# 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.testing_utils import require_torch, require_vision from transformers.utils import is_torch_available, is_vision_available from ...test_image_processing_common import ImageProcessingTester, ImageProcessingTestMixin if is_vision_available(): from PIL import Image if is_torch_available(): import torch class AriaImageProcessingTester(ImageProcessingTester): def __init__(self, **kwargs): # Random test inputs kwargs kwargs.setdefault("max_resolution", 40) kwargs.setdefault("num_images", 1) # Image processor init kwargs kwargs.setdefault("max_image_size", 980) kwargs.setdefault("split_resolutions", [[980, 980]]) kwargs.setdefault("split_image", True) kwargs.setdefault("size", {"longest_edge": 40}) super().__init__(**kwargs) def expected_output_image_shape(self, images): return self.num_channels, self.max_image_size, self.max_image_size 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. """ batch_size = batch_size if batch_size is not None else self.batch_size num_images = num_images if num_images is not None else self.num_images # super() must be called outside list comprehension on Python <= 3.12 prepare_images = super().prepare_image_inputs image_inputs = [ prepare_images( batch_size=num_images, min_resolution=min_resolution, max_resolution=max_resolution, num_channels=num_channels, size_divisor=size_divisor, equal_resolution=equal_resolution, numpify=numpify, torchify=torchify, ) for _ in range(batch_size) ] return image_inputs @require_torch @require_vision class AriaImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase): image_processor_tester_class = AriaImageProcessingTester 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)