# Copyright 2021 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 DETR model.""" import inspect import math import unittest from functools import cached_property from transformers import DetrConfig, ResNetConfig, is_torch_available, is_vision_available from transformers.testing_utils import Expectations, require_timm, require_torch, 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 DetrForObjectDetection, DetrForSegmentation, DetrModel if is_vision_available(): from PIL import Image from transformers import DetrImageProcessor class DetrModelTester: 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, min_size=200, max_size=200, n_targets=8, num_labels=91, ): 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.min_size = min_size self.max_size = max_size self.n_targets = n_targets self.num_labels = num_labels # we also set the expected seq length for both encoder and decoder self.encoder_seq_length = math.ceil(self.min_size / 32) * math.ceil(self.max_size / 32) self.decoder_seq_length = self.num_queries def prepare_config_and_inputs(self): pixel_values = floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size]) pixel_mask = torch.ones([self.batch_size, self.min_size, self.max_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.min_size, self.max_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 DetrConfig( 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, use_timm_backbone=False, backbone_config=resnet_config, backbone=None, 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_detr_model(self, config, pixel_values, pixel_mask, labels): model = DetrModel(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.decoder_seq_length, self.hidden_size) ) def create_and_check_detr_object_detection_head_model(self, config, pixel_values, pixel_mask, labels): model = DetrForObjectDetection(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 + 1)) 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 + 1)) self.parent.assertEqual(result.pred_boxes.shape, (self.batch_size, self.num_queries, 4)) @require_torch class DetrModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase): all_model_classes = ( ( DetrModel, DetrForObjectDetection, DetrForSegmentation, ) if is_torch_available() else () ) pipeline_model_mapping = ( { "image-feature-extraction": DetrModel, "image-segmentation": DetrForSegmentation, "object-detection": DetrForObjectDetection, } if is_torch_available() else {} ) is_encoder_decoder = True test_missing_keys = False zero_init_hidden_state = True 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__ in ["DetrForObjectDetection", "DetrForSegmentation"]: 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.min_size, self.model_tester.max_size, device=torch_device, dtype=torch.float, ) labels.append(target) inputs_dict["labels"] = labels return inputs_dict def setUp(self): self.model_tester = DetrModelTester(self) self.config_tester = ConfigTester(self, config_class=DetrConfig, has_text_modality=False) def test_config(self): self.config_tester.run_common_tests() def test_detr_model(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_detr_model(*config_and_inputs) def test_detr_object_detection_head_model(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_detr_object_detection_head_model(*config_and_inputs) # TODO: check if this works again for PyTorch 2.x.y @unittest.skip(reason="Got `CUDA error: misaligned address` with PyTorch 2.0.0.") def test_multi_gpu_data_parallel_forward(self): pass @unittest.skip(reason="DETR does not use inputs_embeds") def test_inputs_embeds(self): pass @unittest.skip(reason="DETR does not use inputs_embeds") def test_inputs_embeds_matches_input_ids(self): pass @unittest.skip(reason="DETR does not have a get_input_embeddings method") def test_model_get_set_embeddings(self): pass @unittest.skip(reason="DETR is not a generative model") def test_generate_without_input_ids(self): pass @unittest.skip(reason="DETR does not use token embeddings") def test_resize_tokens_embeddings(self): pass @slow @unittest.skip(reason="TODO Niels: fix me!") def test_model_outputs_equivalence(self): pass def test_attention_outputs(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() config.return_dict = True decoder_seq_length = self.model_tester.decoder_seq_length encoder_seq_length = self.model_tester.encoder_seq_length decoder_key_length = self.model_tester.decoder_seq_length encoder_key_length = self.model_tester.encoder_seq_length 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 if config.is_encoder_decoder else outputs.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 if config.is_encoder_decoder else outputs.attentions self.assertEqual(len(attentions), self.model_tester.num_hidden_layers) self.assertListEqual( list(attentions[0].shape[-3:]), [self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length], ) out_len = len(outputs) if self.is_encoder_decoder: correct_outlen = 5 # 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__ == "DetrForObjectDetection": correct_outlen += 2 # Panoptic Segmentation model returns pred_logits, pred_boxes, pred_masks if model_class.__name__ == "DetrForSegmentation": correct_outlen += 3 if "past_key_values" in outputs: correct_outlen += 1 # past_key_values have been returned 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, decoder_seq_length, decoder_key_length], ) # 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, decoder_seq_length, encoder_key_length, ], ) # 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 if config.is_encoder_decoder else outputs.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, encoder_seq_length, encoder_key_length], ) 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) output = outputs[0] 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", "encoder_outputs"] 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": [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__ == "DetrForObjectDetection": expected_shape = ( self.model_tester.batch_size, self.model_tester.num_queries, self.model_tester.num_labels + 1, ) 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), 3) elif model_class.__name__ == "DetrForSegmentation": # Confirm out_indices was propagated to backbone self.assertEqual(len(model.detr.model.backbone.conv_encoder.intermediate_channel_sizes), 3) else: # Confirm out_indices was propagated to backbone self.assertEqual(len(model.backbone.conv_encoder.intermediate_channel_sizes), 3) 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": [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__ == "DetrForObjectDetection": expected_shape = ( self.model_tester.batch_size, self.model_tester.num_queries, self.model_tester.num_labels + 1, ) 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), 3) elif model_class.__name__ == "DetrForSegmentation": # Confirm out_indices was propagated to backbone self.assertEqual(len(model.detr.model.backbone.conv_encoder.intermediate_channel_sizes), 3) else: # Confirm out_indices was propagated to backbone self.assertEqual(len(model.backbone.conv_encoder.intermediate_channel_sizes), 3) self.assertTrue(outputs) def test_greyscale_images(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() # use greyscale pixel values inputs_dict["pixel_values"] = floats_tensor( [self.model_tester.batch_size, 1, self.model_tester.min_size, self.model_tester.max_size] ) # let's set num_channels to 1 config.num_channels = 1 config.backbone_config.num_channels = 1 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)) self.assertTrue(outputs) 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 DetrModelIntegrationTestsTimmBackbone(unittest.TestCase): @cached_property def default_image_processor(self): return DetrImageProcessor.from_pretrained("facebook/detr-resnet-50") if is_vision_available() else None def test_inference_no_head(self): model = DetrModel.from_pretrained("facebook/detr-resnet-50").to(torch_device) image_processor = self.default_image_processor image = prepare_img() encoding = image_processor(images=image, return_tensors="pt").to(torch_device) with torch.no_grad(): outputs = model(**encoding) expected_shape = torch.Size((1, 100, 256)) assert outputs.last_hidden_state.shape == expected_shape expected_slices = Expectations( { (None, None): [ [0.0616, -0.5146, -0.4032], [-0.7629, -0.4934, -1.7153], [-0.4768, -0.6403, -0.7826], ], ("rocm", (9, 5)): [ [ 0.0616, -0.5146, -0.4032], [-0.7629, -0.4934, -1.7153], [-0.4768, -0.6403, -0.7826], ], } ) # fmt: skip expected_slice = torch.tensor(expected_slices.get_expectation(), device=torch_device) torch.testing.assert_close(outputs.last_hidden_state[0, :3, :3], expected_slice, rtol=2e-4, atol=2e-4) def test_inference_object_detection_head(self): model = DetrForObjectDetection.from_pretrained("facebook/detr-resnet-50").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) # verify outputs expected_shape_logits = torch.Size((1, model.config.num_queries, model.config.num_labels + 1)) self.assertEqual(outputs.logits.shape, expected_shape_logits) expected_slices = Expectations( { (None, None): [ [-19.1194, -0.0893, -11.0154], [-17.3640, -1.8035, -14.0219], [-20.0461, -0.5837, -11.1060], ], ("rocm", (9, 5)): [ [-19.1194, -0.0893, -11.0154], [-17.3640, -1.8035, -14.0219], [-20.0461, -0.5837, -11.1060], ], } ) # fmt: skip expected_slice_logits = torch.tensor(expected_slices.get_expectation(), device=torch_device) torch.testing.assert_close(outputs.logits[0, :3, :3], expected_slice_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) expected_slice_boxes = torch.tensor( [ [0.4433, 0.5302, 0.8852], [0.5494, 0.2517, 0.0529], [0.4998, 0.5360, 0.9955], ] ).to(torch_device) torch.testing.assert_close(outputs.pred_boxes[0, :3, :3], expected_slice_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([0.9982, 0.9960, 0.9955, 0.9988, 0.9987]).to(torch_device) expected_labels = [75, 75, 63, 17, 17] expected_slice_boxes = torch.tensor([40.1615, 70.8090, 175.5476, 117.9810]).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_panoptic_segmentation_head(self): model = DetrForSegmentation.from_pretrained("facebook/detr-resnet-50-panoptic").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) # verify outputs expected_shape_logits = torch.Size((1, model.config.num_queries, model.config.num_labels + 1)) self.assertEqual(outputs.logits.shape, expected_shape_logits) expected_slices = Expectations( { (None, None): [ [-18.1565, -1.7568, -13.5029], [-16.8888, -1.4138, -14.1028], [-17.5709, -2.5080, -11.8654], ], ("rocm", (9, 5)): [ [-18.1565, -1.7568, -13.5029], [-16.8888, -1.4138, -14.1028], [-17.5709, -2.5080, -11.8654], ], } ) # fmt: skip expected_slice_logits = torch.tensor(expected_slices.get_expectation(), device=torch_device) torch.testing.assert_close(outputs.logits[0, :3, :3], expected_slice_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) expected_slices = Expectations( { (None, None): [ [0.5344, 0.1789, 0.9285], [0.4420, 0.0572, 0.0875], [0.6630, 0.6887, 0.1017], ], ("rocm", (9, 5)): [ [0.5344, 0.1789, 0.9285], [0.4420, 0.0572, 0.0875], [0.6630, 0.6887, 0.1017], ], } ) # fmt: skip expected_slice_boxes = torch.tensor(expected_slices.get_expectation(), device=torch_device) torch.testing.assert_close(outputs.pred_boxes[0, :3, :3], expected_slice_boxes, rtol=2e-4, atol=2e-4) expected_shape_masks = torch.Size((1, model.config.num_queries, 200, 267)) self.assertEqual(outputs.pred_masks.shape, expected_shape_masks) expected_slices = Expectations( { (None, None): [ [-7.7557, -10.8788, -11.9797], [-11.8880, -16.4328, -17.7450], [-14.7315, -19.7382, -20.3003], ], ("rocm", (9, 5)): [ [ -7.7558, -10.8789, -11.9798], [-11.8882, -16.4330, -17.7452], [-14.7317, -19.7384, -20.3005], ], } ) # fmt: skip expected_slice_masks = torch.tensor(expected_slices.get_expectation(), device=torch_device) torch.testing.assert_close(outputs.pred_masks[0, 0, :3, :3], expected_slice_masks, rtol=2e-3, atol=2e-3) # verify postprocessing results = image_processor.post_process_panoptic_segmentation( outputs, threshold=0.3, target_sizes=[image.size[::-1]] )[0] expected_shape = torch.Size([480, 640]) expected_slice_segmentation = torch.tensor([[4, 4, 4], [4, 4, 4], [4, 4, 4]], dtype=torch.int32).to( torch_device ) expected_number_of_segments = 5 expected_first_segment = {"id": 1, "label_id": 17, "was_fused": False, "score": 0.9941} number_of_unique_segments = len(torch.unique(results["segmentation"])) self.assertTrue( number_of_unique_segments, expected_number_of_segments + 1 ) # we add 1 for the background class self.assertTrue(results["segmentation"].shape, expected_shape) torch.testing.assert_close(results["segmentation"][:3, :3], expected_slice_segmentation, rtol=1e-4, atol=1e-4) self.assertTrue(len(results["segments_info"]), expected_number_of_segments) predicted_first_segment = results["segments_info"][0] self.assertEqual(predicted_first_segment["id"], expected_first_segment["id"]) self.assertEqual(predicted_first_segment["label_id"], expected_first_segment["label_id"]) self.assertEqual(predicted_first_segment["was_fused"], expected_first_segment["was_fused"]) self.assertAlmostEqual(predicted_first_segment["score"], expected_first_segment["score"], places=3) @require_vision @require_torch @slow class DetrModelIntegrationTests(unittest.TestCase): @cached_property def default_image_processor(self): return ( DetrImageProcessor.from_pretrained("facebook/detr-resnet-50", revision="no_timm") if is_vision_available() else None ) def test_inference_no_head(self): model = DetrModel.from_pretrained("facebook/detr-resnet-50", revision="no_timm").to(torch_device) image_processor = self.default_image_processor image = prepare_img() encoding = image_processor(images=image, return_tensors="pt").to(torch_device) with torch.no_grad(): outputs = model(**encoding) expected_shape = torch.Size((1, 100, 256)) assert outputs.last_hidden_state.shape == expected_shape expected_slices = Expectations( { (None, None): [ [0.0616, -0.5146, -0.4032], [-0.7629, -0.4934, -1.7153], [-0.4768, -0.6403, -0.7826], ], ("rocm", (9, 5)): [ [ 0.0616, -0.5146, -0.4032], [-0.7629, -0.4934, -1.7153], [-0.4768, -0.6403, -0.7826] ], } ) # fmt: skip expected_slice = torch.tensor(expected_slices.get_expectation(), device=torch_device) torch.testing.assert_close(outputs.last_hidden_state[0, :3, :3], expected_slice, rtol=1e-4, atol=1e-4)