# 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 inspect import unittest import warnings import numpy as np import pytest from transformers.image_utils import load_image from transformers.testing_utils import ( require_torch, require_torch_accelerator, require_vision, slow, torch_device, ) from transformers.utils import is_torch_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 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_divisor=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_divisor = size_divisor 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_divisor": self.size_divisor, } 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): 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_processing_classes.values(): 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_divisor")) 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_processing_classes.values(): 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_divisor == 0) self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 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_processing_classes.values(): 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_divisor == 0) self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 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_processing_classes.values(): 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_divisor == 0) self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 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_processing_classes.values(): 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_divisor == 0) self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 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_processing_classes.values(): image_processor = image_processing_class(**self.image_processor_dict) encoded_images = image_processor( images=image, trimaps=trimap, input_data_format="channels_last", image_mean=(0.0, 0.0, 0.0, 0.0), image_std=(1.0, 1.0, 1.0, 1.0), 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_divisor == 0) self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0) self.assertTrue(encoded_images.shape[-3] == 5) def test_padding(self): for backend_name, image_processing_class in self.image_processing_classes.items(): image_processing = image_processing_class(**self.image_processor_dict) if backend_name == "pil": 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) else: # torchvision 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): is_tested = False for image_processing_class in self.image_processing_classes.values(): image_processor = image_processing_class(**self.image_processor_dict) # validation done by _valid_processor_keys attribute if hasattr(image_processor, "_valid_processor_keys") and hasattr(image_processor, "preprocess"): preprocess_parameter_names = inspect.getfullargspec(image_processor.preprocess).args preprocess_parameter_names.remove("self") preprocess_parameter_names.sort() valid_processor_keys = image_processor._valid_processor_keys valid_processor_keys.sort() self.assertEqual(preprocess_parameter_names, valid_processor_keys) is_tested = True # validation done by @filter_out_non_signature_kwargs decorator if hasattr(image_processor.preprocess, "_filter_out_non_signature_kwargs"): inputs = self.image_processor_tester.prepare_image_inputs() 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_tested = True # ViTMatte-specific: validation for processors requiring trimaps (no _filter_out_non_signature_kwargs) if "trimaps" in inspect.signature(image_processor.preprocess).parameters: inputs = self.image_processor_tester.prepare_image_inputs() image = inputs[0] trimap = np.random.randint(0, 3, size=image.size[::-1]) # Extra kwargs are rejected (TypeError for strict validation, or warning) with self.assertRaises(TypeError): image_processor(image, trimaps=trimap, extra_argument=True) is_tested = True if not is_tested: self.skipTest(reason="No validation found for `preprocess` method") def test_backends_equivalence(self): 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("http://images.cocodataset.org/val2017/000000039769.jpg")) dummy_trimap = np.random.randint(0, 3, size=dummy_image.size[::-1]) # Create processors for each backend 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, trimaps=dummy_trimap, return_tensors="pt") # Compare all backends to the first one (reference backend) backend_names = list(encodings.keys()) reference_backend = backend_names[0] reference_encoding = encodings[reference_backend].pixel_values for backend_name in backend_names[1:]: self._assert_tensors_equivalence(reference_encoding, encodings[backend_name].pixel_values) def test_backends_equivalence_batched(self): # this only checks on equal resolution, since the slow processor doesn't work otherwise if len(self.image_processing_classes) < 2: self.skipTest(reason="Skipping backends equivalence test as there are less than 2 backends") 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] # Create processors for each backend 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_images, trimaps=dummy_trimaps, return_tensors="pt") # Compare all backends to the first one (reference backend) backend_names = list(encodings.keys()) reference_backend = backend_names[0] reference_encoding = encodings[reference_backend].pixel_values for backend_name in backend_names[1:]: self._assert_tensors_equivalence(reference_encoding, encodings[backend_name].pixel_values) @slow @require_torch_accelerator @require_vision @pytest.mark.torch_compile_test def test_can_compile_torchvision_backend(self): # override as trimaps are needed for the image processor if "torchvision" not in self.image_processing_classes: self.skipTest("Skipping compilation test as torchvision image processor is not defined") 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.image_processing_classes["torchvision"](**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)