# coding=utf-8 # Copyright 2022 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 DETA model. """ import inspect import math import unittest from transformers import DetaConfig, ResNetConfig, is_torch_available, is_torchvision_available, is_vision_available from transformers.file_utils import cached_property from transformers.testing_utils import require_torchvision, require_vision, slow, torch_device from ...generation.test_utils import GenerationTesterMixin from ...test_configuration_common import ConfigTester from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor from ...test_pipeline_mixin import PipelineTesterMixin if is_torch_available(): import torch if is_torchvision_available(): from transformers import DetaForObjectDetection, DetaModel if is_vision_available(): from PIL import Image from transformers import AutoImageProcessor class DetaModelTester: def __init__( self, parent, batch_size=8, is_training=True, use_labels=True, hidden_size=32, num_hidden_layers=2, num_attention_heads=8, intermediate_size=4, hidden_act="gelu", hidden_dropout_prob=0.1, attention_probs_dropout_prob=0.1, num_queries=12, num_channels=3, image_size=196, n_targets=8, num_labels=91, num_feature_levels=4, encoder_n_points=2, decoder_n_points=6, two_stage=False, ): 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.two_stage = two_stage # we also set the expected seq length for both encoder and decoder self.encoder_seq_length = ( 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.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) 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"] = torch.rand(self.n_targets, 4, device=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, labels def get_config(self): resnet_config = ResNetConfig( num_channels=3, embeddings_size=10, hidden_sizes=[10, 20, 30, 40], depths=[1, 1, 2, 1], hidden_act="relu", num_labels=3, out_features=["stage2", "stage3", "stage4"], out_indices=[2, 3, 4], ) return DetaConfig( 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, two_stage=self.two_stage, backbone_config=resnet_config, ) def prepare_config_and_inputs_for_common(self): config, pixel_values, pixel_mask, labels = self.prepare_config_and_inputs() inputs_dict = {"pixel_values": pixel_values, "pixel_mask": pixel_mask} return config, inputs_dict def create_and_check_deta_model(self, config, pixel_values, pixel_mask, labels): model = DetaModel(config=config) model.to(torch_device) model.eval() result = model(pixel_values=pixel_values, pixel_mask=pixel_mask) result = model(pixel_values) self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.num_queries, self.hidden_size)) def create_and_check_deta_object_detection_head_model(self, config, pixel_values, pixel_mask, labels): model = DetaForObjectDetection(config=config) model.to(torch_device) model.eval() result = model(pixel_values=pixel_values, pixel_mask=pixel_mask) result = model(pixel_values) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_queries, self.num_labels)) self.parent.assertEqual(result.pred_boxes.shape, (self.batch_size, self.num_queries, 4)) result = model(pixel_values=pixel_values, pixel_mask=pixel_mask, labels=labels) self.parent.assertEqual(result.loss.shape, ()) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_queries, self.num_labels)) self.parent.assertEqual(result.pred_boxes.shape, (self.batch_size, self.num_queries, 4)) @require_torchvision class DetaModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase): all_model_classes = (DetaModel, DetaForObjectDetection) if is_torchvision_available() else () pipeline_model_mapping = ( {"feature-extraction": DetaModel, "object-detection": DetaForObjectDetection} if is_torchvision_available() else {} ) is_encoder_decoder = True test_torchscript = False test_pruning = False test_head_masking = False test_missing_keys = False # TODO: Fix the failed tests when this model gets more usage def is_pipeline_test_to_skip( self, pipeline_test_casse_name, config_class, model_architecture, tokenizer_name, processor_name ): if pipeline_test_casse_name == "ObjectDetectionPipelineTests": return True return False @unittest.skip("Skip for now. PR #22437 causes some loading issue. See (not merged) #22656 for some discussions.") def test_can_use_safetensors(self): super().test_can_use_safetensors() # 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__ == "DetaForObjectDetection": 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 = DetaModelTester(self) self.config_tester = ConfigTester(self, config_class=DetaConfig, has_text_modality=False) def test_config(self): # we don't test common_properties and arguments_init as these don't apply for DETA 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() def test_deta_model(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_deta_model(*config_and_inputs) def test_deta_object_detection_head_model(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_deta_object_detection_head_model(*config_and_inputs) @unittest.skip(reason="DETA does not use inputs_embeds") def test_inputs_embeds(self): pass @unittest.skip(reason="DETA does not have a get_input_embeddings method") def test_model_common_attributes(self): pass @unittest.skip(reason="DETA is not a generative model") def test_generate_without_input_ids(self): pass @unittest.skip(reason="DETA 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(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 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 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 = 8 # 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 if model_class.__name__ == "DetaForObjectDetection": correct_outlen += 2 self.assertEqual(out_len, correct_outlen) # decoder attentions decoder_attentions = outputs.decoder_attentions 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.cross_attentions 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)) if hasattr(self.model_tester, "num_hidden_states_types"): added_hidden_states = self.model_tester.num_hidden_states_types elif self.is_encoder_decoder: added_hidden_states = 2 else: added_hidden_states = 1 self.assertEqual(out_len + added_hidden_states, len(outputs)) self_attentions = outputs.encoder_attentions 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, ], ) # 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) # we take the second output since last_hidden_state is the second item output = outputs[1] encoder_hidden_states = outputs.encoder_hidden_states[0] encoder_attentions = outputs.encoder_attentions[0] encoder_hidden_states.retain_grad() encoder_attentions.retain_grad() decoder_attentions = outputs.decoder_attentions[0] decoder_attentions.retain_grad() cross_attentions = outputs.cross_attentions[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(decoder_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()] if model.config.is_encoder_decoder: expected_arg_names = ["pixel_values", "pixel_mask"] expected_arg_names.extend( ["head_mask", "decoder_head_mask", "encoder_outputs"] if "head_mask" and "decoder_head_mask" in arg_names else [] ) self.assertListEqual(arg_names[: len(expected_arg_names)], expected_arg_names) else: expected_arg_names = ["pixel_values", "pixel_mask"] self.assertListEqual(arg_names[:1], expected_arg_names) @unittest.skip(reason="Model doesn't use tied weights") def test_tied_model_weights_key_ignore(self): pass def test_initialization(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() configs_no_init = _config_zero_init(config) for model_class in self.all_model_classes: model = model_class(config=configs_no_init) # Skip the check for the backbone for name, module in model.named_modules(): if module.__class__.__name__ == "DetaBackboneWithPositionalEncodings": backbone_params = [f"{name}.{key}" for key in module.state_dict().keys()] break for name, param in model.named_parameters(): if param.requires_grad: if ( "level_embed" in name or "sampling_offsets.bias" in name or "value_proj" in name or "output_proj" in name or "reference_points" in name or name in backbone_params ): continue self.assertIn( ((param.data.mean() * 1e9).round() / 1e9).item(), [0.0, 1.0], msg=f"Parameter {name} of model {model_class} seems not properly initialized", ) TOLERANCE = 1e-4 # 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 @require_torchvision @require_vision @slow class DetaModelIntegrationTests(unittest.TestCase): @cached_property def default_image_processor(self): return AutoImageProcessor.from_pretrained("jozhang97/deta-resnet-50") if is_vision_available() else None def test_inference_object_detection_head(self): model = DetaForObjectDetection.from_pretrained("jozhang97/deta-resnet-50").to(torch_device) image_processor = self.default_image_processor image = prepare_img() inputs = image_processor(images=image, return_tensors="pt").to(torch_device) with torch.no_grad(): outputs = model(**inputs) expected_shape_logits = torch.Size((1, 300, model.config.num_labels)) self.assertEqual(outputs.logits.shape, expected_shape_logits) expected_logits = torch.tensor( [[-7.3978, -2.5406, -4.1668], [-8.2684, -3.9933, -3.8096], [-7.0515, -3.7973, -5.8516]] ).to(torch_device) expected_boxes = torch.tensor( [[0.5043, 0.4973, 0.9998], [0.2542, 0.5489, 0.4748], [0.5490, 0.2765, 0.0570]] ).to(torch_device) self.assertTrue(torch.allclose(outputs.logits[0, :3, :3], expected_logits, atol=1e-4)) expected_shape_boxes = torch.Size((1, 300, 4)) self.assertEqual(outputs.pred_boxes.shape, expected_shape_boxes) self.assertTrue(torch.allclose(outputs.pred_boxes[0, :3, :3], expected_boxes, atol=1e-4)) # verify postprocessing results = image_processor.post_process_object_detection( outputs, threshold=0.3, target_sizes=[image.size[::-1]] )[0] expected_scores = torch.tensor([0.6392, 0.6276, 0.5546, 0.5260, 0.4706], device=torch_device) expected_labels = [75, 17, 17, 75, 63] expected_slice_boxes = torch.tensor([40.5866, 73.2107, 176.1421, 117.1751], device=torch_device) self.assertTrue(torch.allclose(results["scores"], expected_scores, atol=1e-4)) self.assertSequenceEqual(results["labels"].tolist(), expected_labels) self.assertTrue(torch.allclose(results["boxes"][0, :], expected_slice_boxes)) def test_inference_object_detection_head_swin_backbone(self): model = DetaForObjectDetection.from_pretrained("jozhang97/deta-swin-large").to(torch_device) image_processor = self.default_image_processor image = prepare_img() inputs = image_processor(images=image, return_tensors="pt").to(torch_device) with torch.no_grad(): outputs = model(**inputs) expected_shape_logits = torch.Size((1, 300, model.config.num_labels)) self.assertEqual(outputs.logits.shape, expected_shape_logits) expected_logits = torch.tensor( [[-7.6308, -2.8485, -5.3737], [-7.2037, -4.5505, -4.8027], [-7.2943, -4.2611, -4.6617]] ).to(torch_device) expected_boxes = torch.tensor( [[0.4987, 0.4969, 0.9999], [0.2549, 0.5498, 0.4805], [0.5498, 0.2757, 0.0569]] ).to(torch_device) self.assertTrue(torch.allclose(outputs.logits[0, :3, :3], expected_logits, atol=1e-4)) expected_shape_boxes = torch.Size((1, 300, 4)) self.assertEqual(outputs.pred_boxes.shape, expected_shape_boxes) self.assertTrue(torch.allclose(outputs.pred_boxes[0, :3, :3], expected_boxes, atol=1e-4)) # verify postprocessing results = image_processor.post_process_object_detection( outputs, threshold=0.3, target_sizes=[image.size[::-1]] )[0] expected_scores = torch.tensor([0.6831, 0.6826, 0.5684, 0.5464, 0.4392], device=torch_device) expected_labels = [17, 17, 75, 75, 63] expected_slice_boxes = torch.tensor([345.8478, 23.6754, 639.8562, 372.8265], device=torch_device) self.assertTrue(torch.allclose(results["scores"], expected_scores, atol=1e-4)) self.assertSequenceEqual(results["labels"].tolist(), expected_labels) self.assertTrue(torch.allclose(results["boxes"][0, :], expected_slice_boxes))