first commit

tests/models/efficientloftr/test_image_processing_efficientloftr.py

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+# 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 unittest
+
+import numpy as np
+import pytest
+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
+
+from ...test_image_processing_common import ImageProcessingTestMixin, prepare_image_inputs
+
+
+if is_torch_available():
+    import torch
+
+    from transformers.models.efficientloftr.modeling_efficientloftr import EfficientLoFTRKeypointMatchingOutput
+
+
+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):
+    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_processing_classes.values():
+            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_processing_classes.values():
+            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_processing_classes.values():
+            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_processing_classes.values():
+            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_processing_classes.values():
+            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_processing_classes.values():
+            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_processing_classes.values():
+            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_processing_classes.values():
+            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_processing_classes.values():
+            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_processing_classes.values():
+            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_processing_classes.values():
+            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_processing_classes.values():
+            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(keypoints1[:, 0] >= 0) and torch.all(keypoints1[:, 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_processing_classes.values():
+            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)
+
+    @require_vision
+    @require_torch
+    def test_backends_equivalence(self):
+        """Override base test since EfficientLoFTR requires image pairs."""
+        if len(self.image_processing_classes) < 2:
+            self.skipTest(reason="Skipping backends equivalence test as there are less than 2 backends")
+
+        dummy_image = self.image_processor_tester.prepare_image_inputs(
+            equal_resolution=False, numpify=True, batch_size=2, pairs=False
+        )
+        image_processor_pil = self.image_processing_classes["pil"](**self.image_processor_dict)
+        image_processor_torchvision = self.image_processing_classes["torchvision"](**self.image_processor_dict)
+
+        encoding_pil = image_processor_pil(dummy_image, return_tensors="pt")
+        encoding_torchvision = image_processor_torchvision(dummy_image, return_tensors="pt")
+
+        self._assert_tensors_equivalence(encoding_pil.pixel_values, encoding_torchvision.pixel_values)
+
+    @slow
+    @require_torch_accelerator
+    @require_vision
+    @pytest.mark.torch_compile_test
+    def test_can_compile_torchvision_backend(self):
+        """Override the generic test since EfficientLoFTR requires image pairs."""
+        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 = self.image_processor_tester.prepare_image_inputs(equal_resolution=True, torchify=False)
+        image_processor = self.image_processing_classes["torchvision"](**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_tensors_equivalence(
+            output_eager.pixel_values, output_compiled.pixel_values, atol=1e-4, rtol=1e-4, mean_atol=1e-5
+        )

tests/models/efficientloftr/test_modeling_efficientloftr.py

@@ -0,0 +1,448 @@
+# 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 inspect
+import unittest
+from functools import cached_property, reduce
+
+from datasets import load_dataset
+
+from transformers.models.efficientloftr import EfficientLoFTRConfig, EfficientLoFTRModel
+from transformers.testing_utils import (
+    require_torch,
+    require_vision,
+    set_config_for_less_flaky_test,
+    set_model_for_less_flaky_test,
+    slow,
+    torch_device,
+)
+from transformers.utils import is_torch_available, is_vision_available
+
+from ...test_configuration_common import ConfigTester
+from ...test_modeling_common import ModelTesterMixin, floats_tensor
+
+
+if is_torch_available():
+    import torch
+
+    from transformers import EfficientLoFTRForKeypointMatching
+
+if is_vision_available():
+    from transformers import AutoImageProcessor
+
+
+class EfficientLoFTRModelTester:
+    def __init__(
+        self,
+        parent,
+        batch_size=2,
+        image_width=6,  # need to be a multiple of `stage_stride[0] * stage_stride[1]`
+        image_height=4,  # need to be a multiple of `stage_stride[0] * stage_stride[1]`
+        stage_num_blocks: list[int] = [1, 1],
+        out_features: list[int] = [16, 16],  # need to be >= 2 to make `config.fine_fusion_dims > 0`
+        stage_stride: list[int] = [2, 1],
+        q_aggregation_kernel_size: int = 1,
+        kv_aggregation_kernel_size: int = 1,
+        q_aggregation_stride: int = 1,
+        kv_aggregation_stride: int = 1,
+        num_attention_layers: int = 2,
+        num_attention_heads: int = 8,
+        hidden_size: int = 16,
+        coarse_matching_threshold: float = 0.0,
+        fine_kernel_size: int = 2,
+        coarse_matching_border_removal: int = 0,
+    ):
+        self.parent = parent
+        self.batch_size = batch_size
+        self.image_width = image_width
+        self.image_height = image_height
+
+        self.stage_num_blocks = stage_num_blocks
+        self.out_features = out_features
+        self.stage_stride = stage_stride
+        self.q_aggregation_kernel_size = q_aggregation_kernel_size
+        self.kv_aggregation_kernel_size = kv_aggregation_kernel_size
+        self.q_aggregation_stride = q_aggregation_stride
+        self.kv_aggregation_stride = kv_aggregation_stride
+        self.num_attention_layers = num_attention_layers
+        self.num_attention_heads = num_attention_heads
+        self.hidden_size = hidden_size
+        self.coarse_matching_threshold = coarse_matching_threshold
+        self.coarse_matching_border_removal = coarse_matching_border_removal
+        self.fine_kernel_size = fine_kernel_size
+
+    def prepare_config_and_inputs(self):
+        # EfficientLoFTR expects a grayscale image as input
+        pixel_values = floats_tensor([self.batch_size, 2, 3, self.image_height, self.image_width])
+        config = self.get_config()
+        return config, pixel_values
+
+    def get_config(self):
+        return EfficientLoFTRConfig(
+            stage_num_blocks=self.stage_num_blocks,
+            out_features=self.out_features,
+            stage_stride=self.stage_stride,
+            q_aggregation_kernel_size=self.q_aggregation_kernel_size,
+            kv_aggregation_kernel_size=self.kv_aggregation_kernel_size,
+            q_aggregation_stride=self.q_aggregation_stride,
+            kv_aggregation_stride=self.kv_aggregation_stride,
+            num_attention_layers=self.num_attention_layers,
+            num_attention_heads=self.num_attention_heads,
+            hidden_size=self.hidden_size,
+            coarse_matching_threshold=self.coarse_matching_threshold,
+            coarse_matching_border_removal=self.coarse_matching_border_removal,
+            fine_kernel_size=self.fine_kernel_size,
+        )
+
+    def create_and_check_model(self, config, pixel_values):
+        model = EfficientLoFTRForKeypointMatching(config=config)
+        model.to(torch_device)
+        model.eval()
+        result = model(pixel_values)
+        maximum_num_matches = result.matches.shape[-1]
+        self.parent.assertEqual(
+            result.keypoints.shape,
+            (self.batch_size, 2, maximum_num_matches, 2),
+        )
+        self.parent.assertEqual(
+            result.matches.shape,
+            (self.batch_size, 2, maximum_num_matches),
+        )
+        self.parent.assertEqual(
+            result.matching_scores.shape,
+            (self.batch_size, 2, maximum_num_matches),
+        )
+
+    def prepare_config_and_inputs_for_common(self):
+        config_and_inputs = self.prepare_config_and_inputs()
+        config, pixel_values = config_and_inputs
+        inputs_dict = {"pixel_values": pixel_values}
+        return config, inputs_dict
+
+
+@require_torch
+class EfficientLoFTRModelTest(ModelTesterMixin, unittest.TestCase):
+    all_model_classes = (EfficientLoFTRForKeypointMatching, EfficientLoFTRModel) if is_torch_available() else ()
+
+    test_resize_embeddings = False
+    has_attentions = True
+
+    def setUp(self):
+        self.model_tester = EfficientLoFTRModelTester(self)
+        self.config_tester = ConfigTester(self, config_class=EfficientLoFTRConfig, has_text_modality=False)
+
+    def test_config(self):
+        self.config_tester.create_and_test_config_to_json_string()
+        self.config_tester.create_and_test_config_to_json_file()
+        self.config_tester.create_and_test_config_from_and_save_pretrained()
+        self.config_tester.create_and_test_config_with_num_labels()
+        self.config_tester.check_config_can_be_init_without_params()
+        self.config_tester.check_config_arguments_init()
+
+    @unittest.skip(reason="EfficientLoFTRForKeypointMatching does not use inputs_embeds")
+    def test_inputs_embeds(self):
+        pass
+
+    @unittest.skip(reason="EfficientLoFTRForKeypointMatching does not support input and output embeddings")
+    def test_model_get_set_embeddings(self):
+        pass
+
+    @unittest.skip(reason="EfficientLoFTRForKeypointMatching does not use feedforward chunking")
+    def test_feed_forward_chunking(self):
+        pass
+
+    @unittest.skip(reason="This module does not support standalone training")
+    def test_training(self):
+        pass
+
+    @unittest.skip(reason="This module does not support standalone training")
+    def test_training_gradient_checkpointing(self):
+        pass
+
+    @unittest.skip(reason="This module does not support standalone training")
+    def test_training_gradient_checkpointing_use_reentrant_false(self):
+        pass
+
+    @unittest.skip(reason="This module does not support standalone training")
+    def test_training_gradient_checkpointing_use_reentrant_true(self):
+        pass
+
+    @unittest.skip(reason="EfficientLoFTR does not output any loss term in the forward pass")
+    def test_retain_grad_hidden_states_attentions(self):
+        pass
+
+    def test_model(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs()
+        self.model_tester.create_and_check_model(*config_and_inputs)
+
+    def test_forward_signature(self):
+        config, _ = self.model_tester.prepare_config_and_inputs()
+
+        for model_class in self.all_model_classes:
+            model = model_class(config)
+            signature = inspect.signature(model.forward)
+            # signature.parameters is an OrderedDict => so arg_names order is deterministic
+            arg_names = [*signature.parameters.keys()]
+
+            expected_arg_names = ["pixel_values"]
+            self.assertListEqual(arg_names[:1], expected_arg_names)
+
+    def test_hidden_states_output(self):
+        def check_hidden_states_output(inputs_dict, config, model_class):
+            model = model_class(config)
+            model.to(torch_device)
+            model.eval()
+
+            with torch.no_grad():
+                outputs = model(**self._prepare_for_class(inputs_dict, model_class))
+
+            hidden_states = outputs.hidden_states
+
+            expected_num_hidden_states = len(self.model_tester.stage_num_blocks) + 1
+            self.assertEqual(len(hidden_states), expected_num_hidden_states)
+
+            self.assertListEqual(
+                list(hidden_states[0].shape[-2:]),
+                [self.model_tester.image_height, self.model_tester.image_width],
+            )
+
+        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
+
+        for model_class in self.all_model_classes:
+            inputs_dict["output_hidden_states"] = True
+            check_hidden_states_output(inputs_dict, config, model_class)
+
+            # check that output_hidden_states also work using config
+            del inputs_dict["output_hidden_states"]
+            config.output_hidden_states = True
+
+            check_hidden_states_output(inputs_dict, config, model_class)
+
+    def test_attention_outputs(self):
+        def check_attention_output(inputs_dict, config, model_class):
+            config._attn_implementation = "eager"
+            model = model_class(config)
+            model.to(torch_device)
+            model.eval()
+
+            with torch.no_grad():
+                outputs = model(**self._prepare_for_class(inputs_dict, model_class))
+
+            attentions = outputs.attentions
+            total_stride = reduce(lambda a, b: a * b, config.stage_stride)
+            hidden_size = (
+                self.model_tester.image_height // total_stride * self.model_tester.image_width // total_stride
+            )
+
+            expected_attention_shape = [
+                self.model_tester.num_attention_heads,
+                hidden_size,
+                hidden_size,
+            ]
+
+            for i, attention in enumerate(attentions):
+                self.assertListEqual(
+                    list(attention.shape[-3:]),
+                    expected_attention_shape,
+                )
+
+        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
+
+        for model_class in self.all_model_classes:
+            inputs_dict["output_attentions"] = True
+            check_attention_output(inputs_dict, config, model_class)
+
+            # check that output_hidden_states also work using config
+            del inputs_dict["output_attentions"]
+            config.output_attentions = True
+
+            check_attention_output(inputs_dict, config, model_class)
+
+    @slow
+    def test_model_from_pretrained(self):
+        from_pretrained_ids = ["zju-community/efficientloftr"]
+        for model_name in from_pretrained_ids:
+            model = EfficientLoFTRForKeypointMatching.from_pretrained(model_name)
+            self.assertIsNotNone(model)
+
+    def test_forward_labels_should_be_none(self):
+        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
+        for model_class in self.all_model_classes:
+            model = model_class(config)
+            model.to(torch_device)
+            model.eval()
+
+            with torch.no_grad():
+                model_inputs = self._prepare_for_class(inputs_dict, model_class)
+                # Provide an arbitrary sized Tensor as labels to model inputs
+                model_inputs["labels"] = torch.rand((128, 128))
+
+                with self.assertRaises(ValueError) as cm:
+                    model(**model_inputs)
+                self.assertEqual(ValueError, cm.exception.__class__)
+
+    def test_batching_equivalence(self, atol=1e-5, rtol=1e-5):
+        """
+        This test is overwritten because the model outputs do not contain only regressive values but also keypoint
+        locations.
+        Similarly to the problem discussed about SuperGlue implementation
+        [here](https://github.com/huggingface/transformers/pull/29886#issuecomment-2482752787), the consequence of
+        having different scores for matching, makes the maximum indices differ. These indices are being used to compute
+        the keypoint coordinates. The keypoint coordinates, in the model outputs, are floating point tensors, so the
+        original implementation of this test cover this case. But the resulting tensors may have differences exceeding
+        the relative and absolute tolerance.
+        Therefore, similarly to SuperGlue integration test, for the key "keypoints" in the model outputs, we check the
+        number of differences in keypoint coordinates being less than a TODO given number
+        """
+
+        def recursive_check(batched_object, single_row_object, model_name, key):
+            if isinstance(batched_object, (list, tuple)):
+                for batched_object_value, single_row_object_value in zip(batched_object, single_row_object):
+                    recursive_check(batched_object_value, single_row_object_value, model_name, key)
+            elif isinstance(batched_object, dict):
+                for batched_object_value, single_row_object_value in zip(
+                    batched_object.values(), single_row_object.values()
+                ):
+                    recursive_check(batched_object_value, single_row_object_value, model_name, key)
+            # do not compare returned loss (0-dim tensor) / codebook ids (int) / caching objects
+            elif batched_object is None or not isinstance(batched_object, torch.Tensor):
+                return
+            elif batched_object.dim() == 0:
+                return
+            # do not compare int or bool outputs as they are mostly computed with max/argmax/topk methods which are
+            # very sensitive to the inputs (e.g. tiny differences may give totally different results)
+            elif not torch.is_floating_point(batched_object):
+                return
+            else:
+                # indexing the first element does not always work
+                # e.g. models that output similarity scores of size (N, M) would need to index [0, 0]
+                slice_ids = tuple(slice(0, index) for index in single_row_object.shape)
+                batched_row = batched_object[slice_ids]
+                if key == "keypoints":
+                    batched_row = torch.sum(batched_row, dim=-1)
+                    single_row_object = torch.sum(single_row_object, dim=-1)
+                    tolerance = 0.02 * single_row_object.shape[-1]
+                    self.assertTrue(
+                        torch.sum(~torch.isclose(batched_row, single_row_object, rtol=rtol, atol=atol)) < tolerance
+                    )
+                else:
+                    self.assertFalse(
+                        torch.isnan(batched_row).any(), f"Batched output has `nan` in {model_name} for key={key}"
+                    )
+                    self.assertFalse(
+                        torch.isinf(batched_row).any(), f"Batched output has `inf` in {model_name} for key={key}"
+                    )
+                    self.assertFalse(
+                        torch.isnan(single_row_object).any(),
+                        f"Single row output has `nan` in {model_name} for key={key}",
+                    )
+                    self.assertFalse(
+                        torch.isinf(single_row_object).any(),
+                        f"Single row output has `inf` in {model_name} for key={key}",
+                    )
+                    try:
+                        torch.testing.assert_close(batched_row, single_row_object, atol=atol, rtol=rtol)
+                    except AssertionError as e:
+                        msg = f"Batched and Single row outputs are not equal in {model_name} for key={key}.\n\n"
+                        msg += str(e)
+                        raise AssertionError(msg)
+
+        config, batched_input = self.model_tester.prepare_config_and_inputs_for_common()
+        set_config_for_less_flaky_test(config)
+
+        for model_class in self.all_model_classes:
+            config.output_hidden_states = True
+
+            model_name = model_class.__name__
+            if hasattr(self.model_tester, "prepare_config_and_inputs_for_model_class"):
+                config, batched_input = self.model_tester.prepare_config_and_inputs_for_model_class(model_class)
+            batched_input_prepared = self._prepare_for_class(batched_input, model_class)
+            model = model_class(config).to(torch_device).eval()
+            set_model_for_less_flaky_test(model)
+
+            batch_size = self.model_tester.batch_size
+            single_row_input = {}
+            for key, value in batched_input_prepared.items():
+                if isinstance(value, torch.Tensor) and value.shape[0] % batch_size == 0:
+                    # e.g. musicgen has inputs of size (bs*codebooks). in most cases value.shape[0] == batch_size
+                    single_batch_shape = value.shape[0] // batch_size
+                    single_row_input[key] = value[:single_batch_shape]
+                else:
+                    single_row_input[key] = value
+
+            with torch.no_grad():
+                model_batched_output = model(**batched_input_prepared)
+                model_row_output = model(**single_row_input)
+
+            if isinstance(model_batched_output, torch.Tensor):
+                model_batched_output = {"model_output": model_batched_output}
+                model_row_output = {"model_output": model_row_output}
+
+            for key in model_batched_output:
+                # DETR starts from zero-init queries to decoder, leading to cos_similarity = `nan`
+                if hasattr(self, "zero_init_hidden_state") and "decoder_hidden_states" in key:
+                    model_batched_output[key] = model_batched_output[key][1:]
+                    model_row_output[key] = model_row_output[key][1:]
+                recursive_check(model_batched_output[key], model_row_output[key], model_name, key)
+
+
+def prepare_imgs():
+    dataset = load_dataset("hf-internal-testing/image-matching-test-dataset", split="train")
+    image1 = dataset[0]["image"]
+    image2 = dataset[1]["image"]
+    image3 = dataset[2]["image"]
+    return [[image1, image2], [image3, image2]]
+
+
+@require_torch
+@require_vision
+class EfficientLoFTRModelIntegrationTest(unittest.TestCase):
+    @cached_property
+    def default_image_processor(self):
+        return AutoImageProcessor.from_pretrained("zju-community/efficientloftr") if is_vision_available() else None
+
+    @slow
+    def test_inference(self):
+        model = EfficientLoFTRForKeypointMatching.from_pretrained(
+            "zju-community/efficientloftr", attn_implementation="eager"
+        ).to(torch_device)
+        preprocessor = self.default_image_processor
+        images = prepare_imgs()
+        inputs = preprocessor(images=images, return_tensors="pt").to(torch_device)
+        with torch.no_grad():
+            outputs = model(**inputs, output_hidden_states=True, output_attentions=True)
+
+        predicted_top10 = torch.topk(outputs.matching_scores[0, 0], k=10)
+        predicted_top10_matches_indices = predicted_top10.indices
+        predicted_top10_matching_scores = predicted_top10.values
+
+        expected_number_of_matches = 4800
+        expected_matches_shape = torch.Size((len(images), 2, expected_number_of_matches))
+        expected_matching_scores_shape = torch.Size((len(images), 2, expected_number_of_matches))
+
+        expected_top10_matches_indices = torch.tensor(
+            [3145, 3065, 3143, 3144, 1397, 1705, 3151, 2422, 3066, 2342], dtype=torch.int64, device=torch_device
+        )
+        expected_top10_matching_scores = torch.tensor(
+            [0.9998, 0.9997, 0.9997, 0.9996, 0.9996, 0.9996, 0.9996, 0.9995, 0.9995, 0.9995], device=torch_device
+        )
+
+        self.assertEqual(outputs.matches.shape, expected_matches_shape)
+        self.assertEqual(outputs.matching_scores.shape, expected_matching_scores_shape)
+
+        torch.testing.assert_close(
+            predicted_top10_matches_indices, expected_top10_matches_indices, rtol=5e-3, atol=5e-3
+        )
+        torch.testing.assert_close(
+            predicted_top10_matching_scores, expected_top10_matching_scores, rtol=5e-3, atol=5e-3
+        )