# 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 time import unittest import warnings import numpy as np import requests from packaging import version from transformers.testing_utils import ( is_flaky, require_torch, require_torch_accelerator, require_vision, slow, torch_device, ) from transformers.utils import is_torch_available, is_torchvision_available, is_vision_available from ...test_image_processing_common import ImageProcessingTestMixin, prepare_image_inputs if is_torch_available(): import torch if is_vision_available(): from PIL import Image from transformers import VitMatteImageProcessor if is_torchvision_available(): from transformers import VitMatteImageProcessorFast class VitMatteImageProcessingTester: def __init__( self, parent, batch_size=7, num_channels=3, image_size=18, min_resolution=30, max_resolution=400, do_rescale=True, rescale_factor=0.5, do_pad=True, size_divisibility=10, do_normalize=True, image_mean=[0.5, 0.5, 0.5], image_std=[0.5, 0.5, 0.5], ): 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_rescale = do_rescale self.rescale_factor = rescale_factor self.do_pad = do_pad self.size_divisibility = size_divisibility self.do_normalize = do_normalize self.image_mean = image_mean self.image_std = 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_rescale": self.do_rescale, "rescale_factor": self.rescale_factor, "do_pad": self.do_pad, "size_divisibility": self.size_divisibility, } 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 VitMatteImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase): image_processing_class = VitMatteImageProcessor if is_vision_available() else None fast_image_processing_class = VitMatteImageProcessorFast if is_torchvision_available() else None def setUp(self): super().setUp() self.image_processor_tester = VitMatteImageProcessingTester(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_processor_list: 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_rescale")) self.assertTrue(hasattr(image_processing, "rescale_factor")) self.assertTrue(hasattr(image_processing, "do_pad")) self.assertTrue(hasattr(image_processing, "size_divisibility")) def test_call_numpy(self): # create random numpy tensors image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=False, numpify=True) for image in image_inputs: self.assertIsInstance(image, np.ndarray) # Test not batched input (image processor does not support batched inputs) image = image_inputs[0] trimap = np.random.randint(0, 3, size=image.shape[:2]) for image_processing_class in self.image_processor_list: image_processing = image_processing_class(**self.image_processor_dict) encoded_images = image_processing(images=image, trimaps=trimap, return_tensors="pt").pixel_values # Verify that width and height can be divided by size_divisibility and that correct dimensions got merged self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisibility == 0) self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisibility == 0) self.assertTrue(encoded_images.shape[-3] == 4) def test_call_pytorch(self): # create random PyTorch tensors image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=False, torchify=True) for image in image_inputs: self.assertIsInstance(image, torch.Tensor) # Test not batched input (image processor does not support batched inputs) image = image_inputs[0] trimap = np.random.randint(0, 3, size=image.shape[1:]) for image_processing_class in self.image_processor_list: image_processing = image_processing_class(**self.image_processor_dict) encoded_images = image_processing(images=image, trimaps=trimap, return_tensors="pt").pixel_values # Verify that width and height can be divided by size_divisibility and that correct dimensions got merged self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisibility == 0) self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisibility == 0) self.assertTrue(encoded_images.shape[-3] == 4) # create batched tensors image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=True, torchify=True) image_input = torch.stack(image_inputs, dim=0) self.assertIsInstance(image_input, torch.Tensor) self.assertTrue(image_input.shape[1] == 3) trimap_shape = [image_input.shape[0]] + [1] + list(image_input.shape)[2:] trimap_input = torch.randint(0, 3, trimap_shape, dtype=torch.uint8) self.assertIsInstance(trimap_input, torch.Tensor) self.assertTrue(trimap_input.shape[1] == 1) for image_processing_class in self.image_processor_list: image_processing = image_processing_class(**self.image_processor_dict) encoded_images = image_processing(images=image, trimaps=trimap, return_tensors="pt").pixel_values # Verify that width and height can be divided by size_divisibility and that correct dimensions got merged self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisibility == 0) self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisibility == 0) self.assertTrue(encoded_images.shape[-3] == 4) def test_call_pil(self): # create random PIL images image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=False) for image in image_inputs: self.assertIsInstance(image, Image.Image) # Test not batched input (image processor does not support batched inputs) image = image_inputs[0] trimap = np.random.randint(0, 3, size=image.size[::-1]) for image_processing_class in self.image_processor_list: image_processing = image_processing_class(**self.image_processor_dict) encoded_images = image_processing(images=image, trimaps=trimap, return_tensors="pt").pixel_values # Verify that width and height can be divided by size_divisibility and that correct dimensions got merged self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisibility == 0) self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisibility == 0) self.assertTrue(encoded_images.shape[-3] == 4) def test_call_numpy_4_channels(self): # Test that can process images which have an arbitrary number of channels # 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 (image processor does not support batched inputs) image = image_inputs[0] trimap = np.random.randint(0, 3, size=image.shape[:2]) for image_processing_class in self.image_processor_list: image_processor = image_processing_class(**self.image_processor_dict) encoded_images = image_processor( images=image, trimaps=trimap, input_data_format="channels_last", image_mean=0, image_std=1, return_tensors="pt", ).pixel_values # Verify that width and height can be divided by size_divisibility and that correct dimensions got merged self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisibility == 0) self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisibility == 0) self.assertTrue(encoded_images.shape[-3] == 5) def test_padding_slow(self): image_processing = self.image_processing_class(**self.image_processor_dict) image = np.random.randn(3, 249, 491) images = image_processing.pad_image(image) assert images.shape == (3, 256, 512) image = np.random.randn(3, 249, 512) images = image_processing.pad_image(image) assert images.shape == (3, 256, 512) def test_padding_fast(self): # extra test because name is different for fast image processor image_processing = self.fast_image_processing_class(**self.image_processor_dict) image = torch.rand(3, 249, 491) images = image_processing._pad_image(image) assert images.shape == (3, 256, 512) image = torch.rand(3, 249, 512) images = image_processing._pad_image(image) assert images.shape == (3, 256, 512) def test_image_processor_preprocess_arguments(self): # vitmatte require additional trimap input for image_processor # that is why we override original common test for image_processing_class in self.image_processor_list: image_processor = image_processing_class(**self.image_processor_dict) image = self.image_processor_tester.prepare_image_inputs()[0] trimap = np.random.randint(0, 3, size=image.size[::-1]) with warnings.catch_warnings(record=True) as raised_warnings: warnings.simplefilter("always") image_processor(image, trimaps=trimap, extra_argument=True) messages = " ".join([str(w.message) for w in raised_warnings]) self.assertGreaterEqual(len(raised_warnings), 1) self.assertIn("extra_argument", messages) @is_flaky() def test_fast_is_faster_than_slow(self): if not self.test_slow_image_processor or not self.test_fast_image_processor: self.skipTest(reason="Skipping speed test") if self.image_processing_class is None or self.fast_image_processing_class is None: self.skipTest(reason="Skipping speed test as one of the image processors is not defined") def measure_time(image_processor, images, trimaps): # Warmup for _ in range(5): _ = image_processor(images, trimaps=trimaps, return_tensors="pt") all_times = [] for _ in range(10): start = time.time() _ = image_processor(images, trimaps=trimaps, return_tensors="pt") all_times.append(time.time() - start) # Take the average of the fastest 3 runs avg_time = sum(sorted(all_times[:3])) / 3.0 return avg_time dummy_images = torch.randint(0, 255, (4, 3, 400, 800), dtype=torch.uint8) dummy_trimaps = torch.randint(0, 3, (4, 400, 800), dtype=torch.uint8) image_processor_slow = self.image_processing_class(**self.image_processor_dict) image_processor_fast = self.fast_image_processing_class(**self.image_processor_dict) fast_time = measure_time(image_processor_fast, dummy_images, dummy_trimaps) slow_time = measure_time(image_processor_slow, dummy_images, dummy_trimaps) self.assertLessEqual(fast_time, slow_time) 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 = Image.open( requests.get("http://images.cocodataset.org/val2017/000000039769.jpg", stream=True).raw ) dummy_trimap = np.random.randint(0, 3, size=dummy_image.size[::-1]) 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, trimaps=dummy_trimap, return_tensors="pt") encoding_fast = image_processor_fast(dummy_image, trimaps=dummy_trimap, return_tensors="pt") self.assertTrue(torch.allclose(encoding_slow.pixel_values, encoding_fast.pixel_values, atol=1e-1)) self.assertLessEqual( torch.mean(torch.abs(encoding_slow.pixel_values - encoding_fast.pixel_values)).item(), 1e-3 ) def test_slow_fast_equivalence_batched(self): # this only checks on equal resolution, since the slow processor doesn't work otherwise 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") if hasattr(self.image_processor_tester, "do_center_crop") and self.image_processor_tester.do_center_crop: self.skipTest( reason="Skipping as do_center_crop is True and center_crop functions are not equivalent for fast and slow processors" ) dummy_images = self.image_processor_tester.prepare_image_inputs(equal_resolution=True, torchify=True) dummy_trimaps = [np.random.randint(0, 3, size=image.shape[1:]) for image in dummy_images] 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_images, trimaps=dummy_trimaps, return_tensors="pt") encoding_fast = image_processor_fast(dummy_images, trimaps=dummy_trimaps, return_tensors="pt") self.assertTrue(torch.allclose(encoding_slow.pixel_values, encoding_fast.pixel_values, atol=1e-1)) self.assertLessEqual( torch.mean(torch.abs(encoding_slow.pixel_values - encoding_fast.pixel_values)).item(), 1e-3 ) @slow @require_torch_accelerator @require_vision def test_can_compile_fast_image_processor(self): # override as trimaps are needed for the image processor if self.fast_image_processing_class is None: self.skipTest("Skipping compilation test as fast image processor is not defined") if version.parse(torch.__version__) < version.parse("2.3"): self.skipTest(reason="This test requires torch >= 2.3 to run.") torch.compiler.reset() input_image = torch.randint(0, 255, (3, 224, 224), dtype=torch.uint8) dummy_trimap = np.random.randint(0, 3, size=input_image.shape[1:]) image_processor = self.fast_image_processing_class(**self.image_processor_dict) output_eager = image_processor(input_image, dummy_trimap, device=torch_device, return_tensors="pt") image_processor = torch.compile(image_processor, mode="reduce-overhead") output_compiled = image_processor(input_image, dummy_trimap, device=torch_device, return_tensors="pt") torch.testing.assert_close(output_eager.pixel_values, output_compiled.pixel_values, rtol=1e-4, atol=1e-4)