# Copyright 2025 The HuggingFace Team. All rights reserved. # # 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 numpy as np import pytest from packaging import version from parameterized import parameterized from transformers.testing_utils import ( 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 from transformers.models.efficientloftr.modeling_efficientloftr import EfficientLoFTRKeypointMatchingOutput if is_vision_available(): from transformers import EfficientLoFTRImageProcessor if is_torchvision_available(): from transformers import EfficientLoFTRImageProcessorFast def random_array(size): return np.random.randint(255, size=size) def random_tensor(size): return torch.rand(size) class EfficientLoFTRImageProcessingTester: """Tester for EfficientLoFTRImageProcessor""" def __init__( self, parent, batch_size=6, num_channels=3, image_size=18, min_resolution=30, max_resolution=400, do_resize=True, size=None, do_grayscale=True, ): size = size if size is not None else {"height": 480, "width": 640} 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_resize = do_resize self.size = size self.do_grayscale = do_grayscale def prepare_image_processor_dict(self): return { "do_resize": self.do_resize, "size": self.size, "do_grayscale": self.do_grayscale, } def expected_output_image_shape(self, images): return 2, self.num_channels, self.size["height"], self.size["width"] def prepare_image_inputs(self, equal_resolution=False, numpify=False, torchify=False, pairs=True, batch_size=None): batch_size = batch_size if batch_size is not None else self.batch_size image_inputs = prepare_image_inputs( batch_size=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, ) if pairs: image_inputs = [image_inputs[i : i + 2] for i in range(0, len(image_inputs), 2)] return image_inputs def prepare_keypoint_matching_output(self, pixel_values): """Prepare a fake output for the keypoint matching model with random matches between 50 keypoints per image.""" max_number_keypoints = 50 batch_size = len(pixel_values) keypoints = torch.zeros((batch_size, 2, max_number_keypoints, 2)) matches = torch.full((batch_size, 2, max_number_keypoints), -1, dtype=torch.int) scores = torch.zeros((batch_size, 2, max_number_keypoints)) for i in range(batch_size): random_number_keypoints0 = np.random.randint(10, max_number_keypoints) random_number_keypoints1 = np.random.randint(10, max_number_keypoints) random_number_matches = np.random.randint(5, min(random_number_keypoints0, random_number_keypoints1)) keypoints[i, 0, :random_number_keypoints0] = torch.rand((random_number_keypoints0, 2)) keypoints[i, 1, :random_number_keypoints1] = torch.rand((random_number_keypoints1, 2)) random_matches_indices0 = torch.randperm(random_number_keypoints1, dtype=torch.int)[:random_number_matches] random_matches_indices1 = torch.randperm(random_number_keypoints0, dtype=torch.int)[:random_number_matches] matches[i, 0, random_matches_indices1] = random_matches_indices0 matches[i, 1, random_matches_indices0] = random_matches_indices1 scores[i, 0, random_matches_indices1] = torch.rand((random_number_matches,)) scores[i, 1, random_matches_indices0] = torch.rand((random_number_matches,)) return EfficientLoFTRKeypointMatchingOutput(keypoints=keypoints, matches=matches, matching_scores=scores) @require_torch @require_vision class EfficientLoFTRImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase): image_processing_class = EfficientLoFTRImageProcessor if is_vision_available() else None fast_image_processing_class = EfficientLoFTRImageProcessorFast if is_torchvision_available() else None def setUp(self) -> None: super().setUp() self.image_processor_tester = EfficientLoFTRImageProcessingTester(self) @property def image_processor_dict(self): return self.image_processor_tester.prepare_image_processor_dict() def test_image_processing(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_rescale")) self.assertTrue(hasattr(image_processing, "rescale_factor")) self.assertTrue(hasattr(image_processing, "do_grayscale")) def test_image_processor_from_dict_with_kwargs(self): for image_processing_class in self.image_processor_list: image_processor = image_processing_class.from_dict(self.image_processor_dict) self.assertEqual(image_processor.size, {"height": 480, "width": 640}) image_processor = image_processing_class.from_dict( self.image_processor_dict, size={"height": 42, "width": 42} ) self.assertEqual(image_processor.size, {"height": 42, "width": 42}) @unittest.skip(reason="SuperPointImageProcessor is always supposed to return a grayscaled image") def test_call_numpy_4_channels(self): pass def test_number_and_format_of_images_in_input(self): for image_processing_class in self.image_processor_list: image_processor = image_processing_class.from_dict(self.image_processor_dict) # Cases where the number of images and the format of lists in the input is correct image_input = self.image_processor_tester.prepare_image_inputs(pairs=False, batch_size=2) image_processed = image_processor.preprocess(image_input, return_tensors="pt") self.assertEqual((1, 2, 3, 480, 640), tuple(image_processed["pixel_values"].shape)) image_input = self.image_processor_tester.prepare_image_inputs(pairs=True, batch_size=2) image_processed = image_processor.preprocess(image_input, return_tensors="pt") self.assertEqual((1, 2, 3, 480, 640), tuple(image_processed["pixel_values"].shape)) image_input = self.image_processor_tester.prepare_image_inputs(pairs=True, batch_size=4) image_processed = image_processor.preprocess(image_input, return_tensors="pt") self.assertEqual((2, 2, 3, 480, 640), tuple(image_processed["pixel_values"].shape)) image_input = self.image_processor_tester.prepare_image_inputs(pairs=True, batch_size=6) image_processed = image_processor.preprocess(image_input, return_tensors="pt") self.assertEqual((3, 2, 3, 480, 640), tuple(image_processed["pixel_values"].shape)) # Cases where the number of images or the format of lists in the input is incorrect ## List of 4 images image_input = self.image_processor_tester.prepare_image_inputs(pairs=False, batch_size=4) with self.assertRaises(ValueError) as cm: image_processor.preprocess(image_input, return_tensors="pt") self.assertEqual(ValueError, cm.exception.__class__) ## List of 3 images image_input = self.image_processor_tester.prepare_image_inputs(pairs=False, batch_size=3) with self.assertRaises(ValueError) as cm: image_processor.preprocess(image_input, return_tensors="pt") self.assertEqual(ValueError, cm.exception.__class__) ## List of 2 pairs and 1 image image_input = self.image_processor_tester.prepare_image_inputs(pairs=True, batch_size=3) with self.assertRaises(ValueError) as cm: image_processor.preprocess(image_input, return_tensors="pt") self.assertEqual(ValueError, cm.exception.__class__) @parameterized.expand( [ ([random_array((3, 100, 200)), random_array((3, 100, 200))], (1, 2, 3, 480, 640)), ([[random_array((3, 100, 200)), random_array((3, 100, 200))]], (1, 2, 3, 480, 640)), ([random_tensor((3, 100, 200)), random_tensor((3, 100, 200))], (1, 2, 3, 480, 640)), ([random_tensor((3, 100, 200)), random_tensor((3, 100, 200))], (1, 2, 3, 480, 640)), ], ) def test_valid_image_shape_in_input(self, image_input, output): for image_processing_class in self.image_processor_list: image_processor = image_processing_class.from_dict(self.image_processor_dict) image_processed = image_processor.preprocess(image_input, return_tensors="pt") self.assertEqual(output, tuple(image_processed["pixel_values"].shape)) @parameterized.expand( [ (random_array((3, 100, 200)),), ([random_array((3, 100, 200))],), (random_array((1, 3, 100, 200)),), ([[random_array((3, 100, 200))]],), ([[random_array((3, 100, 200))], [random_array((3, 100, 200))]],), ([random_array((1, 3, 100, 200)), random_array((1, 3, 100, 200))],), (random_array((1, 1, 3, 100, 200)),), ], ) def test_invalid_image_shape_in_input(self, image_input): for image_processing_class in self.image_processor_list: image_processor = image_processing_class.from_dict(self.image_processor_dict) with self.assertRaises(ValueError) as cm: image_processor(image_input, return_tensors="pt") self.assertEqual(ValueError, cm.exception.__class__) def test_input_images_properly_paired(self): for image_processing_class in self.image_processor_list: image_processor = image_processing_class.from_dict(self.image_processor_dict) image_inputs = self.image_processor_tester.prepare_image_inputs() pre_processed_images = image_processor(image_inputs, return_tensors="pt") self.assertEqual(len(pre_processed_images["pixel_values"].shape), 5) self.assertEqual(pre_processed_images["pixel_values"].shape[1], 2) def test_input_not_paired_images_raises_error(self): for image_processing_class in self.image_processor_list: image_processor = image_processing_class.from_dict(self.image_processor_dict) image_inputs = self.image_processor_tester.prepare_image_inputs(pairs=False) with self.assertRaises(ValueError): image_processor(image_inputs[0]) def test_input_image_properly_converted_to_grayscale(self): for image_processing_class in self.image_processor_list: image_processor = image_processing_class.from_dict(self.image_processor_dict) image_inputs = self.image_processor_tester.prepare_image_inputs() pre_processed_images = image_processor(image_inputs, return_tensors="pt") for image_pair in pre_processed_images["pixel_values"]: for image in image_pair: self.assertTrue( torch.all(image[0, ...] == image[1, ...]) and torch.all(image[1, ...] == image[2, ...]) ) def test_call_numpy(self): # Test overwritten because SuperGlueImageProcessor combines images by pair to feed it into SuperGlue # Initialize image_processing for image_processing_class in self.image_processor_list: image_processing = image_processing_class(**self.image_processor_dict) # create random numpy tensors image_pairs = self.image_processor_tester.prepare_image_inputs(equal_resolution=False, numpify=True) for image_pair in image_pairs: self.assertEqual(len(image_pair), 2) expected_batch_size = int(self.image_processor_tester.batch_size / 2) # Test with 2 images encoded_images = image_processing(image_pairs[0], return_tensors="pt").pixel_values expected_output_image_shape = self.image_processor_tester.expected_output_image_shape(image_pairs[0]) self.assertEqual(tuple(encoded_images.shape), (1, *expected_output_image_shape)) # Test with list of pairs encoded_images = image_processing(image_pairs, return_tensors="pt").pixel_values expected_output_image_shape = self.image_processor_tester.expected_output_image_shape(image_pairs) self.assertEqual(tuple(encoded_images.shape), (expected_batch_size, *expected_output_image_shape)) # Test without paired images image_pairs = self.image_processor_tester.prepare_image_inputs( equal_resolution=False, numpify=True, pairs=False ) with self.assertRaises(ValueError): image_processing(image_pairs, return_tensors="pt").pixel_values def test_call_pil(self): # Test overwritten because SuperGlueImageProcessor combines images by pair to feed it into SuperGlue # Initialize image_processing for image_processing_class in self.image_processor_list: image_processing = image_processing_class(**self.image_processor_dict) # create random PIL images image_pairs = self.image_processor_tester.prepare_image_inputs(equal_resolution=False) for image_pair in image_pairs: self.assertEqual(len(image_pair), 2) expected_batch_size = int(self.image_processor_tester.batch_size / 2) # Test with 2 images encoded_images = image_processing(image_pairs[0], return_tensors="pt").pixel_values expected_output_image_shape = self.image_processor_tester.expected_output_image_shape(image_pairs[0]) self.assertEqual(tuple(encoded_images.shape), (1, *expected_output_image_shape)) # Test with list of pairs encoded_images = image_processing(image_pairs, return_tensors="pt").pixel_values expected_output_image_shape = self.image_processor_tester.expected_output_image_shape(image_pairs) self.assertEqual(tuple(encoded_images.shape), (expected_batch_size, *expected_output_image_shape)) # Test without paired images image_pairs = self.image_processor_tester.prepare_image_inputs(equal_resolution=False, pairs=False) with self.assertRaises(ValueError): image_processing(image_pairs, return_tensors="pt").pixel_values def test_call_pytorch(self): # Test overwritten because SuperGlueImageProcessor combines images by pair to feed it into SuperGlue # Initialize image_processing for image_processing_class in self.image_processor_list: image_processing = image_processing_class(**self.image_processor_dict) # create random PyTorch tensors image_pairs = self.image_processor_tester.prepare_image_inputs(equal_resolution=False, torchify=True) for image_pair in image_pairs: self.assertEqual(len(image_pair), 2) expected_batch_size = int(self.image_processor_tester.batch_size / 2) # Test with 2 images encoded_images = image_processing(image_pairs[0], return_tensors="pt").pixel_values expected_output_image_shape = self.image_processor_tester.expected_output_image_shape(image_pairs[0]) self.assertEqual(tuple(encoded_images.shape), (1, *expected_output_image_shape)) # Test with list of pairs encoded_images = image_processing(image_pairs, return_tensors="pt").pixel_values expected_output_image_shape = self.image_processor_tester.expected_output_image_shape(image_pairs) self.assertEqual(tuple(encoded_images.shape), (expected_batch_size, *expected_output_image_shape)) # Test without paired images image_pairs = self.image_processor_tester.prepare_image_inputs( equal_resolution=False, torchify=True, pairs=False ) with self.assertRaises(ValueError): image_processing(image_pairs, return_tensors="pt").pixel_values def test_image_processor_with_list_of_two_images(self): for image_processing_class in self.image_processor_list: image_processing = image_processing_class(**self.image_processor_dict) image_pairs = self.image_processor_tester.prepare_image_inputs( equal_resolution=False, numpify=True, batch_size=2, pairs=False ) self.assertEqual(len(image_pairs), 2) self.assertTrue(isinstance(image_pairs[0], np.ndarray)) self.assertTrue(isinstance(image_pairs[1], np.ndarray)) expected_batch_size = 1 encoded_images = image_processing(image_pairs, return_tensors="pt").pixel_values expected_output_image_shape = self.image_processor_tester.expected_output_image_shape(image_pairs[0]) self.assertEqual(tuple(encoded_images.shape), (expected_batch_size, *expected_output_image_shape)) @require_torch def test_post_processing_keypoint_matching(self): def check_post_processed_output(post_processed_output, image_pair_size): for post_processed_output, (image_size0, image_size1) in zip(post_processed_output, image_pair_size): self.assertTrue("keypoints0" in post_processed_output) self.assertTrue("keypoints1" in post_processed_output) self.assertTrue("matching_scores" in post_processed_output) keypoints0 = post_processed_output["keypoints0"] keypoints1 = post_processed_output["keypoints1"] all_below_image_size0 = torch.all(keypoints0[:, 0] <= image_size0[1]) and torch.all( keypoints0[:, 1] <= image_size0[0] ) all_below_image_size1 = torch.all(keypoints1[:, 0] <= image_size1[1]) and torch.all( keypoints1[:, 1] <= image_size1[0] ) all_above_zero0 = torch.all(keypoints0[:, 0] >= 0) and torch.all(keypoints0[:, 1] >= 0) all_above_zero1 = torch.all(keypoints0[:, 0] >= 0) and torch.all(keypoints0[:, 1] >= 0) self.assertTrue(all_below_image_size0) self.assertTrue(all_below_image_size1) self.assertTrue(all_above_zero0) self.assertTrue(all_above_zero1) all_scores_different_from_minus_one = torch.all(post_processed_output["matching_scores"] != -1) self.assertTrue(all_scores_different_from_minus_one) for image_processing_class in self.image_processor_list: image_processor = image_processing_class.from_dict(self.image_processor_dict) image_inputs = self.image_processor_tester.prepare_image_inputs() pre_processed_images = image_processor.preprocess(image_inputs, return_tensors="pt") outputs = self.image_processor_tester.prepare_keypoint_matching_output(**pre_processed_images) tuple_image_sizes = [ ((image_pair[0].size[0], image_pair[0].size[1]), (image_pair[1].size[0], image_pair[1].size[1])) for image_pair in image_inputs ] tuple_post_processed_outputs = image_processor.post_process_keypoint_matching(outputs, tuple_image_sizes) check_post_processed_output(tuple_post_processed_outputs, tuple_image_sizes) tensor_image_sizes = torch.tensor( [(image_pair[0].size, image_pair[1].size) for image_pair in image_inputs] ).flip(2) tensor_post_processed_outputs = image_processor.post_process_keypoint_matching(outputs, tensor_image_sizes) check_post_processed_output(tensor_post_processed_outputs, tensor_image_sizes) @unittest.skip(reason="Many failing cases. This test needs a more deep investigation.") def test_fast_is_faster_than_slow(self): """Override the generic test since EfficientLoFTR requires image pairs.""" if not self.test_slow_image_processor or not self.test_fast_image_processor: self.skipTest(reason="Skipping slow/fast speed test") if self.image_processing_class is None or self.fast_image_processing_class is None: self.skipTest(reason="Skipping slow/fast speed test as one of the image processors is not defined") # Create image pairs for speed test dummy_images = self.image_processor_tester.prepare_image_inputs(equal_resolution=False, torchify=False) image_processor_slow = self.image_processing_class(**self.image_processor_dict) image_processor_fast = self.fast_image_processing_class(**self.image_processor_dict) # Time slow processor start_time = time.time() for _ in range(10): _ = image_processor_slow(dummy_images, return_tensors="pt") slow_time = time.time() - start_time # Time fast processor start_time = time.time() for _ in range(10): _ = image_processor_fast(dummy_images, return_tensors="pt") fast_time = time.time() - start_time # Fast should be faster (or at least not significantly slower) self.assertLessEqual( fast_time, slow_time * 1.2, "Fast processor should not be significantly slower than slow processor" ) @require_vision @require_torch 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 = self.image_processor_tester.prepare_image_inputs( equal_resolution=False, numpify=True, batch_size=2, pairs=False ) 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") self._assert_slow_fast_tensors_equivalence(encoding_slow.pixel_values, encoding_fast.pixel_values) @slow @require_torch_accelerator @require_vision @pytest.mark.torch_compile_test def test_can_compile_fast_image_processor(self): """Override the generic test since EfficientLoFTR requires image pairs.""" 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 = self.image_processor_tester.prepare_image_inputs(equal_resolution=True, torchify=False) image_processor = self.fast_image_processing_class(**self.image_processor_dict) output_eager = image_processor(input_image, device=torch_device, return_tensors="pt") image_processor = torch.compile(image_processor, mode="reduce-overhead") output_compiled = image_processor(input_image, device=torch_device, return_tensors="pt") self._assert_slow_fast_tensors_equivalence( output_eager.pixel_values, output_compiled.pixel_values, atol=1e-4, rtol=1e-4, mean_atol=1e-5 )