Add Prompt Depth Anything Model

docs/source/en/_toctree.yml

@@ -689,6 +689,8 @@
         title: NAT
       - local: model_doc/poolformer
         title: PoolFormer
+      - local: model_doc/prompt_depth_anything
+        title: Prompt Depth Anything
       - local: model_doc/pvt
         title: Pyramid Vision Transformer (PVT)
       - local: model_doc/pvt_v2

docs/source/en/index.md

@@ -275,6 +275,7 @@ Flax), PyTorch, and/or TensorFlow.
 |                        [PLBart](model_doc/plbart)                        |       ✅        |         ❌         |      ❌      |
 |                    [PoolFormer](model_doc/poolformer)                    |       ✅        |         ❌         |      ❌      |
 |                     [Pop2Piano](model_doc/pop2piano)                     |       ✅        |         ❌         |      ❌      |
+|          [PromptDepthAnything](model_doc/prompt_depth_anything)          |       ✅        |         ❌         |      ❌      |
 |                    [ProphetNet](model_doc/prophetnet)                    |       ✅        |         ❌         |      ❌      |
 |                           [PVT](model_doc/pvt)                           |       ✅        |         ❌         |      ❌      |
 |                        [PVTv2](model_doc/pvt_v2)                         |       ✅        |         ❌         |      ❌      |

docs/source/en/model_doc/prompt_depth_anything.md

@@ -0,0 +1,99 @@
+<!--Copyright 2024 The HuggingFace 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.
+
+⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
+rendered properly in your Markdown viewer.
+
+-->
+
+# Prompt Depth Anything
+
+## Overview
+
+The Prompt Depth Anything model was introduced in [Prompting Depth Anything for 4K Resolution Accurate Metric Depth Estimation](https://promptda.github.io/) by Haotong Lin, Sida Peng, Jingxiao Chen, Songyou Peng, Jiaming Sun, Minghuan Liu, Hujun Bao, Jiashi Feng, Xiaowei Zhou, Bingyi Kang. 
+
+<!-- 
+<Tip>
+
+[Prompt Depth Anything V2](prompt_depth_anything_v2) was released in June 2024. It retains the same architecture as the original Prompt Depth Anything, ensuring compatibility with all existing code examples and workflows. However, it utilizes synthetic data and a larger capacity teacher model to deliver more precise and robust depth predictions.
+
+</Tip> -->
+
+The abstract from the paper is as follows:
+
+*Prompts play a critical role in unleashing the power of language and vision foundation models for specific tasks. For the first time, we introduce prompting into depth foundation models, creating a new paradigm for metric depth estimation termed Prompt Depth Anything. Specifically, we use a low-cost LiDAR as the prompt to guide the Depth Anything model for accurate metric depth output, achieving up to 4K resolution. Our approach centers on a concise prompt fusion design that integrates the LiDAR at multiple scales within the depth decoder. To address training challenges posed by limited datasets containing both LiDAR depth and precise GT depth, we propose a scalable data pipeline that includes synthetic data LiDAR simulation and real data pseudo GT depth generation. Our approach sets new state-of-the-arts on the ARKitScenes and ScanNet++ datasets and benefits downstream applications, including 3D reconstruction and generalized robotic grasping.*
+
+<img src="https://promptda.github.io/assets/teaser.jpg"
+alt="drawing" width="600"/>
+
+<small> Prompt Depth Anything overview. Taken from the <a href="https://promptda.github.io/">original paper</a>.</small>
+
+<!-- This model was contributed by [nielsr](https://huggingface.co/nielsr).
+The original code can be found [here](https://github.com/LiheYoung/Depth-Anything). -->
+
+## Usage example
+
+The transformers library allows you to use the model with just a few lines of code:
+
+```python
+>>> from transformers import AutoImageProcessor, AutoModelForDepthEstimation
+>>> import torch
+>>> import numpy as np
+>>> from PIL import Image
+>>> import requests
+
+>>> url = "https://github.com/DepthAnything/PromptDA/blob/main/assets/example_images/image.jpg?raw=true"
+>>> image = Image.open(requests.get(url, stream=True).raw)
+
+>>> image_processor = AutoImageProcessor.from_pretrained("depth-anything/promptda_vits_hf")
+>>> model = AutoModelForDepthEstimation.from_pretrained("depth-anything/promptda_vits_hf")
+
+>>> prompt_depth_url = "https://github.com/DepthAnything/PromptDA/blob/main/assets/example_images/arkit_depth.png?raw=true"
+>>> prompt_depth = Image.open(requests.get(prompt_depth_url, stream=True).raw)
+>>> prompt_depth = torch.tensor((np.asarray(prompt_depth) / 1000.0).astype(np.float32))
+>>> prompt_depth = prompt_depth.unsqueeze(0).unsqueeze(0)
+
+>>> # prepare image for the model
+>>> inputs = image_processor(images=image, return_tensors="pt")
+
+>>> with torch.no_grad():
+...     outputs = model(pixel_values=inputs.pixel_values, prompt_depth=prompt_depth)
+
+>>> # interpolate to original size
+>>> post_processed_output = image_processor.post_process_depth_estimation(
+...     outputs,
+...     target_sizes=[(image.height, image.width)],
+... )
+
+>>> # visualize the prediction
+>>> predicted_depth = post_processed_output[0]["predicted_depth"]
+>>> depth = predicted_depth * 1000 
+>>> depth = depth.detach().cpu().numpy()
+>>> depth = Image.fromarray(depth.astype("uint16")) # mm
+```
+
+## Resources
+
+A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with Prompt Depth Anything.
+
+- [Prompt Depth Anything Demo](https://huggingface.co/spaces/depth-anything/PromptDA)
+- [Prompt Depth Anything Interactive Results](https://promptda.github.io/interactive.html)
+
+If you're interested in submitting a resource to be included here, please feel free to open a Pull Request and we'll review it! The resource should ideally demonstrate something new instead of duplicating an existing resource.
+
+## PromptDepthAnythingConfig
+
+[[autodoc]] PromptDepthAnythingConfig
+
+## PromptDepthAnythingForDepthEstimation
+
+[[autodoc]] PromptDepthAnythingForDepthEstimation
+    - forward

src/transformers/__init__.py

@@ -689,6 +689,7 @@
     "models.plbart": ["PLBartConfig"],
     "models.poolformer": ["PoolFormerConfig"],
     "models.pop2piano": ["Pop2PianoConfig"],
+    "models.prompt_depth_anything": ["PromptDepthAnythingConfig"],
     "models.prophetnet": [
         "ProphetNetConfig",
         "ProphetNetTokenizer",
@@ -3181,6 +3182,12 @@
             "Pop2PianoPreTrainedModel",
         ]
     )
+    _import_structure["models.prompt_depth_anything"].extend(
+        [
+            "PromptDepthAnythingForDepthEstimation",
+            "PromptDepthAnythingPreTrainedModel",
+        ]
+    )
     _import_structure["models.prophetnet"].extend(
         [
             "ProphetNetDecoder",
@@ -5682,6 +5689,7 @@
     from .models.pop2piano import (
         Pop2PianoConfig,
     )
+    from .models.prompt_depth_anything import PromptDepthAnythingConfig
     from .models.prophetnet import (
         ProphetNetConfig,
         ProphetNetTokenizer,
@@ -7819,6 +7827,10 @@
             Pop2PianoForConditionalGeneration,
             Pop2PianoPreTrainedModel,
         )
+        from .models.prompt_depth_anything import (
+            PromptDepthAnythingForDepthEstimation,
+            PromptDepthAnythingPreTrainedModel,
+        )
         from .models.prophetnet import (
             ProphetNetDecoder,
             ProphetNetEncoder,

src/transformers/models/__init__.py

@@ -207,6 +207,7 @@
     plbart,
     poolformer,
     pop2piano,
+    prompt_depth_anything,
     prophetnet,
     pvt,
     pvt_v2,

src/transformers/models/auto/configuration_auto.py

@@ -227,6 +227,7 @@
         ("plbart", "PLBartConfig"),
         ("poolformer", "PoolFormerConfig"),
         ("pop2piano", "Pop2PianoConfig"),
+        ("prompt_depth_anything", "PromptDepthAnythingConfig"),
         ("prophetnet", "ProphetNetConfig"),
         ("pvt", "PvtConfig"),
         ("pvt_v2", "PvtV2Config"),
@@ -554,6 +555,7 @@
         ("plbart", "PLBart"),
         ("poolformer", "PoolFormer"),
         ("pop2piano", "Pop2Piano"),
+        ("prompt_depth_anything", "PromptDepthAnything"),
         ("prophetnet", "ProphetNet"),
         ("pvt", "PVT"),
         ("pvt_v2", "PVTv2"),

src/transformers/models/auto/modeling_auto.py

@@ -893,6 +893,7 @@
         ("depth_anything", "DepthAnythingForDepthEstimation"),
         ("dpt", "DPTForDepthEstimation"),
         ("glpn", "GLPNForDepthEstimation"),
+        ("prompt_depth_anything", "PromptDepthAnythingForDepthEstimation"),
         ("zoedepth", "ZoeDepthForDepthEstimation"),
     ]
 )

src/transformers/models/prompt_depth_anything/__init__.py

@@ -0,0 +1,27 @@
+# Copyright 2024 The HuggingFace 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.
+from typing import TYPE_CHECKING
+
+from ...utils import _LazyModule
+from ...utils.import_utils import define_import_structure
+
+
+if TYPE_CHECKING:
+    from .configuration_prompt_depth_anything import *
+    from .modeling_prompt_depth_anything import *
+else:
+    import sys
+
+    _file = globals()["__file__"]
+    sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__)

src/transformers/models/prompt_depth_anything/configuration_prompt_depth_anything.py

@@ -0,0 +1,159 @@
+#                🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
+#           This file was automatically generated from src/transformers/models/prompt_depth_anything/modular_prompt_depth_anything.py.
+#               Do NOT edit this file manually as any edits will be overwritten by the generation of
+#             the file from the modular. If any change should be done, please apply the change to the
+#                          modular_prompt_depth_anything.py file directly. One of our CI enforces this.
+#                🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
+
+import copy
+
+from ...configuration_utils import PretrainedConfig
+from ...utils import logging
+from ...utils.backbone_utils import verify_backbone_config_arguments
+from ..auto.configuration_auto import CONFIG_MAPPING
+
+
+logger = logging.get_logger(__name__)
+
+
+class PromptDepthAnythingConfig(PretrainedConfig):
+    r"""
+    This is the configuration class to store the configuration of a [`PromptDepthAnythingModel`]. It is used to instantiate a PromptDepthAnything
+    model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
+    defaults will yield a similar configuration to that of the PromptDepthAnything
+    [LiheYoung/depth-anything-small-hf](https://huggingface.co/LiheYoung/depth-anything-small-hf) architecture.
+
+    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+    documentation from [`PretrainedConfig`] for more information.
+
+    Args:
+        backbone_config (`Union[Dict[str, Any], PretrainedConfig]`, *optional*):
+            The configuration of the backbone model. Only used in case `is_hybrid` is `True` or in case you want to
+            leverage the [`AutoBackbone`] API.
+        backbone (`str`, *optional*):
+            Name of backbone to use when `backbone_config` is `None`. If `use_pretrained_backbone` is `True`, this
+            will load the corresponding pretrained weights from the timm or transformers library. If `use_pretrained_backbone`
+            is `False`, this loads the backbone's config and uses that to initialize the backbone with random weights.
+        use_pretrained_backbone (`bool`, *optional*, defaults to `False`):
+            Whether to use pretrained weights for the backbone.
+        use_timm_backbone (`bool`, *optional*, defaults to `False`):
+            Whether or not to use the `timm` library for the backbone. If set to `False`, will use the [`AutoBackbone`]
+            API.
+        backbone_kwargs (`dict`, *optional*):
+            Keyword arguments to be passed to AutoBackbone when loading from a checkpoint
+            e.g. `{'out_indices': (0, 1, 2, 3)}`. Cannot be specified if `backbone_config` is set.
+        patch_size (`int`, *optional*, defaults to 14):
+            The size of the patches to extract from the backbone features.
+        initializer_range (`float`, *optional*, defaults to 0.02):
+            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
+        reassemble_hidden_size (`int`, *optional*, defaults to 384):
+            The number of input channels of the reassemble layers.
+        reassemble_factors (`List[int]`, *optional*, defaults to `[4, 2, 1, 0.5]`):
+            The up/downsampling factors of the reassemble layers.
+        neck_hidden_sizes (`List[str]`, *optional*, defaults to `[48, 96, 192, 384]`):
+            The hidden sizes to project to for the feature maps of the backbone.
+        fusion_hidden_size (`int`, *optional*, defaults to 64):
+            The number of channels before fusion.
+        head_in_index (`int`, *optional*, defaults to -1):
+            The index of the features to use in the depth estimation head.
+        head_hidden_size (`int`, *optional*, defaults to 32):
+            The number of output channels in the second convolution of the depth estimation head.
+        depth_estimation_type (`str`, *optional*, defaults to `"relative"`):
+            The type of depth estimation to use. Can be one of `["relative", "metric"]`.
+        max_depth (`float`, *optional*):
+            The maximum depth to use for the "metric" depth estimation head. 20 should be used for indoor models
+            and 80 for outdoor models. For "relative" depth estimation, this value is ignored.
+
+    Example:
+
+    ```python
+    >>> from transformers import PromptDepthAnythingConfig, PromptDepthAnythingForDepthEstimation
+
+    >>> # Initializing a PromptDepthAnything small style configuration
+    >>> configuration = PromptDepthAnythingConfig()
+
+    >>> # Initializing a model from the PromptDepthAnything small style configuration
+    >>> model = PromptDepthAnythingForDepthEstimation(configuration)
+
+    >>> # Accessing the model configuration
+    >>> configuration = model.config
+    ```"""
+
+    model_type = "prompt_depth_anything"
+
+    def __init__(
+        self,
+        backbone_config=None,
+        backbone=None,
+        use_pretrained_backbone=False,
+        use_timm_backbone=False,
+        backbone_kwargs=None,
+        patch_size=14,
+        initializer_range=0.02,
+        reassemble_hidden_size=384,
+        reassemble_factors=[4, 2, 1, 0.5],
+        neck_hidden_sizes=[48, 96, 192, 384],
+        fusion_hidden_size=64,
+        head_in_index=-1,
+        head_hidden_size=32,
+        depth_estimation_type="relative",
+        max_depth=None,
+        **kwargs,
+    ):
+        super().__init__(**kwargs)
+        if backbone_config is None and backbone is None:
+            logger.info("`backbone_config` is `None`. Initializing the config with the default `Dinov2` backbone.")
+            backbone_config = CONFIG_MAPPING["dinov2"](
+                image_size=518,
+                hidden_size=384,
+                num_attention_heads=6,
+                out_indices=[9, 10, 11, 12],
+                apply_layernorm=True,
+                reshape_hidden_states=False,
+            )
+        elif isinstance(backbone_config, dict):
+            backbone_model_type = backbone_config.get("model_type")
+            config_class = CONFIG_MAPPING[backbone_model_type]
+            backbone_config = config_class.from_dict(backbone_config)
+
+        verify_backbone_config_arguments(
+            use_timm_backbone=use_timm_backbone,
+            use_pretrained_backbone=use_pretrained_backbone,
+            backbone=backbone,
+            backbone_config=backbone_config,
+            backbone_kwargs=backbone_kwargs,
+        )
+
+        self.backbone_config = backbone_config
+        self.backbone = backbone
+        self.use_pretrained_backbone = use_pretrained_backbone
+        self.use_timm_backbone = use_timm_backbone
+        self.backbone_kwargs = backbone_kwargs
+        self.reassemble_hidden_size = reassemble_hidden_size
+        self.patch_size = patch_size
+        self.initializer_range = initializer_range
+        self.reassemble_factors = reassemble_factors
+        self.neck_hidden_sizes = neck_hidden_sizes
+        self.fusion_hidden_size = fusion_hidden_size
+        self.head_in_index = head_in_index
+        self.head_hidden_size = head_hidden_size
+        if depth_estimation_type not in ["relative", "metric"]:
+            raise ValueError("depth_estimation_type must be one of ['relative', 'metric']")
+        self.depth_estimation_type = depth_estimation_type
+        self.max_depth = max_depth if max_depth else 1
+
+    def to_dict(self):
+        """
+        Serializes this instance to a Python dictionary. Override the default [`~PretrainedConfig.to_dict`]. Returns:
+            `Dict[str, any]`: Dictionary of all the attributes that make up this configuration instance,
+        """
+        output = copy.deepcopy(self.__dict__)
+
+        if output["backbone_config"] is not None:
+            output["backbone_config"] = self.backbone_config.to_dict()
+
+        output["model_type"] = self.__class__.model_type
+        return output
+
+
+__all__ = ["PromptDepthAnythingConfig"]