97 lines
4.0 KiB
Python
97 lines
4.0 KiB
Python
# Copyright 2025 The HuggingFace Team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import unittest
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from tests.models.superglue.test_image_processing_superglue import (
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SuperGlueImageProcessingTest,
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SuperGlueImageProcessingTester,
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)
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from transformers.testing_utils import require_torch, require_vision
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from transformers.utils import is_torch_available, is_vision_available
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if is_torch_available():
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import numpy as np
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import torch
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from transformers.models.lightglue.modeling_lightglue import LightGlueKeypointMatchingOutput
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if is_vision_available():
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from transformers import LightGlueImageProcessor
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def random_array(size):
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return np.random.randint(255, size=size)
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def random_tensor(size):
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return torch.rand(size)
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class LightGlueImageProcessingTester(SuperGlueImageProcessingTester):
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"""Tester for LightGlueImageProcessor"""
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def __init__(
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self,
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parent,
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batch_size=6,
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num_channels=3,
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image_size=18,
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min_resolution=30,
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max_resolution=400,
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do_resize=True,
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size=None,
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do_grayscale=True,
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):
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super().__init__(
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parent, batch_size, num_channels, image_size, min_resolution, max_resolution, do_resize, size, do_grayscale
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)
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def prepare_keypoint_matching_output(self, pixel_values):
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"""Prepare a fake output for the keypoint matching model with random matches between 50 keypoints per image."""
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max_number_keypoints = 50
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batch_size = len(pixel_values)
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mask = torch.zeros((batch_size, 2, max_number_keypoints), dtype=torch.int)
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keypoints = torch.zeros((batch_size, 2, max_number_keypoints, 2))
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matches = torch.full((batch_size, 2, max_number_keypoints), -1, dtype=torch.int)
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scores = torch.zeros((batch_size, 2, max_number_keypoints))
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prune = torch.zeros((batch_size, 2, max_number_keypoints), dtype=torch.int)
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for i in range(batch_size):
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random_number_keypoints0 = np.random.randint(10, max_number_keypoints)
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random_number_keypoints1 = np.random.randint(10, max_number_keypoints)
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random_number_matches = np.random.randint(5, min(random_number_keypoints0, random_number_keypoints1))
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mask[i, 0, :random_number_keypoints0] = 1
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mask[i, 1, :random_number_keypoints1] = 1
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keypoints[i, 0, :random_number_keypoints0] = torch.rand((random_number_keypoints0, 2))
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keypoints[i, 1, :random_number_keypoints1] = torch.rand((random_number_keypoints1, 2))
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random_matches_indices0 = torch.randperm(random_number_keypoints1, dtype=torch.int)[:random_number_matches]
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random_matches_indices1 = torch.randperm(random_number_keypoints0, dtype=torch.int)[:random_number_matches]
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matches[i, 0, random_matches_indices1] = random_matches_indices0
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matches[i, 1, random_matches_indices0] = random_matches_indices1
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scores[i, 0, random_matches_indices1] = torch.rand((random_number_matches,))
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scores[i, 1, random_matches_indices0] = torch.rand((random_number_matches,))
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return LightGlueKeypointMatchingOutput(
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mask=mask, keypoints=keypoints, matches=matches, matching_scores=scores, prune=prune
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)
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@require_torch
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@require_vision
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class LightGlueImageProcessingTest(SuperGlueImageProcessingTest, unittest.TestCase):
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image_processing_class = LightGlueImageProcessor if is_vision_available() else None
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def setUp(self) -> None:
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super().setUp()
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self.image_processor_tester = LightGlueImageProcessingTester(self)
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