166 lines
6.7 KiB
Python
166 lines
6.7 KiB
Python
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# coding=utf-8
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# Copyright 2022 Meta Platforms authors and The HuggingFace Team. All rights reserved.
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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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"""
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Image/Text processor class for FLAVA
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"""
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import warnings
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from typing import List, Optional, Union
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from ...image_utils import ImageInput
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from ...processing_utils import ProcessorMixin
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from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
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from ...utils import TensorType
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class FlavaProcessor(ProcessorMixin):
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r"""
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Constructs a FLAVA processor which wraps a FLAVA image processor and a FLAVA tokenizer into a single processor.
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[`FlavaProcessor`] offers all the functionalities of [`FlavaImageProcessor`] and [`BertTokenizerFast`]. See the
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[`~FlavaProcessor.__call__`] and [`~FlavaProcessor.decode`] for more information.
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Args:
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image_processor ([`FlavaImageProcessor`], *optional*): The image processor is a required input.
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tokenizer ([`BertTokenizerFast`], *optional*): The tokenizer is a required input.
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"""
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attributes = ["image_processor", "tokenizer"]
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image_processor_class = "FlavaImageProcessor"
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tokenizer_class = ("BertTokenizer", "BertTokenizerFast")
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def __init__(self, image_processor=None, tokenizer=None, **kwargs):
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feature_extractor = None
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if "feature_extractor" in kwargs:
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warnings.warn(
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"The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
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" instead.",
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FutureWarning,
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)
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feature_extractor = kwargs.pop("feature_extractor")
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image_processor = image_processor if image_processor is not None else feature_extractor
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if image_processor is None:
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raise ValueError("You need to specify an `image_processor`.")
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if tokenizer is None:
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raise ValueError("You need to specify a `tokenizer`.")
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super().__init__(image_processor, tokenizer)
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self.current_processor = self.image_processor
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def __call__(
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self,
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images: Optional[ImageInput] = None,
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text: Optional[Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]]] = None,
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add_special_tokens: bool = True,
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padding: Union[bool, str, PaddingStrategy] = False,
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truncation: Union[bool, str, TruncationStrategy] = False,
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max_length: Optional[int] = None,
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stride: int = 0,
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pad_to_multiple_of: Optional[int] = None,
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return_image_mask: Optional[bool] = None,
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return_codebook_pixels: Optional[bool] = None,
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return_token_type_ids: Optional[bool] = None,
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return_attention_mask: Optional[bool] = None,
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return_overflowing_tokens: bool = False,
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return_special_tokens_mask: bool = False,
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return_offsets_mapping: bool = False,
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return_length: bool = False,
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verbose: bool = True,
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return_tensors: Optional[Union[str, TensorType]] = None,
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**kwargs,
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):
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"""
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This method uses [`FlavaImageProcessor.__call__`] method to prepare image(s) for the model, and
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[`BertTokenizerFast.__call__`] to prepare text for the model.
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Please refer to the docstring of the above two methods for more information.
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"""
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if text is None and images is None:
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raise ValueError("You have to specify either text or images. Both cannot be none.")
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if text is not None:
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encoding = self.tokenizer(
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text=text,
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add_special_tokens=add_special_tokens,
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padding=padding,
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truncation=truncation,
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max_length=max_length,
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stride=stride,
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pad_to_multiple_of=pad_to_multiple_of,
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return_token_type_ids=return_token_type_ids,
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return_attention_mask=return_attention_mask,
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return_overflowing_tokens=return_overflowing_tokens,
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return_special_tokens_mask=return_special_tokens_mask,
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return_offsets_mapping=return_offsets_mapping,
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return_length=return_length,
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verbose=verbose,
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return_tensors=return_tensors,
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**kwargs,
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)
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if images is not None:
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image_features = self.image_processor(
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images,
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return_image_mask=return_image_mask,
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return_codebook_pixels=return_codebook_pixels,
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return_tensors=return_tensors,
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**kwargs,
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)
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if text is not None and images is not None:
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encoding.update(image_features)
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return encoding
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elif text is not None:
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return encoding
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else:
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return BatchEncoding(data=dict(**image_features), tensor_type=return_tensors)
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def batch_decode(self, *args, **kwargs):
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"""
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This method forwards all its arguments to BertTokenizerFast's [`~PreTrainedTokenizer.batch_decode`]. Please
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refer to the docstring of this method for more information.
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"""
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return self.tokenizer.batch_decode(*args, **kwargs)
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def decode(self, *args, **kwargs):
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"""
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This method forwards all its arguments to BertTokenizerFast's [`~PreTrainedTokenizer.decode`]. Please refer to
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the docstring of this method for more information.
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"""
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return self.tokenizer.decode(*args, **kwargs)
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@property
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def model_input_names(self):
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tokenizer_input_names = self.tokenizer.model_input_names
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image_processor_input_names = self.image_processor.model_input_names
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return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))
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@property
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def feature_extractor_class(self):
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warnings.warn(
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"`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.",
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FutureWarning,
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)
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return self.image_processor_class
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@property
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def feature_extractor(self):
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warnings.warn(
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"`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.",
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FutureWarning,
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)
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return self.image_processor
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