# 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 DPT model.""" import unittest import pytest from transformers import DPTConfig from transformers.file_utils import is_torch_available, is_vision_available from transformers.pytorch_utils import is_torch_greater_or_equal_than_2_4 from transformers.testing_utils import Expectations, require_torch, require_vision, slow, torch_device 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 DPTForDepthEstimation, DPTForSemanticSegmentation, DPTModel from transformers.models.auto.modeling_auto import MODEL_MAPPING_NAMES if is_vision_available(): from PIL import Image from transformers import DPTImageProcessor class DPTModelTester: def __init__( self, parent, batch_size=2, image_size=32, patch_size=16, num_channels=3, is_training=True, use_labels=True, hidden_size=32, num_hidden_layers=2, backbone_out_indices=[0, 1, 2, 3], num_attention_heads=4, intermediate_size=37, hidden_act="gelu", hidden_dropout_prob=0.1, attention_probs_dropout_prob=0.1, initializer_range=0.02, num_labels=3, neck_hidden_sizes=[16, 32], is_hybrid=False, scope=None, ): self.parent = parent self.batch_size = batch_size self.image_size = image_size self.patch_size = patch_size self.num_channels = num_channels self.is_training = is_training self.use_labels = use_labels self.hidden_size = hidden_size self.num_hidden_layers = num_hidden_layers self.backbone_out_indices = backbone_out_indices 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.initializer_range = initializer_range self.num_labels = num_labels self.scope = scope self.is_hybrid = is_hybrid self.neck_hidden_sizes = neck_hidden_sizes # sequence length of DPT = num_patches + 1 (we add 1 for the [CLS] token) num_patches = (image_size // patch_size) ** 2 self.seq_length = num_patches + 1 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.image_size, self.image_size], self.num_labels) config = self.get_config() return config, pixel_values, labels def get_config(self): return DPTConfig( image_size=self.image_size, patch_size=self.patch_size, num_channels=self.num_channels, hidden_size=self.hidden_size, fusion_hidden_size=self.hidden_size, num_hidden_layers=self.num_hidden_layers, backbone_out_indices=self.backbone_out_indices, num_attention_heads=self.num_attention_heads, intermediate_size=self.intermediate_size, hidden_act=self.hidden_act, hidden_dropout_prob=self.hidden_dropout_prob, attention_probs_dropout_prob=self.attention_probs_dropout_prob, is_decoder=False, initializer_range=self.initializer_range, is_hybrid=self.is_hybrid, neck_hidden_sizes=self.neck_hidden_sizes, ) def create_and_check_model(self, config, pixel_values, labels): model = DPTModel(config=config) model.to(torch_device) model.eval() result = model(pixel_values) self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size)) def create_and_check_for_depth_estimation(self, config, pixel_values, labels): config.num_labels = self.num_labels model = DPTForDepthEstimation(config) model.to(torch_device) model.eval() result = model(pixel_values) self.parent.assertEqual(result.predicted_depth.shape, (self.batch_size, self.image_size, self.image_size)) def create_and_check_for_semantic_segmentation(self, config, pixel_values, labels): config.num_labels = self.num_labels model = DPTForSemanticSegmentation(config) model.to(torch_device) model.eval() result = model(pixel_values, labels=labels) self.parent.assertEqual( result.logits.shape, (self.batch_size, self.num_labels, self.image_size, self.image_size) ) 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 @require_torch class DPTModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase): """ Here we also overwrite some of the tests of test_modeling_common.py, as DPT does not use input_ids, inputs_embeds, attention_mask and seq_length. """ all_model_classes = (DPTModel, DPTForDepthEstimation, DPTForSemanticSegmentation) if is_torch_available() else () pipeline_model_mapping = ( { "depth-estimation": DPTForDepthEstimation, "image-feature-extraction": DPTModel, "image-segmentation": DPTForSemanticSegmentation, } if is_torch_available() else {} ) test_resize_embeddings = False test_torch_exportable = True def setUp(self): self.model_tester = DPTModelTester(self) self.config_tester = ConfigTester(self, config_class=DPTConfig, has_text_modality=False, hidden_size=37) def test_config(self): self.config_tester.run_common_tests() @unittest.skip(reason="DPT does not use inputs_embeds") def test_inputs_embeds(self): pass 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_model(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_model(*config_and_inputs) def test_for_depth_estimation(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_depth_estimation(*config_and_inputs) def test_for_semantic_segmentation(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_semantic_segmentation(*config_and_inputs) def test_training(self): for model_class in self.all_model_classes: if model_class.__name__ == "DPTForDepthEstimation": continue config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() config.return_dict = True if model_class.__name__ in MODEL_MAPPING_NAMES.values(): continue 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 test_training_gradient_checkpointing(self): for model_class in self.all_model_classes: if model_class.__name__ == "DPTForDepthEstimation": continue config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() config.use_cache = False config.return_dict = True if model_class.__name__ in MODEL_MAPPING_NAMES.values() or not model_class.supports_gradient_checkpointing: continue model = model_class(config) model.to(torch_device) model.gradient_checkpointing_enable() model.train() inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True) loss = model(**inputs).loss loss.backward() @unittest.skip( reason="This architecture seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124" ) def test_training_gradient_checkpointing_use_reentrant(self): pass @unittest.skip( reason="This architecture seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124" ) def test_training_gradient_checkpointing_use_reentrant_false(self): pass @unittest.skip(reason="Inductor error for dynamic shape") @pytest.mark.torch_compile_test def test_sdpa_can_compile_dynamic(self): pass def test_backbone_selection(self): def _validate_backbone_init(): for model_class in self.all_model_classes: model = model_class(config) model.to(torch_device) model.eval() if model.__class__.__name__ == "DPTForDepthEstimation": # Confirm out_indices propagated to backbone self.assertEqual(len(model.backbone.out_indices), 2) config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() config.use_pretrained_backbone = True config.backbone_config = None config.backbone_kwargs = {"out_indices": [-2, -1]} # Force load_backbone path config.is_hybrid = False # Load a timm backbone config.backbone = "resnet18" config.use_timm_backbone = True _validate_backbone_init() # Load a HF backbone config.backbone = "facebook/dinov2-small" config.use_timm_backbone = False _validate_backbone_init() @slow def test_model_from_pretrained(self): model_name = "Intel/dpt-large" model = DPTModel.from_pretrained(model_name) self.assertIsNotNone(model) # 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 DPTModelIntegrationTest(unittest.TestCase): def test_inference_depth_estimation(self): image_processor = DPTImageProcessor.from_pretrained("Intel/dpt-large") model = DPTForDepthEstimation.from_pretrained("Intel/dpt-large").to(torch_device) image = prepare_img() inputs = image_processor(images=image, return_tensors="pt").to(torch_device) # forward pass with torch.no_grad(): outputs = model(**inputs) predicted_depth = outputs.predicted_depth # verify the predicted depth expected_shape = torch.Size((1, 384, 384)) self.assertEqual(predicted_depth.shape, expected_shape) expectations = Expectations( { (None, None): [[6.3199, 6.3629, 6.4148], [6.3850, 6.3615, 6.4166], [6.3519, 6.3176, 6.3575]], ("cuda", 8): [[6.3199, 6.3629, 6.4148], [6.3850, 6.3615, 6.4166], [6.3519, 6.3176, 6.3575]], } ) expected_slice = torch.tensor(expectations.get_expectation()).to(torch_device) torch.testing.assert_close(outputs.predicted_depth[0, :3, :3], expected_slice, rtol=2e-4, atol=2e-4) def test_inference_semantic_segmentation(self): image_processor = DPTImageProcessor.from_pretrained("Intel/dpt-large-ade") model = DPTForSemanticSegmentation.from_pretrained("Intel/dpt-large-ade").to(torch_device) image = prepare_img() inputs = image_processor(images=image, return_tensors="pt").to(torch_device) # forward pass with torch.no_grad(): outputs = model(**inputs) # verify the logits expected_shape = torch.Size((1, 150, 480, 480)) self.assertEqual(outputs.logits.shape, expected_shape) expected_slice = torch.tensor( [[4.0480, 4.2420, 4.4360], [4.3124, 4.5693, 4.8261], [4.5768, 4.8965, 5.2163]] ).to(torch_device) torch.testing.assert_close(outputs.logits[0, 0, :3, :3], expected_slice, rtol=1e-4, atol=1e-4) def test_post_processing_semantic_segmentation(self): image_processor = DPTImageProcessor.from_pretrained("Intel/dpt-large-ade") model = DPTForSemanticSegmentation.from_pretrained("Intel/dpt-large-ade").to(torch_device) image = prepare_img() inputs = image_processor(images=image, return_tensors="pt").to(torch_device) # forward pass with torch.no_grad(): outputs = model(**inputs) outputs.logits = outputs.logits.detach().cpu() segmentation = image_processor.post_process_semantic_segmentation(outputs=outputs, target_sizes=[(500, 300)]) expected_shape = torch.Size((500, 300)) self.assertEqual(segmentation[0].shape, expected_shape) segmentation = image_processor.post_process_semantic_segmentation(outputs=outputs) expected_shape = torch.Size((480, 480)) self.assertEqual(segmentation[0].shape, expected_shape) def test_post_processing_depth_estimation(self): image_processor = DPTImageProcessor.from_pretrained("Intel/dpt-large") model = DPTForDepthEstimation.from_pretrained("Intel/dpt-large") image = prepare_img() inputs = image_processor(images=image, return_tensors="pt") # forward pass with torch.no_grad(): outputs = model(**inputs) predicted_depth = image_processor.post_process_depth_estimation(outputs=outputs)[0]["predicted_depth"] expected_shape = torch.Size((384, 384)) self.assertTrue(predicted_depth.shape == expected_shape) predicted_depth_l = image_processor.post_process_depth_estimation(outputs=outputs, target_sizes=[(500, 500)]) predicted_depth_l = predicted_depth_l[0]["predicted_depth"] expected_shape = torch.Size((500, 500)) self.assertTrue(predicted_depth_l.shape == expected_shape) output_enlarged = torch.nn.functional.interpolate( predicted_depth.unsqueeze(0).unsqueeze(1), size=(500, 500), mode="bicubic", align_corners=False ).squeeze() self.assertTrue(output_enlarged.shape == expected_shape) torch.testing.assert_close(predicted_depth_l, output_enlarged, atol=1e-3, rtol=1e-3) @pytest.mark.torch_export_test def test_export(self): for strict in [True, False]: with self.subTest(strict=strict): if not is_torch_greater_or_equal_than_2_4: self.skipTest(reason="This test requires torch >= 2.4 to run.") model = DPTForSemanticSegmentation.from_pretrained("Intel/dpt-large-ade").to(torch_device).eval() image_processor = DPTImageProcessor.from_pretrained("Intel/dpt-large-ade") image = prepare_img() inputs = image_processor(images=image, return_tensors="pt").to(torch_device) exported_program = torch.export.export( model, args=(inputs["pixel_values"],), strict=strict, ) with torch.no_grad(): eager_outputs = model(**inputs) exported_outputs = exported_program.module().forward(inputs["pixel_values"]) self.assertEqual(eager_outputs.logits.shape, exported_outputs.logits.shape) torch.testing.assert_close(eager_outputs.logits, exported_outputs.logits, rtol=1e-4, atol=1e-4)