/
githubmirror
/
transformers
Обзор
Документация
Войти
/
githubmirror
/
transformers
Код
Запросы
0
Пакеты
0
Релизы
0
Аналитика
Безопасность
main
tests/models/aria/test_image_processing_aria.py
334 строки
16 KB
arnavkewalram
fix(aria): use math.ceil in get_number_of_image_patches to match actual patch count (#46732)
19 июн 2026, 13:06
Не верифицирован
19 июн 2026, 13:06
d8904a2
Код
Авторство
О чём код?
# Copyright 2024 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.image_utils import PILImageResampling 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 if is_vision_available(): from PIL import Image if is_torch_available(): import torch class AriaImageProcessingTester: def __init__( self, parent, batch_size=7, num_channels=3, num_images=1, min_resolution=30, max_resolution=40, size=None, max_image_size=980, min_image_size=336, split_resolutions=None, split_image=True, do_normalize=True, image_mean=[0.5, 0.5, 0.5], image_std=[0.5, 0.5, 0.5], do_convert_rgb=True, resample=PILImageResampling.BICUBIC, ): self.size = size if size is not None else {"longest_edge": max_resolution} self.parent = parent self.batch_size = batch_size self.num_channels = num_channels self.num_images = num_images self.min_resolution = min_resolution self.max_resolution = max_resolution self.resample = resample self.max_image_size = max_image_size self.min_image_size = min_image_size self.split_resolutions = split_resolutions if split_resolutions is not None else [[980, 980]] self.split_image = split_image self.do_normalize = do_normalize self.image_mean = image_mean self.image_std = image_std self.do_convert_rgb = do_convert_rgb def prepare_image_processor_dict(self): return { "image_mean": self.image_mean, "image_std": self.image_std, "max_image_size": self.max_image_size, "min_image_size": self.min_image_size, "split_resolutions": self.split_resolutions, "split_image": self.split_image, "do_convert_rgb": self.do_convert_rgb, "do_normalize": self.do_normalize, "resample": self.resample, } def get_expected_values(self, image_inputs, batched=False): """ This function computes the expected height and width when providing images to AriaImageProcessor, assuming do_resize is set to True. The expected size in that case the max image size. """ return self.max_image_size, self.max_image_size def expected_output_image_shape(self, images): height, width = self.get_expected_values(images, batched=True) return self.num_channels, height, width def prepare_image_inputs( self, batch_size=None, min_resolution=None, max_resolution=None, num_channels=None, num_images=None, size_divisor=None, equal_resolution=False, numpify=False, torchify=False, ): """This function prepares a list of PIL images, or a list of numpy arrays if one specifies numpify=True, or a list of PyTorch tensors if one specifies torchify=True. One can specify whether the images are of the same resolution or not. """ assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time" batch_size = batch_size if batch_size is not None else self.batch_size min_resolution = min_resolution if min_resolution is not None else self.min_resolution max_resolution = max_resolution if max_resolution is not None else self.max_resolution num_channels = num_channels if num_channels is not None else self.num_channels num_images = num_images if num_images is not None else self.num_images images_list = [] for i in range(batch_size): images = [] for j in range(num_images): if equal_resolution: width = height = max_resolution else: # To avoid getting image width/height 0 if size_divisor is not None: # If `size_divisor` is defined, the image needs to have width/size >= `size_divisor` min_resolution = max(size_divisor, min_resolution) width, height = np.random.choice(np.arange(min_resolution, max_resolution), 2) images.append(np.random.randint(255, size=(num_channels, width, height), dtype=np.uint8)) images_list.append(images) if not numpify and not torchify: # PIL expects the channel dimension as last dimension images_list = [[Image.fromarray(np.moveaxis(image, 0, -1)) for image in images] for images in images_list] if torchify: images_list = [[torch.from_numpy(image) for image in images] for images in images_list] if numpify: # Numpy images are typically in channels last format images_list = [[image.transpose(1, 2, 0) for image in images] for images in images_list] return images_list @require_torch @require_vision class AriaImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase): def setUp(self): super().setUp() self.image_processor_tester = AriaImageProcessingTester(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, "do_convert_rgb")) self.assertTrue(hasattr(image_processing, "max_image_size")) self.assertTrue(hasattr(image_processing, "min_image_size")) self.assertTrue(hasattr(image_processing, "do_normalize")) self.assertTrue(hasattr(image_processing, "image_mean")) self.assertTrue(hasattr(image_processing, "image_std")) self.assertTrue(hasattr(image_processing, "split_image")) def test_call_numpy(self): for image_processing_class in self.image_processing_classes.values(): image_processing = image_processing_class(**self.image_processor_dict) # create random numpy tensors image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=False, numpify=True) for sample_images in image_inputs: for image in sample_images: self.assertIsInstance(image, np.ndarray) # Test not batched input encoded_images = image_processing(image_inputs[0], return_tensors="pt").pixel_values expected_output_image_shape = self.image_processor_tester.expected_output_image_shape([image_inputs[0]]) self.assertEqual(tuple(encoded_images.shape), (1, *expected_output_image_shape)) # Test batched encoded_images = image_processing(image_inputs, return_tensors="pt").pixel_values expected_output_image_shape = self.image_processor_tester.expected_output_image_shape(image_inputs) self.assertEqual( tuple(encoded_images.shape), (self.image_processor_tester.batch_size, *expected_output_image_shape) ) def test_call_numpy_4_channels(self): # Aria always processes images as RGB, so it always returns images with 3 channels for image_processing_class in self.image_processing_classes.values(): image_processor_dict = self.image_processor_dict image_processing = image_processing_class(**image_processor_dict) # create random numpy tensors image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=False, numpify=True) for sample_images in image_inputs: for image in sample_images: self.assertIsInstance(image, np.ndarray) # Test not batched input encoded_images = image_processing(image_inputs[0], return_tensors="pt").pixel_values expected_output_image_shape = self.image_processor_tester.expected_output_image_shape([image_inputs[0]]) self.assertEqual(tuple(encoded_images.shape), (1, *expected_output_image_shape)) # Test batched encoded_images = image_processing(image_inputs, return_tensors="pt").pixel_values expected_output_image_shape = self.image_processor_tester.expected_output_image_shape(image_inputs) self.assertEqual( tuple(encoded_images.shape), (self.image_processor_tester.batch_size, *expected_output_image_shape) ) def test_call_pil(self): for image_processing_class in self.image_processing_classes.values(): 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 images in image_inputs: for image in images: self.assertIsInstance(image, Image.Image) # Test not batched input encoded_images = image_processing(image_inputs[0], return_tensors="pt").pixel_values expected_output_image_shape = self.image_processor_tester.expected_output_image_shape([image_inputs[0]]) self.assertEqual(tuple(encoded_images.shape), (1, *expected_output_image_shape)) # Test batched encoded_images = image_processing(image_inputs, return_tensors="pt").pixel_values expected_output_image_shape = self.image_processor_tester.expected_output_image_shape(image_inputs) self.assertEqual( tuple(encoded_images.shape), (self.image_processor_tester.batch_size, *expected_output_image_shape) ) def test_call_pytorch(self): for image_processing_class in self.image_processing_classes.values(): image_processing = image_processing_class(**self.image_processor_dict) # create random PyTorch tensors image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=False, torchify=True) for images in image_inputs: for image in images: self.assertIsInstance(image, torch.Tensor) # Test not batched input encoded_images = image_processing(image_inputs[0], return_tensors="pt").pixel_values expected_output_image_shape = self.image_processor_tester.expected_output_image_shape([image_inputs[0]]) self.assertEqual(tuple(encoded_images.shape), (1, *expected_output_image_shape)) # Test batched expected_output_image_shape = self.image_processor_tester.expected_output_image_shape(image_inputs) encoded_images = image_processing(image_inputs, return_tensors="pt").pixel_values self.assertEqual( tuple(encoded_images.shape), (self.image_processor_tester.batch_size, *expected_output_image_shape), ) def test_pad_for_patching(self): for backend_name, image_processing_class in self.image_processing_classes.items(): numpify = backend_name == "pil" torchify = backend_name == "torchvision" image_processing = image_processing_class(**self.image_processor_dict) # Create odd-sized images image_input = self.image_processor_tester.prepare_image_inputs( batch_size=1, max_resolution=400, num_images=1, equal_resolution=True, numpify=numpify, torchify=torchify, )[0][0] self.assertIn(image_input.shape, [(3, 400, 400), (400, 400, 3)]) # Both backends use channels-first internally; transpose if numpify returned HWC if numpify: image_input = image_input.transpose(2, 0, 1) # Test odd-width image_shape = (400, 601) encoded_images = image_processing._pad_for_patching(image_input, image_shape) self.assertEqual(encoded_images.shape[-2:], image_shape) # Test odd-height image_shape = (503, 400) encoded_images = image_processing._pad_for_patching(image_input, image_shape) self.assertEqual(encoded_images.shape[-2:], image_shape) def test_get_num_patches_without_images(self): for image_processing_class in self.image_processing_classes.values(): image_processing = image_processing_class(**self.image_processor_dict) num_patches = image_processing.get_number_of_image_patches(height=100, width=100, images_kwargs={}) self.assertEqual(num_patches, 1) num_patches = image_processing.get_number_of_image_patches( height=300, width=500, images_kwargs={"split_image": True} ) self.assertEqual(num_patches, 1) # The test fixture uses split_resolutions=[[980, 980]], so best_resolution=(980,980). # divide_to_patches with patch_size=200 iterates range(0,980,200) -> 5 steps each dim -> 25 patches. num_patches = image_processing.get_number_of_image_patches( height=100, width=100, images_kwargs={"split_image": True, "max_image_size": 200} ) self.assertEqual(num_patches, 25) def test_get_num_patches_ceil_matches_actual_patch_count(self): # Regression test for https://github.com/huggingface/transformers/issues/46728. # With max_image_size=980 the default split_resolutions include odd multiples of 490 # (e.g. 490, 1470) that are not divisible by 980. The old floor-division formula # returned wrong counts (as low as 0); ceil division matches what divide_to_patches # actually produces. for image_processing_class in self.image_processing_classes.values(): # Use the full default split_resolutions so odd-multiple slots are reachable. image_processing = image_processing_class(**{**self.image_processor_dict, "split_resolutions": None}) # Portrait image -> best_resolution = [490, 980] -> ceil(490/980)*ceil(980/980) = 1*1 = 1 num_patches = image_processing.get_number_of_image_patches( height=300, width=600, images_kwargs={"split_image": True, "max_image_size": 980} ) self.assertEqual(num_patches, 1) # Landscape image -> best_resolution = [980, 490] -> ceil(980/980)*ceil(490/980) = 1*1 = 1 num_patches = image_processing.get_number_of_image_patches( height=600, width=300, images_kwargs={"split_image": True, "max_image_size": 980} ) self.assertEqual(num_patches, 1) # Wide image -> best_resolution = [490, 1470] -> ceil(490/980)*ceil(1470/980) = 1*2 = 2 num_patches = image_processing.get_number_of_image_patches( height=300, width=1470, images_kwargs={"split_image": True, "max_image_size": 980} ) self.assertEqual(num_patches, 2)