Auto processor (#14465)
* Add AutoProcessor class * Init and tests * Add doc * Fix init * Update src/transformers/models/auto/processing_auto.py Co-authored-by: Lysandre Debut <lysandre@huggingface.co> * Reverts to tokenizer or feature extractor when available * Adapt test Co-authored-by: Lysandre Debut <lysandre@huggingface.co>
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@ -76,6 +76,13 @@ AutoFeatureExtractor
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:members:
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AutoProcessor
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.AutoProcessor
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:members:
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AutoModel
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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@ -154,9 +154,11 @@ _import_structure = {
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"CONFIG_MAPPING",
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"FEATURE_EXTRACTOR_MAPPING",
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"MODEL_NAMES_MAPPING",
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"PROCESSOR_MAPPING",
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"TOKENIZER_MAPPING",
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"AutoConfig",
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"AutoFeatureExtractor",
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"AutoProcessor",
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"AutoTokenizer",
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],
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"models.bart": ["BartConfig", "BartTokenizer"],
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@ -2125,9 +2127,11 @@ if TYPE_CHECKING:
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CONFIG_MAPPING,
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FEATURE_EXTRACTOR_MAPPING,
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MODEL_NAMES_MAPPING,
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PROCESSOR_MAPPING,
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TOKENIZER_MAPPING,
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AutoConfig,
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AutoFeatureExtractor,
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AutoProcessor,
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AutoTokenizer,
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)
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from .models.bart import BartConfig, BartTokenizer
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@ -25,6 +25,7 @@ _import_structure = {
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"auto_factory": ["get_values"],
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"configuration_auto": ["ALL_PRETRAINED_CONFIG_ARCHIVE_MAP", "CONFIG_MAPPING", "MODEL_NAMES_MAPPING", "AutoConfig"],
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"feature_extraction_auto": ["FEATURE_EXTRACTOR_MAPPING", "AutoFeatureExtractor"],
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"processing_auto": ["PROCESSOR_MAPPING", "AutoProcessor"],
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"tokenization_auto": ["TOKENIZER_MAPPING", "AutoTokenizer"],
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}
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@ -130,6 +131,7 @@ if TYPE_CHECKING:
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from .auto_factory import get_values
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from .configuration_auto import ALL_PRETRAINED_CONFIG_ARCHIVE_MAP, CONFIG_MAPPING, MODEL_NAMES_MAPPING, AutoConfig
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from .feature_extraction_auto import FEATURE_EXTRACTOR_MAPPING, AutoFeatureExtractor
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from .processing_auto import PROCESSOR_MAPPING, AutoProcessor
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from .tokenization_auto import TOKENIZER_MAPPING, AutoTokenizer
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if is_torch_available():
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@ -81,9 +81,9 @@ class AutoFeatureExtractor:
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r"""
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Instantiate one of the feature extractor classes of the library from a pretrained model vocabulary.
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The tokenizer class to instantiate is selected based on the :obj:`model_type` property of the config object
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(either passed as an argument or loaded from :obj:`pretrained_model_name_or_path` if possible), or when it's
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missing, by falling back to using pattern matching on :obj:`pretrained_model_name_or_path`:
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The feature extractor class to instantiate is selected based on the :obj:`model_type` property of the config
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object (either passed as an argument or loaded from :obj:`pretrained_model_name_or_path` if possible), or when
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it's missing, by falling back to using pattern matching on :obj:`pretrained_model_name_or_path`:
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List options
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@ -136,10 +136,10 @@ class AutoFeatureExtractor:
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>>> from transformers import AutoFeatureExtractor
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>>> # Download vocabulary from huggingface.co and cache.
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>>> # Download feature extractor from huggingface.co and cache.
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>>> feature_extractor = AutoFeatureExtractor.from_pretrained('facebook/wav2vec2-base-960h')
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>>> # If vocabulary files are in a directory (e.g. feature extractor was saved using `save_pretrained('./test/saved_model/')`)
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>>> # If feature extractor files are in a directory (e.g. feature extractor was saved using `save_pretrained('./test/saved_model/')`)
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>>> feature_extractor = AutoFeatureExtractor.from_pretrained('./test/saved_model/')
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"""
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@ -0,0 +1,189 @@
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# coding=utf-8
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# Copyright 2021 The HuggingFace Inc. team.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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""" AutoProcessor class. """
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import importlib
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from collections import OrderedDict
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# Build the list of all feature extractors
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from ...configuration_utils import PretrainedConfig
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from ...feature_extraction_utils import FeatureExtractionMixin
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from ...file_utils import CONFIG_NAME, FEATURE_EXTRACTOR_NAME, get_list_of_files
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from .auto_factory import _LazyAutoMapping
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from .configuration_auto import (
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CONFIG_MAPPING_NAMES,
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AutoConfig,
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config_class_to_model_type,
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model_type_to_module_name,
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replace_list_option_in_docstrings,
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)
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from .feature_extraction_auto import FEATURE_EXTRACTOR_MAPPING_NAMES, AutoFeatureExtractor
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from .tokenization_auto import TOKENIZER_MAPPING_NAMES, AutoTokenizer
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PROCESSOR_MAPPING_NAMES = OrderedDict(
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[
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("clip", "CLIPProcessor"),
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("layoutlmv2", "LayoutLMv2Processor"),
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("layoutxlm", "LayoutXLMProcessor"),
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("speech_to_text", "Speech2TextProcessor"),
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("speech_to_text_2", "Speech2Text2Processor"),
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("trocr", "TrOCRProcessor"),
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("wav2vec2", "Wav2Vec2Processor"),
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]
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)
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PROCESSOR_MAPPING = _LazyAutoMapping(CONFIG_MAPPING_NAMES, PROCESSOR_MAPPING_NAMES)
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def processor_class_from_name(class_name: str):
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for module_name, processors in PROCESSOR_MAPPING_NAMES.items():
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if class_name in processors:
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module_name = model_type_to_module_name(module_name)
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module = importlib.import_module(f".{module_name}", "transformers.models")
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return getattr(module, class_name)
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break
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return None
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class AutoProcessor:
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r"""
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This is a generic processor class that will be instantiated as one of the processor classes of the library when
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created with the :meth:`AutoProcessor.from_pretrained` class method.
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This class cannot be instantiated directly using ``__init__()`` (throws an error).
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"""
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def __init__(self):
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raise EnvironmentError(
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"AutoProcessor is designed to be instantiated "
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"using the `AutoProcessor.from_pretrained(pretrained_model_name_or_path)` method."
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)
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@classmethod
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@replace_list_option_in_docstrings(PROCESSOR_MAPPING_NAMES)
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def from_pretrained(cls, pretrained_model_name_or_path, **kwargs):
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r"""
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Instantiate one of the processor classes of the library from a pretrained model vocabulary.
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The processor class to instantiate is selected based on the :obj:`model_type` property of the config object
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(either passed as an argument or loaded from :obj:`pretrained_model_name_or_path` if possible):
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List options
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For other types of models, this class will return the appropriate tokenizer (if available) or feature
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extractor.
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Params:
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pretrained_model_name_or_path (:obj:`str` or :obj:`os.PathLike`):
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This can be either:
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- a string, the `model id` of a pretrained feature_extractor hosted inside a model repo on
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huggingface.co. Valid model ids can be located at the root-level, like ``bert-base-uncased``, or
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namespaced under a user or organization name, like ``dbmdz/bert-base-german-cased``.
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- a path to a `directory` containing a processor files saved using the :obj:`save_pretrained()` method,
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e.g., ``./my_model_directory/``.
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cache_dir (:obj:`str` or :obj:`os.PathLike`, `optional`):
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Path to a directory in which a downloaded pretrained model feature extractor should be cached if the
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standard cache should not be used.
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force_download (:obj:`bool`, `optional`, defaults to :obj:`False`):
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Whether or not to force to (re-)download the feature extractor files and override the cached versions
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if they exist.
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resume_download (:obj:`bool`, `optional`, defaults to :obj:`False`):
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Whether or not to delete incompletely received file. Attempts to resume the download if such a file
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exists.
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proxies (:obj:`Dict[str, str]`, `optional`):
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A dictionary of proxy servers to use by protocol or endpoint, e.g., :obj:`{'http': 'foo.bar:3128',
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'http://hostname': 'foo.bar:4012'}.` The proxies are used on each request.
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use_auth_token (:obj:`str` or `bool`, `optional`):
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The token to use as HTTP bearer authorization for remote files. If :obj:`True`, will use the token
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generated when running :obj:`transformers-cli login` (stored in :obj:`~/.huggingface`).
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revision (:obj:`str`, `optional`, defaults to :obj:`"main"`):
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The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
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git-based system for storing models and other artifacts on huggingface.co, so ``revision`` can be any
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identifier allowed by git.
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return_unused_kwargs (:obj:`bool`, `optional`, defaults to :obj:`False`):
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If :obj:`False`, then this function returns just the final feature extractor object. If :obj:`True`,
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then this functions returns a :obj:`Tuple(feature_extractor, unused_kwargs)` where `unused_kwargs` is a
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dictionary consisting of the key/value pairs whose keys are not feature extractor attributes: i.e., the
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part of ``kwargs`` which has not been used to update ``feature_extractor`` and is otherwise ignored.
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kwargs (:obj:`Dict[str, Any]`, `optional`):
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The values in kwargs of any keys which are feature extractor attributes will be used to override the
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loaded values. Behavior concerning key/value pairs whose keys are *not* feature extractor attributes is
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controlled by the ``return_unused_kwargs`` keyword parameter.
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.. note::
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Passing :obj:`use_auth_token=True` is required when you want to use a private model.
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Examples::
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>>> from transformers import AutoProcessor
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>>> # Download processor from huggingface.co and cache.
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>>> processor = AutoProcessor.from_pretrained('facebook/wav2vec2-base-960h')
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>>> # If processor files are in a directory (e.g. processor was saved using `save_pretrained('./test/saved_model/')`)
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>>> processor = AutoProcessor.from_pretrained('./test/saved_model/')
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"""
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config = kwargs.pop("config", None)
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kwargs["_from_auto"] = True
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# First, let's see if we have a preprocessor config.
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# get_list_of_files only takes three of the kwargs we have, so we filter them.
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get_list_of_files_kwargs = {
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key: kwargs[key] for key in ["revision", "use_auth_token", "local_files_only"] if key in kwargs
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}
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model_files = get_list_of_files(pretrained_model_name_or_path, **get_list_of_files_kwargs)
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if FEATURE_EXTRACTOR_NAME in model_files:
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config_dict, _ = FeatureExtractionMixin.get_feature_extractor_dict(pretrained_model_name_or_path, **kwargs)
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if "processor_class" in config_dict:
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processor_class = processor_class_from_name(config_dict["processor_class"])
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return processor_class.from_pretrained(pretrained_model_name_or_path, **kwargs)
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# Otherwise, load config, if it can be loaded.
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if not isinstance(config, PretrainedConfig):
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config = AutoConfig.from_pretrained(pretrained_model_name_or_path, **kwargs)
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model_type = config_class_to_model_type(type(config).__name__)
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if getattr(config, "processor_class", None) is not None:
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processor_class = config.processor_class
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return processor_class.from_pretrained(pretrained_model_name_or_path, **kwargs)
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model_type = config_class_to_model_type(type(config).__name__)
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if model_type is not None and model_type in PROCESSOR_MAPPING_NAMES:
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return PROCESSOR_MAPPING[type(config)].from_pretrained(pretrained_model_name_or_path, **kwargs)
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# At this stage there doesn't seem to be a `Processor` class available for this model, so let's try a tokenizer
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if model_type in TOKENIZER_MAPPING_NAMES:
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return AutoTokenizer.from_pretrained(pretrained_model_name_or_path, **kwargs)
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# At this stage there doesn't seem to be a `Processor` class available for this model, so let's try a tokenizer
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if model_type in FEATURE_EXTRACTOR_MAPPING_NAMES:
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return AutoFeatureExtractor.from_pretrained(pretrained_model_name_or_path, **kwargs)
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all_model_types = set(
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PROCESSOR_MAPPING_NAMES.keys() + TOKENIZER_MAPPING_NAMES.keys() + FEATURE_EXTRACTOR_MAPPING_NAMES.keys()
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)
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all_model_types = list(all_model_types)
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all_model_types.sort()
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raise ValueError(
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f"Unrecognized processor in {pretrained_model_name_or_path}. Should have a `processor_type` key in "
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f"its {FEATURE_EXTRACTOR_NAME}, or one of the following `model_type` keys in its {CONFIG_NAME}: "
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f"{', '.join(all_model_types)}"
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)
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@ -1,3 +1,4 @@
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{
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"feature_extractor_type": "Wav2Vec2FeatureExtractor"
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"feature_extractor_type": "Wav2Vec2FeatureExtractor",
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"processor_class": "Wav2Vec2Processor"
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}
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@ -0,0 +1,56 @@
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# coding=utf-8
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# Copyright 2021 the HuggingFace Inc. team.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import os
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import tempfile
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import unittest
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from transformers import AutoProcessor, BeitFeatureExtractor, BertTokenizerFast, Wav2Vec2Config, Wav2Vec2Processor
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from transformers.testing_utils import require_torch
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SAMPLE_PROCESSOR_CONFIG_DIR = os.path.join(os.path.dirname(os.path.abspath(__file__)), "fixtures")
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SAMPLE_PROCESSOR_CONFIG = os.path.join(
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os.path.dirname(os.path.abspath(__file__)), "fixtures/dummy_feature_extractor_config.json"
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)
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SAMPLE_CONFIG = os.path.join(os.path.dirname(os.path.abspath(__file__)), "fixtures/dummy-config.json")
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class AutoFeatureExtractorTest(unittest.TestCase):
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def test_processor_from_model_shortcut(self):
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processor = AutoProcessor.from_pretrained("facebook/wav2vec2-base-960h")
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self.assertIsInstance(processor, Wav2Vec2Processor)
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def test_processor_from_local_directory_from_config(self):
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with tempfile.TemporaryDirectory() as tmpdirname:
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model_config = Wav2Vec2Config()
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processor = AutoProcessor.from_pretrained("facebook/wav2vec2-base-960h")
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# save in new folder
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model_config.save_pretrained(tmpdirname)
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processor.save_pretrained(tmpdirname)
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processor = AutoProcessor.from_pretrained(tmpdirname)
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self.assertIsInstance(processor, Wav2Vec2Processor)
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def test_auto_processor_reverts_to_tokenizer(self):
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processor = AutoProcessor.from_pretrained("bert-base-cased")
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self.assertIsInstance(processor, BertTokenizerFast)
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@require_torch
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def test_auto_processor_reverts_to_feature_extractor(self):
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processor = AutoProcessor.from_pretrained("microsoft/beit-base-patch16-224")
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self.assertIsInstance(processor, BeitFeatureExtractor)
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