229 lines
9.8 KiB
Python
229 lines
9.8 KiB
Python
# coding=utf-8
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# Copyright 2022 HuggingFace Inc.
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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 unittest
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import numpy as np
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from transformers.testing_utils import require_torch, require_vision
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from transformers.utils import is_torch_available, is_vision_available
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from ...test_image_processing_common import ImageProcessingTestMixin, prepare_video_inputs
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if is_torch_available():
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import torch
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if is_vision_available():
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from PIL import Image
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from transformers import VivitImageProcessor
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class VivitImageProcessingTester(unittest.TestCase):
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def __init__(
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self,
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parent,
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batch_size=7,
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num_channels=3,
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num_frames=10,
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image_size=18,
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min_resolution=30,
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max_resolution=400,
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do_resize=True,
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size=None,
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do_normalize=True,
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image_mean=[0.5, 0.5, 0.5],
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image_std=[0.5, 0.5, 0.5],
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crop_size=None,
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):
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size = size if size is not None else {"shortest_edge": 18}
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crop_size = crop_size if crop_size is not None else {"height": 18, "width": 18}
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self.parent = parent
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self.batch_size = batch_size
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self.num_channels = num_channels
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self.num_frames = num_frames
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self.image_size = image_size
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self.min_resolution = min_resolution
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self.max_resolution = max_resolution
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self.do_resize = do_resize
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self.size = size
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self.do_normalize = do_normalize
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self.image_mean = image_mean
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self.image_std = image_std
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self.crop_size = crop_size
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def prepare_image_processor_dict(self):
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return {
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"image_mean": self.image_mean,
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"image_std": self.image_std,
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"do_normalize": self.do_normalize,
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"do_resize": self.do_resize,
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"size": self.size,
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"crop_size": self.crop_size,
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}
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def expected_output_image_shape(self, images):
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return self.num_frames, self.num_channels, self.crop_size["height"], self.crop_size["width"]
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def prepare_video_inputs(self, equal_resolution=False, numpify=False, torchify=False):
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return prepare_video_inputs(
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batch_size=self.batch_size,
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num_channels=self.num_channels,
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num_frames=self.num_frames,
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min_resolution=self.min_resolution,
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max_resolution=self.max_resolution,
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equal_resolution=equal_resolution,
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numpify=numpify,
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torchify=torchify,
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)
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@require_torch
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@require_vision
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class VivitImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase):
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image_processing_class = VivitImageProcessor if is_vision_available() else None
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def setUp(self):
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self.image_processor_tester = VivitImageProcessingTester(self)
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@property
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def image_processor_dict(self):
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return self.image_processor_tester.prepare_image_processor_dict()
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def test_image_processor_properties(self):
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image_processing = self.image_processing_class(**self.image_processor_dict)
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self.assertTrue(hasattr(image_processing, "image_mean"))
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self.assertTrue(hasattr(image_processing, "image_std"))
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self.assertTrue(hasattr(image_processing, "do_normalize"))
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self.assertTrue(hasattr(image_processing, "do_resize"))
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self.assertTrue(hasattr(image_processing, "do_center_crop"))
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self.assertTrue(hasattr(image_processing, "size"))
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def test_image_processor_from_dict_with_kwargs(self):
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image_processor = self.image_processing_class.from_dict(self.image_processor_dict)
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self.assertEqual(image_processor.size, {"shortest_edge": 18})
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self.assertEqual(image_processor.crop_size, {"height": 18, "width": 18})
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image_processor = self.image_processing_class.from_dict(self.image_processor_dict, size=42, crop_size=84)
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self.assertEqual(image_processor.size, {"shortest_edge": 42})
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self.assertEqual(image_processor.crop_size, {"height": 84, "width": 84})
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def test_rescale(self):
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# ViVit optionally rescales between -1 and 1 instead of the usual 0 and 1
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image = np.arange(0, 256, 1, dtype=np.uint8).reshape(1, 8, 32)
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image_processor = self.image_processing_class(**self.image_processor_dict)
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rescaled_image = image_processor.rescale(image, scale=1 / 127.5)
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expected_image = (image * (1 / 127.5)).astype(np.float32) - 1
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self.assertTrue(np.allclose(rescaled_image, expected_image))
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rescaled_image = image_processor.rescale(image, scale=1 / 255, offset=False)
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expected_image = (image / 255.0).astype(np.float32)
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self.assertTrue(np.allclose(rescaled_image, expected_image))
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def test_call_pil(self):
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# Initialize image_processing
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image_processing = self.image_processing_class(**self.image_processor_dict)
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# create random PIL videos
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video_inputs = self.image_processor_tester.prepare_video_inputs(equal_resolution=False)
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for video in video_inputs:
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self.assertIsInstance(video, list)
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self.assertIsInstance(video[0], Image.Image)
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# Test not batched input
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encoded_videos = image_processing(video_inputs[0], return_tensors="pt").pixel_values
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expected_output_video_shape = self.image_processor_tester.expected_output_image_shape([encoded_videos[0]])
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self.assertEqual(tuple(encoded_videos.shape), (1, *expected_output_video_shape))
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# Test batched
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encoded_videos = image_processing(video_inputs, return_tensors="pt").pixel_values
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expected_output_video_shape = self.image_processor_tester.expected_output_image_shape(encoded_videos)
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self.assertEqual(
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tuple(encoded_videos.shape), (self.image_processor_tester.batch_size, *expected_output_video_shape)
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)
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def test_call_numpy(self):
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# Initialize image_processing
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image_processing = self.image_processing_class(**self.image_processor_dict)
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# create random numpy tensors
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video_inputs = self.image_processor_tester.prepare_video_inputs(equal_resolution=False, numpify=True)
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for video in video_inputs:
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self.assertIsInstance(video, list)
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self.assertIsInstance(video[0], np.ndarray)
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# Test not batched input
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encoded_videos = image_processing(video_inputs[0], return_tensors="pt").pixel_values
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expected_output_video_shape = self.image_processor_tester.expected_output_image_shape([encoded_videos[0]])
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self.assertEqual(tuple(encoded_videos.shape), (1, *expected_output_video_shape))
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# Test batched
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encoded_videos = image_processing(video_inputs, return_tensors="pt").pixel_values
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expected_output_video_shape = self.image_processor_tester.expected_output_image_shape(encoded_videos)
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self.assertEqual(
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tuple(encoded_videos.shape), (self.image_processor_tester.batch_size, *expected_output_video_shape)
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)
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def test_call_numpy_4_channels(self):
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# Initialize image_processing
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image_processing = self.image_processing_class(**self.image_processor_dict)
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# create random numpy tensors
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self.image_processor_tester.num_channels = 4
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video_inputs = self.image_processor_tester.prepare_video_inputs(equal_resolution=False, numpify=True)
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for video in video_inputs:
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self.assertIsInstance(video, list)
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self.assertIsInstance(video[0], np.ndarray)
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# Test not batched input
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encoded_videos = image_processing(
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video_inputs[0], return_tensors="pt", image_mean=0, image_std=1, input_data_format="channels_first"
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).pixel_values
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expected_output_video_shape = self.image_processor_tester.expected_output_image_shape([encoded_videos[0]])
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self.assertEqual(tuple(encoded_videos.shape), (1, *expected_output_video_shape))
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# Test batched
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encoded_videos = image_processing(
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video_inputs, return_tensors="pt", image_mean=0, image_std=1, input_data_format="channels_first"
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).pixel_values
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expected_output_video_shape = self.image_processor_tester.expected_output_image_shape(encoded_videos)
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self.assertEqual(
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tuple(encoded_videos.shape), (self.image_processor_tester.batch_size, *expected_output_video_shape)
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)
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self.image_processor_tester.num_channels = 3
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def test_call_pytorch(self):
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# Initialize image_processing
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image_processing = self.image_processing_class(**self.image_processor_dict)
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# create random PyTorch tensors
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video_inputs = self.image_processor_tester.prepare_video_inputs(equal_resolution=False, torchify=True)
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for video in video_inputs:
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self.assertIsInstance(video, list)
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self.assertIsInstance(video[0], torch.Tensor)
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# Test not batched input
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encoded_videos = image_processing(video_inputs[0], return_tensors="pt").pixel_values
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expected_output_video_shape = self.image_processor_tester.expected_output_image_shape([encoded_videos[0]])
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self.assertEqual(tuple(encoded_videos.shape), (1, *expected_output_video_shape))
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# Test batched
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encoded_videos = image_processing(video_inputs, return_tensors="pt").pixel_values
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expected_output_video_shape = self.image_processor_tester.expected_output_image_shape(encoded_videos)
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self.assertEqual(
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tuple(encoded_videos.shape), (self.image_processor_tester.batch_size, *expected_output_video_shape)
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)
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