# 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 ZoeDepth model.""" import unittest import numpy as np from transformers import Dinov2Config, ZoeDepthConfig from transformers.file_utils import is_torch_available, is_vision_available from transformers.testing_utils import 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 ZoeDepthForDepthEstimation if is_vision_available(): from PIL import Image from transformers import ZoeDepthImageProcessor class ZoeDepthModelTester: 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, bottleneck_features=6, num_out_features=[6, 6, 6, 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 self.bottleneck_features = bottleneck_features self.num_out_features = num_out_features # ZoeDepth'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 ZoeDepthConfig( backbone_config=self.get_backbone_config(), backbone=None, neck_hidden_sizes=self.neck_hidden_sizes, fusion_hidden_size=self.fusion_hidden_size, bottleneck_features=self.bottleneck_features, num_out_features=self.num_out_features, ) 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 = ZoeDepthForDepthEstimation(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 ZoeDepthModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase): """ Here we also overwrite some of the tests of test_modeling_common.py, as ZoeDepth does not use input_ids, inputs_embeds, attention_mask and seq_length. """ all_model_classes = (ZoeDepthForDepthEstimation,) if is_torch_available() else () pipeline_model_mapping = {"depth-estimation": ZoeDepthForDepthEstimation} if is_torch_available() else {} test_resize_embeddings = False # `strict=True/False` are both failing with torch 2.7, see #38677 test_torch_exportable = get_torch_major_and_minor_version() != "2.7" def setUp(self): self.model_tester = ZoeDepthModelTester(self) self.config_tester = ConfigTester( self, config_class=ZoeDepthConfig, has_text_modality=False, hidden_size=37, common_properties=[] ) def test_config(self): self.config_tester.run_common_tests() @unittest.skip(reason="ZoeDepth with AutoBackbone does not have a base model and hence no input_embeddings") def test_inputs_embeds(self): pass @unittest.skip(reason="ZoeDepth with AutoBackbone does not have a base model and hence no input_embeddings") def test_model_get_set_embeddings(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) @unittest.skip(reason="ZoeDepth with AutoBackbone does not have a base model and hence no input_embeddings") def test_model_common_attributes(self): pass @unittest.skip(reason="ZoeDepth does not support training yet") def test_training(self): pass @unittest.skip(reason="ZoeDepth does not support training yet") def test_training_gradient_checkpointing(self): pass @unittest.skip(reason="ZoeDepth does not support training yet") def test_training_gradient_checkpointing_use_reentrant(self): pass @unittest.skip(reason="ZoeDepth does not support training yet") def test_training_gradient_checkpointing_use_reentrant_false(self): pass @slow def test_model_from_pretrained(self): model_name = "Intel/zoedepth-nyu" model = ZoeDepthForDepthEstimation.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 ZoeDepthModelIntegrationTest(unittest.TestCase): expected_slice_post_processing = { (False, False): [ [[1.1348238, 1.1193453, 1.130562], [1.1754476, 1.1613507, 1.1701596], [1.2287744, 1.2101802, 1.2148322]], [[2.7170, 2.6550, 2.6839], [2.9827, 2.9438, 2.9587], [3.2340, 3.1817, 3.1602]], ], (False, True): [ [[1.0610938, 1.1042216, 1.1429265], [1.1099341, 1.148696, 1.1817775], [1.1656011, 1.1988826, 1.2268101]], [[2.5848, 2.7391, 2.8694], [2.7882, 2.9872, 3.1244], [2.9436, 3.1812, 3.3188]], ], (True, False): [ [[1.8382794, 1.8380532, 1.8375976], [1.848761, 1.8485023, 1.8479986], [1.8571457, 1.8568444, 1.8562847]], [[6.2030, 6.1902, 6.1777], [6.2303, 6.2176, 6.2053], [6.2561, 6.2436, 6.2312]], ], (True, True): [ [[1.8306141, 1.8305621, 1.8303483], [1.8410318, 1.8409299, 1.8406585], [1.8492792, 1.8491366, 1.8488203]], [[6.2616, 6.2520, 6.2435], [6.2845, 6.2751, 6.2667], [6.3065, 6.2972, 6.2887]], ], } # (pad, flip) def test_inference_depth_estimation(self): image_processor = ZoeDepthImageProcessor.from_pretrained("Intel/zoedepth-nyu") model = ZoeDepthForDepthEstimation.from_pretrained("Intel/zoedepth-nyu").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, 512)) self.assertEqual(predicted_depth.shape, expected_shape) expected_slice = torch.tensor( [[1.0020, 1.0219, 1.0389], [1.0349, 1.0816, 1.1000], [1.0576, 1.1094, 1.1249]], ).to(torch_device) torch.testing.assert_close(outputs.predicted_depth[0, :3, :3], expected_slice, rtol=1e-4, atol=1e-4) def test_inference_depth_estimation_multiple_heads(self): image_processor = ZoeDepthImageProcessor.from_pretrained("Intel/zoedepth-nyu-kitti") model = ZoeDepthForDepthEstimation.from_pretrained("Intel/zoedepth-nyu-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, 384, 512)) self.assertEqual(predicted_depth.shape, expected_shape) expected_slice = torch.tensor( [[1.1571, 1.1438, 1.1783], [1.2163, 1.2036, 1.2320], [1.2688, 1.2461, 1.2734]], ).to(torch_device) torch.testing.assert_close(outputs.predicted_depth[0, :3, :3], expected_slice, rtol=1e-4, atol=1e-4) def check_target_size( self, image_processor, pad_input, images, outputs, raw_outputs, raw_outputs_flipped=None, ): outputs_large = image_processor.post_process_depth_estimation( raw_outputs, [img.size[::-1] for img in images], outputs_flipped=raw_outputs_flipped, target_sizes=[tuple(np.array(img.size[::-1]) * 2) for img in images], do_remove_padding=pad_input, ) for img, out, out_l in zip(images, outputs, outputs_large): out = out["predicted_depth"] out_l = out_l["predicted_depth"] out_l_reduced = torch.nn.functional.interpolate( out_l.unsqueeze(0).unsqueeze(1), size=img.size[::-1], mode="bicubic", align_corners=False ) out_l_reduced = out_l_reduced.squeeze(0).squeeze(0) torch.testing.assert_close(out, out_l_reduced, rtol=2e-2, atol=2e-2) def check_post_processing_test(self, image_processor, images, model, pad_input=True, flip_aug=True): inputs = image_processor(images=images, return_tensors="pt", do_pad=pad_input).to(torch_device) with torch.no_grad(): raw_outputs = model(**inputs) raw_outputs_flipped = None if flip_aug: raw_outputs_flipped = model(pixel_values=torch.flip(inputs.pixel_values, dims=[3])) outputs = image_processor.post_process_depth_estimation( raw_outputs, [img.size[::-1] for img in images], outputs_flipped=raw_outputs_flipped, do_remove_padding=pad_input, ) expected_slices = torch.tensor(self.expected_slice_post_processing[pad_input, flip_aug]).to(torch_device) for img, out, expected_slice in zip(images, outputs, expected_slices): out = out["predicted_depth"] self.assertTrue(img.size == out.shape[::-1]) torch.testing.assert_close(expected_slice, out[:3, :3], rtol=1e-3, atol=1e-3) self.check_target_size(image_processor, pad_input, images, outputs, raw_outputs, raw_outputs_flipped) def test_post_processing_depth_estimation_post_processing_nopad_noflip(self): images = [prepare_img(), Image.open("./tests/fixtures/tests_samples/COCO/000000004016.png")] image_processor = ZoeDepthImageProcessor.from_pretrained("Intel/zoedepth-nyu-kitti", keep_aspect_ratio=False) model = ZoeDepthForDepthEstimation.from_pretrained("Intel/zoedepth-nyu-kitti").to(torch_device) self.check_post_processing_test(image_processor, images, model, pad_input=False, flip_aug=False) def test_inference_depth_estimation_post_processing_nopad_flip(self): images = [prepare_img(), Image.open("./tests/fixtures/tests_samples/COCO/000000004016.png")] image_processor = ZoeDepthImageProcessor.from_pretrained("Intel/zoedepth-nyu-kitti", keep_aspect_ratio=False) model = ZoeDepthForDepthEstimation.from_pretrained("Intel/zoedepth-nyu-kitti").to(torch_device) self.check_post_processing_test(image_processor, images, model, pad_input=False, flip_aug=True) def test_inference_depth_estimation_post_processing_pad_noflip(self): images = [prepare_img(), Image.open("./tests/fixtures/tests_samples/COCO/000000004016.png")] image_processor = ZoeDepthImageProcessor.from_pretrained("Intel/zoedepth-nyu-kitti", keep_aspect_ratio=False) model = ZoeDepthForDepthEstimation.from_pretrained("Intel/zoedepth-nyu-kitti").to(torch_device) self.check_post_processing_test(image_processor, images, model, pad_input=True, flip_aug=False) def test_inference_depth_estimation_post_processing_pad_flip(self): images = [prepare_img(), Image.open("./tests/fixtures/tests_samples/COCO/000000004016.png")] image_processor = ZoeDepthImageProcessor.from_pretrained("Intel/zoedepth-nyu-kitti", keep_aspect_ratio=False) model = ZoeDepthForDepthEstimation.from_pretrained("Intel/zoedepth-nyu-kitti").to(torch_device) self.check_post_processing_test(image_processor, images, model, pad_input=True, flip_aug=True)