117 lines
4.2 KiB
Python
117 lines
4.2 KiB
Python
# Copyright 2023 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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import shutil
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import tempfile
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import unittest
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import numpy as np
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import pytest
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from transformers import is_speech_available, is_vision_available
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from transformers.testing_utils import require_torch
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if is_vision_available():
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from transformers import TvltImageProcessor
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if is_speech_available():
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from transformers import TvltFeatureExtractor
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from transformers import TvltProcessor
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@require_torch
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class TvltProcessorTest(unittest.TestCase):
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def setUp(self):
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self.checkpoint = "ZinengTang/tvlt-base"
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self.tmpdirname = tempfile.mkdtemp()
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def get_image_processor(self, **kwargs):
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return TvltImageProcessor.from_pretrained(self.checkpoint, **kwargs)
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def get_feature_extractor(self, **kwargs):
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return TvltFeatureExtractor.from_pretrained(self.checkpoint, **kwargs)
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def tearDown(self):
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shutil.rmtree(self.tmpdirname)
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def test_save_load_pretrained_default(self):
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image_processor = self.get_image_processor()
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feature_extractor = self.get_feature_extractor()
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processor = TvltProcessor(image_processor=image_processor, feature_extractor=feature_extractor)
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processor.save_pretrained(self.tmpdirname)
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processor = TvltProcessor.from_pretrained(self.tmpdirname)
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self.assertIsInstance(processor.feature_extractor, TvltFeatureExtractor)
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self.assertIsInstance(processor.image_processor, TvltImageProcessor)
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def test_feature_extractor(self):
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image_processor = self.get_image_processor()
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feature_extractor = self.get_feature_extractor()
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processor = TvltProcessor(image_processor=image_processor, feature_extractor=feature_extractor)
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audio = np.ones([12000])
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audio_dict = feature_extractor(audio, return_tensors="np")
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input_processor = processor(audio=audio, return_tensors="np")
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for key in audio_dict.keys():
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self.assertAlmostEqual(audio_dict[key].sum(), input_processor[key].sum(), delta=1e-2)
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def test_image_processor(self):
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image_processor = self.get_image_processor()
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feature_extractor = self.get_feature_extractor()
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processor = TvltProcessor(image_processor=image_processor, feature_extractor=feature_extractor)
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images = np.ones([3, 224, 224])
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image_dict = image_processor(images, return_tensors="np")
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input_processor = processor(images=images, return_tensors="np")
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for key in image_dict.keys():
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self.assertAlmostEqual(image_dict[key].sum(), input_processor[key].sum(), delta=1e-2)
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def test_processor(self):
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image_processor = self.get_image_processor()
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feature_extractor = self.get_feature_extractor()
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processor = TvltProcessor(image_processor=image_processor, feature_extractor=feature_extractor)
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audio = np.ones([12000])
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images = np.ones([3, 224, 224])
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inputs = processor(audio=audio, images=images)
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self.assertListEqual(list(inputs.keys()), ["audio_values", "audio_mask", "pixel_values", "pixel_mask"])
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# test if it raises when no input is passed
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with pytest.raises(ValueError):
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processor()
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def test_model_input_names(self):
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image_processor = self.get_image_processor()
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feature_extractor = self.get_feature_extractor()
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processor = TvltProcessor(image_processor=image_processor, feature_extractor=feature_extractor)
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self.assertListEqual(
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processor.model_input_names,
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image_processor.model_input_names + feature_extractor.model_input_names,
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msg="`processor` and `image_processor`+`feature_extractor` model input names do not match",
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)
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