Add DINOv2 depth estimation (#26092)
* First draft * Fix style * More improvements * Fix tests * Fix tests * Convert checkpoint * Improve DPTImageProcessor * Remove scripts, improve conversion script * Remove print statements * Fix test * Improve docstring * More improvements * Fix style * Fix image processor * Add tests * Address comments * Address comments * Make bias backwards compatible * Address comment * Address comment * Address comment * Apply suggestions from code review Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com> * Address comments * Add flag * Add tests * Make tests smaller * Use regular BackboneOutput * Fix all tests * Update test * Convert more checkpoints * Convert giant checkpoints, add integration test * Rename size_divisibility to size_divisor --------- Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
This commit is contained in:
NielsRogge
@@ -16,6 +16,8 @@
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import unittest
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import numpy as np
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from transformers.file_utils import is_vision_available
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from transformers.testing_utils import require_torch, require_vision
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@@ -97,6 +99,10 @@ class DPTImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase):
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self.assertTrue(hasattr(image_processing, "do_normalize"))
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self.assertTrue(hasattr(image_processing, "do_resize"))
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self.assertTrue(hasattr(image_processing, "size"))
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self.assertTrue(hasattr(image_processing, "do_rescale"))
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self.assertTrue(hasattr(image_processing, "rescale_factor"))
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self.assertTrue(hasattr(image_processing, "do_pad"))
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self.assertTrue(hasattr(image_processing, "size_divisor"))
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def test_image_processor_from_dict_with_kwargs(self):
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image_processor = self.image_processing_class.from_dict(self.image_processor_dict)
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@@ -104,3 +110,19 @@ class DPTImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase):
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image_processor = self.image_processing_class.from_dict(self.image_processor_dict, size=42)
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self.assertEqual(image_processor.size, {"height": 42, "width": 42})
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def test_padding(self):
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image_processing = self.image_processing_class(**self.image_processor_dict)
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image = np.random.randn(3, 249, 491)
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# test individual method
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image = image_processing.pad_image(image, size_divisor=4)
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self.assertTrue(image.shape[1] % 4 == 0)
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self.assertTrue(image.shape[2] % 4 == 0)
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# test by calling
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pixel_values = image_processing.preprocess(
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image, do_rescale=False, do_resize=False, do_pad=True, size_divisor=4, return_tensors="pt"
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).pixel_values
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self.assertTrue(pixel_values.shape[2] % 4 == 0)
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self.assertTrue(pixel_values.shape[3] % 4 == 0)
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294
tests/models/dpt/test_modeling_dpt_auto_backbone.py
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294
tests/models/dpt/test_modeling_dpt_auto_backbone.py
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@@ -0,0 +1,294 @@
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# coding=utf-8
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# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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""" Testing suite for the PyTorch DPT model. """
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import inspect
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import unittest
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from transformers import Dinov2Config, DPTConfig
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from transformers.file_utils import is_torch_available, is_vision_available
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from transformers.models.auto import get_values
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from transformers.testing_utils import require_torch, require_vision, slow, torch_device
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from ...test_configuration_common import ConfigTester
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from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
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from ...test_pipeline_mixin import PipelineTesterMixin
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if is_torch_available():
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import torch
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from transformers import MODEL_MAPPING, DPTForDepthEstimation
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from transformers.models.dpt.modeling_dpt import DPT_PRETRAINED_MODEL_ARCHIVE_LIST
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if is_vision_available():
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from PIL import Image
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from transformers import DPTImageProcessor
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class DPTModelTester:
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def __init__(
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self,
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parent,
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batch_size=2,
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num_channels=3,
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image_size=32,
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patch_size=16,
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use_labels=True,
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num_labels=3,
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is_training=True,
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hidden_size=4,
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num_hidden_layers=2,
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num_attention_heads=2,
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intermediate_size=8,
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out_features=["stage1", "stage2"],
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apply_layernorm=False,
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reshape_hidden_states=False,
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neck_hidden_sizes=[2, 2],
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fusion_hidden_size=6,
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):
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self.parent = parent
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self.batch_size = batch_size
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self.num_channels = num_channels
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self.image_size = image_size
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self.patch_size = patch_size
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self.hidden_size = hidden_size
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self.num_hidden_layers = num_hidden_layers
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self.num_attention_heads = num_attention_heads
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self.intermediate_size = intermediate_size
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self.out_features = out_features
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self.apply_layernorm = apply_layernorm
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self.reshape_hidden_states = reshape_hidden_states
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self.use_labels = use_labels
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self.num_labels = num_labels
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self.is_training = is_training
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self.neck_hidden_sizes = neck_hidden_sizes
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self.fusion_hidden_size = fusion_hidden_size
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# DPT's sequence length
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self.seq_length = (self.image_size // self.patch_size) ** 2 + 1
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def prepare_config_and_inputs(self):
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pixel_values = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])
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labels = None
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if self.use_labels:
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labels = ids_tensor([self.batch_size, self.image_size, self.image_size], self.num_labels)
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config = self.get_config()
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return config, pixel_values, labels
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def get_config(self):
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return DPTConfig(
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backbone_config=self.get_backbone_config(),
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neck_hidden_sizes=self.neck_hidden_sizes,
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fusion_hidden_size=self.fusion_hidden_size,
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)
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def get_backbone_config(self):
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return Dinov2Config(
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image_size=self.image_size,
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patch_size=self.patch_size,
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num_channels=self.num_channels,
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hidden_size=self.hidden_size,
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num_hidden_layers=self.num_hidden_layers,
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num_attention_heads=self.num_attention_heads,
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intermediate_size=self.intermediate_size,
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is_training=self.is_training,
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out_features=self.out_features,
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reshape_hidden_states=self.reshape_hidden_states,
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)
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def create_and_check_for_depth_estimation(self, config, pixel_values, labels):
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config.num_labels = self.num_labels
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model = DPTForDepthEstimation(config)
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model.to(torch_device)
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model.eval()
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result = model(pixel_values)
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self.parent.assertEqual(result.predicted_depth.shape, (self.batch_size, self.image_size, self.image_size))
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def prepare_config_and_inputs_for_common(self):
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config_and_inputs = self.prepare_config_and_inputs()
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config, pixel_values, labels = config_and_inputs
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inputs_dict = {"pixel_values": pixel_values}
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return config, inputs_dict
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@require_torch
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class DPTModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
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"""
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Here we also overwrite some of the tests of test_modeling_common.py, as DPT does not use input_ids, inputs_embeds,
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attention_mask and seq_length.
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"""
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all_model_classes = (DPTForDepthEstimation,) if is_torch_available() else ()
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pipeline_model_mapping = {"depth-estimation": DPTForDepthEstimation} if is_torch_available() else {}
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test_pruning = False
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test_resize_embeddings = False
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test_head_masking = False
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def setUp(self):
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self.model_tester = DPTModelTester(self)
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self.config_tester = ConfigTester(self, config_class=DPTConfig, has_text_modality=False, hidden_size=37)
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def test_config(self):
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self.config_tester.run_common_tests()
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@unittest.skip(reason="DPT with AutoBackbone does not have a base model and hence no input_embeddings")
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def test_inputs_embeds(self):
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pass
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def test_forward_signature(self):
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config, _ = self.model_tester.prepare_config_and_inputs_for_common()
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for model_class in self.all_model_classes:
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model = model_class(config)
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signature = inspect.signature(model.forward)
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# signature.parameters is an OrderedDict => so arg_names order is deterministic
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arg_names = [*signature.parameters.keys()]
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expected_arg_names = ["pixel_values"]
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self.assertListEqual(arg_names[:1], expected_arg_names)
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def test_for_depth_estimation(self):
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config_and_inputs = self.model_tester.prepare_config_and_inputs()
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self.model_tester.create_and_check_for_depth_estimation(*config_and_inputs)
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def test_training(self):
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for model_class in self.all_model_classes:
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if model_class.__name__ == "DPTForDepthEstimation":
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continue
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config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
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config.return_dict = True
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if model_class in get_values(MODEL_MAPPING):
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continue
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model = model_class(config)
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model.to(torch_device)
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model.train()
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inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
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loss = model(**inputs).loss
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loss.backward()
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def test_training_gradient_checkpointing(self):
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for model_class in self.all_model_classes:
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if model_class.__name__ == "DPTForDepthEstimation":
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continue
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config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
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config.use_cache = False
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config.return_dict = True
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if model_class in get_values(MODEL_MAPPING) or not model_class.supports_gradient_checkpointing:
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continue
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model = model_class(config)
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model.to(torch_device)
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model.gradient_checkpointing_enable()
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model.train()
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inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
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loss = model(**inputs).loss
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loss.backward()
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def test_initialization(self):
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config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
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configs_no_init = _config_zero_init(config)
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for model_class in self.all_model_classes:
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model = model_class(config=configs_no_init)
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# Skip the check for the backbone
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backbone_params = []
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for name, module in model.named_modules():
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if module.__class__.__name__ == "DPTViTHybridEmbeddings":
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backbone_params = [f"{name}.{key}" for key in module.state_dict().keys()]
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break
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for name, param in model.named_parameters():
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if param.requires_grad:
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if name in backbone_params:
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continue
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self.assertIn(
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((param.data.mean() * 1e9).round() / 1e9).item(),
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[0.0, 1.0],
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msg=f"Parameter {name} of model {model_class} seems not properly initialized",
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)
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@unittest.skip(reason="DPT with AutoBackbone does not have a base model and hence no input_embeddings")
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def test_model_common_attributes(self):
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pass
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@unittest.skip(reason="DPT with AutoBackbone does not have a base model")
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def test_save_load_fast_init_from_base(self):
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pass
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@unittest.skip(reason="DPT with AutoBackbone does not have a base model")
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def test_save_load_fast_init_to_base(self):
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pass
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@unittest.skip(
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reason="This architecure seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124"
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)
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def test_training_gradient_checkpointing_use_reentrant(self):
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pass
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@unittest.skip(
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reason="This architecure seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124"
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)
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def test_training_gradient_checkpointing_use_reentrant_false(self):
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pass
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@slow
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def test_model_from_pretrained(self):
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for model_name in DPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
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model = DPTForDepthEstimation.from_pretrained(model_name)
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self.assertIsNotNone(model)
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# We will verify our results on an image of cute cats
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def prepare_img():
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image = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
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return image
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@require_torch
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@require_vision
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@slow
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class DPTModelIntegrationTest(unittest.TestCase):
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def test_inference_depth_estimation(self):
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image_processor = DPTImageProcessor.from_pretrained("facebook/dpt-dinov2-small-kitti")
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model = DPTForDepthEstimation.from_pretrained("facebook/dpt-dinov2-small-kitti").to(torch_device)
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image = prepare_img()
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inputs = image_processor(images=image, return_tensors="pt").to(torch_device)
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# forward pass
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with torch.no_grad():
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outputs = model(**inputs)
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predicted_depth = outputs.predicted_depth
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# verify the predicted depth
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expected_shape = torch.Size((1, 576, 736))
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self.assertEqual(predicted_depth.shape, expected_shape)
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expected_slice = torch.tensor(
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[[6.0433, 7.1636, 7.4268], [6.9047, 7.2471, 7.2355], [7.9261, 8.0631, 8.0244]]
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).to(torch_device)
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self.assertTrue(torch.allclose(outputs.predicted_depth[0, :3, :3], expected_slice, atol=1e-4))
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