90 lines
3.4 KiB
Python
90 lines
3.4 KiB
Python
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# coding=utf-8
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# Copyright 2023 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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"""
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Processor class for TVLT.
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"""
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from ...processing_utils import ProcessorMixin
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class TvltProcessor(ProcessorMixin):
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r"""
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Constructs a TVLT processor which wraps a TVLT image processor and TVLT feature extractor into a single processor.
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[`TvltProcessor`] offers all the functionalities of [`TvltImageProcessor`] and [`TvltFeatureExtractor`]. See the
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docstring of [`~TvltProcessor.__call__`] for more information.
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Args:
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image_processor (`TvltImageProcessor`):
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An instance of [`TvltImageProcessor`]. The image processor is a required input.
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feature_extractor (`TvltFeatureExtractor`):
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An instance of [`TvltFeatureExtractor`]. The feature extractor is a required input.
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"""
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attributes = ["image_processor", "feature_extractor"]
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image_processor_class = "TvltImageProcessor"
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feature_extractor_class = "TvltFeatureExtractor"
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def __init__(self, image_processor, feature_extractor):
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super().__init__(image_processor=image_processor, feature_extractor=feature_extractor)
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self.image_processor = image_processor
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self.feature_extractor = feature_extractor
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def __call__(
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self,
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images=None,
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audio=None,
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images_mixed=None,
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sampling_rate=None,
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mask_audio=False,
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mask_pixel=False,
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*args,
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**kwargs,
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):
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"""
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Forwards the `images` argument to TvltImageProcessor's [`~TvltImageProcessor.preprocess`] and the `audio`
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argument to TvltFeatureExtractor's [`~TvltFeatureExtractor.__call__`]. Please refer to the docstring of the
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above two methods for more information.
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"""
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if images is None and audio is None:
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raise ValueError("You need to specify either an `images` or `audio` input to process.")
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images_mixed_dict = None
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if images is not None:
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images_dict = self.image_processor(images, mask_pixel=mask_pixel, *args, **kwargs)
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if images_mixed is not None:
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images_mixed_dict = self.image_processor(images_mixed, is_mixed=True, *args, **kwargs)
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if audio is not None:
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audio_dict = self.feature_extractor(
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audio, *args, sampling_rate=sampling_rate, mask_audio=mask_audio, **kwargs
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)
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output_dict = {}
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if audio is not None:
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output_dict.update(audio_dict)
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if images is not None:
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output_dict.update(images_dict)
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if images_mixed_dict is not None:
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output_dict.update(images_mixed_dict)
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return output_dict
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@property
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def model_input_names(self):
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image_processor_input_names = self.image_processor.model_input_names
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feature_extractor_input_names = self.feature_extractor.model_input_names
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return list(dict.fromkeys(image_processor_input_names + feature_extractor_input_names))
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