# Copyright 2024 The HuggingFace Inc. 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. """Testing suite for the PyTorch Grounding DINO model.""" import collections import inspect import math import re import unittest from functools import cached_property from datasets import load_dataset from transformers import ( GroundingDinoConfig, SwinConfig, is_torch_available, is_vision_available, ) from transformers.testing_utils import ( Expectations, is_flaky, require_timm, require_torch, require_torch_accelerator, require_vision, slow, torch_device, ) from ...test_configuration_common import ConfigTester from ...test_modeling_common import ModelTesterMixin, floats_tensor from ...test_pipeline_mixin import PipelineTesterMixin if is_torch_available(): import torch from transformers import GroundingDinoConfig, GroundingDinoForObjectDetection, GroundingDinoModel from transformers.pytorch_utils import id_tensor_storage if is_vision_available(): from PIL import Image from transformers import AutoProcessor def generate_fake_bounding_boxes(n_boxes): """Generate bounding boxes in the format (center_x, center_y, width, height)""" # Validate the input if not isinstance(n_boxes, int): raise TypeError("n_boxes must be an integer") if n_boxes <= 0: raise ValueError("n_boxes must be a positive integer") # Generate random bounding boxes in the format (center_x, center_y, width, height) bounding_boxes = torch.rand((n_boxes, 4)) # Extract the components center_x = bounding_boxes[:, 0] center_y = bounding_boxes[:, 1] width = bounding_boxes[:, 2] height = bounding_boxes[:, 3] # Ensure width and height do not exceed bounds width = torch.min(width, torch.tensor(1.0)) height = torch.min(height, torch.tensor(1.0)) # Ensure the bounding box stays within the normalized space center_x = torch.where(center_x - width / 2 < 0, width / 2, center_x) center_x = torch.where(center_x + width / 2 > 1, 1 - width / 2, center_x) center_y = torch.where(center_y - height / 2 < 0, height / 2, center_y) center_y = torch.where(center_y + height / 2 > 1, 1 - height / 2, center_y) # Combine back into bounding boxes bounding_boxes = torch.stack([center_x, center_y, width, height], dim=1) return bounding_boxes class GroundingDinoModelTester: def __init__( self, parent, batch_size=4, is_training=True, use_labels=True, hidden_size=32, num_hidden_layers=2, num_attention_heads=4, intermediate_size=4, hidden_act="gelu", hidden_dropout_prob=0.1, attention_probs_dropout_prob=0.1, num_queries=2, num_channels=3, image_size=98, n_targets=8, num_labels=2, num_feature_levels=4, encoder_n_points=2, decoder_n_points=6, max_text_len=7, ): self.parent = parent self.batch_size = batch_size self.is_training = is_training self.use_labels = use_labels self.hidden_size = hidden_size self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads self.intermediate_size = intermediate_size self.hidden_act = hidden_act self.hidden_dropout_prob = hidden_dropout_prob self.attention_probs_dropout_prob = attention_probs_dropout_prob self.num_queries = num_queries self.num_channels = num_channels self.image_size = image_size self.n_targets = n_targets self.num_labels = num_labels self.num_feature_levels = num_feature_levels self.encoder_n_points = encoder_n_points self.decoder_n_points = decoder_n_points self.max_text_len = max_text_len # we also set the expected seq length for both encoder and decoder self.encoder_seq_length_vision = ( math.ceil(self.image_size / 8) ** 2 + math.ceil(self.image_size / 16) ** 2 + math.ceil(self.image_size / 32) ** 2 + math.ceil(self.image_size / 64) ** 2 ) self.encoder_seq_length_text = self.max_text_len self.decoder_seq_length = self.num_queries def prepare_config_and_inputs(self): pixel_values = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]) pixel_mask = torch.ones([self.batch_size, self.image_size, self.image_size], device=torch_device) # When using `GroundingDino` the text input template is '{label1}. {label2}. {label3. ... {labelN}.' # Therefore to avoid errors when running tests with `labels` `input_ids` have to follow this structure. # Otherwise when running `build_label_maps` it will throw an error when trying to split the input_ids into segments. input_ids = torch.tensor([101, 3869, 1012, 11420, 3869, 1012, 102], device=torch_device) input_ids = input_ids.unsqueeze(0).expand(self.batch_size, -1) labels = None if self.use_labels: # labels is a list of Dict (each Dict being the labels for a given example in the batch) labels = [] for i in range(self.batch_size): target = {} target["class_labels"] = torch.randint( high=self.num_labels, size=(self.n_targets,), device=torch_device ) target["boxes"] = generate_fake_bounding_boxes(self.n_targets).to(torch_device) target["masks"] = torch.rand(self.n_targets, self.image_size, self.image_size, device=torch_device) labels.append(target) config = self.get_config() return config, pixel_values, pixel_mask, input_ids, labels def get_config(self): swin_config = SwinConfig( window_size=7, embed_dim=8, depths=[1, 1, 1, 1], num_heads=[1, 1, 1, 1], image_size=self.image_size, out_features=["stage2", "stage3", "stage4"], out_indices=[2, 3, 4], ) text_backbone = { "hidden_size": 8, "num_hidden_layers": 2, "num_attention_heads": 2, "intermediate_size": 8, "max_position_embeddings": 8, "model_type": "bert", } return GroundingDinoConfig( d_model=self.hidden_size, encoder_layers=self.num_hidden_layers, decoder_layers=self.num_hidden_layers, encoder_attention_heads=self.num_attention_heads, decoder_attention_heads=self.num_attention_heads, encoder_ffn_dim=self.intermediate_size, decoder_ffn_dim=self.intermediate_size, dropout=self.hidden_dropout_prob, attention_dropout=self.attention_probs_dropout_prob, num_queries=self.num_queries, num_labels=self.num_labels, num_feature_levels=self.num_feature_levels, encoder_n_points=self.encoder_n_points, decoder_n_points=self.decoder_n_points, use_timm_backbone=False, backbone_config=swin_config, max_text_len=self.max_text_len, text_config=text_backbone, ) def prepare_config_and_inputs_for_common(self): config, pixel_values, pixel_mask, input_ids, labels = self.prepare_config_and_inputs() inputs_dict = {"pixel_values": pixel_values, "pixel_mask": pixel_mask, "input_ids": input_ids} return config, inputs_dict def create_and_check_model(self, config, pixel_values, pixel_mask, input_ids, labels): model = GroundingDinoModel(config=config) model.to(torch_device) model.eval() result = model(pixel_values=pixel_values, pixel_mask=pixel_mask, input_ids=input_ids) self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.num_queries, self.hidden_size)) def create_and_check_object_detection_head_model(self, config, pixel_values, pixel_mask, input_ids, labels): model = GroundingDinoForObjectDetection(config=config) model.to(torch_device) model.eval() result = model(pixel_values=pixel_values, pixel_mask=pixel_mask, input_ids=input_ids) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_queries, config.max_text_len)) self.parent.assertEqual(result.pred_boxes.shape, (self.batch_size, self.num_queries, 4)) result = model(pixel_values=pixel_values, pixel_mask=pixel_mask, input_ids=input_ids, labels=labels) self.parent.assertEqual(result.loss.shape, ()) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_queries, config.max_text_len)) self.parent.assertEqual(result.pred_boxes.shape, (self.batch_size, self.num_queries, 4)) @require_torch class GroundingDinoModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase): all_model_classes = (GroundingDinoModel, GroundingDinoForObjectDetection) if is_torch_available() else () is_encoder_decoder = True test_missing_keys = False pipeline_model_mapping = ( {"image-feature-extraction": GroundingDinoModel, "zero-shot-object-detection": GroundingDinoForObjectDetection} if is_torch_available() else {} ) # special case for head models def _prepare_for_class(self, inputs_dict, model_class, return_labels=False): inputs_dict = super()._prepare_for_class(inputs_dict, model_class, return_labels=return_labels) if return_labels: if model_class.__name__ == "GroundingDinoForObjectDetection": labels = [] for i in range(self.model_tester.batch_size): target = {} target["class_labels"] = torch.ones( size=(self.model_tester.n_targets,), device=torch_device, dtype=torch.long ) target["boxes"] = torch.ones( self.model_tester.n_targets, 4, device=torch_device, dtype=torch.float ) target["masks"] = torch.ones( self.model_tester.n_targets, self.model_tester.image_size, self.model_tester.image_size, device=torch_device, dtype=torch.float, ) labels.append(target) inputs_dict["labels"] = labels return inputs_dict def setUp(self): self.model_tester = GroundingDinoModelTester(self) self.config_tester = ConfigTester( self, config_class=GroundingDinoConfig, has_text_modality=False, common_properties=["d_model", "encoder_attention_heads", "decoder_attention_heads"], ) def test_config(self): self.config_tester.run_common_tests() 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_object_detection_head_model(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_object_detection_head_model(*config_and_inputs) @unittest.skip(reason="Grounding DINO does not use inputs_embeds") def test_inputs_embeds(self): pass @unittest.skip(reason="Grounding DINO does not have a get_input_embeddings method") def test_model_get_set_embeddings(self): pass @unittest.skip(reason="Grounding DINO does not use token embeddings") def test_resize_tokens_embeddings(self): pass @unittest.skip(reason="Feed forward chunking is not implemented") def test_feed_forward_chunking(self): pass def test_attention_outputs(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() config.return_dict = True for model_class in self.all_model_classes: inputs_dict["output_attentions"] = True inputs_dict["output_hidden_states"] = False config.return_dict = True model = model_class._from_config(config, attn_implementation="eager") config = model.config model.to(torch_device) model.eval() with torch.no_grad(): outputs = model(**self._prepare_for_class(inputs_dict, model_class)) attentions = outputs.encoder_attentions[-1] self.assertEqual(len(attentions), self.model_tester.num_hidden_layers) # check that output_attentions also work using config del inputs_dict["output_attentions"] config.output_attentions = True 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.encoder_attentions[-1] self.assertEqual(len(attentions), self.model_tester.num_hidden_layers) self.assertListEqual( list(attentions[0].shape[-3:]), [ self.model_tester.num_attention_heads, self.model_tester.num_feature_levels, self.model_tester.encoder_n_points, ], ) out_len = len(outputs) correct_outlen = 12 # loss is at first position if "labels" in inputs_dict: correct_outlen += 1 # loss is added to beginning # Object Detection model returns pred_logits and pred_boxes and input_ids if model_class.__name__ == "GroundingDinoForObjectDetection": correct_outlen += 3 self.assertEqual(out_len, correct_outlen) # decoder attentions decoder_attentions = outputs.decoder_attentions[0] self.assertIsInstance(decoder_attentions, (list, tuple)) self.assertEqual(len(decoder_attentions), self.model_tester.num_hidden_layers) self.assertListEqual( list(decoder_attentions[0].shape[-3:]), [self.model_tester.num_attention_heads, self.model_tester.num_queries, self.model_tester.num_queries], ) # cross attentions cross_attentions = outputs.decoder_attentions[-1] self.assertIsInstance(cross_attentions, (list, tuple)) self.assertEqual(len(cross_attentions), self.model_tester.num_hidden_layers) self.assertListEqual( list(cross_attentions[0].shape[-3:]), [ self.model_tester.num_attention_heads, self.model_tester.num_feature_levels, self.model_tester.decoder_n_points, ], ) # Check attention is always last and order is fine inputs_dict["output_attentions"] = True inputs_dict["output_hidden_states"] = True model = model_class(config) model.to(torch_device) model.eval() with torch.no_grad(): outputs = model(**self._prepare_for_class(inputs_dict, model_class)) self.assertEqual(out_len + 3, len(outputs)) self_attentions = outputs.encoder_attentions[-1] self.assertEqual(len(self_attentions), self.model_tester.num_hidden_layers) self.assertListEqual( list(self_attentions[0].shape[-3:]), [ self.model_tester.num_attention_heads, self.model_tester.num_feature_levels, self.model_tester.encoder_n_points, ], ) # overwrite since hidden_states are called encoder_text_hidden_states 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.encoder_vision_hidden_states expected_num_layers = getattr( self.model_tester, "expected_num_hidden_layers", self.model_tester.num_hidden_layers + 1 ) self.assertEqual(len(hidden_states), expected_num_layers) seq_len = self.model_tester.encoder_seq_length_vision self.assertListEqual( list(hidden_states[0].shape[-2:]), [seq_len, self.model_tester.hidden_size], ) hidden_states = outputs.encoder_text_hidden_states self.assertEqual(len(hidden_states), expected_num_layers) seq_len = self.model_tester.encoder_seq_length_text self.assertListEqual( list(hidden_states[0].shape[-2:]), [seq_len, self.model_tester.hidden_size], ) hidden_states = outputs.decoder_hidden_states self.assertIsInstance(hidden_states, (list, tuple)) self.assertEqual(len(hidden_states), expected_num_layers) seq_len = getattr(self.model_tester, "seq_length", None) decoder_seq_length = getattr(self.model_tester, "decoder_seq_length", seq_len) self.assertListEqual( list(hidden_states[0].shape[-2:]), [decoder_seq_length, self.model_tester.hidden_size], ) 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) # removed retain_grad and grad on decoder_hidden_states, as queries don't require grad def test_retain_grad_hidden_states_attentions(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() config.output_hidden_states = True config.output_attentions = True # no need to test all models as different heads yield the same functionality model_class = self.all_model_classes[0] model = model_class(config) model.to(torch_device) inputs = self._prepare_for_class(inputs_dict, model_class) outputs = model(**inputs) output = outputs[0] encoder_hidden_states = outputs.encoder_vision_hidden_states[0] encoder_attentions = outputs.encoder_attentions[0][0] encoder_hidden_states.retain_grad() encoder_attentions.retain_grad() cross_attentions = outputs.decoder_attentions[-1][0] cross_attentions.retain_grad() output.flatten()[0].backward(retain_graph=True) self.assertIsNotNone(encoder_hidden_states.grad) self.assertIsNotNone(encoder_attentions.grad) self.assertIsNotNone(cross_attentions.grad) def test_forward_signature(self): config, _ = self.model_tester.prepare_config_and_inputs_for_common() 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", "input_ids"] self.assertListEqual(arg_names[: len(expected_arg_names)], expected_arg_names) def test_different_timm_backbone(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() # let's pick a random timm backbone config.backbone = "tf_mobilenetv3_small_075" config.use_timm_backbone = True config.backbone_config = None config.backbone_kwargs = {"in_chans": 3, "out_indices": (2, 3, 4)} for model_class in self.all_model_classes: model = model_class(config) model.to(torch_device) model.eval() with torch.no_grad(): outputs = model(**self._prepare_for_class(inputs_dict, model_class)) if model_class.__name__ == "GroundingDinoForObjectDetection": expected_shape = ( self.model_tester.batch_size, self.model_tester.num_queries, config.max_text_len, ) self.assertEqual(outputs.logits.shape, expected_shape) self.assertTrue(outputs) @require_timm def test_hf_backbone(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() # Load a pretrained HF checkpoint as backbone config.backbone = "microsoft/resnet-18" config.backbone_config = None config.use_timm_backbone = False config.use_pretrained_backbone = True config.backbone_kwargs = {"out_indices": [2, 3, 4]} for model_class in self.all_model_classes: model = model_class(config) model.to(torch_device) model.eval() with torch.no_grad(): outputs = model(**self._prepare_for_class(inputs_dict, model_class)) if model_class.__name__ == "GroundingDinoForObjectDetection": expected_shape = ( self.model_tester.batch_size, self.model_tester.num_queries, config.max_text_len, ) self.assertEqual(outputs.logits.shape, expected_shape) self.assertTrue(outputs) # Copied from tests.models.deformable_detr.test_modeling_deformable_detr.DeformableDetrModelTest.test_two_stage_training with DeformableDetr->GroundingDino def test_two_stage_training(self): model_class = GroundingDinoForObjectDetection config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() config.return_dict = True config.two_stage = True config.auxiliary_loss = True config.with_box_refine = True model = model_class(config) model.to(torch_device) model.train() inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True) loss = model(**inputs).loss loss.backward() def test_tied_weights_keys(self): config, _ = self.model_tester.prepare_config_and_inputs_for_common() config.tie_word_embeddings = True for model_class in self.all_model_classes: model_tied = model_class(config) ptrs = collections.defaultdict(list) for name, tensor in model_tied.state_dict().items(): ptrs[id_tensor_storage(tensor)].append(name) # These are all the pointers of shared tensors. tied_params = [names for _, names in ptrs.items() if len(names) > 1] tied_weight_keys = model_tied._tied_weights_keys if model_tied._tied_weights_keys is not None else [] # Detect we get a hit for each key for key in tied_weight_keys: if not any(re.search(key, p) for group in tied_params for p in group): raise ValueError(f"{key} is not a tied weight key for {model_class}.") # Removed tied weights found from tied params -> there should only be one left after for key in tied_weight_keys: for i in range(len(tied_params)): tied_params[i] = [p for p in tied_params[i] if re.search(key, p) is None] # GroundingDino when sharing weights also uses the shared ones in GroundingDinoDecoder # Therefore, differently from DeformableDetr, we expect the group lens to be 2 # one for self.bbox_embed in GroundingDinoForObjectDetection and another one # in the decoder tied_params = [group for group in tied_params if len(group) > 2] self.assertListEqual( tied_params, [], f"Missing `_tied_weights_keys` for {model_class}: add all of {tied_params} except one.", ) # We will verify our results on an image of cute cats def prepare_img(): image = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png") return image def prepare_text(): text = "a cat." return text @require_timm @require_vision @slow class GroundingDinoModelIntegrationTests(unittest.TestCase): @cached_property def default_processor(self): return AutoProcessor.from_pretrained("IDEA-Research/grounding-dino-tiny") if is_vision_available() else None def test_inference_object_detection_head(self): model = GroundingDinoForObjectDetection.from_pretrained("IDEA-Research/grounding-dino-tiny").to(torch_device) processor = self.default_processor image = prepare_img() text = prepare_text() encoding = processor(images=image, text=text, return_tensors="pt").to(torch_device) with torch.no_grad(): outputs = model(**encoding) expected_shape_logits = torch.Size((1, model.config.num_queries, model.config.d_model)) self.assertEqual(outputs.logits.shape, expected_shape_logits) expectations = Expectations( { (None, None): [[0.7674, 0.4136, 0.4572], [0.2566, 0.5463, 0.4760], [0.2585, 0.5442, 0.4641]], ("cuda", 8): [[0.7674, 0.4135, 0.4571], [0.2566, 0.5463, 0.4760], [0.2585, 0.5442, 0.4640]], } ) expected_boxes = torch.tensor(expectations.get_expectation()).to(torch_device) expectations = Expectations( { (None, None): [[-4.8913, -0.1900, -0.2161], [-4.9653, -0.3719, -0.3950], [-5.9599, -3.3765, -3.3104]], ("cuda", 8): [[-4.8915, -0.1900, -0.2161], [-4.9658, -0.3716, -0.3948], [-5.9596, -3.3763, -3.3103]], } ) expected_logits = torch.tensor(expectations.get_expectation()).to(torch_device) torch.testing.assert_close(outputs.logits[0, :3, :3], expected_logits, rtol=1e-3, atol=1e-3) expected_shape_boxes = torch.Size((1, model.config.num_queries, 4)) self.assertEqual(outputs.pred_boxes.shape, expected_shape_boxes) torch.testing.assert_close(outputs.pred_boxes[0, :3, :3], expected_boxes, rtol=2e-4, atol=2e-4) # verify postprocessing results = processor.image_processor.post_process_object_detection( outputs, threshold=0.35, target_sizes=[(image.height, image.width)] )[0] expectations = Expectations( { (None, None): [0.4526, 0.4082], ("cuda", 8): [0.4524, 0.4074], } ) expected_scores = torch.tensor(expectations.get_expectation()).to(torch_device) expectations = Expectations( { (None, None): [344.8143, 23.1796, 637.4004, 373.8295], ("cuda", 8): [344.8210, 23.1831, 637.3943, 373.8227], } ) expected_slice_boxes = torch.tensor(expectations.get_expectation()).to(torch_device) self.assertEqual(len(results["scores"]), 2) torch.testing.assert_close(results["scores"], expected_scores, rtol=1e-3, atol=1e-3) torch.testing.assert_close(results["boxes"][0, :], expected_slice_boxes, rtol=1e-2, atol=1e-2) # verify grounded postprocessing expected_labels = ["a cat", "a cat"] results = processor.post_process_grounded_object_detection( outputs=outputs, input_ids=encoding.input_ids, threshold=0.35, text_threshold=0.3, target_sizes=[(image.height, image.width)], )[0] torch.testing.assert_close(results["scores"], expected_scores, rtol=1e-3, atol=1e-3) torch.testing.assert_close(results["boxes"][0, :], expected_slice_boxes, rtol=1e-2, atol=1e-2) self.assertListEqual(results["text_labels"], expected_labels) @require_torch_accelerator @is_flaky() def test_inference_object_detection_head_equivalence_cpu_accelerator(self): processor = self.default_processor image = prepare_img() text = prepare_text() encoding = processor(images=image, text=text, return_tensors="pt") # 1. run model on CPU model = GroundingDinoForObjectDetection.from_pretrained("IDEA-Research/grounding-dino-tiny") with torch.no_grad(): cpu_outputs = model(**encoding) # 2. run model on accelerator model.to(torch_device) encoding = encoding.to(torch_device) with torch.no_grad(): gpu_outputs = model(**encoding) # 3. assert equivalence for key in cpu_outputs: torch.testing.assert_close(cpu_outputs[key], gpu_outputs[key].cpu(), rtol=1e-3, atol=1e-3) expected_logits = torch.tensor( [[-4.8915, -0.1900, -0.2161], [-4.9658, -0.3716, -0.3948], [-5.9596, -3.3763, -3.3103]] ) torch.testing.assert_close(cpu_outputs.logits[0, :3, :3], expected_logits, rtol=1e-3, atol=1e-3) # assert postprocessing results_cpu = processor.image_processor.post_process_object_detection( cpu_outputs, threshold=0.35, target_sizes=[(image.height, image.width)] )[0] result_gpu = processor.image_processor.post_process_object_detection( gpu_outputs, threshold=0.35, target_sizes=[(image.height, image.width)] )[0] torch.testing.assert_close(results_cpu["scores"], result_gpu["scores"].cpu(), rtol=1e-3, atol=1e-3) torch.testing.assert_close(results_cpu["boxes"], result_gpu["boxes"].cpu(), rtol=1e-3, atol=1e-3) @is_flaky() def test_cross_attention_mask(self): model = GroundingDinoForObjectDetection.from_pretrained("IDEA-Research/grounding-dino-tiny").to(torch_device) processor = self.default_processor image = prepare_img() text1 = "a cat." text2 = "a remote control." text_batched = [text1, text2] encoding1 = processor(images=image, text=text1, return_tensors="pt").to(torch_device) encoding2 = processor(images=image, text=text2, return_tensors="pt").to(torch_device) # If we batch the text and cross attention masking is working the batched result should be equal to # The single text result encoding_batched = processor( images=[image] * len(text_batched), text=text_batched, padding="longest", return_tensors="pt" ).to(torch_device) with torch.no_grad(): outputs1 = model(**encoding1) outputs2 = model(**encoding2) outputs_batched = model(**encoding_batched) torch.testing.assert_close(outputs1.logits, outputs_batched.logits[:1], rtol=1e-3, atol=1e-3) # For some reason 12 elements are > 1e-3, but the rest are fine self.assertTrue(torch.allclose(outputs2.logits, outputs_batched.logits[1:], atol=1.8e-3)) def test_grounding_dino_loss(self): ds = load_dataset("EduardoPacheco/aquarium-sample", split="train") image_processor = self.default_processor.image_processor tokenizer = self.default_processor.tokenizer id2label = {0: "fish", 1: "jellyfish", 2: "penguins", 3: "sharks", 4: "puffins", 5: "stingrays", 6: "starfish"} prompt = ". ".join(id2label.values()) + "." text_inputs = tokenizer([prompt, prompt], return_tensors="pt") image_inputs = image_processor( images=list(ds["image"]), annotations=list(ds["annotations"]), return_tensors="pt" ) # Passing auxiliary_loss=True to compare with the expected loss model = GroundingDinoForObjectDetection.from_pretrained( "IDEA-Research/grounding-dino-tiny", auxiliary_loss=True, ) # Interested in the loss only model.eval() with torch.no_grad(): outputs = model(**text_inputs, **image_inputs) # Loss differs by CPU and accelerator, also this can be changed in future. expected_loss_dicts = Expectations( { ("xpu", 3): { "loss_ce": torch.tensor(1.1147), "loss_bbox": torch.tensor(0.2031), "loss_giou": torch.tensor(0.5819), "loss_ce_0": torch.tensor(1.1941), "loss_bbox_0": torch.tensor(0.1978), "loss_giou_0": torch.tensor(0.5524), "loss_ce_1": torch.tensor(1.1621), "loss_bbox_1": torch.tensor(0.1909), "loss_giou_1": torch.tensor(0.5892), "loss_ce_2": torch.tensor(1.1641), "loss_bbox_2": torch.tensor(0.1892), "loss_giou_2": torch.tensor(0.5626), "loss_ce_3": torch.tensor(1.1943), "loss_bbox_3": torch.tensor(0.1941), "loss_giou_3": torch.tensor(0.5592), "loss_ce_4": torch.tensor(1.0956), "loss_bbox_4": torch.tensor(0.2037), "loss_giou_4": torch.tensor(0.5813), "loss_ce_enc": torch.tensor(16226.3164), "loss_bbox_enc": torch.tensor(0.3063), "loss_giou_enc": torch.tensor(0.7380), }, ("cuda", None): { "loss_ce": torch.tensor(1.1147), "loss_bbox": torch.tensor(0.2031), "loss_giou": torch.tensor(0.5819), "loss_ce_0": torch.tensor(1.1941), "loss_bbox_0": torch.tensor(0.1978), "loss_giou_0": torch.tensor(0.5524), "loss_ce_1": torch.tensor(1.1621), "loss_bbox_1": torch.tensor(0.1909), "loss_giou_1": torch.tensor(0.5892), "loss_ce_2": torch.tensor(1.1641), "loss_bbox_2": torch.tensor(0.1892), "loss_giou_2": torch.tensor(0.5626), "loss_ce_3": torch.tensor(1.1943), "loss_bbox_3": torch.tensor(0.1941), "loss_giou_3": torch.tensor(0.5607), "loss_ce_4": torch.tensor(1.0956), "loss_bbox_4": torch.tensor(0.2008), "loss_giou_4": torch.tensor(0.5836), "loss_ce_enc": torch.tensor(16226.3164), "loss_bbox_enc": torch.tensor(0.3063), "loss_giou_enc": torch.tensor(0.7380), }, } ) # fmt: skip expected_loss_dict = expected_loss_dicts.get_expectation() expected_loss = torch.tensor(32482.2305) for key in expected_loss_dict: torch.testing.assert_close(outputs.loss_dict[key], expected_loss_dict[key], rtol=1e-5, atol=1e-3) self.assertTrue(torch.allclose(outputs.loss, expected_loss, atol=1e-3))