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 Log-Mel Spectrogram features, feature extraction from images e.g. cropping image image files, but also padding, normalization, and conversion to Numpy, PyTorch, and TensorFlow tensors.
This is a feature extraction mixin used to provide saving/loading functionality for sequential and image feature extractors.
( pretrained_model_name_or_path: typing.Union[str, os.PathLike] **kwargs )
Parameters
str
or os.PathLike
) —
This can be either:
bert-base-uncased
, or
namespaced under a user or organization name, like dbmdz/bert-base-german-cased
../my_model_directory/
../my_model_directory/preprocessor_config.json
.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.
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.
bool
, optional, defaults to False
) —
Whether or not to delete incompletely received file. Attempts to resume the download if such a file
exists.
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.
str
or bool, optional) —
The token to use as HTTP bearer authorization for remote files. If True
, will use the token generated
when running huggingface-cli login
(stored in ~/.huggingface
).
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.
bool
, optional, defaults to False
) —
If False
, then this function returns just the final feature extractor object. If True
, then this
functions returns a Tuple(feature_extractor, unused_kwargs)
where unused_kwargs is a dictionary
consisting of the key/value pairs whose keys are not feature extractor attributes: i.e., the part of
kwargs
which has not been used to update feature_extractor
and is otherwise ignored.
Dict[str, Any]
, optional) —
The values in kwargs of any keys which are feature extractor attributes will be used to override the
loaded values. Behavior concerning key/value pairs whose keys are not feature extractor attributes is
controlled by the return_unused_kwargs
keyword parameter.
Instantiate a type of FeatureExtractionMixin from a feature extractor, e.g. a derived class of SequenceFeatureExtractor.
Passing use_auth_token=True
is required when you want to use a private model.
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_directory: typing.Union[str, os.PathLike] push_to_hub: bool = False **kwargs )
Parameters
str
or os.PathLike
) —
Directory where the feature extractor JSON file will be saved (will be created if it does not exist).
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 —
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.
( feature_size: int sampling_rate: int padding_value: float **kwargs )
This is a general feature extraction class for speech recognition.
( 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
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.
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).int
, optional) —
Maximum length of the returned list and optionally padding length (see above).
bool
) —
Activates truncation to cut input sequences longer than max_length
to max_length
.
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.
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.
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.
( data: typing.Union[typing.Dict[str, typing.Any], NoneType] = None tensor_type: typing.Union[NoneType, str, transformers.utils.generic.TensorType] = None )
Parameters
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.
( tensor_type: typing.Union[str, transformers.utils.generic.TensorType, NoneType] = None )
Parameters
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.
( device: typing.Union[str, ForwardRef('torch.device')] ) → BatchFeature
Parameters
Returns
The same instance after modification.
Send all values to device by calling v.to(device)
(PyTorch only).
Mixin that contain utilities for preparing image features.
( image size ) → new_image
Parameters
PIL.Image.Image
or np.ndarray
or torch.Tensor
of shape (n_channels, height, width) or (height, width, n_channels)) —
The image to resize.
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).
Converts PIL.Image.Image
to RGB format.
( image )
Expands 2-dimensional image
to 3 dimensions.
( image )
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.
( image mean std rescale = False )
Parameters
PIL.Image.Image
or np.ndarray
or torch.Tensor
) —
The image to normalize.
List[float]
or np.ndarray
or torch.Tensor
) —
The mean (per channel) to use for normalization.
List[float]
or np.ndarray
or torch.Tensor
) —
The standard deviation (per channel) to use for normalization.
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 a numpy image by scale amount
( image size resample = None default_to_square = True max_size = None ) → image
Parameters
PIL.Image.Image
or np.ndarray
or torch.Tensor
) —
The image to resize.
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).
int
, optional, defaults to PIL.Image.Resampling.BILINEAR
) —
The filter to user for resampling.
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
.
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.
( image angle resample = None expand = 0 center = None translate = None fillcolor = None ) → 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.
( image rescale = None channel_first = True )
Parameters
PIL.Image.Image
or np.ndarray
or torch.Tensor
) —
The image to convert to a NumPy array.
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.
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.
( image rescale = None )
Parameters
PIL.Image.Image
or numpy.ndarray
or torch.Tensor
) —
The image to convert to the PIL Image format.
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.