# Copyright 2023 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 huggingface_hub import hf_hub_download from transformers.image_utils import IMAGENET_STANDARD_MEAN, IMAGENET_STANDARD_STD, SizeDict, load_image 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 from ...test_processing_common import url_to_local_path if is_torch_available(): import torch if is_vision_available(): from PIL import Image class NougatImageProcessingTester(ImageProcessingTester): def __init__(self, **kwargs): # Image processor init kwargs kwargs.setdefault("size", {"height": 20, "width": 20}) # test_expected_output pins the pixel mean with this normalization. kwargs.setdefault("image_mean", IMAGENET_STANDARD_MEAN.copy()) kwargs.setdefault("image_std", IMAGENET_STANDARD_STD.copy()) kwargs.setdefault("data_format", "channels_first") super().__init__(**kwargs) def prepare_dummy_image(self): revision = "ec57bf8c8b1653a209c13f6e9ee66b12df0fc2db" filepath = hf_hub_download( repo_id="hf-internal-testing/fixtures_docvqa", filename="nougat_pdf.png", repo_type="dataset", revision=revision, ) image = Image.open(filepath).convert("RGB") return image @require_torch @require_vision class NougatImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase): image_processor_tester_class = NougatImageProcessingTester def test_expected_output(self): dummy_image = self.image_processor_tester.prepare_dummy_image() for image_processing_class in self.image_processing_classes.values(): image_processor = image_processing_class(**self.image_processor_dict) inputs = image_processor(dummy_image, return_tensors="pt") torch.testing.assert_close(inputs["pixel_values"].mean(), torch.tensor(0.4906), rtol=1e-3, atol=1e-3) def test_crop_margin_all_white(self): image = np.uint8(np.ones((3, 100, 100)) * 255) for backend_name, image_processing_class in self.image_processing_classes.items(): if backend_name == "torchvision": image = torch.from_numpy(image) image_processor = image_processing_class(**self.image_processor_dict) cropped_image = image_processor.crop_margin(image) self.assertTrue(torch.equal(image, cropped_image)) else: image_processor = image_processing_class(**self.image_processor_dict) cropped_image = image_processor.crop_margin(image) self.assertTrue(np.array_equal(image, cropped_image)) def test_crop_margin_centered_black_square(self): image = np.ones((3, 100, 100), dtype=np.uint8) * 255 image[:, 45:55, 45:55] = 0 expected_cropped = image[:, 45:55, 45:55] for backend_name, image_processing_class in self.image_processing_classes.items(): if backend_name == "torchvision": image = torch.from_numpy(image) expected_cropped = torch.from_numpy(expected_cropped) image_processor = image_processing_class(**self.image_processor_dict) cropped_image = image_processor.crop_margin(image) self.assertTrue(torch.equal(expected_cropped, cropped_image)) else: image_processor = image_processing_class(**self.image_processor_dict) cropped_image = image_processor.crop_margin(image) self.assertTrue(np.array_equal(expected_cropped, cropped_image)) def test_align_long_axis_no_rotation(self): image = np.uint8(np.ones((3, 100, 200)) * 255) for backend_name, image_processing_class in self.image_processing_classes.items(): size = SizeDict(height=200, width=300) image_processor = image_processing_class(**self.image_processor_dict) if backend_name == "torchvision": image = torch.from_numpy(image) aligned_image = image_processor.align_long_axis(image, size) self.assertEqual(image.shape, aligned_image.shape) else: aligned_image = image_processor.align_long_axis(image, size) self.assertEqual(image.shape, aligned_image.shape) def test_align_long_axis_with_rotation(self): image = np.uint8(np.ones((3, 200, 100)) * 255) for backend_name, image_processing_class in self.image_processing_classes.items(): image_processor = image_processing_class(**self.image_processor_dict) size = SizeDict(height=300, width=200) if backend_name == "torchvision": image = torch.from_numpy(image) aligned_image = image_processor.align_long_axis(image, size) self.assertEqual(torch.Size([3, 200, 100]), aligned_image.shape) else: aligned_image = image_processor.align_long_axis(image, size) self.assertEqual((3, 200, 100), aligned_image.shape) def test_align_long_axis_data_format(self): image = np.uint8(np.ones((3, 100, 200)) * 255) for backend_name, image_processing_class in self.image_processing_classes.items(): size = SizeDict(height=200, width=300) image_processor = image_processing_class(**self.image_processor_dict) if backend_name == "torchvision": image = torch.from_numpy(image) aligned_image = image_processor.align_long_axis(image, size) self.assertEqual(torch.Size([3, 100, 200]), aligned_image.shape) else: aligned_image = image_processor.align_long_axis(image, size) self.assertEqual((3, 100, 200), aligned_image.shape) def prepare_dummy_np_image(self): revision = "ec57bf8c8b1653a209c13f6e9ee66b12df0fc2db" filepath = hf_hub_download( repo_id="hf-internal-testing/fixtures_docvqa", filename="nougat_pdf.png", repo_type="dataset", revision=revision, ) image = Image.open(filepath).convert("RGB") return np.array(image).transpose(2, 0, 1) def test_crop_margin_equality_cv2_python(self): image = self.prepare_dummy_np_image() for backend_name, image_processing_class in self.image_processing_classes.items(): image_processor = image_processing_class(**self.image_processor_dict) if backend_name == "torchvision": image = torch.from_numpy(image) image_cropped_python = image_processor.crop_margin(image) self.assertEqual(image_cropped_python.shape, torch.Size([3, 850, 685])) self.assertAlmostEqual(image_cropped_python.float().mean().item(), 237.43881150708458, delta=0.001) else: image_cropped_python = image_processor.crop_margin(image) self.assertEqual(image_cropped_python.shape, (3, 850, 685)) self.assertAlmostEqual(image_cropped_python.mean(), 237.43881150708458, delta=0.001) def test_call_numpy_4_channels(self): for backend_name, image_processing_class in self.image_processing_classes.items(): if backend_name == "pil": # Test that can process images which have an arbitrary number of channels # Initialize image_processing image_processor = image_processing_class(**self.image_processor_dict) # create random numpy tensors self.image_processor_tester.num_channels = 4 image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=False, numpify=True) # Test not batched input encoded_images = image_processor( image_inputs[0], return_tensors="pt", input_data_format="channels_last", image_mean=(0.0, 0.0, 0.0, 0.0), image_std=(1.0, 1.0, 1.0, 1.0), ).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_processor( image_inputs, return_tensors="pt", input_data_format="channels_last", image_mean=(0.0, 0.0, 0.0, 0.0), image_std=(1.0, 1.0, 1.0, 1.0), ).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_backends_equivalence(self): """Test equivalence across backends. PIL backend delegates to Torchvision for pixel-perfect match.""" if len(self.image_processing_classes) < 2: self.skipTest(reason="Skipping backends equivalence test as there are less than 2 backends") dummy_image = load_image( url_to_local_path( "https://huggingface.co/datasets/hf-internal-testing/fixtures-coco/resolve/main/val2017/000000039769.jpg" ) ) 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") backend_names = list(encodings.keys()) reference_backend = backend_names[0] reference_pixel_values = encodings[reference_backend].pixel_values for backend_name in backend_names[1:]: self._assert_tensors_equivalence(reference_pixel_values, encodings[backend_name].pixel_values)