TPUs which benefit from having sequence lengths be a multiple of 128.

- return_attention_mask (`bool`, optional) — Whether to return the attention mask. If left to the default, will return the attention mask according to the specific feature_extractor’s default.

  What are attention masks?

- return_tensors (`str` or TensorType, optional) — If set, will return tensors instead of list of python integers. Acceptable values are:
  - `'tf'`: Return TensorFlow `tf.constant` objects.
  - `'pt'`: Return PyTorch `torch.Tensor` objects.
  - `'np'`: Return Numpy `np.ndarray` objects.

Pad input values / input vectors or a batch of input values / input vectors up to predefined length or to the max sequence length in the batch.

Padding side (left/right) padding values are defined at the feature extractor level (with `self.padding_side`, `self.padding_value`)

If the `processed_features` passed are dictionary of numpy arrays, PyTorch tensors or TensorFlow tensors, the result will use the same type unless you provide a different tensor type with `return_tensors`. In the case of PyTorch tensors, you will lose the specific device of your tensors however.

##
BatchFeature

###
class transformers.BatchFeature

< source >

( data: typing.Union[typing.Dict[str, typing.Any], NoneType] = None tensor_type: typing.Union[NoneType, str, transformers.utils.generic.TensorType] = None )

Parameters

- data (`dict`, optional) — Dictionary of lists/arrays/tensors returned by the call/pad methods (‘input_values’, ‘attention_mask’, etc.).
- tensor_type (`Union[None, str, TensorType]`, optional) — You can give a tensor_type here to convert the lists of integers in PyTorch/TensorFlow/Numpy Tensors at initialization.

Holds the output of the pad() and feature extractor specific `__call__` methods.

This class is derived from a python dictionary and can be used as a dictionary.

####
convert_to_tensors

< source >

( tensor_type: typing.Union[str, transformers.utils.generic.TensorType, NoneType] = None )

Parameters

- tensor_type (`str` or TensorType, optional) — The type of tensors to use. If `str`, should be one of the values of the enum TensorType. If `None`, no modification is done.

Convert the inner content to tensors.

####
to

< source >

( *args **kwargs ) →
BatchFeature

Parameters

- args (`Tuple`) — Will be passed to the `to(...)` function of the tensors.
- kwargs (`Dict`, optional) — Will be passed to the `to(...)` function of the tensors.

Returns

BatchFeature

The same instance after modification.

Send all values to device by calling `v.to(*args, **kwargs)` (PyTorch only). This should support casting in different `dtypes` and sending the `BatchFeature` to a different `device`.

##
ImageFeatureExtractionMixin

###
class transformers.ImageFeatureExtractionMixin

< source >

( )

Mixin that contain utilities for preparing image features.

####
center_crop

< source >

( image size ) →
new_image

Parameters

- image (`PIL.Image.Image` or `np.ndarray` or `torch.Tensor` of shape (n_channels, height, width) or (height, width, n_channels)) — The image to resize.
- size (`int` or `Tuple[int, int]`) — The size to which crop the image.

Returns

new_image

A center cropped `PIL.Image.Image` or `np.ndarray` or `torch.Tensor` of shape: (n_channels, height, width).

Crops `image` to the given size using a center crop. Note that if the image is too small to be cropped to the size given, it will be padded (so the returned result has the size asked).

####
convert_rgb

< source >

( image )

Parameters

- image (`PIL.Image.Image`) — The image to convert.

Converts `PIL.Image.Image` to RGB format.

####
expand_dims

< source >

( image )

Parameters

- image (`PIL.Image.Image` or `np.ndarray` or `torch.Tensor`) — The image to expand.

Expands 2-dimensional `image` to 3 dimensions.

####
flip_channel_order

< source >

( image )

Parameters

- image (`PIL.Image.Image` or `np.ndarray` or `torch.Tensor`) — The image whose color channels to flip. If `np.ndarray` or `torch.Tensor`, the channel dimension should be first.

Flips the channel order of `image` from RGB to BGR, or vice versa. Note that this will trigger a conversion of `image` to a NumPy array if it’s a PIL Image.

####
normalize

< source >

( image mean std rescale = False )

Parameters

- image (`PIL.Image.Image` or `np.ndarray` or `torch.Tensor`) — The image to normalize.
- mean (`List[float]` or `np.ndarray` or `torch.Tensor`) — The mean (per channel) to use for normalization.
- std (`List[float]` or `np.ndarray` or `torch.Tensor`) — The standard deviation (per channel) to use for normalization.
- rescale (`bool`, optional, defaults to `False`) — Whether or not to rescale the image to be between 0 and 1. If a PIL image is provided, scaling will happen automatically.

Normalizes `image` with `mean` and `std`. Note that this will trigger a conversion of `image` to a NumPy array if it’s a PIL Image.

####
rescale

< source >

( image: ndarray scale: typing.Union[float, int] )

Rescale a numpy image by scale amount

####
resize

< source >

( image size resample = None default_to_square = True max_size = None ) →
image

Parameters

- image (`PIL.Image.Image` or `np.ndarray` or `torch.Tensor`) — The image to resize.
- size (`int` or `Tuple[int, int]`) — The size to use for resizing the image. If `size` is a sequence like (h, w), output size will be matched to this.

  If `size` is an int and `default_to_square` is `True`, then image will be resized to (size, size). If `size` is an int and `default_to_square` is `False`, then smaller edge of the image will be matched to this number. i.e, if height > width, then image will be rescaled to (size * height / width, size).

- resample (`int`, optional, defaults to `PILImageResampling.BILINEAR`) — The filter to user for resampling.
- default_to_square (`bool`, optional, defaults to `True`) — How to convert `size` when it is a single int. If set to `True`, the `size` will be converted to a square (`size`,`size`). If set to `False`, will replicate `torchvision.transforms.Resize` with support for resizing only the smallest edge and providing an optional `max_size`.
- max_size (`int`, optional, defaults to `None`) — The maximum allowed for the longer edge of the resized image: if the longer edge of the image is greater than `max_size` after being resized according to `size`, then the image is resized again so that the longer edge is equal to `max_size`. As a result, `size` might be overruled, i.e the smaller edge may be shorter than `size`. Only used if `default_to_square` is `False`.

Returns

image

A resized `PIL.Image.Image`.

Resizes `image`. Enforces conversion of input to PIL.Image.

####
rotate

< source >

( image angle resample = None expand = 0 center = None translate = None fillcolor = None ) →
image

Parameters

- image (`PIL.Image.Image` or `np.ndarray` or `torch.Tensor`) — The image to rotate. If `np.ndarray` or `torch.Tensor`, will be converted to `PIL.Image.Image` before rotating.

Returns

image

A rotated `PIL.Image.Image`.

Returns a rotated copy of `image`. This method returns a copy of `image`, rotated the given number of degrees counter clockwise around its centre.

####
to_numpy_array

< source >

( image rescale = None channel_first = True )

Parameters

- image (`PIL.Image.Image` or `np.ndarray` or `torch.Tensor`) — The image to convert to a NumPy array.
- rescale (`bool`, optional) — Whether or not to apply the scaling factor (to make pixel values floats between 0. and 1.). Will default to `True` if the image is a PIL Image or an array/tensor of integers, `False` otherwise.
- channel_first (`bool`, optional, defaults to `True`) — Whether or not to permute the dimensions of the image to put the channel dimension first.

Converts `image` to a numpy array. Optionally rescales it and puts the channel dimension as the first dimension.

####
to_pil_image

< source >

( image rescale = None )

Parameters

- image (`PIL.Image.Image` or `numpy.ndarray` or `torch.Tensor`) — The image to convert to the PIL Image format.
- rescale (`bool`, optional) — Whether or not to apply the scaling factor (to make pixel values integers between 0 and 255). Will default to `True` if the image type is a floating type, `False` otherwise.

Converts `image` to a PIL Image. Optionally rescales it and puts the channel dimension back as the last axis if needed.