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f0ae65c198
* bug fix * update with more cases * more entries * Fix --------- Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>
385 lines
19 KiB
Python
385 lines
19 KiB
Python
# Copyright 2024 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 parameterized import parameterized
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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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from ...test_image_processing_common import (
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ImageProcessingTestMixin,
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prepare_image_inputs,
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)
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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.superglue.modeling_superglue import KeypointMatchingOutput
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if is_vision_available():
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from transformers import SuperGlueImageProcessor
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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 SuperGlueImageProcessingTester:
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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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size = size if size is not None else {"height": 480, "width": 640}
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self.parent = parent
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self.batch_size = batch_size
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self.num_channels = num_channels
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self.image_size = image_size
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self.min_resolution = min_resolution
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self.max_resolution = max_resolution
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self.do_resize = do_resize
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self.size = size
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self.do_grayscale = do_grayscale
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def prepare_image_processor_dict(self):
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return {
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"do_resize": self.do_resize,
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"size": self.size,
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"do_grayscale": self.do_grayscale,
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}
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def expected_output_image_shape(self, images):
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return 2, self.num_channels, self.size["height"], self.size["width"]
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def prepare_image_inputs(self, equal_resolution=False, numpify=False, torchify=False, pairs=True, batch_size=None):
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batch_size = batch_size if batch_size is not None else self.batch_size
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image_inputs = prepare_image_inputs(
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batch_size=batch_size,
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num_channels=self.num_channels,
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min_resolution=self.min_resolution,
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max_resolution=self.max_resolution,
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equal_resolution=equal_resolution,
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numpify=numpify,
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torchify=torchify,
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)
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if pairs:
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image_inputs = [image_inputs[i : i + 2] for i in range(0, len(image_inputs), 2)]
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return image_inputs
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def prepare_keypoint_matching_output(self, pixel_values):
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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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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 KeypointMatchingOutput(mask=mask, keypoints=keypoints, matches=matches, matching_scores=scores)
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@require_torch
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@require_vision
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class SuperGlueImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase):
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image_processing_class = SuperGlueImageProcessor 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 = SuperGlueImageProcessingTester(self)
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@property
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def image_processor_dict(self):
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return self.image_processor_tester.prepare_image_processor_dict()
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def test_image_processing(self):
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image_processing = self.image_processing_class(**self.image_processor_dict)
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self.assertTrue(hasattr(image_processing, "do_resize"))
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self.assertTrue(hasattr(image_processing, "size"))
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self.assertTrue(hasattr(image_processing, "do_rescale"))
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self.assertTrue(hasattr(image_processing, "rescale_factor"))
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self.assertTrue(hasattr(image_processing, "do_grayscale"))
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def test_image_processor_from_dict_with_kwargs(self):
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image_processor = self.image_processing_class.from_dict(self.image_processor_dict)
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self.assertEqual(image_processor.size, {"height": 480, "width": 640})
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image_processor = self.image_processing_class.from_dict(
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self.image_processor_dict, size={"height": 42, "width": 42}
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)
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self.assertEqual(image_processor.size, {"height": 42, "width": 42})
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@unittest.skip(reason="SuperPointImageProcessor is always supposed to return a grayscaled image")
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def test_call_numpy_4_channels(self):
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pass
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def test_number_and_format_of_images_in_input(self):
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image_processor = self.image_processing_class.from_dict(self.image_processor_dict)
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# Cases where the number of images and the format of lists in the input is correct
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image_input = self.image_processor_tester.prepare_image_inputs(pairs=False, batch_size=2)
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image_processed = image_processor.preprocess(image_input, return_tensors="pt")
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self.assertEqual((1, 2, 3, 480, 640), tuple(image_processed["pixel_values"].shape))
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image_input = self.image_processor_tester.prepare_image_inputs(pairs=True, batch_size=2)
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image_processed = image_processor.preprocess(image_input, return_tensors="pt")
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self.assertEqual((1, 2, 3, 480, 640), tuple(image_processed["pixel_values"].shape))
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image_input = self.image_processor_tester.prepare_image_inputs(pairs=True, batch_size=4)
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image_processed = image_processor.preprocess(image_input, return_tensors="pt")
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self.assertEqual((2, 2, 3, 480, 640), tuple(image_processed["pixel_values"].shape))
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image_input = self.image_processor_tester.prepare_image_inputs(pairs=True, batch_size=6)
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image_processed = image_processor.preprocess(image_input, return_tensors="pt")
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self.assertEqual((3, 2, 3, 480, 640), tuple(image_processed["pixel_values"].shape))
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# Cases where the number of images or the format of lists in the input is incorrect
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## List of 4 images
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image_input = self.image_processor_tester.prepare_image_inputs(pairs=False, batch_size=4)
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with self.assertRaises(ValueError) as cm:
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image_processor.preprocess(image_input, return_tensors="pt")
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self.assertEqual(ValueError, cm.exception.__class__)
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## List of 3 images
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image_input = self.image_processor_tester.prepare_image_inputs(pairs=False, batch_size=3)
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with self.assertRaises(ValueError) as cm:
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image_processor.preprocess(image_input, return_tensors="pt")
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self.assertEqual(ValueError, cm.exception.__class__)
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## List of 2 pairs and 1 image
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image_input = self.image_processor_tester.prepare_image_inputs(pairs=True, batch_size=3)
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with self.assertRaises(ValueError) as cm:
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image_processor.preprocess(image_input, return_tensors="pt")
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self.assertEqual(ValueError, cm.exception.__class__)
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@parameterized.expand(
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[
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([random_array((3, 100, 200)), random_array((3, 100, 200))], (1, 2, 3, 480, 640)),
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([[random_array((3, 100, 200)), random_array((3, 100, 200))]], (1, 2, 3, 480, 640)),
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([random_tensor((3, 100, 200)), random_tensor((3, 100, 200))], (1, 2, 3, 480, 640)),
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([random_tensor((3, 100, 200)), random_tensor((3, 100, 200))], (1, 2, 3, 480, 640)),
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],
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)
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def test_valid_image_shape_in_input(self, image_input, output):
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image_processor = self.image_processing_class.from_dict(self.image_processor_dict)
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image_processed = image_processor.preprocess(image_input, return_tensors="pt")
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self.assertEqual(output, tuple(image_processed["pixel_values"].shape))
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@parameterized.expand(
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[
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(random_array((3, 100, 200)),),
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([random_array((3, 100, 200))],),
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(random_array((1, 3, 100, 200)),),
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([[random_array((3, 100, 200))]],),
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([[random_array((3, 100, 200))], [random_array((3, 100, 200))]],),
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([random_array((1, 3, 100, 200)), random_array((1, 3, 100, 200))],),
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(random_array((1, 1, 3, 100, 200)),),
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],
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)
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def test_invalid_image_shape_in_input(self, image_input):
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image_processor = self.image_processing_class.from_dict(self.image_processor_dict)
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with self.assertRaises(ValueError) as cm:
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image_processor.preprocess(image_input, return_tensors="pt")
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self.assertEqual(ValueError, cm.exception.__class__)
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def test_input_images_properly_paired(self):
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image_processor = self.image_processing_class.from_dict(self.image_processor_dict)
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image_inputs = self.image_processor_tester.prepare_image_inputs()
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pre_processed_images = image_processor.preprocess(image_inputs, return_tensors="np")
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self.assertEqual(len(pre_processed_images["pixel_values"].shape), 5)
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self.assertEqual(pre_processed_images["pixel_values"].shape[1], 2)
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def test_input_not_paired_images_raises_error(self):
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image_processor = self.image_processing_class.from_dict(self.image_processor_dict)
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image_inputs = self.image_processor_tester.prepare_image_inputs(pairs=False)
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with self.assertRaises(ValueError):
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image_processor.preprocess(image_inputs[0])
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def test_input_image_properly_converted_to_grayscale(self):
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image_processor = self.image_processing_class.from_dict(self.image_processor_dict)
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image_inputs = self.image_processor_tester.prepare_image_inputs()
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pre_processed_images = image_processor.preprocess(image_inputs)
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for image_pair in pre_processed_images["pixel_values"]:
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for image in image_pair:
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self.assertTrue(np.all(image[0, ...] == image[1, ...]) and np.all(image[1, ...] == image[2, ...]))
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def test_call_numpy(self):
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# Test overwritten because SuperGlueImageProcessor combines images by pair to feed it into SuperGlue
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# Initialize image_processing
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image_processing = self.image_processing_class(**self.image_processor_dict)
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# create random numpy tensors
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image_pairs = self.image_processor_tester.prepare_image_inputs(equal_resolution=False, numpify=True)
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for image_pair in image_pairs:
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self.assertEqual(len(image_pair), 2)
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expected_batch_size = int(self.image_processor_tester.batch_size / 2)
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# Test with 2 images
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encoded_images = image_processing(image_pairs[0], return_tensors="pt").pixel_values
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expected_output_image_shape = self.image_processor_tester.expected_output_image_shape(image_pairs[0])
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self.assertEqual(tuple(encoded_images.shape), (1, *expected_output_image_shape))
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# Test with list of pairs
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encoded_images = image_processing(image_pairs, return_tensors="pt").pixel_values
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expected_output_image_shape = self.image_processor_tester.expected_output_image_shape(image_pairs)
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self.assertEqual(tuple(encoded_images.shape), (expected_batch_size, *expected_output_image_shape))
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# Test without paired images
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image_pairs = self.image_processor_tester.prepare_image_inputs(
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equal_resolution=False, numpify=True, pairs=False
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)
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with self.assertRaises(ValueError):
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image_processing(image_pairs, return_tensors="pt").pixel_values
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def test_call_pil(self):
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# Test overwritten because SuperGlueImageProcessor combines images by pair to feed it into SuperGlue
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# Initialize image_processing
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image_processing = self.image_processing_class(**self.image_processor_dict)
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# create random PIL images
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image_pairs = self.image_processor_tester.prepare_image_inputs(equal_resolution=False)
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for image_pair in image_pairs:
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self.assertEqual(len(image_pair), 2)
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expected_batch_size = int(self.image_processor_tester.batch_size / 2)
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# Test with 2 images
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encoded_images = image_processing(image_pairs[0], return_tensors="pt").pixel_values
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expected_output_image_shape = self.image_processor_tester.expected_output_image_shape(image_pairs[0])
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self.assertEqual(tuple(encoded_images.shape), (1, *expected_output_image_shape))
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# Test with list of pairs
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encoded_images = image_processing(image_pairs, return_tensors="pt").pixel_values
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expected_output_image_shape = self.image_processor_tester.expected_output_image_shape(image_pairs)
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self.assertEqual(tuple(encoded_images.shape), (expected_batch_size, *expected_output_image_shape))
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# Test without paired images
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image_pairs = self.image_processor_tester.prepare_image_inputs(equal_resolution=False, pairs=False)
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with self.assertRaises(ValueError):
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image_processing(image_pairs, return_tensors="pt").pixel_values
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def test_call_pytorch(self):
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# Test overwritten because SuperGlueImageProcessor combines images by pair to feed it into SuperGlue
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# Initialize image_processing
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image_processing = self.image_processing_class(**self.image_processor_dict)
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# create random PyTorch tensors
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image_pairs = self.image_processor_tester.prepare_image_inputs(equal_resolution=False, torchify=True)
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for image_pair in image_pairs:
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self.assertEqual(len(image_pair), 2)
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expected_batch_size = int(self.image_processor_tester.batch_size / 2)
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# Test with 2 images
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encoded_images = image_processing(image_pairs[0], return_tensors="pt").pixel_values
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expected_output_image_shape = self.image_processor_tester.expected_output_image_shape(image_pairs[0])
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self.assertEqual(tuple(encoded_images.shape), (1, *expected_output_image_shape))
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# Test with list of pairs
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encoded_images = image_processing(image_pairs, return_tensors="pt").pixel_values
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expected_output_image_shape = self.image_processor_tester.expected_output_image_shape(image_pairs)
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self.assertEqual(tuple(encoded_images.shape), (expected_batch_size, *expected_output_image_shape))
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# Test without paired images
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image_pairs = self.image_processor_tester.prepare_image_inputs(
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equal_resolution=False, torchify=True, pairs=False
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)
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with self.assertRaises(ValueError):
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image_processing(image_pairs, return_tensors="pt").pixel_values
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def test_image_processor_with_list_of_two_images(self):
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image_processing = self.image_processing_class(**self.image_processor_dict)
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image_pairs = self.image_processor_tester.prepare_image_inputs(
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equal_resolution=False, numpify=True, batch_size=2, pairs=False
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)
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self.assertEqual(len(image_pairs), 2)
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self.assertTrue(isinstance(image_pairs[0], np.ndarray))
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self.assertTrue(isinstance(image_pairs[1], np.ndarray))
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expected_batch_size = 1
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encoded_images = image_processing(image_pairs, return_tensors="pt").pixel_values
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expected_output_image_shape = self.image_processor_tester.expected_output_image_shape(image_pairs[0])
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self.assertEqual(tuple(encoded_images.shape), (expected_batch_size, *expected_output_image_shape))
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@require_torch
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def test_post_processing_keypoint_matching(self):
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image_processor = self.image_processing_class.from_dict(self.image_processor_dict)
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image_inputs = self.image_processor_tester.prepare_image_inputs()
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pre_processed_images = image_processor.preprocess(image_inputs, return_tensors="pt")
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outputs = self.image_processor_tester.prepare_keypoint_matching_output(**pre_processed_images)
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def check_post_processed_output(post_processed_output, image_pair_size):
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for post_processed_output, (image_size0, image_size1) in zip(post_processed_output, image_pair_size):
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self.assertTrue("keypoints0" in post_processed_output)
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self.assertTrue("keypoints1" in post_processed_output)
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self.assertTrue("matching_scores" in post_processed_output)
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keypoints0 = post_processed_output["keypoints0"]
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keypoints1 = post_processed_output["keypoints1"]
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all_below_image_size0 = torch.all(keypoints0[:, 0] <= image_size0[1]) and torch.all(
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keypoints0[:, 1] <= image_size0[0]
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)
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all_below_image_size1 = torch.all(keypoints1[:, 0] <= image_size1[1]) and torch.all(
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keypoints1[:, 1] <= image_size1[0]
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)
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all_above_zero0 = torch.all(keypoints0[:, 0] >= 0) and torch.all(keypoints0[:, 1] >= 0)
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all_above_zero1 = torch.all(keypoints0[:, 0] >= 0) and torch.all(keypoints0[:, 1] >= 0)
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self.assertTrue(all_below_image_size0)
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self.assertTrue(all_below_image_size1)
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self.assertTrue(all_above_zero0)
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self.assertTrue(all_above_zero1)
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all_scores_different_from_minus_one = torch.all(post_processed_output["matching_scores"] != -1)
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self.assertTrue(all_scores_different_from_minus_one)
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tuple_image_sizes = [
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((image_pair[0].size[0], image_pair[0].size[1]), (image_pair[1].size[0], image_pair[1].size[1]))
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for image_pair in image_inputs
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]
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tuple_post_processed_outputs = image_processor.post_process_keypoint_matching(outputs, tuple_image_sizes)
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check_post_processed_output(tuple_post_processed_outputs, tuple_image_sizes)
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tensor_image_sizes = torch.tensor(
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[(image_pair[0].size, image_pair[1].size) for image_pair in image_inputs]
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).flip(2)
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tensor_post_processed_outputs = image_processor.post_process_keypoint_matching(outputs, tensor_image_sizes)
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check_post_processed_output(tensor_post_processed_outputs, tensor_image_sizes)
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