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