# Copyright 2024 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import inspect import unittest from functools import cached_property from datasets import load_dataset from transformers.models.superglue.configuration_superglue import SuperGlueConfig from transformers.testing_utils import require_torch, require_vision, slow, torch_device from transformers.utils import is_torch_available, is_vision_available from ...test_configuration_common import ConfigTester from ...test_modeling_common import ModelTesterMixin, floats_tensor if is_torch_available(): import torch from transformers import SuperGlueForKeypointMatching if is_vision_available(): from transformers import AutoImageProcessor class SuperGlueModelTester: def __init__( self, parent, batch_size=2, image_width=80, image_height=60, keypoint_detector_config=None, hidden_size: int = 64, keypoint_encoder_sizes: list[int] = [32, 64], gnn_layers_types: list[str] = ["self", "cross"] * 2, num_attention_heads: int = 4, sinkhorn_iterations: int = 100, matching_threshold: float = 0.2, ): if keypoint_detector_config is None: keypoint_detector_config = { "encoder_hidden_sizes": [32, 64], "decoder_hidden_size": 64, "keypoint_decoder_dim": 65, "descriptor_decoder_dim": 64, "keypoint_threshold": 0.005, "max_keypoints": 256, "nms_radius": 4, "border_removal_distance": 4, } self.parent = parent self.batch_size = batch_size self.image_width = image_width self.image_height = image_height self.keypoint_detector_config = keypoint_detector_config self.hidden_size = hidden_size self.keypoint_encoder_sizes = keypoint_encoder_sizes self.gnn_layers_types = gnn_layers_types self.num_attention_heads = num_attention_heads self.sinkhorn_iterations = sinkhorn_iterations self.matching_threshold = matching_threshold def prepare_config_and_inputs(self): # SuperGlue expects a grayscale image as input pixel_values = floats_tensor([self.batch_size, 2, 3, self.image_height, self.image_width]) config = self.get_config() return config, pixel_values def get_config(self): return SuperGlueConfig( keypoint_detector_config=self.keypoint_detector_config, hidden_size=self.hidden_size, keypoint_encoder_sizes=self.keypoint_encoder_sizes, gnn_layers_types=self.gnn_layers_types, num_attention_heads=self.num_attention_heads, sinkhorn_iterations=self.sinkhorn_iterations, matching_threshold=self.matching_threshold, ) def create_and_check_model(self, config, pixel_values): model = SuperGlueForKeypointMatching(config=config) model.to(torch_device) model.eval() result = model(pixel_values) maximum_num_matches = result.mask.shape[-1] self.parent.assertEqual( result.keypoints.shape, (self.batch_size, 2, maximum_num_matches, 2), ) self.parent.assertEqual( result.matches.shape, (self.batch_size, 2, maximum_num_matches), ) self.parent.assertEqual( result.matching_scores.shape, (self.batch_size, 2, maximum_num_matches), ) def prepare_config_and_inputs_for_common(self): config_and_inputs = self.prepare_config_and_inputs() config, pixel_values = config_and_inputs inputs_dict = {"pixel_values": pixel_values} return config, inputs_dict @require_torch class SuperGlueModelTest(ModelTesterMixin, unittest.TestCase): all_model_classes = (SuperGlueForKeypointMatching,) if is_torch_available() else () test_resize_embeddings = False has_attentions = True def setUp(self): self.model_tester = SuperGlueModelTester(self) self.config_tester = ConfigTester(self, config_class=SuperGlueConfig, has_text_modality=False, hidden_size=64) def test_config(self): self.config_tester.create_and_test_config_to_json_string() self.config_tester.create_and_test_config_to_json_file() self.config_tester.create_and_test_config_from_and_save_pretrained() self.config_tester.create_and_test_config_with_num_labels() self.config_tester.check_config_can_be_init_without_params() self.config_tester.check_config_arguments_init() @unittest.skip(reason="SuperGlueForKeypointMatching does not use inputs_embeds") def test_inputs_embeds(self): pass @unittest.skip(reason="SuperGlueForKeypointMatching does not support input and output embeddings") def test_model_get_set_embeddings(self): pass @unittest.skip(reason="SuperGlueForKeypointMatching does not use feedforward chunking") def test_feed_forward_chunking(self): pass @unittest.skip(reason="SuperGlueForKeypointMatching is not trainable") def test_training(self): pass @unittest.skip(reason="SuperGlueForKeypointMatching is not trainable") def test_training_gradient_checkpointing(self): pass @unittest.skip(reason="SuperGlueForKeypointMatching is not trainable") def test_training_gradient_checkpointing_use_reentrant(self): pass @unittest.skip(reason="SuperGlueForKeypointMatching is not trainable") def test_training_gradient_checkpointing_use_reentrant_false(self): pass @unittest.skip(reason="SuperGlue does not output any loss term in the forward pass") def test_retain_grad_hidden_states_attentions(self): pass def test_model(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_model(*config_and_inputs) def test_forward_signature(self): config, _ = self.model_tester.prepare_config_and_inputs() for model_class in self.all_model_classes: model = model_class(config) signature = inspect.signature(model.forward) # signature.parameters is an OrderedDict => so arg_names order is deterministic arg_names = [*signature.parameters.keys()] expected_arg_names = ["pixel_values"] self.assertListEqual(arg_names[:1], expected_arg_names) def test_hidden_states_output(self): def check_hidden_states_output(inputs_dict, config, model_class): model = model_class(config) model.to(torch_device) model.eval() with torch.no_grad(): outputs = model(**self._prepare_for_class(inputs_dict, model_class)) hidden_states = outputs.hidden_states maximum_num_matches = outputs.mask.shape[-1] hidden_states_sizes = ( self.model_tester.keypoint_encoder_sizes + [self.model_tester.hidden_size] + [self.model_tester.hidden_size, self.model_tester.hidden_size * 2] * len(self.model_tester.gnn_layers_types) + [self.model_tester.hidden_size] * 2 ) for i, hidden_states_size in enumerate(hidden_states_sizes): self.assertListEqual( list(hidden_states[i].shape[-2:]), [hidden_states_size, maximum_num_matches], ) config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: inputs_dict["output_hidden_states"] = True check_hidden_states_output(inputs_dict, config, model_class) # check that output_hidden_states also work using config del inputs_dict["output_hidden_states"] config.output_hidden_states = True check_hidden_states_output(inputs_dict, config, model_class) def test_attention_outputs(self): def check_attention_output(inputs_dict, config, model_class): model = model_class(config) model.to(torch_device) model.eval() with torch.no_grad(): outputs = model(**self._prepare_for_class(inputs_dict, model_class)) attentions = outputs.attentions maximum_num_matches = outputs.mask.shape[-1] expected_attention_shape = [ self.model_tester.num_attention_heads, maximum_num_matches, maximum_num_matches, ] for i, attention in enumerate(attentions): self.assertListEqual( list(attention.shape[-3:]), expected_attention_shape, ) config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: inputs_dict["output_attentions"] = True check_attention_output(inputs_dict, config, model_class) # check that output_hidden_states also work using config del inputs_dict["output_attentions"] config.output_attentions = True check_attention_output(inputs_dict, config, model_class) @slow def test_model_from_pretrained(self): from_pretrained_ids = ["magic-leap-community/superglue_indoor", "magic-leap-community/superglue_outdoor"] for model_name in from_pretrained_ids: model = SuperGlueForKeypointMatching.from_pretrained(model_name) self.assertIsNotNone(model) def test_forward_labels_should_be_none(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: model = model_class(config) model.to(torch_device) model.eval() with torch.no_grad(): model_inputs = self._prepare_for_class(inputs_dict, model_class) # Provide an arbitrary sized Tensor as labels to model inputs model_inputs["labels"] = torch.rand((128, 128)) with self.assertRaises(ValueError) as cm: model(**model_inputs) self.assertEqual(ValueError, cm.exception.__class__) def test_batching_equivalence(self): """ Overwriting ModelTesterMixin.test_batching_equivalence since SuperGlue returns `matching_scores` tensors full of zeros which causes the test to fail, because cosine_similarity of two zero tensors is 0. Discussed here : https://github.com/huggingface/transformers/pull/29886#issuecomment-2481539481 """ def recursive_check(batched_object, single_row_object, model_name, key): if isinstance(batched_object, (list, tuple)): for batched_object_value, single_row_object_value in zip(batched_object, single_row_object): recursive_check(batched_object_value, single_row_object_value, model_name, key) elif isinstance(batched_object, dict): for batched_object_value, single_row_object_value in zip( batched_object.values(), single_row_object.values() ): recursive_check(batched_object_value, single_row_object_value, model_name, key) # do not compare returned loss (0-dim tensor) / codebook ids (int) / caching objects elif batched_object is None or not isinstance(batched_object, torch.Tensor): return elif batched_object.dim() == 0: return else: # indexing the first element does not always work # e.g. models that output similarity scores of size (N, M) would need to index [0, 0] slice_ids = [slice(0, index) for index in single_row_object.shape] batched_row = batched_object[slice_ids] self.assertFalse( torch.isnan(batched_row).any(), f"Batched output has `nan` in {model_name} for key={key}" ) self.assertFalse( torch.isinf(batched_row).any(), f"Batched output has `inf` in {model_name} for key={key}" ) self.assertFalse( torch.isnan(single_row_object).any(), f"Single row output has `nan` in {model_name} for key={key}" ) self.assertFalse( torch.isinf(single_row_object).any(), f"Single row output has `inf` in {model_name} for key={key}" ) self.assertTrue( (equivalence(batched_row, single_row_object)) <= 1e-03, msg=( f"Batched and Single row outputs are not equal in {model_name} for key={key}. " f"Difference={equivalence(batched_row, single_row_object)}." ), ) def equivalence(tensor1, tensor2): return torch.max(torch.abs(tensor1 - tensor2)) config, batched_input = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: config.output_hidden_states = True model_name = model_class.__name__ batched_input_prepared = self._prepare_for_class(batched_input, model_class) model = model_class(config).to(torch_device).eval() batch_size = self.model_tester.batch_size single_row_input = {} for key, value in batched_input_prepared.items(): if isinstance(value, torch.Tensor) and value.shape[0] % batch_size == 0: # e.g. musicgen has inputs of size (bs*codebooks). in most cases value.shape[0] == batch_size single_batch_shape = value.shape[0] // batch_size single_row_input[key] = value[:single_batch_shape] else: single_row_input[key] = value with torch.no_grad(): model_batched_output = model(**batched_input_prepared) model_row_output = model(**single_row_input) if isinstance(model_batched_output, torch.Tensor): model_batched_output = {"model_output": model_batched_output} model_row_output = {"model_output": model_row_output} for key in model_batched_output: recursive_check(model_batched_output[key], model_row_output[key], model_name, key) def prepare_imgs(): dataset = load_dataset("hf-internal-testing/image-matching-test-dataset", split="train") image1 = dataset[0]["image"] image2 = dataset[1]["image"] image3 = dataset[2]["image"] return [[image1, image2], [image3, image2]] @require_torch @require_vision class SuperGlueModelIntegrationTest(unittest.TestCase): @cached_property def default_image_processor(self): return ( AutoImageProcessor.from_pretrained("magic-leap-community/superglue_outdoor") if is_vision_available() else None ) @slow def test_inference(self): model = SuperGlueForKeypointMatching.from_pretrained("magic-leap-community/superglue_outdoor").to(torch_device) preprocessor = self.default_image_processor images = prepare_imgs() inputs = preprocessor(images=images, return_tensors="pt").to(torch_device) with torch.no_grad(): outputs = model(**inputs, output_hidden_states=True, output_attentions=True) predicted_number_of_matches = torch.sum(outputs.matches[0][0] != -1).item() predicted_matches_values = outputs.matches[0, 0, :30] predicted_matching_scores_values = outputs.matching_scores[0, 0, :20] expected_number_of_matches = 282 expected_matches_values = torch.tensor([125,630,137,138,136,143,135,-1,-1,153, 154,156,117,160,-1,149,147,152,168,-1, 165,182,-1,190,187,188,189,112,-1,193], device=predicted_matches_values.device) # fmt:skip expected_matching_scores_values = torch.tensor([0.9899,0.0033,0.9897,0.9889,0.9879,0.7464,0.7109,0.0,0.0,0.9841, 0.9889,0.9639,0.0114,0.9559,0.0,0.9735,0.8018,0.5190,0.9157,0.0], device=predicted_matches_values.device) # fmt:skip """ Because of inconsistencies introduced between CUDA versions, the checks here are less strict. SuperGlue relies on SuperPoint, which may, depending on CUDA version, return different number of keypoints (866 or 867 in this specific test example). The consequence of having different number of keypoints is that the number of matches will also be different. In the 20 first matches being checked, having one keypoint less will result in 1 less match. The matching scores will also be different, as the keypoints are different. The checks here are less strict to account for these inconsistencies. Therefore, the test checks that the predicted number of matches, matches and matching scores are close to the expected values, individually. Here, the tolerance of the number of values changing is set to 2. This was discussed [here](https://github.com/huggingface/transformers/pull/29886#issuecomment-2482752787) Such CUDA inconsistencies can be found [here](https://github.com/huggingface/transformers/pull/33200/files#r1785980300) """ self.assertTrue(abs(predicted_number_of_matches - expected_number_of_matches) < 4) self.assertTrue( torch.sum(~torch.isclose(predicted_matching_scores_values, expected_matching_scores_values, atol=1e-2)) < 4 ) self.assertTrue(torch.sum(predicted_matches_values != expected_matches_values) < 4) self.assertTrue(torch.all(outputs.matches[0, 1] < torch.sum(outputs.mask[0, 0]))) self.assertTrue(torch.all(outputs.matches[0, 0] < torch.sum(outputs.mask[0, 1])))