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tests/models/lightglue/test_image_processing_lightglue.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
+
+from tests.models.superglue.test_image_processing_superglue import (
+    SuperGlueImageProcessingTest,
+    SuperGlueImageProcessingTester,
+)
+from transformers.testing_utils import require_torch, require_vision
+from transformers.utils import is_torch_available
+
+
+if is_torch_available():
+    import numpy as np
+    import torch
+
+    from transformers.models.lightglue.modeling_lightglue import LightGlueKeypointMatchingOutput
+
+
+def random_array(size):
+    return np.random.randint(255, size=size)
+
+
+def random_tensor(size):
+    return torch.rand(size)
+
+
+class LightGlueImageProcessingTester(SuperGlueImageProcessingTester):
+    """Tester for LightGlueImageProcessor"""
+
+    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,
+    ):
+        super().__init__(
+            parent, batch_size, num_channels, image_size, min_resolution, max_resolution, do_resize, size, do_grayscale
+        )
+
+    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)
+        mask = torch.zeros((batch_size, 2, max_number_keypoints), dtype=torch.int)
+        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))
+        prune = torch.zeros((batch_size, 2, max_number_keypoints), dtype=torch.int)
+        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))
+            mask[i, 0, :random_number_keypoints0] = 1
+            mask[i, 1, :random_number_keypoints1] = 1
+            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 LightGlueKeypointMatchingOutput(
+            mask=mask, keypoints=keypoints, matches=matches, matching_scores=scores, prune=prune
+        )
+
+
+@require_torch
+@require_vision
+class LightGlueImageProcessingTest(SuperGlueImageProcessingTest, unittest.TestCase):
+    def setUp(self) -> None:
+        super().setUp()
+        self.image_processor_tester = LightGlueImageProcessingTester(self)