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tests/models/flava/test_image_processing_flava.py
426 строк
18 KB
Pradeep Biswas
Refactor image loading in tests to use load_test_image helper (#47218)
17 июл 2026, 11:08
Не верифицирован
17 июл 2026, 11:08
760d519
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# Copyright 2022 Meta Platforms authors and 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 random 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, load_coco_image, prepare_image_inputs, ) if is_torch_available(): import torch if is_vision_available(): import PIL from transformers.image_utils import PILImageResampling from transformers.models.flava.image_processing_flava import ( FLAVA_CODEBOOK_MEAN, FLAVA_CODEBOOK_STD, FLAVA_IMAGE_MEAN, FLAVA_IMAGE_STD, ) else: FLAVA_IMAGE_MEAN = FLAVA_IMAGE_STD = FLAVA_CODEBOOK_MEAN = FLAVA_CODEBOOK_STD = None class FlavaImageProcessingTester: def __init__( self, parent, batch_size=7, num_channels=3, min_resolution=30, max_resolution=400, do_resize=True, size=None, do_center_crop=True, crop_size=None, resample=None, do_rescale=True, rescale_factor=1 / 255, do_normalize=True, image_mean=FLAVA_IMAGE_MEAN, image_std=FLAVA_IMAGE_STD, input_size_patches=14, total_mask_patches=75, mask_group_min_patches=16, mask_group_min_aspect_ratio=0.3, mask_group_max_aspect_ratio=None, codebook_do_resize=True, codebook_size=None, codebook_resample=None, codebook_do_center_crop=True, codebook_crop_size=None, codebook_do_map_pixels=True, codebook_do_normalize=True, codebook_image_mean=FLAVA_CODEBOOK_MEAN, codebook_image_std=FLAVA_CODEBOOK_STD, ): size = size if size is not None else {"height": 224, "width": 224} crop_size = crop_size if crop_size is not None else {"height": 224, "width": 224} codebook_size = codebook_size if codebook_size is not None else {"height": 112, "width": 112} codebook_crop_size = codebook_crop_size if codebook_crop_size is not None else {"height": 112, "width": 112} self.parent = parent self.batch_size = batch_size self.num_channels = num_channels self.do_resize = do_resize self.do_rescale = do_rescale self.rescale_factor = rescale_factor self.min_resolution = min_resolution self.max_resolution = max_resolution self.size = size self.resample = resample if resample is not None else PILImageResampling.BICUBIC self.do_normalize = do_normalize self.image_mean = image_mean self.image_std = image_std self.do_center_crop = do_center_crop self.crop_size = crop_size self.input_size_patches = input_size_patches self.total_mask_patches = total_mask_patches self.mask_group_min_patches = mask_group_min_patches self.mask_group_min_aspect_ratio = mask_group_min_aspect_ratio self.mask_group_max_aspect_ratio = mask_group_max_aspect_ratio self.codebook_do_resize = codebook_do_resize self.codebook_size = codebook_size # LANCZOS resample is natively supported with torchvision >= 0.27. # On older versions, the base class falls back to BICUBIC automatically. self.codebook_resample = codebook_resample if codebook_resample is not None else PILImageResampling.LANCZOS self.codebook_do_center_crop = codebook_do_center_crop self.codebook_crop_size = codebook_crop_size self.codebook_do_map_pixels = codebook_do_map_pixels self.codebook_do_normalize = codebook_do_normalize self.codebook_image_mean = codebook_image_mean self.codebook_image_std = codebook_image_std 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, "resample": self.resample, "do_rescale": self.do_rescale, "rescale_factor": self.rescale_factor, "do_center_crop": self.do_center_crop, "crop_size": self.crop_size, "input_size_patches": self.input_size_patches, "total_mask_patches": self.total_mask_patches, "mask_group_min_patches": self.mask_group_min_patches, "mask_group_min_aspect_ratio": self.mask_group_min_aspect_ratio, "mask_group_max_aspect_ratio": self.mask_group_min_aspect_ratio, "codebook_do_resize": self.codebook_do_resize, "codebook_size": self.codebook_size, "codebook_resample": self.codebook_resample, "codebook_do_center_crop": self.codebook_do_center_crop, "codebook_crop_size": self.codebook_crop_size, "codebook_do_map_pixels": self.codebook_do_map_pixels, "codebook_do_normalize": self.codebook_do_normalize, "codebook_image_mean": self.codebook_image_mean, "codebook_image_std": self.codebook_image_std, } def get_expected_image_size(self): return (self.size["height"], self.size["width"]) def get_expected_mask_size(self): return ( (self.input_size_patches, self.input_size_patches) if not isinstance(self.input_size_patches, tuple) else self.input_size_patches ) def get_expected_codebook_image_size(self): return (self.codebook_size["height"], self.codebook_size["width"]) def prepare_image_inputs(self, equal_resolution=False, numpify=False, torchify=False): return prepare_image_inputs( batch_size=self.batch_size, num_channels=self.num_channels, min_resolution=self.min_resolution, max_resolution=self.max_resolution, equal_resolution=equal_resolution, numpify=numpify, torchify=torchify, ) @require_torch @require_vision class FlavaImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase): maxDiff = None def setUp(self): super().setUp() self.image_processor_tester = FlavaImageProcessingTester(self) @property def image_processor_dict(self): return self.image_processor_tester.prepare_image_processor_dict() def test_image_processor_properties(self): for image_processing_class in self.image_processing_classes.values(): image_processing = 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, "resample")) self.assertTrue(hasattr(image_processing, "crop_size")) self.assertTrue(hasattr(image_processing, "do_center_crop")) self.assertTrue(hasattr(image_processing, "do_rescale")) self.assertTrue(hasattr(image_processing, "rescale_factor")) self.assertTrue(hasattr(image_processing, "masking_generator")) self.assertTrue(hasattr(image_processing, "codebook_do_resize")) self.assertTrue(hasattr(image_processing, "codebook_size")) self.assertTrue(hasattr(image_processing, "codebook_resample")) self.assertTrue(hasattr(image_processing, "codebook_do_center_crop")) self.assertTrue(hasattr(image_processing, "codebook_crop_size")) self.assertTrue(hasattr(image_processing, "codebook_do_map_pixels")) self.assertTrue(hasattr(image_processing, "codebook_do_normalize")) self.assertTrue(hasattr(image_processing, "codebook_image_mean")) self.assertTrue(hasattr(image_processing, "codebook_image_std")) def test_image_processor_from_dict_with_kwargs(self): for image_processing_class in self.image_processing_classes.values(): image_processor = image_processing_class.from_dict(self.image_processor_dict) self.assertEqual(image_processor.size, {"height": 224, "width": 224}) self.assertEqual(image_processor.crop_size, {"height": 224, "width": 224}) self.assertEqual(image_processor.codebook_size, {"height": 112, "width": 112}) self.assertEqual(image_processor.codebook_crop_size, {"height": 112, "width": 112}) image_processor = image_processing_class.from_dict( self.image_processor_dict, size=42, crop_size=84, codebook_size=33, codebook_crop_size=66 ) self.assertEqual(image_processor.size, {"height": 42, "width": 42}) self.assertEqual(image_processor.crop_size, {"height": 84, "width": 84}) self.assertEqual(image_processor.codebook_size, {"height": 33, "width": 33}) self.assertEqual(image_processor.codebook_crop_size, {"height": 66, "width": 66}) def test_call_pil(self): for image_processing_class in self.image_processing_classes.values(): # Initialize image_processing image_processing = image_processing_class(**self.image_processor_dict) # create random PIL images image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=False) for image in image_inputs: self.assertIsInstance(image, PIL.Image.Image) # Test not batched input encoded_images = image_processing(image_inputs[0], return_tensors="pt") # Test no bool masked pos self.assertFalse("bool_masked_pos" in encoded_images) expected_height, expected_width = self.image_processor_tester.get_expected_image_size() self.assertEqual( encoded_images.pixel_values.shape, (1, self.image_processor_tester.num_channels, expected_height, expected_width), ) # Test batched encoded_images = image_processing(image_inputs, return_tensors="pt") expected_height, expected_width = self.image_processor_tester.get_expected_image_size() # Test no bool masked pos self.assertFalse("bool_masked_pos" in encoded_images) self.assertEqual( encoded_images.pixel_values.shape, ( self.image_processor_tester.batch_size, self.image_processor_tester.num_channels, expected_height, expected_width, ), ) def _test_call_framework(self, instance_class, prepare_kwargs): for image_processing_class in self.image_processing_classes.values(): # Initialize image_processing image_processing = image_processing_class(**self.image_processor_dict) # create random tensors image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=False, **prepare_kwargs) for image in image_inputs: self.assertIsInstance(image, instance_class) # Test not batched input encoded_images = image_processing(image_inputs[0], return_tensors="pt") expected_height, expected_width = self.image_processor_tester.get_expected_image_size() self.assertEqual( encoded_images.pixel_values.shape, (1, self.image_processor_tester.num_channels, expected_height, expected_width), ) encoded_images = image_processing(image_inputs, return_image_mask=True, return_tensors="pt") expected_height, expected_width = self.image_processor_tester.get_expected_image_size() self.assertEqual( encoded_images.pixel_values.shape, ( self.image_processor_tester.batch_size, self.image_processor_tester.num_channels, expected_height, expected_width, ), ) expected_height, expected_width = self.image_processor_tester.get_expected_mask_size() self.assertEqual( encoded_images.bool_masked_pos.shape, ( self.image_processor_tester.batch_size, expected_height, expected_width, ), ) # Test batched encoded_images = image_processing(image_inputs, return_tensors="pt").pixel_values expected_height, expected_width = self.image_processor_tester.get_expected_image_size() self.assertEqual( encoded_images.shape, ( self.image_processor_tester.batch_size, self.image_processor_tester.num_channels, expected_height, expected_width, ), ) # Test masking encoded_images = image_processing(image_inputs, return_image_mask=True, return_tensors="pt") expected_height, expected_width = self.image_processor_tester.get_expected_image_size() self.assertEqual( encoded_images.pixel_values.shape, ( self.image_processor_tester.batch_size, self.image_processor_tester.num_channels, expected_height, expected_width, ), ) expected_height, expected_width = self.image_processor_tester.get_expected_mask_size() self.assertEqual( encoded_images.bool_masked_pos.shape, ( self.image_processor_tester.batch_size, expected_height, expected_width, ), ) def test_call_numpy(self): self._test_call_framework(np.ndarray, prepare_kwargs={"numpify": True}) def test_call_numpy_4_channels(self): # Get the first backend class to modify num_channels first_backend_class = list(self.image_processing_classes.values())[0] original_num_channels = ( first_backend_class.num_channels if hasattr(first_backend_class, "num_channels") else None ) first_backend_class.num_channels = 4 self._test_call_framework(np.ndarray, prepare_kwargs={"numpify": True}) if original_num_channels is not None: first_backend_class.num_channels = original_num_channels else: delattr(first_backend_class, "num_channels") def test_call_pytorch(self): self._test_call_framework(torch.Tensor, prepare_kwargs={"torchify": True}) def test_masking(self): for image_processing_class in self.image_processing_classes.values(): # Initialize image_processing random.seed(1234) image_processing = image_processing_class(**self.image_processor_dict) image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=False, torchify=True) # Test not batched input encoded_images = image_processing(image_inputs[0], return_image_mask=True, return_tensors="pt") self.assertEqual(encoded_images.bool_masked_pos.sum().item(), 75) def test_codebook_pixels(self): for image_processing_class in self.image_processing_classes.values(): # Initialize image_processing image_processing = image_processing_class(**self.image_processor_dict) # create random PIL images image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=False) for image in image_inputs: self.assertIsInstance(image, PIL.Image.Image) # Test not batched input encoded_images = image_processing(image_inputs[0], return_codebook_pixels=True, return_tensors="pt") expected_height, expected_width = self.image_processor_tester.get_expected_codebook_image_size() self.assertEqual( encoded_images.codebook_pixel_values.shape, (1, self.image_processor_tester.num_channels, expected_height, expected_width), ) # Test batched encoded_images = image_processing(image_inputs, return_codebook_pixels=True, return_tensors="pt") expected_height, expected_width = self.image_processor_tester.get_expected_codebook_image_size() self.assertEqual( encoded_images.codebook_pixel_values.shape, ( self.image_processor_tester.batch_size, self.image_processor_tester.num_channels, expected_height, expected_width, ), ) @require_vision @require_torch def test_slow_fast_equivalence(self): if len(self.image_processing_classes) < 2: self.skipTest(reason="Skipping backends equivalence test as there are less than 2 backends") dummy_image = load_coco_image("000000039769.jpg") # Create processors for each backend encodings = {} for backend_name, image_processing_class in self.image_processing_classes.items(): image_processor = image_processing_class(**self.image_processor_dict) encodings[backend_name] = image_processor( dummy_image, return_tensors="pt", return_codebook_pixels=True, return_image_mask=True ) # Compare all backends to the first one (reference backend) backend_names = list(encodings.keys()) reference_backend = backend_names[0] reference_encoding = encodings[reference_backend] for backend_name in backend_names[1:]: self._assert_tensors_equivalence(reference_encoding.pixel_values, encodings[backend_name].pixel_values) self._assert_tensors_equivalence( reference_encoding.codebook_pixel_values, encodings[backend_name].codebook_pixel_values )