# Copyright 2026 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. import unittest from functools import cached_property from transformers import ( DeformableDetrImageProcessorPil, LwDetrConfig, LwDetrViTConfig, is_torch_available, is_vision_available, ) from transformers.testing_utils import ( Expectations, require_torch, require_vision, slow, torch_device, ) from ...test_backbone_common import BackboneTesterMixin from ...test_configuration_common import ConfigTester from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor from ...test_pipeline_mixin import PipelineTesterMixin if is_torch_available(): import torch from torch import nn from transformers import LwDetrForObjectDetection, LwDetrModel, LwDetrViTBackbone if is_vision_available(): from PIL import Image CHECKPOINT = { "tiny": "stevenbucaille/lwdetr_tiny_30e_objects365", "xlarge": "stevenbucaille/lwdetr_xlarge_30e_objects365", } class LwDetrVitModelTester: def __init__( self, parent, batch_size=3, num_labels=3, num_channels=3, use_labels=True, is_training=True, image_size=256, hidden_size=16, num_hidden_layers=2, num_attention_heads=2, window_block_indices=[1], out_indices=[0], num_windows=16, dropout_prob=0.0, attn_implementation="eager", ): self.parent = parent self.batch_size = batch_size self.num_labels = num_labels self.num_channels = num_channels self.use_labels = use_labels self.image_size = image_size self.hidden_size = hidden_size self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads self.window_block_indices = window_block_indices self.out_indices = out_indices self.num_windows = num_windows self.dropout_prob = dropout_prob self.attn_implementation = attn_implementation self.is_training = is_training def prepare_config_and_inputs(self): pixel_values = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]) labels = None if self.use_labels: labels = ids_tensor([self.batch_size], self.num_labels) config = self.get_config() return config, pixel_values, labels def get_config(self): return LwDetrViTConfig( hidden_size=self.hidden_size, num_hidden_layers=self.num_hidden_layers, num_attention_heads=self.num_attention_heads, window_block_indices=self.window_block_indices, out_indices=self.out_indices, num_windows=self.num_windows, hidden_dropout_prob=self.dropout_prob, attention_probs_dropout_prob=self.dropout_prob, attn_implementation=self.attn_implementation, ) def prepare_config_and_inputs_for_common(self): config_and_inputs = self.prepare_config_and_inputs() config, pixel_values, labels = config_and_inputs inputs_dict = {"pixel_values": pixel_values} return config, inputs_dict def create_and_check_backbone(self, config, pixel_values, labels): model = LwDetrViTBackbone(config=config) model.to(torch_device) model.eval() result = model(pixel_values) # verify hidden states self.parent.assertEqual(len(result.feature_maps), len(config.out_features)) self.parent.assertListEqual( list(result.feature_maps[0].shape), [ self.batch_size, self.hidden_size, self.get_config().num_windows_side ** 2, self.get_config().num_windows_side ** 2, ], ) # verify channels self.parent.assertEqual(len(model.channels), len(config.out_features)) self.parent.assertListEqual(model.channels, [config.hidden_size]) # verify backbone works with out_features=None config.out_features = None model = LwDetrViTBackbone(config=config) model.to(torch_device) model.eval() result = model(pixel_values) # verify feature maps self.parent.assertEqual(len(result.feature_maps), 1) self.parent.assertListEqual( list(result.feature_maps[0].shape), [self.batch_size, config.hidden_size, config.patch_size, config.patch_size], ) # verify channels self.parent.assertEqual(len(model.channels), 1) self.parent.assertListEqual(model.channels, [config.hidden_size]) @require_torch class LwDetrViTBackboneTest(ModelTesterMixin, BackboneTesterMixin, unittest.TestCase): all_model_classes = (LwDetrViTBackbone,) if is_torch_available() else () config_class = LwDetrViTConfig test_resize_embeddings = False test_torch_exportable = True model_split_percents = [0.5, 0.87, 0.9] def setUp(self): self.model_tester = LwDetrVitModelTester(self) def test_backbone(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_backbone(*config_and_inputs) def test_model_get_set_embeddings(self): config, _ = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: model = model_class(config) self.assertIsInstance(model.get_input_embeddings(), (nn.Module)) x = model.get_output_embeddings() self.assertTrue(x is None or isinstance(x, nn.Linear)) def test_attention_outputs(self): def check_attention_output(inputs_dict, config, model_class): config._attn_implementation = "eager" model = model_class(config) model.to(torch_device) model.eval() with torch.no_grad(): outputs = model(**self._prepare_for_class(inputs_dict, model_class)) attentions = outputs.attentions windowed_attentions = [attentions[i] for i in self.model_tester.window_block_indices] unwindowed_attentions = [attentions[i] for i in self.model_tester.out_indices] expected_windowed_attention_shape = [ self.model_tester.batch_size * self.model_tester.num_windows, self.model_tester.num_attention_heads, self.model_tester.get_config().num_windows_side ** 2, self.model_tester.get_config().num_windows_side ** 2, ] expected_unwindowed_attention_shape = [ self.model_tester.batch_size, self.model_tester.num_attention_heads, self.model_tester.image_size, self.model_tester.image_size, ] for i, attention in enumerate(windowed_attentions): self.assertListEqual( list(attention.shape), expected_windowed_attention_shape, ) for i, attention in enumerate(unwindowed_attentions): self.assertListEqual( list(attention.shape), expected_unwindowed_attention_shape, ) config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: inputs_dict["output_attentions"] = True check_attention_output(inputs_dict, config, model_class) # check that output_hidden_states also work using config del inputs_dict["output_attentions"] config.output_attentions = True check_attention_output(inputs_dict, config, model_class) def test_hidden_states_output(self): def check_hidden_states_output(inputs_dict, config, model_class): model = model_class(config) model.to(torch_device) model.eval() with torch.no_grad(): outputs = model(**self._prepare_for_class(inputs_dict, model_class)) hidden_states = outputs.hidden_states expected_num_stages = self.model_tester.num_hidden_layers self.assertEqual(len(hidden_states), expected_num_stages + 1) # VitDet's feature maps are of shape (batch_size, num_channels, height, width) self.assertListEqual( list(hidden_states[0].shape[-2:]), [ self.model_tester.hidden_size, 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) # overwrite since LwDetrVitDet only supports retraining gradients of hidden states 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 = self.has_attentions # 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.feature_maps[0] # Encoder-/Decoder-only models hidden_states = outputs.hidden_states[0] hidden_states.retain_grad() output.flatten()[0].backward(retain_graph=True) self.assertIsNotNone(hidden_states.grad) def prepare_img(): image = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png") return image class LwDetrModelTester: def __init__( self, parent, batch_size=3, is_training=True, image_size=256, num_labels=5, n_targets=4, use_labels=True, initializer_range=0.02, layer_norm_eps=1e-5, batch_norm_eps=1e-5, # backbone backbone_config=None, # projector projector_scale_factors=[0.5, 2.0], # decoder d_model=32, decoder_ffn_dim=32, decoder_layers=2, decoder_self_attention_heads=2, decoder_cross_attention_heads=4, # model num_queries=10, group_detr=2, dropout=0.0, activation_dropout=0.0, attention_dropout=0.0, attn_implementation="eager", ): self.parent = parent self.batch_size = batch_size self.is_training = is_training self.num_channels = 3 self.image_size = image_size self.num_labels = num_labels self.n_targets = n_targets self.use_labels = use_labels self.initializer_range = initializer_range self.layer_norm_eps = layer_norm_eps self.batch_norm_eps = batch_norm_eps self.backbone_config = backbone_config self.projector_scale_factors = projector_scale_factors self.d_model = d_model self.decoder_ffn_dim = decoder_ffn_dim self.decoder_layers = decoder_layers self.decoder_self_attention_heads = decoder_self_attention_heads self.decoder_cross_attention_heads = decoder_cross_attention_heads self.num_queries = num_queries self.group_detr = group_detr self.dropout = dropout self.activation_dropout = activation_dropout self.attention_dropout = attention_dropout self.attn_implementation = attn_implementation 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 = [] 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, dtype=pixel_values.dtype) labels.append(target) config = self.get_config() config.num_labels = self.num_labels return config, pixel_values, pixel_mask, labels def get_config(self): backbone_config = LwDetrViTConfig( hidden_size=16, num_hidden_layers=4, num_attention_heads=2, window_block_indices=[0, 2], out_indices=[1, 3], num_windows=16, image_size=self.image_size, dropout_prob=self.dropout, attn_implementation=self.attn_implementation, ) return LwDetrConfig( backbone_config=backbone_config, d_model=self.d_model, projector_scale_factors=self.projector_scale_factors, decoder_ffn_dim=self.decoder_ffn_dim, decoder_layers=self.decoder_layers, decoder_self_attention_heads=self.decoder_self_attention_heads, decoder_cross_attention_heads=self.decoder_cross_attention_heads, num_queries=self.num_queries, group_detr=self.group_detr, dropout=self.dropout, activation_dropout=self.activation_dropout, attention_dropout=self.attention_dropout, attn_implementation=self.attn_implementation, _attn_implementation=self.attn_implementation, ) 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_lw_detr_model(self, config, pixel_values, pixel_mask, labels): model = LwDetrModel(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.d_model)) def create_and_check_lw_detr_object_detection_head_model(self, config, pixel_values, pixel_mask, labels): model = LwDetrForObjectDetection(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 LwDetrModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase): all_model_classes = (LwDetrModel, LwDetrForObjectDetection) if is_torch_available() else () pipeline_model_mapping = ( {"image-feature-extraction": LwDetrModel, "object-detection": LwDetrForObjectDetection} if is_torch_available() else {} ) is_encoder_decoder = False test_missing_keys = False test_torch_exportable = True model_split_percents = [0.5, 0.87, 0.9] # 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__ == "LwDetrForObjectDetection": 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 ) labels.append(target) inputs_dict["labels"] = labels return inputs_dict def setUp(self): self.model_tester = LwDetrModelTester(self) self.config_tester = ConfigTester( self, config_class=LwDetrConfig, has_text_modality=False, common_properties=["d_model", "decoder_self_attention_heads"], ) def test_config(self): self.config_tester.run_common_tests() def test_lw_detr_model(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_lw_detr_model(*config_and_inputs) def test_lw_detr_object_detection_head_model(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_lw_detr_object_detection_head_model(*config_and_inputs) @unittest.skip(reason="LwDetr does not use inputs_embeds") def test_inputs_embeds(self): pass @unittest.skip(reason="LwDetr does not use test_inputs_embeds_matches_input_ids") def test_inputs_embeds_matches_input_ids(self): pass @unittest.skip(reason="LwDetr does not support input and output embeddings") def test_model_get_set_embeddings(self): pass @unittest.skip(reason="LwDetr does not support input and output embeddings") def test_model_common_attributes(self): pass @unittest.skip(reason="LwDetr 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): def check_attention_outputs(inputs_dict, config, model_class): 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.attentions self.assertEqual(len(attentions), self.model_tester.decoder_layers) expected_attentions_shape = [ self.model_tester.batch_size, self.model_tester.decoder_self_attention_heads, self.model_tester.num_queries, self.model_tester.num_queries, ] for i in range(self.model_tester.decoder_layers): self.assertEqual(expected_attentions_shape, list(attentions[i].shape)) # check cross_attentions outputs expected_attentions_shape = [ self.model_tester.batch_size, self.model_tester.num_queries, self.model_tester.decoder_cross_attention_heads, config.num_feature_levels, config.decoder_n_points, ] cross_attentions = outputs.cross_attentions self.assertEqual(len(cross_attentions), self.model_tester.decoder_layers) for i in range(self.model_tester.decoder_layers): self.assertEqual(expected_attentions_shape, list(cross_attentions[i].shape)) out_len = len(outputs) correct_outlen = 8 # 6 + attentions + cross_attentions # Object Detection model returns pred_logits, pred_boxes and auxiliary outputs if model_class.__name__ == "LwDetrForObjectDetection": correct_outlen += 2 if "labels" in inputs_dict: correct_outlen += 3 # loss, loss_dict and auxiliary outputs is added to beginning self.assertEqual(correct_outlen, out_len) config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() config.return_dict = True inputs_dict["output_hidden_states"] = False for model_class in self.all_model_classes: inputs_dict["output_attentions"] = True check_attention_outputs(inputs_dict, config, model_class) # check that output_attentions also work using config del inputs_dict["output_attentions"] config.output_attentions = True check_attention_outputs(inputs_dict, config, model_class) def test_hidden_states_output(self): def check_hidden_states_output(inputs_dict, config, model_class): model = model_class(config) model.to(torch_device) model.eval() with torch.no_grad(): outputs = model(**self._prepare_for_class(inputs_dict, model_class)) hidden_states = outputs.hidden_states expected_num_hidden_states = self.model_tester.decoder_layers + 1 self.assertEqual(len(hidden_states), expected_num_hidden_states) for i in range(expected_num_hidden_states): self.assertListEqual( list(hidden_states[i].shape), [ self.model_tester.batch_size, self.model_tester.num_queries, self.model_tester.d_model, ], ) config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: inputs_dict["output_attentions"] = False inputs_dict["output_hidden_states"] = True check_hidden_states_output(inputs_dict, config, model_class) # check that output_hidden_states also work using config del inputs_dict["output_hidden_states"] config.output_hidden_states = True check_hidden_states_output(inputs_dict, config, model_class) def test_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 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 first output since last_hidden_state is the first item output = outputs.last_hidden_state hidden_states = outputs.hidden_states[0] attentions = outputs.attentions[0] hidden_states.retain_grad() attentions.retain_grad() output.flatten()[0].backward(retain_graph=True) self.assertIsNotNone(hidden_states.grad) self.assertIsNotNone(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.decoder_layers - 1) @require_torch @require_vision class LwDetrModelIntegrationTest(unittest.TestCase): @cached_property def default_image_processor(self): if is_vision_available(): return { "tiny": DeformableDetrImageProcessorPil.from_pretrained(CHECKPOINT["tiny"]), "xlarge": DeformableDetrImageProcessorPil.from_pretrained(CHECKPOINT["xlarge"]), } @slow def test_inference_object_detection_head_tiny(self): size = "tiny" model = LwDetrForObjectDetection.from_pretrained(CHECKPOINT[size], attn_implementation="eager").to( torch_device ) image_processor = self.default_image_processor[size] 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_logits_shape = torch.Size((1, model.config.num_queries, model.config.num_labels)) self.assertEqual(outputs.logits.shape, expected_logits_shape) expectations = Expectations( { ("cuda", (8, 0)): [-7.7648, -4.1330, -2.9003, -4.0559, -2.9635], ("xpu", (3, 0)): [-7.7693, -4.1270, -2.9018, -4.0605, -2.9575], } ) expected_logits = torch.tensor(expectations.get_expectation()).to(torch_device) torch.testing.assert_close(outputs.logits.flatten()[:5], expected_logits, rtol=2e-4, atol=2e-4) expected_boxes_shape = torch.Size((1, model.config.num_queries, 4)) self.assertEqual(outputs.pred_boxes.shape, expected_boxes_shape) expectations = Expectations( { ("cuda", (8, 0)): [0.1694, 0.1979, 0.2121, 0.0912, 0.2537], ("xpu", (3, 0)): [0.1694, 0.1979, 0.2121, 0.0912, 0.2537], } ) expected_boxes = torch.tensor(expectations.get_expectation()).to(torch_device) torch.testing.assert_close(outputs.pred_boxes.flatten()[:5], expected_boxes, rtol=2e-4, atol=2e-4) results = image_processor.post_process_object_detection( outputs, threshold=0.0, target_sizes=[image.size[::-1]] )[0] expectations = Expectations( { ("cuda", (8, 0)): [0.8684, 0.7492, 0.7146, 0.4362], ("xpu", (3, 0)): [0.8676, 0.7527, 0.7177, 0.4391], } ) expected_scores = torch.tensor(expectations.get_expectation()).to(torch_device) expected_labels = [140, 133, 140, 133] expectations = Expectations( { ("cuda", (8, 0)): [ [4.9333, 56.6130, 319.7758, 474.7774], [40.5547, 73.0968, 176.2951, 116.8605], [340.3403, 25.1044, 640.2798, 368.7382], [334.2971, 77.0087, 371.2877, 189.8089], ], ("xpu", (3, 0)): [ [4.8948, 56.5549, 319.8077, 474.7937], [40.5620, 73.1059, 176.2996, 116.8567], [340.3327, 25.1026, 640.3193, 368.6754], [334.2945, 76.9876, 371.2914, 189.8221], ], } ) expected_slice_boxes = torch.tensor(expectations.get_expectation()).to(torch_device) torch.testing.assert_close(results["scores"][:4], expected_scores, atol=1e-3, rtol=2e-4) self.assertSequenceEqual(results["labels"][:4].tolist(), expected_labels) torch.testing.assert_close(results["boxes"][:4], expected_slice_boxes, atol=1e-3, rtol=2e-4) @slow def test_inference_object_detection_head_xlarge(self): size = "xlarge" model = LwDetrForObjectDetection.from_pretrained(CHECKPOINT[size], attn_implementation="eager").to( torch_device ) image_processor = self.default_image_processor[size] 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_logits_shape = torch.Size((1, model.config.num_queries, model.config.num_labels)) self.assertEqual(outputs.logits.shape, expected_logits_shape) expectations = Expectations( { ("cuda", (8, 0)): [-11.9394, -4.3419, -4.4172, -5.0299, -6.9282], ("xpu", (3, 0)): [-11.9292, -4.3307, -4.4075, -5.0207, -6.9211], } ) expected_logits = torch.tensor(expectations.get_expectation()).to(torch_device) torch.testing.assert_close(outputs.logits.flatten()[:5], expected_logits, rtol=2e-4, atol=2e-4) expected_boxes_shape = torch.Size((1, model.config.num_queries, 4)) self.assertEqual(outputs.pred_boxes.shape, expected_boxes_shape) expectations = Expectations( { ("cuda", (8, 0)): [0.7689, 0.4107, 0.4617, 0.7244, 0.2526], ("xpu", (3, 0)): [0.7688, 0.4106, 0.4618, 0.7245, 0.2526], } ) expected_boxes = torch.tensor(expectations.get_expectation()).to(torch_device) torch.testing.assert_close(outputs.pred_boxes.flatten()[:5], expected_boxes, rtol=2e-4, atol=2e-4) results = image_processor.post_process_object_detection( outputs, threshold=0.0, target_sizes=[image.size[::-1]] )[0] expectations = Expectations( { ("cuda", (8, 0)): [0.9746, 0.9717, 0.9344, 0.8182], ("xpu", (3, 0)): [0.9745, 0.9715, 0.9339, 0.8163], } ) expected_scores = torch.tensor(expectations.get_expectation()).to(torch_device) expected_labels = [140, 140, 133, 133] expectations = Expectations( { ("cuda", (8, 0)): [ [7.4541, 54.2878, 315.8890, 474.8681], [344.3325, 23.2591, 639.7999, 370.9900], [40.4797, 73.3092, 175.6086, 116.9654], [333.9930, 77.1547, 370.4000, 186.1230], ], ("xpu", (3, 0)): [ [7.4487, 54.2931, 315.8945, 474.8726], [344.2597, 23.2305, 639.8082, 370.9894], [40.4780, 73.3095, 175.6083, 116.9673], [333.9890, 77.1453, 370.4069, 186.1300], ], } ) expected_slice_boxes = torch.tensor(expectations.get_expectation()).to(torch_device) torch.testing.assert_close(results["scores"][:4], expected_scores, atol=1e-3, rtol=2e-4) self.assertSequenceEqual(results["labels"][:4].tolist(), expected_labels) torch.testing.assert_close(results["boxes"][:4], expected_slice_boxes, atol=1e-3, rtol=2e-4)