# 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 from functools import cached_property import numpy as np from huggingface_hub import hf_hub_download from transformers.image_utils import SizeDict, load_image from transformers.testing_utils import require_torch, require_vision from transformers.utils import is_torch_available, is_torchvision_available, is_vision_available from ...test_image_processing_common import ImageProcessingTestMixin, prepare_image_inputs from ...test_processing_common import url_to_local_path if is_torch_available(): import torch if is_vision_available(): from PIL import Image from transformers import NougatImageProcessor if is_torchvision_available(): from transformers import NougatImageProcessorFast class NougatImageProcessingTester: def __init__( self, parent, batch_size=7, num_channels=3, image_size=18, min_resolution=30, max_resolution=400, do_crop_margin=True, do_resize=True, size=None, do_thumbnail=True, do_align_long_axis: bool = False, do_pad=True, do_normalize: bool = True, image_mean=[0.5, 0.5, 0.5], image_std=[0.5, 0.5, 0.5], ): size = size if size is not None else {"height": 20, "width": 20} self.parent = parent self.batch_size = batch_size self.num_channels = num_channels self.image_size = image_size self.min_resolution = min_resolution self.max_resolution = max_resolution self.do_crop_margin = do_crop_margin self.do_resize = do_resize self.size = size self.do_thumbnail = do_thumbnail self.do_align_long_axis = do_align_long_axis self.do_pad = do_pad self.do_normalize = do_normalize self.image_mean = image_mean self.image_std = image_std self.data_format = "channels_first" self.input_data_format = "channels_first" def prepare_image_processor_dict(self): return { "do_crop_margin": self.do_crop_margin, "do_resize": self.do_resize, "size": self.size, "do_thumbnail": self.do_thumbnail, "do_align_long_axis": self.do_align_long_axis, "do_pad": self.do_pad, "do_normalize": self.do_normalize, "image_mean": self.image_mean, "image_std": self.image_std, } def expected_output_image_shape(self, images): return self.num_channels, self.size["height"], self.size["width"] 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 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 NougatImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase): image_processing_class = NougatImageProcessor if is_vision_available() else None fast_image_processing_class = NougatImageProcessorFast if is_torchvision_available() else None def setUp(self): super().setUp() self.image_processor_tester = NougatImageProcessingTester(self) @property def image_processor_dict(self): return self.image_processor_tester.prepare_image_processor_dict() @cached_property def image_processor(self): return self.image_processing_class(**self.image_processor_dict) @unittest.skip(reason="FIXME: @yoni.") def test_slow_fast_equivalence_batched(self): pass def test_image_processor_properties(self): for image_processing_class in self.image_processor_list: image_processing = image_processing_class(**self.image_processor_dict) self.assertTrue(hasattr(image_processing, "do_resize")) self.assertTrue(hasattr(image_processing, "size")) self.assertTrue(hasattr(image_processing, "do_normalize")) self.assertTrue(hasattr(image_processing, "image_mean")) self.assertTrue(hasattr(image_processing, "image_std")) def test_image_processor_from_dict_with_kwargs(self): for image_processing_class in self.image_processor_list: image_processor = image_processing_class(**self.image_processor_dict) self.assertEqual(image_processor.size, {"height": 20, "width": 20}) kwargs = dict(self.image_processor_dict) kwargs.pop("size", None) image_processor = self.image_processing_class(**kwargs, size=42) self.assertEqual(image_processor.size, {"height": 42, "width": 42}) def test_expected_output(self): dummy_image = self.image_processor_tester.prepare_dummy_image() for image_processing_class in self.image_processor_list: 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 image_processing_class in self.image_processor_list: if image_processing_class == NougatImageProcessorFast: 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 image_processing_class in self.image_processor_list: if image_processing_class == NougatImageProcessorFast: 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 image_processing_class in self.image_processor_list: if image_processing_class == NougatImageProcessorFast: image = torch.from_numpy(image) size = SizeDict(height=200, width=300) image_processor = image_processing_class(**self.image_processor_dict) aligned_image = image_processor.align_long_axis(image, size) self.assertEqual(image.shape, aligned_image.shape) else: size = {"height": 200, "width": 300} image_processor = image_processing_class(**self.image_processor_dict) 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 image_processing_class in self.image_processor_list: image_processor = image_processing_class(**self.image_processor_dict) if image_processing_class == NougatImageProcessorFast: image = torch.from_numpy(image) size = SizeDict(height=300, width=200) image_processor = image_processing_class(**self.image_processor_dict) aligned_image = image_processor.align_long_axis(image, size) self.assertEqual(torch.Size([3, 200, 100]), aligned_image.shape) else: size = {"height": 300, "width": 200} image_processor = image_processing_class(**self.image_processor_dict) 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 image_processing_class in self.image_processor_list: if image_processing_class == NougatImageProcessorFast: image = torch.from_numpy(image) image_processor = image_processing_class(**self.image_processor_dict) size = SizeDict(height=200, width=300) aligned_image = image_processor.align_long_axis(image, size) self.assertEqual(torch.Size([3, 100, 200]), aligned_image.shape) else: size = {"height": 200, "width": 300} data_format = "channels_first" image_processor = image_processing_class(**self.image_processor_dict) aligned_image = image_processor.align_long_axis(image, size, data_format) 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 image_processing_class in self.image_processor_list: if image_processing_class == NougatImageProcessorFast: image = torch.from_numpy(image) image_processor = image_processing_class(**self.image_processor_dict) 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_processor = image_processing_class(**self.image_processor_dict) 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 image_processing_class in self.image_processor_list: if image_processing_class == NougatImageProcessor: # 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_slow_fast_equivalence(self): if not self.test_slow_image_processor or not self.test_fast_image_processor: self.skipTest(reason="Skipping slow/fast equivalence test") if self.image_processing_class is None or self.fast_image_processing_class is None: self.skipTest(reason="Skipping slow/fast equivalence test as one of the image processors is not defined") dummy_image = load_image(url_to_local_path("http://images.cocodataset.org/val2017/000000039769.jpg")) image_processor_slow = self.image_processing_class(**self.image_processor_dict) image_processor_fast = self.fast_image_processing_class(**self.image_processor_dict) encoding_slow = image_processor_slow(dummy_image, return_tensors="pt") encoding_fast = image_processor_fast(dummy_image, return_tensors="pt") # Adding a larger than usual tolerance because the slow processor uses reducing_gap=2.0 during resizing. torch.testing.assert_close(encoding_slow.pixel_values, encoding_fast.pixel_values, atol=2e-1, rtol=0) self.assertLessEqual( torch.mean(torch.abs(encoding_slow.pixel_values - encoding_fast.pixel_values)).item(), 2e-2 )