194 lines
7.2 KiB
Python
194 lines
7.2 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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import datasets
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from transformers.models.auto.modeling_auto import MODEL_FOR_KEYPOINT_MATCHING_MAPPING
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from transformers.pipelines import KeypointMatchingPipeline, pipeline
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from transformers.testing_utils import (
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is_pipeline_test,
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is_vision_available,
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require_torch,
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require_vision,
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)
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from .test_pipelines_common import ANY
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if is_vision_available():
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from PIL import Image
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@is_pipeline_test
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@require_torch
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@require_vision
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class KeypointMatchingPipelineTests(unittest.TestCase):
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model_mapping = MODEL_FOR_KEYPOINT_MATCHING_MAPPING
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_dataset = None
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@classmethod
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def _load_dataset(cls):
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# Lazy loading of the dataset. Because it is a class method, it will only be loaded once per pytest process.
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if cls._dataset is None:
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cls._dataset = datasets.load_dataset("hf-internal-testing/image-matching-dataset", split="train")
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def get_test_pipeline(
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self,
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model,
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tokenizer=None,
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image_processor=None,
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feature_extractor=None,
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processor=None,
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torch_dtype="float32",
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):
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image_matcher = KeypointMatchingPipeline(
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model=model,
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tokenizer=tokenizer,
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feature_extractor=feature_extractor,
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image_processor=image_processor,
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processor=processor,
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torch_dtype=torch_dtype,
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)
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examples = [
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Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"),
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"http://images.cocodataset.org/val2017/000000039769.jpg",
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]
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return image_matcher, examples
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def run_pipeline_test(self, image_matcher, examples):
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self._load_dataset()
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outputs = image_matcher(
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[
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Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"),
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"http://images.cocodataset.org/val2017/000000039769.jpg",
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]
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)
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self.assertEqual(
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outputs,
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[
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{
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"keypoint_image_0": {"x": ANY(float), "y": ANY(float)},
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"keypoint_image_1": {"x": ANY(float), "y": ANY(float)},
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"score": ANY(float),
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}
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]
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* 2, # 2 matches per image pair
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)
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# Accepts URL + PIL.Image + lists
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outputs = image_matcher(
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[
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[
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Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"),
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"http://images.cocodataset.org/val2017/000000039769.jpg",
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],
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[self._dataset[0]["image"], self._dataset[1]["image"]],
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[self._dataset[1]["image"], self._dataset[2]["image"]],
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[self._dataset[2]["image"], self._dataset[0]["image"]],
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]
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)
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self.assertEqual(
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outputs,
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[
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[
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{
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"keypoint_image_0": {"x": ANY(float), "y": ANY(float)},
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"keypoint_image_1": {"x": ANY(float), "y": ANY(float)},
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"score": ANY(float),
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}
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]
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* 2 # 2 matches per image pair
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]
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* 4, # 4 image pairs
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)
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@require_torch
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def test_single_image(self):
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self._load_dataset()
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small_model = "magic-leap-community/superglue_outdoor"
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image_matcher = pipeline("keypoint-matching", model=small_model)
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with self.assertRaises(ValueError):
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image_matcher(
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self._dataset[0]["image"],
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threshold=0.0,
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)
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with self.assertRaises(ValueError):
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image_matcher(
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[self._dataset[0]["image"]],
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threshold=0.0,
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)
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@require_torch
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def test_single_pair(self):
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self._load_dataset()
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small_model = "magic-leap-community/superglue_outdoor"
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image_matcher = pipeline("keypoint-matching", model=small_model)
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image_0: Image.Image = self._dataset[0]["image"]
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image_1: Image.Image = self._dataset[1]["image"]
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outputs = image_matcher((image_0, image_1), threshold=0.0)
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output = outputs[0] # first match from image pair
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self.assertAlmostEqual(output["keypoint_image_0"]["x"], 698, places=1)
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self.assertAlmostEqual(output["keypoint_image_0"]["y"], 469, places=1)
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self.assertAlmostEqual(output["keypoint_image_1"]["x"], 434, places=1)
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self.assertAlmostEqual(output["keypoint_image_1"]["y"], 440, places=1)
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self.assertAlmostEqual(output["score"], 0.9905, places=3)
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@require_torch
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def test_multiple_pairs(self):
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self._load_dataset()
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small_model = "magic-leap-community/superglue_outdoor"
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image_matcher = pipeline("keypoint-matching", model=small_model)
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image_0: Image.Image = self._dataset[0]["image"]
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image_1: Image.Image = self._dataset[1]["image"]
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image_2: Image.Image = self._dataset[2]["image"]
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outputs = image_matcher(
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[
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(image_0, image_1),
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(image_1, image_2),
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(image_2, image_0),
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],
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threshold=1e-4,
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)
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# Test first pair (image_0, image_1)
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output_0 = outputs[0][0] # First match from first pair
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self.assertAlmostEqual(output_0["keypoint_image_0"]["x"], 698, places=1)
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self.assertAlmostEqual(output_0["keypoint_image_0"]["y"], 469, places=1)
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self.assertAlmostEqual(output_0["keypoint_image_1"]["x"], 434, places=1)
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self.assertAlmostEqual(output_0["keypoint_image_1"]["y"], 440, places=1)
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self.assertAlmostEqual(output_0["score"], 0.9905, places=3)
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# Test second pair (image_1, image_2)
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output_1 = outputs[1][0] # First match from second pair
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self.assertAlmostEqual(output_1["keypoint_image_0"]["x"], 272, places=1)
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self.assertAlmostEqual(output_1["keypoint_image_0"]["y"], 310, places=1)
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self.assertAlmostEqual(output_1["keypoint_image_1"]["x"], 228, places=1)
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self.assertAlmostEqual(output_1["keypoint_image_1"]["y"], 568, places=1)
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self.assertAlmostEqual(output_1["score"], 0.9890, places=3)
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# Test third pair (image_2, image_0)
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output_2 = outputs[2][0] # First match from third pair
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self.assertAlmostEqual(output_2["keypoint_image_0"]["x"], 385, places=1)
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self.assertAlmostEqual(output_2["keypoint_image_0"]["y"], 677, places=1)
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self.assertAlmostEqual(output_2["keypoint_image_1"]["x"], 689, places=1)
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self.assertAlmostEqual(output_2["keypoint_image_1"]["y"], 351, places=1)
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self.assertAlmostEqual(output_2["score"], 0.9900, places=3)
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