# 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 Deformable DETR model.""" import inspect import math import unittest from functools import cached_property from transformers import DeformableDetrConfig, ResNetConfig, is_torch_available, is_vision_available from transformers.testing_utils import ( require_timm, require_torch, require_torch_accelerator, require_torch_bf16, 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 DeformableDetrForObjectDetection, DeformableDetrModel if is_vision_available(): from PIL import Image from transformers import AutoImageProcessor class DeformableDetrModelTester: 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, ): 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 # 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 DeformableDetrConfig( 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=None, backbone_config=resnet_config, use_pretrained_backbone=False, ) 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_deformable_detr_model(self, config, pixel_values, pixel_mask, labels): model = DeformableDetrModel(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_deformable_detr_object_detection_head_model(self, config, pixel_values, pixel_mask, labels): model = DeformableDetrForObjectDetection(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_torch class DeformableDetrModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase): all_model_classes = (DeformableDetrModel, DeformableDetrForObjectDetection) if is_torch_available() else () pipeline_model_mapping = ( {"image-feature-extraction": DeformableDetrModel, "object-detection": DeformableDetrForObjectDetection} if is_torch_available() else {} ) is_encoder_decoder = True test_missing_keys = False test_torch_exportable = True # 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__ == "DeformableDetrForObjectDetection": 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 = DeformableDetrModelTester(self) self.config_tester = ConfigTester( self, config_class=DeformableDetrConfig, has_text_modality=False, common_properties=["num_channels", "d_model", "encoder_attention_heads", "decoder_attention_heads"], ) def test_config(self): self.config_tester.run_common_tests() def test_deformable_detr_model(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_deformable_detr_model(*config_and_inputs) def test_deformable_detr_object_detection_head_model(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_deformable_detr_object_detection_head_model(*config_and_inputs) @unittest.skip(reason="Deformable DETR does not use inputs_embeds") def test_inputs_embeds(self): pass @unittest.skip(reason="Deformable DETR does not use inputs_embeds") def test_inputs_embeds_matches_input_ids(self): pass @unittest.skip(reason="Deformable DETR does not have a get_input_embeddings method") def test_model_get_set_embeddings(self): pass @unittest.skip(reason="Deformable DETR is not a generative model") def test_generate_without_input_ids(self): pass @unittest.skip(reason="Deformable DETR 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 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__ == "DeformableDetrForObjectDetection": 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, ], ) def test_model_outputs_equivalence(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() def set_nan_tensor_to_zero(t): t[t != t] = 0 return t def check_equivalence(model, tuple_inputs, dict_inputs, additional_kwargs={}): with torch.no_grad(): tuple_output = model(**tuple_inputs, return_dict=False, **additional_kwargs) dict_output = model(**dict_inputs, return_dict=True, **additional_kwargs).to_tuple() def recursive_check(tuple_object, dict_object): if isinstance(tuple_object, (list, tuple)): for tuple_iterable_value, dict_iterable_value in zip(tuple_object, dict_object): recursive_check(tuple_iterable_value, dict_iterable_value) elif isinstance(tuple_object, dict): for tuple_iterable_value, dict_iterable_value in zip( tuple_object.values(), dict_object.values() ): recursive_check(tuple_iterable_value, dict_iterable_value) elif tuple_object is None: return else: self.assertTrue( torch.allclose( set_nan_tensor_to_zero(tuple_object), set_nan_tensor_to_zero(dict_object), atol=1e-5 ), msg=( "Tuple and dict output are not equal. Difference:" f" {torch.max(torch.abs(tuple_object - dict_object))}. Tuple has `nan`:" f" {torch.isnan(tuple_object).any()} and `inf`: {torch.isinf(tuple_object)}. Dict has" f" `nan`: {torch.isnan(dict_object).any()} and `inf`: {torch.isinf(dict_object)}." ), ) recursive_check(tuple_output, dict_output) for model_class in self.all_model_classes: print("Model class:", model_class) model = model_class(config) model.to(torch_device) model.eval() tuple_inputs = self._prepare_for_class(inputs_dict, model_class) dict_inputs = self._prepare_for_class(inputs_dict, model_class) check_equivalence(model, tuple_inputs, dict_inputs) tuple_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True) dict_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True) check_equivalence(model, tuple_inputs, dict_inputs) tuple_inputs = self._prepare_for_class(inputs_dict, model_class) dict_inputs = self._prepare_for_class(inputs_dict, model_class) check_equivalence(model, tuple_inputs, dict_inputs, {"output_hidden_states": True}) tuple_inputs = self._prepare_for_class(inputs_dict, model_class) dict_inputs = self._prepare_for_class(inputs_dict, model_class) check_equivalence(model, tuple_inputs, dict_inputs, {"output_attentions": True}) tuple_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True) dict_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True) check_equivalence(model, tuple_inputs, dict_inputs, {"output_hidden_states": True}) tuple_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True) dict_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True) check_equivalence(model, tuple_inputs, dict_inputs, {"output_attentions": True}) tuple_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True) dict_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True) check_equivalence( model, tuple_inputs, dict_inputs, {"output_hidden_states": True, "output_attentions": True} ) def test_retain_grad_hidden_states_attentions(self): # removed retain_grad and grad on decoder_hidden_states, as queries don't require grad 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_auxiliary_loss(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() config.auxiliary_loss = True # only test for object detection and segmentation model for model_class in self.all_model_classes[1:]: model = model_class(config) model.to(torch_device) inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True) outputs = model(**inputs) self.assertIsNotNone(outputs.auxiliary_outputs) self.assertEqual(len(outputs.auxiliary_outputs), self.model_tester.num_hidden_layers - 1) 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", "decoder_attention_mask"] 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) 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.backbone_config = None config.use_timm_backbone = True config.backbone_kwargs = {"out_indices": [1, 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__ == "DeformableDetrForObjectDetection": expected_shape = ( self.model_tester.batch_size, self.model_tester.num_queries, self.model_tester.num_labels, ) self.assertEqual(outputs.logits.shape, expected_shape) # Confirm out_indices was propagated to backbone self.assertEqual(len(model.model.backbone.conv_encoder.intermediate_channel_sizes), 4) else: # Confirm out_indices was propagated to backbone self.assertEqual(len(model.backbone.conv_encoder.intermediate_channel_sizes), 4) self.assertTrue(outputs) 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": [1, 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__ == "DeformableDetrForObjectDetection": expected_shape = ( self.model_tester.batch_size, self.model_tester.num_queries, self.model_tester.num_labels, ) self.assertEqual(outputs.logits.shape, expected_shape) # Confirm out_indices was propagated to backbone self.assertEqual(len(model.model.backbone.conv_encoder.intermediate_channel_sizes), 4) else: # Confirm out_indices was propagated to backbone self.assertEqual(len(model.backbone.conv_encoder.intermediate_channel_sizes), 4) self.assertTrue(outputs) def test_two_stage_training(self): model_class = DeformableDetrForObjectDetection 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 create_and_check_model_fp16_forward(self): model_class = DeformableDetrForObjectDetection config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() model = model_class(config) model.to(torch_device) model.half() model.eval() inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True) output = model(**inputs)["last_hidden_state"] self.parent.assertFalse(torch.isnan(output).any().item()) @require_torch_bf16 def create_and_check_model_bf16_forward(self): model_class = DeformableDetrForObjectDetection config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() model = model_class(config, dtype=torch.bfloat16) model.to(torch_device) model.eval() inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True) output = model(**inputs)["last_hidden_state"] self.parent.assertFalse(torch.isnan(output).any().item()) 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_timm @require_vision @slow class DeformableDetrModelIntegrationTests(unittest.TestCase): @cached_property def default_image_processor(self): return AutoImageProcessor.from_pretrained("SenseTime/deformable-detr") if is_vision_available() else None def test_inference_object_detection_head(self): model = DeformableDetrForObjectDetection.from_pretrained("SenseTime/deformable-detr").to(torch_device) image_processor = self.default_image_processor image = prepare_img() encoding = image_processor(images=image, return_tensors="pt").to(torch_device) pixel_values = encoding["pixel_values"].to(torch_device) pixel_mask = encoding["pixel_mask"].to(torch_device) with torch.no_grad(): outputs = model(pixel_values, pixel_mask) expected_shape_logits = torch.Size((1, model.config.num_queries, model.config.num_labels)) self.assertEqual(outputs.logits.shape, expected_shape_logits) expected_logits = torch.tensor( [ [-9.6645, -4.3449, -5.8705], [-9.7035, -3.8504, -5.0724], [-10.5634, -5.3379, -7.5116], ] ).to(torch_device) expected_boxes = torch.tensor( [ [0.8693, 0.2290, 0.2492], [0.3150, 0.5489, 0.5845], [0.5563, 0.7580, 0.8518], ] ).to(torch_device) torch.testing.assert_close(outputs.logits[0, :3, :3], expected_logits, rtol=2e-4, atol=2e-4) 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 = image_processor.post_process_object_detection( outputs, threshold=0.3, target_sizes=[image.size[::-1]] )[0] expected_scores = torch.tensor([0.7999, 0.7895, 0.6332, 0.4719, 0.4382]).to(torch_device) expected_labels = [17, 17, 75, 75, 63] expected_slice_boxes = torch.tensor([16.5028, 52.8391, 318.2544, 470.7841]).to(torch_device) self.assertEqual(len(results["scores"]), 5) torch.testing.assert_close(results["scores"], expected_scores, rtol=2e-4, atol=2e-4) self.assertSequenceEqual(results["labels"].tolist(), expected_labels) torch.testing.assert_close(results["boxes"][0, :], expected_slice_boxes, rtol=2e-4, atol=2e-4) def test_inference_object_detection_head_with_box_refine_two_stage(self): model = DeformableDetrForObjectDetection.from_pretrained( "SenseTime/deformable-detr-with-box-refine-two-stage" ).to(torch_device) image_processor = self.default_image_processor image = prepare_img() encoding = image_processor(images=image, return_tensors="pt").to(torch_device) pixel_values = encoding["pixel_values"].to(torch_device) pixel_mask = encoding["pixel_mask"].to(torch_device) with torch.no_grad(): outputs = model(pixel_values, pixel_mask) expected_shape_logits = torch.Size((1, model.config.num_queries, model.config.num_labels)) self.assertEqual(outputs.logits.shape, expected_shape_logits) expected_logits = torch.tensor( [ [-6.7108, -4.3213, -6.3777], [-8.9014, -6.1799, -6.7240], [-6.9315, -4.4735, -6.2298], ] ).to(torch_device) expected_boxes = torch.tensor( [ [0.2583, 0.5499, 0.4683], [0.7652, 0.9068, 0.4882], [0.5490, 0.2763, 0.0564], ] ).to(torch_device) torch.testing.assert_close(outputs.logits[0, :3, :3], expected_logits, rtol=2e-4, atol=2e-4) 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) @require_torch_accelerator def test_inference_object_detection_head_equivalence_cpu_accelerator(self): image_processor = self.default_image_processor image = prepare_img() encoding = image_processor(images=image, return_tensors="pt") pixel_values = encoding["pixel_values"] pixel_mask = encoding["pixel_mask"] # 1. run model on CPU model = DeformableDetrForObjectDetection.from_pretrained("SenseTime/deformable-detr-single-scale") with torch.no_grad(): cpu_outputs = model(pixel_values, pixel_mask) # 2. run model on accelerator model.to(torch_device) with torch.no_grad(): gpu_outputs = model(pixel_values.to(torch_device), pixel_mask.to(torch_device)) # 3. assert equivalence # (on A10, the differences get larger than on T4) for key in cpu_outputs: torch.testing.assert_close(cpu_outputs[key], gpu_outputs[key].cpu(), atol=2e-2, rtol=2e-2) expected_logits = torch.tensor( [ [-9.9051, -4.2541, -6.4852], [-9.6947, -4.0854, -6.8033], [-10.0665, -5.8470, -7.7003], ] ) assert torch.allclose(cpu_outputs.logits[0, :3, :3], expected_logits, atol=2e-4)