# coding = utf-8 # Copyright 2024 The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Testing suite for the PyTorch RT_DETR model.""" import inspect import math import tempfile import unittest from functools import cached_property from parameterized import parameterized from transformers import ( RTDetrConfig, RTDetrImageProcessor, RTDetrResNetConfig, is_torch_available, is_vision_available, ) from transformers.testing_utils import ( Expectations, require_torch, require_torch_accelerator, require_vision, slow, torch_device, ) from ...test_configuration_common import ConfigTester from ...test_modeling_common import ModelTesterMixin, floats_tensor from ...test_pipeline_mixin import PipelineTesterMixin if is_torch_available(): import torch from transformers import RTDetrForObjectDetection, RTDetrModel if is_vision_available(): from PIL import Image CHECKPOINT = "PekingU/rtdetr_r50vd" # TODO: replace class RTDetrModelTester: def __init__( self, parent, batch_size=3, is_training=True, use_labels=True, n_targets=3, num_labels=10, initializer_range=0.02, layer_norm_eps=1e-5, batch_norm_eps=1e-5, # backbone backbone_config=None, # encoder HybridEncoder encoder_hidden_dim=32, encoder_in_channels=[128, 256, 512], feat_strides=[8, 16, 32], encoder_layers=1, encoder_ffn_dim=64, encoder_attention_heads=2, dropout=0.0, activation_dropout=0.0, encode_proj_layers=[2], positional_encoding_temperature=10000, encoder_activation_function="gelu", activation_function="silu", eval_size=None, normalize_before=False, # decoder RTDetrTransformer d_model=32, num_queries=30, decoder_in_channels=[32, 32, 32], decoder_ffn_dim=64, num_feature_levels=3, decoder_n_points=4, decoder_layers=2, decoder_attention_heads=2, decoder_activation_function="relu", attention_dropout=0.0, num_denoising=0, label_noise_ratio=0.5, box_noise_scale=1.0, learn_initial_query=False, anchor_image_size=None, image_size=64, disable_custom_kernels=True, with_box_refine=True, ): self.parent = parent self.batch_size = batch_size self.num_channels = 3 self.is_training = is_training self.use_labels = use_labels self.n_targets = n_targets self.num_labels = num_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.encoder_hidden_dim = encoder_hidden_dim self.encoder_in_channels = encoder_in_channels self.feat_strides = feat_strides self.encoder_layers = encoder_layers self.encoder_ffn_dim = encoder_ffn_dim self.encoder_attention_heads = encoder_attention_heads self.dropout = dropout self.activation_dropout = activation_dropout self.encode_proj_layers = encode_proj_layers self.positional_encoding_temperature = positional_encoding_temperature self.encoder_activation_function = encoder_activation_function self.activation_function = activation_function self.eval_size = eval_size self.normalize_before = normalize_before self.d_model = d_model self.num_queries = num_queries self.decoder_in_channels = decoder_in_channels self.decoder_ffn_dim = decoder_ffn_dim self.num_feature_levels = num_feature_levels self.decoder_n_points = decoder_n_points self.decoder_layers = decoder_layers self.decoder_attention_heads = decoder_attention_heads self.decoder_activation_function = decoder_activation_function self.attention_dropout = attention_dropout self.num_denoising = num_denoising self.label_noise_ratio = label_noise_ratio self.box_noise_scale = box_noise_scale self.learn_initial_query = learn_initial_query self.anchor_image_size = anchor_image_size self.image_size = image_size self.disable_custom_kernels = disable_custom_kernels self.with_box_refine = with_box_refine self.encoder_seq_length = math.ceil(self.image_size / 32) * math.ceil(self.image_size / 32) 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) labels.append(target) config = self.get_config() config.num_labels = self.num_labels return config, pixel_values, pixel_mask, labels def get_config(self): hidden_sizes = [10, 20, 30, 40] backbone_config = RTDetrResNetConfig( embeddings_size=10, hidden_sizes=hidden_sizes, depths=[1, 1, 2, 1], out_features=["stage2", "stage3", "stage4"], out_indices=[2, 3, 4], ) return RTDetrConfig( backbone_config=backbone_config, encoder_hidden_dim=self.encoder_hidden_dim, encoder_in_channels=hidden_sizes[1:], feat_strides=self.feat_strides, encoder_layers=self.encoder_layers, encoder_ffn_dim=self.encoder_ffn_dim, encoder_attention_heads=self.encoder_attention_heads, dropout=self.dropout, activation_dropout=self.activation_dropout, encode_proj_layers=self.encode_proj_layers, positional_encoding_temperature=self.positional_encoding_temperature, encoder_activation_function=self.encoder_activation_function, activation_function=self.activation_function, eval_size=self.eval_size, normalize_before=self.normalize_before, d_model=self.d_model, num_queries=self.num_queries, decoder_in_channels=self.decoder_in_channels, decoder_ffn_dim=self.decoder_ffn_dim, num_feature_levels=self.num_feature_levels, decoder_n_points=self.decoder_n_points, decoder_layers=self.decoder_layers, decoder_attention_heads=self.decoder_attention_heads, decoder_activation_function=self.decoder_activation_function, attention_dropout=self.attention_dropout, num_denoising=self.num_denoising, label_noise_ratio=self.label_noise_ratio, box_noise_scale=self.box_noise_scale, learn_initial_query=self.learn_initial_query, anchor_image_size=self.anchor_image_size, image_size=self.image_size, disable_custom_kernels=self.disable_custom_kernels, with_box_refine=self.with_box_refine, ) 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} return config, inputs_dict def create_and_check_rt_detr_model(self, config, pixel_values, pixel_mask, labels): model = RTDetrModel(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_rt_detr_object_detection_head_model(self, config, pixel_values, pixel_mask, labels): model = RTDetrForObjectDetection(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 RTDetrModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase): all_model_classes = (RTDetrModel, RTDetrForObjectDetection) if is_torch_available() else () pipeline_model_mapping = ( {"image-feature-extraction": RTDetrModel, "object-detection": RTDetrForObjectDetection} 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__ == "RTDetrForObjectDetection": 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 = RTDetrModelTester(self) self.config_tester = ConfigTester( self, config_class=RTDetrConfig, has_text_modality=False, common_properties=["hidden_size", "num_attention_heads"], ) def test_config(self): self.config_tester.run_common_tests() def test_rt_detr_model(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_rt_detr_model(*config_and_inputs) def test_rt_detr_object_detection_head_model(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_rt_detr_object_detection_head_model(*config_and_inputs) @unittest.skip(reason="RTDetr does not use inputs_embeds") def test_inputs_embeds(self): pass @unittest.skip(reason="RTDetr does not use test_inputs_embeds_matches_input_ids") def test_inputs_embeds_matches_input_ids(self): pass @unittest.skip(reason="RTDetr does not support input and output embeddings") def test_model_get_set_embeddings(self): pass @unittest.skip(reason="RTDetr does not support input and output embeddings") def test_model_common_attributes(self): pass @unittest.skip(reason="RTDetr 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.encoder_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.encoder_layers) self.assertListEqual( list(attentions[0].shape[-3:]), [ self.model_tester.encoder_attention_heads, self.model_tester.encoder_seq_length, self.model_tester.encoder_seq_length, ], ) out_len = len(outputs) correct_outlen = 13 # 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__ == "RTDetrForObjectDetection": 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.decoder_layers) self.assertListEqual( list(decoder_attentions[0].shape[-3:]), [ self.model_tester.decoder_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.decoder_layers) self.assertListEqual( list(cross_attentions[0].shape[-3:]), [ self.model_tester.decoder_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 else: # RTDetr should maintin encoder_hidden_states output added_hidden_states = 2 self.assertEqual(out_len + added_hidden_states, len(outputs)) self_attentions = outputs.encoder_attentions self.assertEqual(len(self_attentions), self.model_tester.encoder_layers) self.assertListEqual( list(self_attentions[0].shape[-3:]), [ self.model_tester.encoder_attention_heads, self.model_tester.encoder_seq_length, self.model_tester.encoder_seq_length, ], ) def test_hidden_states_output(self): def check_hidden_states_output(inputs_dict, config, model_class): model = model_class(config) model.to(torch_device) model.eval() with torch.no_grad(): outputs = model(**self._prepare_for_class(inputs_dict, model_class)) hidden_states = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states expected_num_layers = getattr( self.model_tester, "expected_num_hidden_layers", len(self.model_tester.encoder_in_channels) - 1 ) self.assertEqual(len(hidden_states), expected_num_layers) self.assertListEqual( list(hidden_states[1].shape[-2:]), [ self.model_tester.image_size // self.model_tester.feat_strides[-1], self.model_tester.image_size // self.model_tester.feat_strides[-1], ], ) if config.is_encoder_decoder: hidden_states = outputs.decoder_hidden_states expected_num_layers = getattr( self.model_tester, "expected_num_hidden_layers", self.model_tester.decoder_layers + 1 ) self.assertIsInstance(hidden_states, (list, tuple)) self.assertEqual(len(hidden_states), expected_num_layers) self.assertListEqual( list(hidden_states[0].shape[-2:]), [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_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[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_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) arg_names = [*signature.parameters.keys()] expected_arg_names = ["pixel_values"] 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__ == "RTDetrForObjectDetection": 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.intermediate_channel_sizes), 3) else: # Confirm out_indices was propagated to backbone self.assertEqual(len(model.backbone.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__ == "RTDetrForObjectDetection": 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.intermediate_channel_sizes), 3) else: # Confirm out_indices was propagated to backbone self.assertEqual(len(model.backbone.intermediate_channel_sizes), 3) self.assertTrue(outputs) @parameterized.expand(["float32", "float16", "bfloat16"]) @require_torch_accelerator @slow def test_inference_with_different_dtypes(self, dtype_str): dtype = { "float32": torch.float32, "float16": torch.float16, "bfloat16": torch.bfloat16, }[dtype_str] config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: model = model_class(config) model.to(torch_device).to(dtype) model.eval() for key, tensor in inputs_dict.items(): if tensor.dtype == torch.float32: inputs_dict[key] = tensor.to(dtype) with torch.no_grad(): _ = model(**self._prepare_for_class(inputs_dict, model_class)) @parameterized.expand(["float32", "float16", "bfloat16"]) @require_torch_accelerator @slow def test_inference_equivalence_for_static_and_dynamic_anchors(self, dtype_str): dtype = { "float32": torch.float32, "float16": torch.float16, "bfloat16": torch.bfloat16, }[dtype_str] config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() h, w = inputs_dict["pixel_values"].shape[-2:] # convert inputs to the desired dtype for key, tensor in inputs_dict.items(): if tensor.dtype == torch.float32: inputs_dict[key] = tensor.to(dtype) for model_class in self.all_model_classes: with tempfile.TemporaryDirectory() as tmpdirname: model_class(config).save_pretrained(tmpdirname) model_static = model_class.from_pretrained( tmpdirname, anchor_image_size=[h, w], device_map=torch_device, dtype=dtype ).eval() model_dynamic = model_class.from_pretrained( tmpdirname, anchor_image_size=None, device_map=torch_device, dtype=dtype ).eval() self.assertIsNotNone(model_static.config.anchor_image_size) self.assertIsNone(model_dynamic.config.anchor_image_size) with torch.no_grad(): outputs_static = model_static(**self._prepare_for_class(inputs_dict, model_class)) outputs_dynamic = model_dynamic(**self._prepare_for_class(inputs_dict, model_class)) self.assertTrue( torch.allclose( outputs_static.last_hidden_state, outputs_dynamic.last_hidden_state, rtol=1e-4, atol=1e-4 ), f"Max diff: {(outputs_static.last_hidden_state - outputs_dynamic.last_hidden_state).abs().max()}", ) 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_torch @require_vision @slow class RTDetrModelIntegrationTest(unittest.TestCase): @cached_property def default_image_processor(self): return RTDetrImageProcessor.from_pretrained(CHECKPOINT) if is_vision_available() else None def test_inference_object_detection_head(self): model = RTDetrForObjectDetection.from_pretrained(CHECKPOINT).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) expectations = Expectations( { (None, None): [ [-4.64763879776001, -5.001153945922852, -4.978509902954102], [-4.159348487854004, -4.703853607177734, -5.946484565734863], [-4.437461853027344, -4.65836238861084, -6.235235691070557], ], ("cuda", 8): [[-4.6471, -5.0008, -4.9786], [-4.1599, -4.7041, -5.9458], [-4.4374, -4.6582, -6.2340]], } ) expected_logits = torch.tensor(expectations.get_expectation()).to(torch_device) expectations = Expectations( { (None, None): [ [0.1688060760498047, 0.19992263615131378, 0.21225441992282867], [0.768376350402832, 0.41226309537887573, 0.4636859893798828], [0.25953856110572815, 0.5483334064483643, 0.4777486026287079], ], ("cuda", 8): [[0.1688, 0.1999, 0.2123], [0.7684, 0.4123, 0.4637], [0.2596, 0.5483, 0.4777]], } ) expected_boxes = torch.tensor(expectations.get_expectation()).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, 300, 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=0.0, target_sizes=[image.size[::-1]] )[0] expectations = Expectations( { (None, None): [0.9703017473220825, 0.9599503874778748, 0.9575679302215576, 0.9506784677505493], ("cuda", 8): [0.9704, 0.9599, 0.9576, 0.9507], } ) expected_scores = torch.tensor(expectations.get_expectation()).to(torch_device) expected_labels = [57, 15, 15, 65] expectations = Expectations( { (None, None): [ [0.13774872, 0.37821293, 640.13074, 476.21088], [343.38132, 24.276838, 640.1404, 371.49573], [13.225126, 54.179348, 318.98422, 472.2207], [40.114475, 73.44104, 175.9573, 118.48469], ], ("cuda", 8): [ [1.3775e-01, 3.7821e-01, 6.4013e02, 4.7621e02], [3.4338e02, 2.4277e01, 6.4014e02, 3.7150e02], [1.3225e01, 5.4179e01, 3.1898e02, 4.7222e02], [4.0114e01, 7.3441e01, 1.7596e02, 1.1848e02], ], } ) expected_slice_boxes = torch.tensor(expectations.get_expectation()).to(torch_device) torch.testing.assert_close(results["scores"][:4], expected_scores, rtol=2e-4, atol=2e-4) self.assertSequenceEqual(results["labels"][:4].tolist(), expected_labels) torch.testing.assert_close(results["boxes"][:4], expected_slice_boxes, rtol=2e-4, atol=2e-4)