# Copyright 2023 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 from transformers import Dinov2Config, DPTConfig from transformers.file_utils import is_torch_available, is_vision_available from transformers.testing_utils import Expectations, require_torch, require_vision, slow, torch_device from transformers.utils.import_utils import get_torch_major_and_minor_version 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 transformers import DPTForDepthEstimation 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, num_channels=3, image_size=32, patch_size=16, use_labels=True, num_labels=3, is_training=True, hidden_size=4, num_hidden_layers=2, num_attention_heads=2, intermediate_size=8, out_features=["stage1", "stage2"], apply_layernorm=False, reshape_hidden_states=False, neck_hidden_sizes=[2, 2], fusion_hidden_size=6, ): self.parent = parent self.batch_size = batch_size self.num_channels = num_channels self.image_size = image_size self.patch_size = patch_size 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.out_features = out_features self.apply_layernorm = apply_layernorm self.reshape_hidden_states = reshape_hidden_states self.use_labels = use_labels self.num_labels = num_labels self.is_training = is_training self.neck_hidden_sizes = neck_hidden_sizes self.fusion_hidden_size = fusion_hidden_size # DPT's sequence length self.seq_length = (self.image_size // self.patch_size) ** 2 + 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( backbone_config=self.get_backbone_config(), backbone=None, neck_hidden_sizes=self.neck_hidden_sizes, fusion_hidden_size=self.fusion_hidden_size, ) def get_backbone_config(self): return Dinov2Config( image_size=self.image_size, patch_size=self.patch_size, num_channels=self.num_channels, hidden_size=self.hidden_size, num_hidden_layers=self.num_hidden_layers, num_attention_heads=self.num_attention_heads, intermediate_size=self.intermediate_size, is_training=self.is_training, out_features=self.out_features, reshape_hidden_states=self.reshape_hidden_states, ) 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 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 = (DPTForDepthEstimation,) if is_torch_available() else () pipeline_model_mapping = {"depth-estimation": DPTForDepthEstimation} if is_torch_available() else {} test_resize_embeddings = False test_torch_exportable = True test_torch_exportable_strictly = get_torch_major_and_minor_version() != "2.7" 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 with AutoBackbone does not have a base model and hence no input_embeddings") def test_inputs_embeds(self): pass 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_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="DPT with AutoBackbone does not have a base model and hence no input_embeddings") def test_model_get_set_embeddings(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(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 @slow def test_model_from_pretrained(self): model_name = "Intel/dpt-large" model = DPTForDepthEstimation.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_dinov2(self): image_processor = DPTImageProcessor.from_pretrained("facebook/dpt-dinov2-small-kitti") model = DPTForDepthEstimation.from_pretrained("facebook/dpt-dinov2-small-kitti").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, 576, 736)) self.assertEqual(predicted_depth.shape, expected_shape) expectations = Expectations( { (None, None): [[6.0336, 7.1502, 7.4130], [6.8977, 7.2383, 7.2268], [7.9180, 8.0525, 8.0134]], ("cuda", 8): [[6.0350, 7.1518, 7.4144], [6.8992, 7.2396, 7.2280], [7.9194, 8.0538, 8.0145]], } ) 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_depth_estimation_beit(self): image_processor = DPTImageProcessor.from_pretrained("Intel/dpt-beit-base-384") model = DPTForDepthEstimation.from_pretrained("Intel/dpt-beit-base-384").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): [ [2669.7061, 2663.7144, 2674.9399], [2633.9326, 2650.9092, 2665.4270], [2621.8271, 2632.0129, 2637.2290], ], ("cuda", 8): [ [2669.4292, 2663.4121, 2674.6233], [2633.7400, 2650.7026, 2665.2085], [2621.6572, 2631.8452, 2637.0525], ], } ) 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_depth_estimation_swinv2(self): image_processor = DPTImageProcessor.from_pretrained("Intel/dpt-swinv2-tiny-256") model = DPTForDepthEstimation.from_pretrained("Intel/dpt-swinv2-tiny-256").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, 256, 256)) self.assertEqual(predicted_depth.shape, expected_shape) expectations = Expectations( { (None, None): [ [1032.7719, 1025.1886, 1030.2661], [1023.7619, 1021.0075, 1024.9121], [1022.5667, 1018.8522, 1021.4145], ], ("cuda", 8): [ [1032.7170, 1025.0629, 1030.1941], [1023.7309, 1020.9786, 1024.8594], [1022.5233, 1018.8235, 1021.3312], ], } ) 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)