Add SuperGlue model (#29886)
* Initial commit with template code generated by transformers-cli * Multiple additions to SuperGlue implementation : - Added the SuperGlueConfig - Added the SuperGlueModel and its implementation - Added basic weight conversion script - Added new ImageMatchingOutput dataclass * Few changes for SuperGlue * Multiple changes : - Added keypoint detection config to SuperGlueConfig - Completed convert_superglue_to_pytorch and succesfully run inference * Reverted unintentional change * Multiple changes : - Added SuperGlue to a bunch of places - Divided SuperGlue into SuperGlueForImageMatching and SuperGlueModel - Added testing images * Moved things in init files * Added docs (to be finished depending on the final implementation) * Added necessary imports and some doc * Removed unnecessary import * Fixed make fix-copies bug and ran it * Deleted SuperGlueModel Fixed convert script * Added SuperGlueImageProcessor * Changed SuperGlue to support batching pairs of images and modified ImageMatchingOutput in consequences * Changed convert_superglue_to_hf.py script to experiment different ways of reading an image and seeing its impact on performances * Added initial tests for SuperGlueImageProcessor * Added AutoModelForImageMatching in missing places and tests * Fixed keypoint_detector_output instructions * Fix style * Adapted to latest main changes * Added integration test * Fixed bugs to pass tests * Added keypoints returned by keypoint detector in the output of SuperGlue * Added doc to SuperGlue * SuperGlue returning all attention and hidden states for a fixed number of keypoints * Make style * Changed SuperGlueImageProcessor tests * Revert "SuperGlue returning all attention and hidden states for a fixed number of keypoints" Changed tests accordingly This reverts commit 5b3b669c * Added back hidden_states and attentions masked outputs with tests * Renamed ImageMatching occurences into KeypointMatching * Changed SuperGlueImageProcessor to raise error when batch_size is not even * Added docs and clarity to hidden state and attention grouping function * Fixed some code and done refactoring * Fixed typo in SuperPoint output doc * Fixed some of the formatting and variable naming problems * Removed useless function call * Removed AutoModelForKeypointMatching * Fixed SuperGlueImageProcessor to only accept paris of images * Added more fixes to SuperGlueImageProcessor * Simplified the batching of attention and hidden states * Simplified stack functions * Moved attention instructions into class * Removed unused do_batch_norm argument * Moved weight initialization to the proper place * Replaced deepcopy for instantiation * Fixed small bug * Changed from stevenbucaille to magic-leap repo * Renamed London Bridge images to Tower Bridge * Fixed formatting * Renamed remaining "london" to "tower" * Apply suggestions from code review Small changes in the docs Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com> * Added AutoModelForKeypointMatching * Changed images used in example * Several changes to image_processing_superglue and style * Fixed resample type hint * Changed SuperGlueImageProcessor and added test case for list of 2 images * Changed list_of_tuples implementation * Fix in dummy objects * Added normalize_keypoint, log_sinkhorn_iterations and log_optimal_transport docstring * Added missing docstring * Apply suggestions from code review Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com> * Apply suggestions from code review Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com> * Moved forward block at bottom * Added docstring to forward method * Added docstring to match_image_pair method * Changed test_model_common_attributes to test_model_get_set_embeddings test method signature * Removed AutoModelForKeypointMatching * Removed image fixtures and added load_dataset * Added padding of images in SuperGlueImageProcessor * Cleaned up convert_superglue_to_hf script * Added missing docs and fixed unused argument * Fixed SuperGlueImageProcessor tests * Transposed all hidden states from SuperGlue to reflect the standard (..., seq_len, feature_dim) shape * Added SuperGlueForKeypointMatching back to modeling_auto * Fixed image processor padding test * Changed SuperGlue docs * changes: - Abstraction to batch, concat and stack of inconsistent tensors - Changed conv1d's to linears to match standard attention implementations - Renamed all tensors to be tensor0 and not tensor_0 and be consistent - Changed match image pair to run keypoint detection on all image first, create batching tensors and then filling these tensors matches after matches - Various changes in docs, etc * Changes to SuperGlueImageProcessor: - Reworked the input image pairs checking function and added tests accordingly - Added Copied from statements - Added do_grayscale tag (also for SuperPointImageProcessor) - Misc changes for better code * Formatting changes * Reverted conv1d to linear conversion because of numerical differences * fix: changed some code to be more straightforward (e.g. filtering keypoints) and converted plot from opencv to matplotlib * fix: removed unnecessary test * chore: removed commented code and added back hidden states transpositions * chore: changed from "inconsistent" to "ragged" function names as suggested Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com> * docs: applied suggestions Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com> * docs: updated to display matched output * chore: applied suggestion for check_image_pairs_input function Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com> * chore: changed check_image_pairs_input function name to validate_and_format_image_pairs and used validate_preprocess_arguments function * tests: simplified tests for image input format and shapes * feat: converted SuperGlue's use of Conv1d with kernel_size of 1 with Linear layers. Changed tests and conversion script accordingly * feat: several changes to address comments Conversion script: - Reverted fuse batchnorm to linear conversion - Changed all 'nn.Module' to respective SuperGlue models - Changed conversion script to use regex mapping and match other recent scripts Modeling SuperGlue: - Added batching with mask and padding to attention - Removed unnecessary concat, stack and batch ragged pairs functions - Reverted batchnorm layer - Renamed query, key, value and merge layers into q, k, v, out proj - Removed Union of different Module into nn.Module in _init_weights method typehint - Changed several method's signature to combine image0 and image1 inputs with appropriate doc changes - Updated SuperGlue's doc with torch.no_grad() Updated test to reflect changes in SuperGlue model * refactor: changed validate_and_format_image_pairs function with clarity * refactor: changed from one SuperGlueMLP class to a list of SuperGlueMLP class * fix: fixed forgotten init weight change from last commit * fix: fixed rebase mistake * fix: removed leftover commented code * fix: added typehint and changed some of arguments default values * fix: fixed attribute default values for SuperGlueConfig * feat: added SuperGlueImageProcessor post process keypoint matching method with tests * fix: fixed SuperGlue attention and hidden state tuples aggregation * chore: fixed mask optionality and reordered tensor reshapes to be cleaner * chore: fixed docs and error message returned in validate_and_format_image_pairs function * fix: fixed returned keypoints to be the ones that SuperPoint returns * fix: fixed check on number of image sizes for post process compared to the pairs in outputs of SuperGlue * fix: fixed check on number of image sizes for post process compared to the pairs in outputs of SuperGlue (bis) * fix: Changed SuperGlueMultiLayerPerceptron instantiation to avoid if statement * fix: Changed convert_superglue_to_hf script to reflect latest SuperGlue changes and got rid of nn.Modules * WIP: implement Attention from an existing class (like BERT) * docs: Changed docs to include more appealing matching plot * WIP: Implement Attention * chore: minor typehint change * chore: changed convert superglue script by removing all classes and apply conv to linear conversion in state dict + rearrange keys to comply with changes in model's layers organisation * Revert "Fixed typo in SuperPoint output doc" This reverts commit 2120390e827f94fcd631c8e5728d9a4980f4a503. * chore: added comments in SuperGlueImageProcessor * chore: changed SuperGlue organization HF repo to magic-leap-community * [run-slow] refactor: small change in layer instantiation * [run-slow] chore: replaced remaining stevenbucaille org to magic-leap-community * [run-slow] chore: make style * chore: update image matching fixture dataset HF repository * [run-slow] superglue * tests: overwriting test_batching_equivalence * [run-slow] superglue * tests: changed test to cope with value changing depending on cuda version * [run-slow] superglue * tests: changed matching_threshold value * [run-slow] superglue * [run-slow] superglue * tests: changed tests for integration * [run-slow] superglue * fix: Changed tensor view and permutations to match original implementation results * fix: updated convert script and integration test to include last change in model * fix: increase tolerance for CUDA variances * Apply suggestions from code review Co-authored-by: Pavel Iakubovskii <qubvel@gmail.com> * [run-slow] superglue * chore: removed blank whitespaces * [run-slow] superglue * Revert SuperPoint image processor accident changes * [run-slow] superglue * refactor: reverted copy from BERT class * tests: lower the tolerance in integration tests for SuperGlue * [run-slow] superglue * chore: set do_grayscale to False in SuperPoint and SuperGlue image processors * [run-slow] superglue * fix: fixed imports in SuperGlue files * chore: changed do_grayscale SuperGlueImageProcessing default value to True * docs: added typehint to post_process_keypoint_matching method in SuperGlueImageProcessor * fix: set matching_threshold default value to 0.0 instead of 0.2 * feat: added matching_threshold to post_process_keypoint_matching method * docs: update superglue.md to include matching_threshold parameter * docs: updated SuperGlueConfig docstring for matching_threshold default value * refactor: removed unnecessary parameters in SuperGlueConfig * fix: changed from matching_threshold to threshold * fix: re-revert changes to make SuperGlue attention classes copies of BERT * [run-slow] superglue * fix: added missing device argument in post_processing method * [run-slow] superglue * fix: add matches different from -1 to compute valid matches in post_process_keypoint_matching (and docstring) * fix: add device to image_sizes tensor instantiation * tests: added checks on do_grayscale test * chore: reordered and added Optional typehint to KeypointMatchingOutput * LightGluePR suggestions: - use `post_process_keypoint_matching` as default docs example - add `post_process_keypoint_matching` in autodoc - add `SuperPointConfig` import under TYPE_CHECKING condition - format SuperGlueConfig docstring - add device in convert_superglue_to_hf - Fix typo - Fix KeypointMatchingOutput docstring - Removed unnecessary line - Added missing SuperGlueConfig in __init__ methods * LightGluePR suggestions: - use batching to get keypoint detection * refactor: processing images done in 1 for loop instead of 4 * fix: use @ instead of torch.einsum for scores computation * style: added #fmt skip to long tensor values * refactor: rollbacked validate_and_format_image_pairs valid and invalid case to more simple ones * refactor: prepare_imgs * refactor: simplified `validate_and_format_image_pairs` * docs: fixed doc --------- Co-authored-by: steven <steven.bucaillle@gmail.com> Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com> Co-authored-by: Steven Bucaille <steven.bucaille@buawei.com> Co-authored-by: Pavel Iakubovskii <qubvel@gmail.com>
This commit is contained in:
StevenBucaille
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tests/models/superglue/test_image_processing_superglue.py
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tests/models/superglue/test_image_processing_superglue.py
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# 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(unittest.TestCase):
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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)
|
||||
# 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
|
||||
image_processing = self.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
|
||||
image_processing = self.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):
|
||||
image_processing = self.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):
|
||||
image_processor = self.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)
|
||||
|
||||
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(keypoints0[:, 0] >= 0) and torch.all(keypoints0[:, 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)
|
||||
|
||||
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)
|
||||
Reference in New Issue
Block a user