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| # Copyright 2023 The HuggingFace Team. All rights reserved. | |
| # | |
| # Licensed under the Apache License, Version 2.0 (the "License"); | |
| # you may not use this file except in compliance with the License. | |
| # You may obtain a copy of the License at | |
| # | |
| # http://www.apache.org/licenses/LICENSE-2.0 | |
| # | |
| # Unless required by applicable law or agreed to in writing, software | |
| # distributed under the License is distributed on an "AS IS" BASIS, | |
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |
| # See the License for the specific language governing permissions and | |
| # limitations under the License. | |
| import shutil | |
| import tempfile | |
| import unittest | |
| import numpy as np | |
| import pytest | |
| from transformers import is_speech_available, is_vision_available | |
| from transformers.testing_utils import require_torch | |
| if is_vision_available(): | |
| from transformers import TvltImageProcessor | |
| if is_speech_available(): | |
| from transformers import TvltFeatureExtractor | |
| from transformers import TvltProcessor | |
| class TvltProcessorTest(unittest.TestCase): | |
| def setUp(self): | |
| self.checkpoint = "ZinengTang/tvlt-base" | |
| self.tmpdirname = tempfile.mkdtemp() | |
| def get_image_processor(self, **kwargs): | |
| return TvltImageProcessor.from_pretrained(self.checkpoint, **kwargs) | |
| def get_feature_extractor(self, **kwargs): | |
| return TvltFeatureExtractor.from_pretrained(self.checkpoint, **kwargs) | |
| def tearDown(self): | |
| shutil.rmtree(self.tmpdirname) | |
| def test_save_load_pretrained_default(self): | |
| image_processor = self.get_image_processor() | |
| feature_extractor = self.get_feature_extractor() | |
| processor = TvltProcessor(image_processor=image_processor, feature_extractor=feature_extractor) | |
| processor.save_pretrained(self.tmpdirname) | |
| processor = TvltProcessor.from_pretrained(self.tmpdirname) | |
| self.assertIsInstance(processor.feature_extractor, TvltFeatureExtractor) | |
| self.assertIsInstance(processor.image_processor, TvltImageProcessor) | |
| def test_feature_extractor(self): | |
| image_processor = self.get_image_processor() | |
| feature_extractor = self.get_feature_extractor() | |
| processor = TvltProcessor(image_processor=image_processor, feature_extractor=feature_extractor) | |
| audio = np.ones([12000]) | |
| audio_dict = feature_extractor(audio, return_tensors="np") | |
| input_processor = processor(audio=audio, return_tensors="np") | |
| for key in audio_dict.keys(): | |
| self.assertAlmostEqual(audio_dict[key].sum(), input_processor[key].sum(), delta=1e-2) | |
| def test_image_processor(self): | |
| image_processor = self.get_image_processor() | |
| feature_extractor = self.get_feature_extractor() | |
| processor = TvltProcessor(image_processor=image_processor, feature_extractor=feature_extractor) | |
| images = np.ones([3, 224, 224]) | |
| image_dict = image_processor(images, return_tensors="np") | |
| input_processor = processor(images=images, return_tensors="np") | |
| for key in image_dict.keys(): | |
| self.assertAlmostEqual(image_dict[key].sum(), input_processor[key].sum(), delta=1e-2) | |
| def test_processor(self): | |
| image_processor = self.get_image_processor() | |
| feature_extractor = self.get_feature_extractor() | |
| processor = TvltProcessor(image_processor=image_processor, feature_extractor=feature_extractor) | |
| audio = np.ones([12000]) | |
| images = np.ones([3, 224, 224]) | |
| inputs = processor(audio=audio, images=images) | |
| self.assertListEqual(list(inputs.keys()), ["audio_values", "audio_mask", "pixel_values", "pixel_mask"]) | |
| # test if it raises when no input is passed | |
| with pytest.raises(ValueError): | |
| processor() | |
| def test_model_input_names(self): | |
| image_processor = self.get_image_processor() | |
| feature_extractor = self.get_feature_extractor() | |
| processor = TvltProcessor(image_processor=image_processor, feature_extractor=feature_extractor) | |
| self.assertListEqual( | |
| processor.model_input_names, | |
| image_processor.model_input_names + feature_extractor.model_input_names, | |
| msg="`processor` and `image_processor`+`feature_extractor` model input names do not match", | |
| ) | |