Update spaces

.gitattributes

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+assets/rendered_video/teaser.gif filter=lfs diff=lfs merge=lfs -text

.gitignore

@@ -0,0 +1,52 @@
+# Ignore Python cache files
+**/__pycache__
+
+# Ignore compiled Python files
+*.pyc
+
+# Ignore editor-specific files
+.vscode/
+.idea/
+
+# Ignore operating system files
+.DS_Store
+Thumbs.db
+
+# Ignore log files
+*.log
+
+# Ignore temporary and cache files
+*.tmp
+*.cache
+
+# Ignore build artifacts
+/build/
+/dist/
+
+# Ignore virtual environment files
+/venv/
+/.venv/
+
+# Ignore package manager files
+/node_modules/
+/yarn.lock
+/package-lock.json
+
+# Ignore database files
+*.db
+*.sqlite
+
+# Ignore secret files
+*.secret
+
+# Ignore compiled binaries
+*.exe
+*.dll
+*.so
+*.dylib
+
+# Ignore backup files
+*.bak
+*.swp
+*.swo
+*.~*

LICENSE

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LICENSE_NVIDIA

@@ -0,0 +1,99 @@
+Copyright (c) 2021-2022, NVIDIA Corporation & affiliates. All rights
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LICENSE_WEIGHT

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README.md

@@ -0,0 +1,117 @@
+# OpenLRM: Open-Source Large Reconstruction Models
+
+[![Code License](https://img.shields.io/badge/Code%20License-Apache_2.0-yellow.svg)](LICENSE)
+[![Weight License](https://img.shields.io/badge/Weight%20License-CC%20By%20NC%204.0-red)](LICENSE_WEIGHT)
+[![LRM](https://img.shields.io/badge/LRM-Arxiv%20Link-green)](https://arxiv.org/abs/2311.04400)
+
+[![HF Models](https://img.shields.io/badge/Models-Huggingface%20Models-bron)](https://huggingface.co/zxhezexin)
+[![HF Demo](https://img.shields.io/badge/Demo-Huggingface%20Demo-blue)](https://huggingface.co/spaces/zxhezexin/OpenLRM)
+
+<img src="assets/rendered_video/teaser.gif" width="75%" height="auto"/>
+
+<div style="text-align: left">
+    <img src="assets/mesh_snapshot/crop.owl.ply00.png" width="12%" height="auto"/>
+    <img src="assets/mesh_snapshot/crop.owl.ply01.png" width="12%" height="auto"/>
+    <img src="assets/mesh_snapshot/crop.building.ply00.png" width="12%" height="auto"/>
+    <img src="assets/mesh_snapshot/crop.building.ply01.png" width="12%" height="auto"/>
+    <img src="assets/mesh_snapshot/crop.rose.ply00.png" width="12%" height="auto"/>
+    <img src="assets/mesh_snapshot/crop.rose.ply01.png" width="12%" height="auto"/>
+</div>
+
+## News
+
+- [2024.03.04] Version update v1.1. Release model weights trained on both Objaverse and MVImgNet. Codebase is majorly refactored for better usability and extensibility. Please refer to [v1.1.0](https://github.com/3DTopia/OpenLRM/releases/tag/v1.1.0) for details.
+- [2024.01.09] Updated all v1.0 models trained on Objaverse. Please refer to [HF Models](https://huggingface.co/zxhezexin) and overwrite previous model weights.
+- [2023.12.21] [Hugging Face Demo](https://huggingface.co/spaces/zxhezexin/OpenLRM) is online. Have a try!
+- [2023.12.20] Release weights of the base and large models trained on Objaverse.
+- [2023.12.20] We release this project OpenLRM, which is an open-source implementation of the paper [LRM](https://arxiv.org/abs/2311.04400).
+
+## Setup
+
+### Installation
+```
+git clone https://github.com/3DTopia/OpenLRM.git
+cd OpenLRM
+```
+
+### Environment
+- Install requirements for OpenLRM first.
+  ```
+  pip install -r requirements.txt
+  ```
+- Please then follow the [xFormers installation guide](https://github.com/facebookresearch/xformers?tab=readme-ov-file#installing-xformers) to enable memory efficient attention inside [DINOv2 encoder](openlrm/models/encoders/dinov2/layers/attention.py).
+
+## Quick Start
+
+### Pretrained Models
+
+- Model weights are released on [Hugging Face](https://huggingface.co/zxhezexin).
+- Weights will be downloaded automatically when you run the inference script for the first time.
+- Please be aware of the [license](LICENSE_WEIGHT) before using the weights.
+
+| Model | Training Data | Layers | Feat. Dim | Trip. Dim. | In. Res. | Link |
+| :--- | :--- | :--- | :--- | :--- | :--- | :--- |
+| openlrm-obj-small-1.1 | Objaverse | 12 | 512 | 32 | 224 | [HF](https://huggingface.co/zxhezexin/openlrm-obj-small-1.1) |
+| openlrm-obj-base-1.1 | Objaverse | 12 | 768 | 48 | 336 | [HF](https://huggingface.co/zxhezexin/openlrm-obj-base-1.1) |
+| openlrm-obj-large-1.1 | Objaverse | 16 | 1024 | 80 | 448 | [HF](https://huggingface.co/zxhezexin/openlrm-obj-large-1.1) |
+| openlrm-mix-small-1.1 | Objaverse + MVImgNet | 12 | 512 | 32 | 224 | [HF](https://huggingface.co/zxhezexin/openlrm-mix-small-1.1) |
+| openlrm-mix-base-1.1 | Objaverse + MVImgNet | 12 | 768 | 48 | 336 | [HF](https://huggingface.co/zxhezexin/openlrm-mix-base-1.1) |
+| openlrm-mix-large-1.1 | Objaverse + MVImgNet | 16 | 1024 | 80 | 448 | [HF](https://huggingface.co/zxhezexin/openlrm-mix-large-1.1) |
+
+Model cards with additional details can be found in [model_card.md](model_card.md).
+
+### Prepare Images
+- We put some sample inputs under `assets/sample_input`, and you can quickly try them.
+- Prepare RGBA images or RGB images with white background (with some background removal tools, e.g., [Rembg](https://github.com/danielgatis/rembg), [Clipdrop](https://clipdrop.co)).
+
+### Inference
+- Run the inference script to get 3D assets.
+- You may specify which form of output to generate by setting the flags `EXPORT_VIDEO=true` and `EXPORT_MESH=true`.
+- Please set default `INFER_CONFIG` according to the model you want to use. E.g., `infer-b.yaml` for base models and `infer-s.yaml` for small models.
+- An example usage is as follows:
+
+  ```
+  # Example usage
+  EXPORT_VIDEO=true
+  EXPORT_MESH=true
+  INFER_CONFIG="./configs/infer-b.yaml"
+  MODEL_NAME="zxhezexin/openlrm-mix-base-1.1"
+  IMAGE_INPUT="./assets/sample_input/owl.png"
+
+  python -m openlrm.launch infer.lrm --infer $INFER_CONFIG model_name=$MODEL_NAME image_input=$IMAGE_INPUT export_video=$EXPORT_VIDEO export_mesh=$EXPORT_MESH
+  ```
+
+## Training
+To be released soon.
+
+## Acknowledgement
+
+- We thank the authors of the [original paper](https://arxiv.org/abs/2311.04400) for their great work! Special thanks to Kai Zhang and Yicong Hong for assistance during the reproduction.
+- This project is supported by Shanghai AI Lab by providing the computing resources.
+- This project is advised by Ziwei Liu and Jiaya Jia.
+
+## Citation
+
+If you find this work useful for your research, please consider citing:
+```
+@article{hong2023lrm,
+  title={Lrm: Large reconstruction model for single image to 3d},
+  author={Hong, Yicong and Zhang, Kai and Gu, Jiuxiang and Bi, Sai and Zhou, Yang and Liu, Difan and Liu, Feng and Sunkavalli, Kalyan and Bui, Trung and Tan, Hao},
+  journal={arXiv preprint arXiv:2311.04400},
+  year={2023}
+}
+```
+
+```
+@misc{openlrm,
+  title = {OpenLRM: Open-Source Large Reconstruction Models},
+  author = {Zexin He and Tengfei Wang},
+  year = {2023},
+  howpublished = {\url{https://github.com/3DTopia/OpenLRM}},
+}
+```
+
+## License
+
+- OpenLRM as a whole is licensed under the [Apache License, Version 2.0](LICENSE), while certain components are covered by [NVIDIA's proprietary license](LICENSE_NVIDIA). Users are responsible for complying with the respective licensing terms of each component.
+- Model weights are licensed under the [Creative Commons Attribution-NonCommercial 4.0 International License](LICENSE_WEIGHT). They are provided for research purposes only, and CANNOT be used commercially.

app.py

@@ -0,0 +1,210 @@
+# Copyright (c) 2023-2024, Zexin He
+#
+# 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
+#
+#     https://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.
+
+
+import os
+from PIL import Image
+import numpy as np
+import gradio as gr
+
+
+def assert_input_image(input_image):
+    if input_image is None:
+        raise gr.Error("No image selected or uploaded!")
+
+def prepare_working_dir():
+    import tempfile
+    working_dir = tempfile.TemporaryDirectory()
+    return working_dir
+
+def init_preprocessor():
+    from openlrm.utils.preprocess import Preprocessor
+    global preprocessor
+    preprocessor = Preprocessor()
+
+def preprocess_fn(image_in: np.ndarray, remove_bg: bool, recenter: bool, working_dir):
+    image_raw = os.path.join(working_dir.name, "raw.png")
+    with Image.fromarray(image_in) as img:
+        img.save(image_raw)
+    image_out = os.path.join(working_dir.name, "rembg.png")
+    success = preprocessor.preprocess(image_path=image_raw, save_path=image_out, rmbg=remove_bg, recenter=recenter)
+    assert success, f"Failed under preprocess_fn!"
+    return image_out
+
+
+def demo_openlrm(infer_impl):
+
+    def core_fn(image: str, source_cam_dist: float, working_dir):
+        dump_video_path = os.path.join(working_dir.name, "output.mp4")
+        dump_mesh_path = os.path.join(working_dir.name, "output.ply")
+        infer_impl(
+            image_path=image,
+            source_cam_dist=source_cam_dist,
+            export_video=True,
+            export_mesh=False,
+            dump_video_path=dump_video_path,
+            dump_mesh_path=dump_mesh_path,
+        )
+        return dump_video_path
+
+    def example_fn(image: np.ndarray):
+        from gradio.utils import get_cache_folder
+        working_dir = get_cache_folder()
+        image = preprocess_fn(
+            image_in=image,
+            remove_bg=True,
+            recenter=True,
+            working_dir=working_dir,
+        )
+        video = core_fn(
+            image=image,
+            source_cam_dist=2.0,
+            working_dir=working_dir,
+        )
+        return image, video
+
+
+    _TITLE = '''OpenLRM: Open-Source Large Reconstruction Models'''
+
+    _DESCRIPTION = '''
+        <div>
+            <a style="display:inline-block" href='https://github.com/3DTopia/OpenLRM'><img src='https://img.shields.io/github/stars/3DTopia/OpenLRM?style=social'/></a>
+            <a style="display:inline-block; margin-left: .5em" href="https://huggingface.co/zxhezexin"><img src='https://img.shields.io/badge/Model-Weights-blue'/></a>
+        </div>
+        OpenLRM is an open-source implementation of Large Reconstruction Models.
+
+        <strong>Image-to-3D in 10 seconds!</strong>
+
+        <strong>Disclaimer:</strong> This demo uses `openlrm-mix-base-1.1` model with 288x288 rendering resolution here for a quick demonstration.
+    '''
+
+    with gr.Blocks(analytics_enabled=False) as demo:
+
+        # HEADERS
+        with gr.Row():
+            with gr.Column(scale=1):
+                gr.Markdown('# ' + _TITLE)
+        with gr.Row():
+            gr.Markdown(_DESCRIPTION)
+
+        # DISPLAY
+        with gr.Row():
+
+            with gr.Column(variant='panel', scale=1):
+                with gr.Tabs(elem_id="openlrm_input_image"):
+                    with gr.TabItem('Input Image'):
+                        with gr.Row():
+                            input_image = gr.Image(label="Input Image", image_mode="RGBA", width="auto", sources="upload", type="numpy", elem_id="content_image")
+
+            with gr.Column(variant='panel', scale=1):
+                with gr.Tabs(elem_id="openlrm_processed_image"):
+                    with gr.TabItem('Processed Image'):
+                        with gr.Row():
+                            processed_image = gr.Image(label="Processed Image", image_mode="RGBA", type="filepath", elem_id="processed_image", width="auto", interactive=False)
+
+            with gr.Column(variant='panel', scale=1):
+                with gr.Tabs(elem_id="openlrm_render_video"):
+                    with gr.TabItem('Rendered Video'):
+                        with gr.Row():
+                            output_video = gr.Video(label="Rendered Video", format="mp4", width="auto", autoplay=True)
+
+        # SETTING
+        with gr.Row():
+            with gr.Column(variant='panel', scale=1):
+                with gr.Tabs(elem_id="openlrm_attrs"):
+                    with gr.TabItem('Settings'):
+                        with gr.Column(variant='panel'):
+                            gr.Markdown(
+                                """
+                                <strong>Best Practice</strong>:
+                                    Centered objects in reasonable sizes. Try adjusting source camera distances.
+                                """
+                            )
+                            checkbox_rembg = gr.Checkbox(True, label='Remove background')
+                            checkbox_recenter = gr.Checkbox(True, label='Recenter the object')
+                            slider_cam_dist = gr.Slider(1.0, 3.5, value=2.0, step=0.1, label="Source Camera Distance")
+                            submit = gr.Button('Generate', elem_id="openlrm_generate", variant='primary')
+
+        # EXAMPLES
+        with gr.Row():
+            examples = [
+                ['assets/sample_input/owl.png'],
+                ['assets/sample_input/building.png'],
+                ['assets/sample_input/mailbox.png'],
+                ['assets/sample_input/fire.png'],
+                ['assets/sample_input/girl.png'],
+                ['assets/sample_input/lamp.png'],
+                ['assets/sample_input/hydrant.png'],
+                ['assets/sample_input/hotdogs.png'],
+                ['assets/sample_input/traffic.png'],
+                ['assets/sample_input/ceramic.png'],
+            ]
+            gr.Examples(
+                examples=examples,
+                inputs=[input_image], 
+                outputs=[processed_image, output_video],
+                fn=example_fn,
+                cache_examples=os.getenv('SYSTEM') != 'spaces',
+                examples_per_page=20,
+            )
+
+        working_dir = gr.State()
+        submit.click(
+            fn=assert_input_image,
+            inputs=[input_image],
+            queue=False,
+        ).success(
+            fn=prepare_working_dir,
+            outputs=[working_dir],
+            queue=False,
+        ).success(
+            fn=preprocess_fn,
+            inputs=[input_image, checkbox_rembg, checkbox_recenter, working_dir],
+            outputs=[processed_image],
+        ).success(
+            fn=core_fn,
+            inputs=[processed_image, slider_cam_dist, working_dir],
+            outputs=[output_video],
+        )
+
+        demo.queue()
+        demo.launch()
+
+
+def launch_gradio_app():
+
+    os.environ.update({
+        "APP_ENABLED": "1",
+        "APP_MODEL_NAME": "zxhezexin/openlrm-mix-base-1.1",
+        "APP_INFER": "./configs/infer-gradio.yaml",
+        "APP_TYPE": "infer.lrm",
+        "NUMBA_THREADING_LAYER": 'omp',
+    })
+
+    from openlrm.runners import REGISTRY_RUNNERS
+    from openlrm.runners.infer.base_inferrer import Inferrer
+    InferrerClass : Inferrer = REGISTRY_RUNNERS[os.getenv("APP_TYPE")]
+    with InferrerClass() as inferrer:
+        init_preprocessor()
+        if os.getenv('SYSTEM') != 'spaces':
+            from openlrm.utils.proxy import no_proxy
+            demo = no_proxy(demo_openlrm)
+        else:
+            demo = demo_openlrm
+        demo(infer_impl=inferrer.infer_single)
+
+
+if __name__ == '__main__':
+
+    launch_gradio_app()

configs/infer-b.yaml

@@ -0,0 +1,8 @@
+source_size: 336
+source_cam_dist: 2.0
+render_size: 288
+render_views: 160
+render_fps: 40
+frame_size: 4
+mesh_size: 384
+mesh_thres: 3.0

configs/infer-gradio.yaml

@@ -0,0 +1,7 @@
+source_size: 336
+render_size: 288
+render_views: 100
+render_fps: 25
+frame_size: 2
+mesh_size: 384
+mesh_thres: 3.0

configs/infer-l.yaml

@@ -0,0 +1,8 @@
+source_size: 448
+source_cam_dist: 2.0
+render_size: 384
+render_views: 160
+render_fps: 40
+frame_size: 2
+mesh_size: 384
+mesh_thres: 3.0

configs/infer-s.yaml

@@ -0,0 +1,8 @@
+source_size: 224
+source_cam_dist: 2.0
+render_size: 192
+render_views: 160
+render_fps: 40
+frame_size: 4
+mesh_size: 384
+mesh_thres: 3.0

model_card.md

@@ -0,0 +1,67 @@
+# Model Card for OpenLRM V1.1
+
+## Overview
+
+- This model card is for the [OpenLRM](https://github.com/3DTopia/OpenLRM) project, which is an open-source implementation of the paper [LRM](https://arxiv.org/abs/2311.04400).
+- Information contained in this model card corresponds to [Version 1.1](https://github.com/3DTopia/OpenLRM/releases).
+
+## Model Details
+
+- Training data
+
+    | Model | Training Data |
+    | :---: | :---: |
+    | [openlrm-obj-small-1.1](https://huggingface.co/zxhezexin/openlrm-obj-small-1.1) | Objaverse |
+    | [openlrm-obj-base-1.1](https://huggingface.co/zxhezexin/openlrm-obj-base-1.1) | Objaverse |
+    | [openlrm-obj-large-1.1](https://huggingface.co/zxhezexin/openlrm-obj-large-1.1) | Objaverse |
+    | [openlrm-mix-small-1.1](https://huggingface.co/zxhezexin/openlrm-mix-small-1.1) | Objaverse + MVImgNet |
+    | [openlrm-mix-base-1.1](https://huggingface.co/zxhezexin/openlrm-mix-base-1.1) | Objaverse + MVImgNet |
+    | [openlrm-mix-large-1.1](https://huggingface.co/zxhezexin/openlrm-mix-large-1.1) | Objaverse + MVImgNet |
+
+- Model architecture (version==1.1)
+
+    | Type  | Layers | Feat. Dim | Attn. Heads | Triplane Dim. | Input Res. | Image Encoder     | Size  |
+    | :---: | :----: | :-------: | :---------: | :-----------: | :--------: | :---------------: | :---: |
+    | small |   12   |    512    |      8      |      32       |    224     | dinov2_vits14_reg | 446M  |
+    | base  |   12   |    768    |     12      |      48       |    336     | dinov2_vitb14_reg | 1.04G |
+    | large |   16   |   1024    |     16      |      80       |    448     | dinov2_vitb14_reg | 1.81G |
+
+- Training settings
+
+    | Type  | Rend. Res. | Rend. Patch | Ray Samples |
+    | :---: | :--------: | :---------: | :---------: |
+    | small |    192     |     64      |     96      |
+    | base  |    288     |     96      |     96      |
+    | large |    384     |    128      |    128      |
+
+## Notable Differences from the Original Paper
+
+- We do not use the deferred back-propagation technique in the original paper.
+- We used random background colors during training.
+- The image encoder is based on the [DINOv2](https://github.com/facebookresearch/dinov2) model with register tokens.
+- The triplane decoder contains 4 layers in our implementation.
+
+## License
+
+- The model weights are released under the [Creative Commons Attribution-NonCommercial 4.0 International License](LICENSE_WEIGHT).
+- They are provided for research purposes only, and CANNOT be used commercially.
+
+## Disclaimer
+
+This model is an open-source implementation and is NOT the official release of the original research paper. While it aims to reproduce the original results as faithfully as possible, there may be variations due to model implementation, training data, and other factors.
+
+### Ethical Considerations
+
+- This model should be used responsibly and ethically, and should not be used for malicious purposes.
+- Users should be aware of potential biases in the training data.
+- The model should not be used under the circumstances that could lead to harm or unfair treatment of individuals or groups.
+
+### Usage Considerations
+
+- The model is provided "as is" without warranty of any kind.
+- Users are responsible for ensuring that their use complies with all relevant laws and regulations.
+- The developers and contributors of this model are not liable for any damages or losses arising from the use of this model.
+
+---
+
+*This model card is subject to updates and modifications. Users are advised to check for the latest version regularly.*

openlrm/__init__.py

@@ -0,0 +1,15 @@
+# Copyright (c) 2023-2024, Zexin He
+#
+# 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
+#
+#     https://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.
+#
+# Empty

openlrm/datasets/__init__.py

@@ -0,0 +1,16 @@
+# Copyright (c) 2023-2024, Zexin He
+#
+# 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
+#
+#     https://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 .mixer import MixerDataset

openlrm/datasets/base.py

@@ -0,0 +1,68 @@
+# Copyright (c) 2023-2024, Zexin He
+#
+# 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
+#
+#     https://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 abc import ABC, abstractmethod
+import json
+import numpy as np
+import torch
+from PIL import Image
+from megfile import smart_open, smart_path_join, smart_exists
+
+
+class BaseDataset(torch.utils.data.Dataset, ABC):
+    def __init__(self, root_dirs: list[str], meta_path: str):
+        super().__init__()
+        self.root_dirs = root_dirs
+        self.uids = self._load_uids(meta_path)
+
+    def __len__(self):
+        return len(self.uids)
+
+    @abstractmethod
+    def inner_get_item(self, idx):
+        pass
+
+    def __getitem__(self, idx):
+        try:
+            return self.inner_get_item(idx)
+        except Exception as e:
+            print(f"[DEBUG-DATASET] Error when loading {self.uids[idx]}")
+            # return self.__getitem__(idx+1)
+            raise e
+
+    @staticmethod
+    def _load_uids(meta_path: str):
+        # meta_path is a json file
+        with open(meta_path, 'r') as f:
+            uids = json.load(f)
+        return uids
+
+    @staticmethod
+    def _load_rgba_image(file_path, bg_color: float = 1.0):
+        ''' Load and blend RGBA image to RGB with certain background, 0-1 scaled '''
+        rgba = np.array(Image.open(smart_open(file_path, 'rb')))
+        rgba = torch.from_numpy(rgba).float() / 255.0
+        rgba = rgba.permute(2, 0, 1).unsqueeze(0)
+        rgb = rgba[:, :3, :, :] * rgba[:, 3:4, :, :] + bg_color * (1 - rgba[:, 3:, :, :])
+        rgba[:, :3, ...] * rgba[:, 3:, ...] + (1 - rgba[:, 3:, ...])
+        return rgb
+
+    @staticmethod
+    def _locate_datadir(root_dirs, uid, locator: str):
+        for root_dir in root_dirs:
+            datadir = smart_path_join(root_dir, uid, locator)
+            if smart_exists(datadir):
+                return root_dir
+        raise FileNotFoundError(f"Cannot find valid data directory for uid {uid}")

openlrm/datasets/cam_utils.py

@@ -0,0 +1,179 @@
+# Copyright (c) 2023-2024, Zexin He
+#
+# 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
+#
+#     https://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.
+
+
+import math
+import torch
+
+"""
+R: (N, 3, 3)
+T: (N, 3)
+E: (N, 4, 4)
+vector: (N, 3)
+"""
+
+
+def compose_extrinsic_R_T(R: torch.Tensor, T: torch.Tensor):
+    """
+    Compose the standard form extrinsic matrix from R and T.
+    Batched I/O.
+    """
+    RT = torch.cat((R, T.unsqueeze(-1)), dim=-1)
+    return compose_extrinsic_RT(RT)
+
+
+def compose_extrinsic_RT(RT: torch.Tensor):
+    """
+    Compose the standard form extrinsic matrix from RT.
+    Batched I/O.
+    """
+    return torch.cat([
+        RT,
+        torch.tensor([[[0, 0, 0, 1]]], dtype=RT.dtype, device=RT.device).repeat(RT.shape[0], 1, 1)
+        ], dim=1)
+
+
+def decompose_extrinsic_R_T(E: torch.Tensor):
+    """
+    Decompose the standard extrinsic matrix into R and T.
+    Batched I/O.
+    """
+    RT = decompose_extrinsic_RT(E)
+    return RT[:, :, :3], RT[:, :, 3]
+
+
+def decompose_extrinsic_RT(E: torch.Tensor):
+    """
+    Decompose the standard extrinsic matrix into RT.
+    Batched I/O.
+    """
+    return E[:, :3, :]
+
+
+def get_normalized_camera_intrinsics(intrinsics: torch.Tensor):
+    """
+    intrinsics: (N, 3, 2), [[fx, fy], [cx, cy], [width, height]]
+    Return batched fx, fy, cx, cy
+    """
+    fx, fy = intrinsics[:, 0, 0], intrinsics[:, 0, 1]
+    cx, cy = intrinsics[:, 1, 0], intrinsics[:, 1, 1]
+    width, height = intrinsics[:, 2, 0], intrinsics[:, 2, 1]
+    fx, fy = fx / width, fy / height
+    cx, cy = cx / width, cy / height
+    return fx, fy, cx, cy
+
+
+def build_camera_principle(RT: torch.Tensor, intrinsics: torch.Tensor):
+    """
+    RT: (N, 3, 4)
+    intrinsics: (N, 3, 2), [[fx, fy], [cx, cy], [width, height]]
+    """
+    fx, fy, cx, cy = get_normalized_camera_intrinsics(intrinsics)
+    return torch.cat([
+        RT.reshape(-1, 12),
+        fx.unsqueeze(-1), fy.unsqueeze(-1), cx.unsqueeze(-1), cy.unsqueeze(-1),
+    ], dim=-1)
+
+
+def build_camera_standard(RT: torch.Tensor, intrinsics: torch.Tensor):
+    """
+    RT: (N, 3, 4)
+    intrinsics: (N, 3, 2), [[fx, fy], [cx, cy], [width, height]]
+    """
+    E = compose_extrinsic_RT(RT)
+    fx, fy, cx, cy = get_normalized_camera_intrinsics(intrinsics)
+    I = torch.stack([
+        torch.stack([fx, torch.zeros_like(fx), cx], dim=-1),
+        torch.stack([torch.zeros_like(fy), fy, cy], dim=-1),
+        torch.tensor([[0, 0, 1]], dtype=torch.float32, device=RT.device).repeat(RT.shape[0], 1),
+    ], dim=1)
+    return torch.cat([
+        E.reshape(-1, 16),
+        I.reshape(-1, 9),
+    ], dim=-1)
+
+
+def center_looking_at_camera_pose(
+    camera_position: torch.Tensor, look_at: torch.Tensor = None, up_world: torch.Tensor = None,
+    device: torch.device = torch.device('cpu'),
+    ):
+    """
+    camera_position: (M, 3)
+    look_at: (3)
+    up_world: (3)
+    return: (M, 3, 4)
+    """
+    # by default, looking at the origin and world up is pos-z
+    if look_at is None:
+        look_at = torch.tensor([0, 0, 0], dtype=torch.float32, device=device)
+    if up_world is None:
+        up_world = torch.tensor([0, 0, 1], dtype=torch.float32, device=device)
+    look_at = look_at.unsqueeze(0).repeat(camera_position.shape[0], 1)
+    up_world = up_world.unsqueeze(0).repeat(camera_position.shape[0], 1)
+
+    z_axis = camera_position - look_at
+    z_axis = z_axis / z_axis.norm(dim=-1, keepdim=True)
+    x_axis = torch.cross(up_world, z_axis)
+    x_axis = x_axis / x_axis.norm(dim=-1, keepdim=True)
+    y_axis = torch.cross(z_axis, x_axis)
+    y_axis = y_axis / y_axis.norm(dim=-1, keepdim=True)
+    extrinsics = torch.stack([x_axis, y_axis, z_axis, camera_position], dim=-1)
+    return extrinsics
+
+
+def surrounding_views_linspace(n_views: int, radius: float = 2.0, height: float = 0.8, device: torch.device = torch.device('cpu')):
+    """
+    n_views: number of surrounding views
+    radius: camera dist to center
+    height: height of the camera
+    return: (M, 3, 4)
+    """
+    assert n_views > 0
+    assert radius > 0
+
+    theta = torch.linspace(-torch.pi / 2, 3 * torch.pi / 2, n_views, device=device)
+    projected_radius = math.sqrt(radius ** 2 - height ** 2)
+    x = torch.cos(theta) * projected_radius
+    y = torch.sin(theta) * projected_radius
+    z = torch.full((n_views,), height, device=device)
+
+    camera_positions = torch.stack([x, y, z], dim=1)
+    extrinsics = center_looking_at_camera_pose(camera_positions, device=device)
+
+    return extrinsics
+
+
+def create_intrinsics(
+    f: float,
+    c: float = None, cx: float = None, cy: float = None,
+    w: float = 1., h: float = 1.,
+    dtype: torch.dtype = torch.float32,
+    device: torch.device = torch.device('cpu'),
+    ):
+    """
+    return: (3, 2)
+    """
+    fx = fy = f
+    if c is not None:
+        assert cx is None and cy is None, "c and cx/cy cannot be used together"
+        cx = cy = c
+    else:
+        assert cx is not None and cy is not None, "cx/cy must be provided when c is not provided"
+    fx, fy, cx, cy, w, h = fx/w, fy/h, cx/w, cy/h, 1., 1.
+    intrinsics = torch.tensor([
+        [fx, fy],
+        [cx, cy],
+        [w, h],
+    ], dtype=dtype, device=device)
+    return intrinsics

openlrm/launch.py

@@ -0,0 +1,36 @@
+# Copyright (c) 2023-2024, Zexin He
+#
+# 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
+#
+#     https://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.
+
+
+import argparse
+
+from openlrm.runners import REGISTRY_RUNNERS
+
+
+def main():
+
+    parser = argparse.ArgumentParser(description='OpenLRM launcher')
+    parser.add_argument('runner', type=str, help='Runner to launch')
+    args, unknown = parser.parse_known_args()
+
+    if args.runner not in REGISTRY_RUNNERS:
+        raise ValueError('Runner {} not found'.format(args.runner))
+
+    RunnerClass = REGISTRY_RUNNERS[args.runner]
+    with RunnerClass() as runner:
+        runner.run()
+
+
+if __name__ == '__main__':
+    main()

openlrm/losses/__init__.py

@@ -0,0 +1,18 @@
+# Copyright (c) 2023-2024, Zexin He
+#
+# 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
+#
+#     https://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 .pixelwise import *
+from .perceptual import *
+from .tvloss import *

openlrm/losses/perceptual.py

@@ -0,0 +1,70 @@
+# Copyright (c) 2023-2024, Zexin He
+#
+# 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
+#
+#     https://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.
+
+
+import torch
+import torch.nn as nn
+
+__all__ = ['LPIPSLoss']
+
+
+class LPIPSLoss(nn.Module):
+    """
+    Compute LPIPS loss between two images.
+    """
+
+    def __init__(self, device, prefech: bool = False):
+        super().__init__()
+        self.device = device
+        self.cached_models = {}
+        if prefech:
+            self.prefetch_models()
+
+    def _get_model(self, model_name: str):
+        if model_name not in self.cached_models:
+            import warnings
+            with warnings.catch_warnings():
+                warnings.filterwarnings('ignore', category=UserWarning)
+                import lpips
+                _model = lpips.LPIPS(net=model_name, eval_mode=True, verbose=False).to(self.device)
+            _model = torch.compile(_model)
+            self.cached_models[model_name] = _model
+        return self.cached_models[model_name]
+
+    def prefetch_models(self):
+        _model_names = ['alex', 'vgg']
+        for model_name in _model_names:
+            self._get_model(model_name)
+
+    def forward(self, x, y, is_training: bool = True):
+        """
+        Assume images are 0-1 scaled and channel first.
+        
+        Args:
+            x: [N, M, C, H, W]
+            y: [N, M, C, H, W]
+            is_training: whether to use VGG or AlexNet.
+        
+        Returns:
+            Mean-reduced LPIPS loss across batch.
+        """
+        model_name = 'vgg' if is_training else 'alex'
+        loss_fn = self._get_model(model_name)
+        N, M, C, H, W = x.shape
+        x = x.reshape(N*M, C, H, W)
+        y = y.reshape(N*M, C, H, W)
+        image_loss = loss_fn(x, y, normalize=True).mean(dim=[1, 2, 3])
+        batch_loss = image_loss.reshape(N, M).mean(dim=1)
+        all_loss = batch_loss.mean()
+        return all_loss

openlrm/losses/pixelwise.py

@@ -0,0 +1,58 @@
+# Copyright (c) 2023-2024, Zexin He
+#
+# 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
+#
+#     https://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.
+
+
+import torch
+import torch.nn as nn
+
+__all__ = ['PixelLoss']
+
+
+class PixelLoss(nn.Module):
+    """
+    Pixel-wise loss between two images.
+    """
+
+    def __init__(self, option: str = 'mse'):
+        super().__init__()
+        self.loss_fn = self._build_from_option(option)
+
+    @staticmethod
+    def _build_from_option(option: str, reduction: str = 'none'):
+        if option == 'mse':
+            return nn.MSELoss(reduction=reduction)
+        elif option == 'l1':
+            return nn.L1Loss(reduction=reduction)
+        else:
+            raise NotImplementedError(f'Unknown pixel loss option: {option}')
+
+    @torch.compile
+    def forward(self, x, y):
+        """
+        Assume images are channel first.
+        
+        Args:
+            x: [N, M, C, H, W]
+            y: [N, M, C, H, W]
+        
+        Returns:
+            Mean-reduced pixel loss across batch.
+        """
+        N, M, C, H, W = x.shape
+        x = x.reshape(N*M, C, H, W)
+        y = y.reshape(N*M, C, H, W)
+        image_loss = self.loss_fn(x, y).mean(dim=[1, 2, 3])
+        batch_loss = image_loss.reshape(N, M).mean(dim=1)
+        all_loss = batch_loss.mean()
+        return all_loss

openlrm/losses/tvloss.py

@@ -0,0 +1,55 @@
+# Copyright (c) 2023-2024, Zexin He
+#
+# 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
+#
+#     https://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.
+
+
+import torch
+import torch.nn as nn
+
+__all__ = ['TVLoss']
+
+
+class TVLoss(nn.Module):
+    """
+    Total variance loss.
+    """
+
+    def __init__(self):
+        super().__init__()
+
+    def numel_excluding_first_dim(self, x):
+        return x.numel() // x.shape[0]
+
+    @torch.compile
+    def forward(self, x):
+        """
+        Assume batched and channel first with inner sizes.
+
+        Args:
+            x: [N, M, C, H, W]
+
+        Returns:
+            Mean-reduced TV loss with element-level scaling.
+        """
+        N, M, C, H, W = x.shape
+        x = x.reshape(N*M, C, H, W)
+        diff_i = x[..., 1:, :] - x[..., :-1, :]
+        diff_j = x[..., :, 1:] - x[..., :, :-1]
+        div_i = self.numel_excluding_first_dim(diff_i)
+        div_j = self.numel_excluding_first_dim(diff_j)
+        tv_i = diff_i.pow(2).sum(dim=[1,2,3]) / div_i
+        tv_j = diff_j.pow(2).sum(dim=[1,2,3]) / div_j
+        tv = tv_i + tv_j
+        batch_tv = tv.reshape(N, M).mean(dim=1)
+        all_tv = batch_tv.mean()
+        return all_tv

openlrm/models/__init__.py

@@ -0,0 +1,21 @@
+# Copyright (c) 2023-2024, Zexin He
+#
+# 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
+#
+#     https://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 .modeling_lrm import ModelLRM
+
+
+model_dict = {
+    'lrm': ModelLRM,
+}

openlrm/models/block.py

@@ -0,0 +1,124 @@
+# Copyright (c) 2023-2024, Zexin He
+#
+# 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
+#
+#     https://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.
+
+
+import torch.nn as nn
+
+from .modulate import ModLN
+
+
+class BasicBlock(nn.Module):
+    """
+    Transformer block that is in its simplest form.
+    Designed for PF-LRM architecture.
+    """
+    # Block contains a self-attention layer and an MLP
+    def __init__(self, inner_dim: int, num_heads: int, eps: float,
+                 attn_drop: float = 0., attn_bias: bool = False,
+                 mlp_ratio: float = 4., mlp_drop: float = 0.):
+        super().__init__()
+        self.norm1 = nn.LayerNorm(inner_dim, eps=eps)
+        self.self_attn = nn.MultiheadAttention(
+            embed_dim=inner_dim, num_heads=num_heads,
+            dropout=attn_drop, bias=attn_bias, batch_first=True)
+        self.norm2 = nn.LayerNorm(inner_dim, eps=eps)
+        self.mlp = nn.Sequential(
+            nn.Linear(inner_dim, int(inner_dim * mlp_ratio)),
+            nn.GELU(),
+            nn.Dropout(mlp_drop),
+            nn.Linear(int(inner_dim * mlp_ratio), inner_dim),
+            nn.Dropout(mlp_drop),
+        )
+
+    def forward(self, x):
+        # x: [N, L, D]
+        before_sa = self.norm1(x)
+        x = x + self.self_attn(before_sa, before_sa, before_sa, need_weights=False)[0]
+        x = x + self.mlp(self.norm2(x))
+        return x
+
+
+class ConditionBlock(nn.Module):
+    """
+    Transformer block that takes in a cross-attention condition.
+    Designed for SparseLRM architecture.
+    """
+    # Block contains a cross-attention layer, a self-attention layer, and an MLP
+    def __init__(self, inner_dim: int, cond_dim: int, num_heads: int, eps: float,
+                 attn_drop: float = 0., attn_bias: bool = False,
+                 mlp_ratio: float = 4., mlp_drop: float = 0.):
+        super().__init__()
+        self.norm1 = nn.LayerNorm(inner_dim, eps=eps)
+        self.cross_attn = nn.MultiheadAttention(
+            embed_dim=inner_dim, num_heads=num_heads, kdim=cond_dim, vdim=cond_dim,
+            dropout=attn_drop, bias=attn_bias, batch_first=True)
+        self.norm2 = nn.LayerNorm(inner_dim, eps=eps)
+        self.self_attn = nn.MultiheadAttention(
+            embed_dim=inner_dim, num_heads=num_heads,
+            dropout=attn_drop, bias=attn_bias, batch_first=True)
+        self.norm3 = nn.LayerNorm(inner_dim, eps=eps)
+        self.mlp = nn.Sequential(
+            nn.Linear(inner_dim, int(inner_dim * mlp_ratio)),
+            nn.GELU(),
+            nn.Dropout(mlp_drop),
+            nn.Linear(int(inner_dim * mlp_ratio), inner_dim),
+            nn.Dropout(mlp_drop),
+        )
+
+    def forward(self, x, cond):
+        # x: [N, L, D]
+        # cond: [N, L_cond, D_cond]
+        x = x + self.cross_attn(self.norm1(x), cond, cond, need_weights=False)[0]
+        before_sa = self.norm2(x)
+        x = x + self.self_attn(before_sa, before_sa, before_sa, need_weights=False)[0]
+        x = x + self.mlp(self.norm3(x))
+        return x
+
+
+class ConditionModulationBlock(nn.Module):
+    """
+    Transformer block that takes in a cross-attention condition and another modulation vector applied to sub-blocks.
+    Designed for raw LRM architecture.
+    """
+    # Block contains a cross-attention layer, a self-attention layer, and an MLP
+    def __init__(self, inner_dim: int, cond_dim: int, mod_dim: int, num_heads: int, eps: float,
+                 attn_drop: float = 0., attn_bias: bool = False,
+                 mlp_ratio: float = 4., mlp_drop: float = 0.):
+        super().__init__()
+        self.norm1 = ModLN(inner_dim, mod_dim, eps)
+        self.cross_attn = nn.MultiheadAttention(
+            embed_dim=inner_dim, num_heads=num_heads, kdim=cond_dim, vdim=cond_dim,
+            dropout=attn_drop, bias=attn_bias, batch_first=True)
+        self.norm2 = ModLN(inner_dim, mod_dim, eps)
+        self.self_attn = nn.MultiheadAttention(
+            embed_dim=inner_dim, num_heads=num_heads,
+            dropout=attn_drop, bias=attn_bias, batch_first=True)
+        self.norm3 = ModLN(inner_dim, mod_dim, eps)
+        self.mlp = nn.Sequential(
+            nn.Linear(inner_dim, int(inner_dim * mlp_ratio)),
+            nn.GELU(),
+            nn.Dropout(mlp_drop),
+            nn.Linear(int(inner_dim * mlp_ratio), inner_dim),
+            nn.Dropout(mlp_drop),
+        )
+
+    def forward(self, x, cond, mod):
+        # x: [N, L, D]
+        # cond: [N, L_cond, D_cond]
+        # mod: [N, D_mod]
+        x = x + self.cross_attn(self.norm1(x, mod), cond, cond, need_weights=False)[0]
+        before_sa = self.norm2(x, mod)
+        x = x + self.self_attn(before_sa, before_sa, before_sa, need_weights=False)[0]
+        x = x + self.mlp(self.norm3(x, mod))
+        return x

openlrm/models/embedder.py

@@ -0,0 +1,37 @@
+# Copyright (c) 2023-2024, Zexin He
+#
+# 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
+#
+#     https://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.
+
+
+import torch
+import torch.nn as nn
+
+
+class CameraEmbedder(nn.Module):
+    """
+    Embed camera features to a high-dimensional vector.
+    
+    Reference:
+    DiT: https://github.com/facebookresearch/DiT/blob/main/models.py#L27
+    """
+    def __init__(self, raw_dim: int, embed_dim: int):
+        super().__init__()
+        self.mlp = nn.Sequential(
+            nn.Linear(raw_dim, embed_dim),
+            nn.SiLU(),
+            nn.Linear(embed_dim, embed_dim),
+        )
+
+    @torch.compile
+    def forward(self, x):
+        return self.mlp(x)

openlrm/models/encoders/__init__.py

@@ -0,0 +1,15 @@
+# Copyright (c) 2023-2024, Zexin He
+#
+# 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
+#
+#     https://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.
+#
+# Empty

openlrm/models/encoders/dino_wrapper.py

@@ -0,0 +1,68 @@
+# Copyright (c) 2023-2024, Zexin He
+#
+# 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
+#
+#     https://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.
+
+
+import torch
+import torch.nn as nn
+from transformers import ViTImageProcessor, ViTModel
+from accelerate.logging import get_logger
+
+
+logger = get_logger(__name__)
+
+
+class DinoWrapper(nn.Module):
+    """
+    Dino v1 wrapper using huggingface transformer implementation.
+    """
+    def __init__(self, model_name: str, freeze: bool = True):
+        super().__init__()
+        self.model, self.processor = self._build_dino(model_name)
+        if freeze:
+            self._freeze()
+
+    @torch.compile
+    def forward_model(self, inputs):
+        return self.model(**inputs, interpolate_pos_encoding=True)
+
+    def forward(self, image):
+        # image: [N, C, H, W], on cpu
+        # RGB image with [0,1] scale and properly sized
+        inputs = self.processor(images=image, return_tensors="pt", do_rescale=False, do_resize=False).to(self.model.device)
+        # This resampling of positional embedding uses bicubic interpolation
+        outputs = self.forward_model(inputs)
+        last_hidden_states = outputs.last_hidden_state
+        return last_hidden_states
+
+    def _freeze(self):
+        logger.warning(f"======== Freezing DinoWrapper ========")
+        self.model.eval()
+        for name, param in self.model.named_parameters():
+            param.requires_grad = False
+
+    @staticmethod
+    def _build_dino(model_name: str, proxy_error_retries: int = 3, proxy_error_cooldown: int = 5):
+        import requests
+        try:
+            model = ViTModel.from_pretrained(model_name, add_pooling_layer=False)
+            processor = ViTImageProcessor.from_pretrained(model_name)
+            return model, processor
+        except requests.exceptions.ProxyError as err:
+            if proxy_error_retries > 0:
+                print(f"Huggingface ProxyError: Retrying ({proxy_error_retries}) in {proxy_error_cooldown} seconds...")
+                import time
+                time.sleep(proxy_error_cooldown)
+                return DinoWrapper._build_dino(model_name, proxy_error_retries - 1, proxy_error_cooldown)
+            else:
+                raise err

openlrm/models/encoders/dinov2/__init__.py

@@ -0,0 +1,15 @@
+# Copyright (c) 2023-2024, Zexin He
+#
+# 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
+#
+#     https://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.
+#
+# Empty

openlrm/models/encoders/dinov2/hub/__init__.py

@@ -0,0 +1,4 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+#
+# This source code is licensed under the Apache License, Version 2.0
+# found in the LICENSE file in the root directory of this source tree.

openlrm/models/encoders/dinov2/hub/backbones.py

@@ -0,0 +1,166 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+#
+# This source code is licensed under the Apache License, Version 2.0
+# found in the LICENSE file in the root directory of this source tree.
+
+from enum import Enum
+from typing import Union
+
+import torch
+
+from .utils import _DINOV2_BASE_URL, _make_dinov2_model_name
+
+
+class Weights(Enum):
+    LVD142M = "LVD142M"
+
+
+def _make_dinov2_model(
+    *,
+    arch_name: str = "vit_large",
+    img_size: int = 518,
+    patch_size: int = 14,
+    init_values: float = 1.0,
+    ffn_layer: str = "mlp",
+    block_chunks: int = 0,
+    num_register_tokens: int = 0,
+    interpolate_antialias: bool = False,
+    interpolate_offset: float = 0.1,
+    pretrained: bool = True,
+    weights: Union[Weights, str] = Weights.LVD142M,
+    **kwargs,
+):
+    from ..models import vision_transformer as vits
+
+    if isinstance(weights, str):
+        try:
+            weights = Weights[weights]
+        except KeyError:
+            raise AssertionError(f"Unsupported weights: {weights}")
+
+    model_base_name = _make_dinov2_model_name(arch_name, patch_size)
+    vit_kwargs = dict(
+        img_size=img_size,
+        patch_size=patch_size,
+        init_values=init_values,
+        ffn_layer=ffn_layer,
+        block_chunks=block_chunks,
+        num_register_tokens=num_register_tokens,
+        interpolate_antialias=interpolate_antialias,
+        interpolate_offset=interpolate_offset,
+    )
+    vit_kwargs.update(**kwargs)
+    model = vits.__dict__[arch_name](**vit_kwargs)
+
+    if pretrained:
+        model_full_name = _make_dinov2_model_name(arch_name, patch_size, num_register_tokens)
+        url = _DINOV2_BASE_URL + f"/{model_base_name}/{model_full_name}_pretrain.pth"
+        state_dict = torch.hub.load_state_dict_from_url(url, map_location="cpu")
+        # ********** Modified by Zexin He in 2023-2024 **********
+        state_dict = {k: v for k, v in state_dict.items() if 'mask_token' not in k}  # DDP concern
+        if vit_kwargs.get("modulation_dim") is not None:
+            state_dict = {
+                k.replace('norm1', 'norm1.norm').replace('norm2', 'norm2.norm'): v
+                for k, v in state_dict.items()
+            }
+            model.load_state_dict(state_dict, strict=False)
+        else:
+            model.load_state_dict(state_dict, strict=True)
+        # ********************************************************
+
+    return model
+
+
+def dinov2_vits14(*, pretrained: bool = True, weights: Union[Weights, str] = Weights.LVD142M, **kwargs):
+    """
+    DINOv2 ViT-S/14 model (optionally) pretrained on the LVD-142M dataset.
+    """
+    return _make_dinov2_model(arch_name="vit_small", pretrained=pretrained, weights=weights, **kwargs)
+
+
+def dinov2_vitb14(*, pretrained: bool = True, weights: Union[Weights, str] = Weights.LVD142M, **kwargs):
+    """
+    DINOv2 ViT-B/14 model (optionally) pretrained on the LVD-142M dataset.
+    """
+    return _make_dinov2_model(arch_name="vit_base", pretrained=pretrained, weights=weights, **kwargs)
+
+
+def dinov2_vitl14(*, pretrained: bool = True, weights: Union[Weights, str] = Weights.LVD142M, **kwargs):
+    """
+    DINOv2 ViT-L/14 model (optionally) pretrained on the LVD-142M dataset.
+    """
+    return _make_dinov2_model(arch_name="vit_large", pretrained=pretrained, weights=weights, **kwargs)
+
+
+def dinov2_vitg14(*, pretrained: bool = True, weights: Union[Weights, str] = Weights.LVD142M, **kwargs):
+    """
+    DINOv2 ViT-g/14 model (optionally) pretrained on the LVD-142M dataset.
+    """
+    return _make_dinov2_model(
+        arch_name="vit_giant2",
+        ffn_layer="swiglufused",
+        weights=weights,
+        pretrained=pretrained,
+        **kwargs,
+    )
+
+
+def dinov2_vits14_reg(*, pretrained: bool = True, weights: Union[Weights, str] = Weights.LVD142M, **kwargs):
+    """
+    DINOv2 ViT-S/14 model with registers (optionally) pretrained on the LVD-142M dataset.
+    """
+    return _make_dinov2_model(
+        arch_name="vit_small",
+        pretrained=pretrained,
+        weights=weights,
+        num_register_tokens=4,
+        interpolate_antialias=True,
+        interpolate_offset=0.0,
+        **kwargs,
+    )
+
+
+def dinov2_vitb14_reg(*, pretrained: bool = True, weights: Union[Weights, str] = Weights.LVD142M, **kwargs):
+    """
+    DINOv2 ViT-B/14 model with registers (optionally) pretrained on the LVD-142M dataset.
+    """
+    return _make_dinov2_model(
+        arch_name="vit_base",
+        pretrained=pretrained,
+        weights=weights,
+        num_register_tokens=4,
+        interpolate_antialias=True,
+        interpolate_offset=0.0,
+        **kwargs,
+    )
+
+
+def dinov2_vitl14_reg(*, pretrained: bool = True, weights: Union[Weights, str] = Weights.LVD142M, **kwargs):
+    """
+    DINOv2 ViT-L/14 model with registers (optionally) pretrained on the LVD-142M dataset.
+    """
+    return _make_dinov2_model(
+        arch_name="vit_large",
+        pretrained=pretrained,
+        weights=weights,
+        num_register_tokens=4,
+        interpolate_antialias=True,
+        interpolate_offset=0.0,
+        **kwargs,
+    )
+
+
+def dinov2_vitg14_reg(*, pretrained: bool = True, weights: Union[Weights, str] = Weights.LVD142M, **kwargs):
+    """
+    DINOv2 ViT-g/14 model with registers (optionally) pretrained on the LVD-142M dataset.
+    """
+    return _make_dinov2_model(
+        arch_name="vit_giant2",
+        ffn_layer="swiglufused",
+        weights=weights,
+        pretrained=pretrained,
+        num_register_tokens=4,
+        interpolate_antialias=True,
+        interpolate_offset=0.0,
+        **kwargs,
+    )

openlrm/models/encoders/dinov2/hub/classifiers.py

@@ -0,0 +1,268 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+#
+# This source code is licensed under the Apache License, Version 2.0
+# found in the LICENSE file in the root directory of this source tree.
+
+from enum import Enum
+from typing import Union
+
+import torch
+import torch.nn as nn
+
+from .backbones import _make_dinov2_model
+from .utils import _DINOV2_BASE_URL, _make_dinov2_model_name
+
+
+class Weights(Enum):
+    IMAGENET1K = "IMAGENET1K"
+
+
+def _make_dinov2_linear_classification_head(
+    *,
+    arch_name: str = "vit_large",
+    patch_size: int = 14,
+    embed_dim: int = 1024,
+    layers: int = 4,
+    pretrained: bool = True,
+    weights: Union[Weights, str] = Weights.IMAGENET1K,
+    num_register_tokens: int = 0,
+    **kwargs,
+):
+    if layers not in (1, 4):
+        raise AssertionError(f"Unsupported number of layers: {layers}")
+    if isinstance(weights, str):
+        try:
+            weights = Weights[weights]
+        except KeyError:
+            raise AssertionError(f"Unsupported weights: {weights}")
+
+    linear_head = nn.Linear((1 + layers) * embed_dim, 1_000)
+
+    if pretrained:
+        model_base_name = _make_dinov2_model_name(arch_name, patch_size)
+        model_full_name = _make_dinov2_model_name(arch_name, patch_size, num_register_tokens)
+        layers_str = str(layers) if layers == 4 else ""
+        url = _DINOV2_BASE_URL + f"/{model_base_name}/{model_full_name}_linear{layers_str}_head.pth"
+        state_dict = torch.hub.load_state_dict_from_url(url, map_location="cpu")
+        linear_head.load_state_dict(state_dict, strict=True)
+
+    return linear_head
+
+
+class _LinearClassifierWrapper(nn.Module):
+    def __init__(self, *, backbone: nn.Module, linear_head: nn.Module, layers: int = 4):
+        super().__init__()
+        self.backbone = backbone
+        self.linear_head = linear_head
+        self.layers = layers
+
+    def forward(self, x):
+        if self.layers == 1:
+            x = self.backbone.forward_features(x)
+            cls_token = x["x_norm_clstoken"]
+            patch_tokens = x["x_norm_patchtokens"]
+            # fmt: off
+            linear_input = torch.cat([
+                cls_token,
+                patch_tokens.mean(dim=1),
+            ], dim=1)
+            # fmt: on
+        elif self.layers == 4:
+            x = self.backbone.get_intermediate_layers(x, n=4, return_class_token=True)
+            # fmt: off
+            linear_input = torch.cat([
+                x[0][1],
+                x[1][1],
+                x[2][1],
+                x[3][1],
+                x[3][0].mean(dim=1),
+            ], dim=1)
+            # fmt: on
+        else:
+            assert False, f"Unsupported number of layers: {self.layers}"
+        return self.linear_head(linear_input)
+
+
+def _make_dinov2_linear_classifier(
+    *,
+    arch_name: str = "vit_large",
+    layers: int = 4,
+    pretrained: bool = True,
+    weights: Union[Weights, str] = Weights.IMAGENET1K,
+    num_register_tokens: int = 0,
+    interpolate_antialias: bool = False,
+    interpolate_offset: float = 0.1,
+    **kwargs,
+):
+    backbone = _make_dinov2_model(
+        arch_name=arch_name,
+        pretrained=pretrained,
+        num_register_tokens=num_register_tokens,
+        interpolate_antialias=interpolate_antialias,
+        interpolate_offset=interpolate_offset,
+        **kwargs,
+    )
+
+    embed_dim = backbone.embed_dim
+    patch_size = backbone.patch_size
+    linear_head = _make_dinov2_linear_classification_head(
+        arch_name=arch_name,
+        patch_size=patch_size,
+        embed_dim=embed_dim,
+        layers=layers,
+        pretrained=pretrained,
+        weights=weights,
+        num_register_tokens=num_register_tokens,
+    )
+
+    return _LinearClassifierWrapper(backbone=backbone, linear_head=linear_head, layers=layers)
+
+
+def dinov2_vits14_lc(
+    *,
+    layers: int = 4,
+    pretrained: bool = True,
+    weights: Union[Weights, str] = Weights.IMAGENET1K,
+    **kwargs,
+):
+    """
+    Linear classifier (1 or 4 layers) on top of a DINOv2 ViT-S/14 backbone (optionally) pretrained on the LVD-142M dataset and trained on ImageNet-1k.
+    """
+    return _make_dinov2_linear_classifier(
+        arch_name="vit_small",
+        layers=layers,
+        pretrained=pretrained,
+        weights=weights,
+        **kwargs,
+    )
+
+
+def dinov2_vitb14_lc(
+    *,
+    layers: int = 4,
+    pretrained: bool = True,
+    weights: Union[Weights, str] = Weights.IMAGENET1K,
+    **kwargs,
+):
+    """
+    Linear classifier (1 or 4 layers) on top of a DINOv2 ViT-B/14 backbone (optionally) pretrained on the LVD-142M dataset and trained on ImageNet-1k.
+    """
+    return _make_dinov2_linear_classifier(
+        arch_name="vit_base",
+        layers=layers,
+        pretrained=pretrained,
+        weights=weights,
+        **kwargs,
+    )
+
+
+def dinov2_vitl14_lc(
+    *,
+    layers: int = 4,
+    pretrained: bool = True,
+    weights: Union[Weights, str] = Weights.IMAGENET1K,
+    **kwargs,
+):
+    """
+    Linear classifier (1 or 4 layers) on top of a DINOv2 ViT-L/14 backbone (optionally) pretrained on the LVD-142M dataset and trained on ImageNet-1k.
+    """
+    return _make_dinov2_linear_classifier(
+        arch_name="vit_large",
+        layers=layers,
+        pretrained=pretrained,
+        weights=weights,
+        **kwargs,
+    )
+
+
+def dinov2_vitg14_lc(
+    *,
+    layers: int = 4,
+    pretrained: bool = True,
+    weights: Union[Weights, str] = Weights.IMAGENET1K,
+    **kwargs,
+):
+    """
+    Linear classifier (1 or 4 layers) on top of a DINOv2 ViT-g/14 backbone (optionally) pretrained on the LVD-142M dataset and trained on ImageNet-1k.
+    """
+    return _make_dinov2_linear_classifier(
+        arch_name="vit_giant2",
+        layers=layers,
+        ffn_layer="swiglufused",
+        pretrained=pretrained,
+        weights=weights,
+        **kwargs,
+    )
+
+
+def dinov2_vits14_reg_lc(
+    *, layers: int = 4, pretrained: bool = True, weights: Union[Weights, str] = Weights.IMAGENET1K, **kwargs
+):
+    """
+    Linear classifier (1 or 4 layers) on top of a DINOv2 ViT-S/14 backbone with registers (optionally) pretrained on the LVD-142M dataset and trained on ImageNet-1k.
+    """
+    return _make_dinov2_linear_classifier(
+        arch_name="vit_small",
+        layers=layers,
+        pretrained=pretrained,
+        weights=weights,
+        num_register_tokens=4,
+        interpolate_antialias=True,
+        interpolate_offset=0.0,
+        **kwargs,
+    )
+
+
+def dinov2_vitb14_reg_lc(
+    *, layers: int = 4, pretrained: bool = True, weights: Union[Weights, str] = Weights.IMAGENET1K, **kwargs
+):
+    """
+    Linear classifier (1 or 4 layers) on top of a DINOv2 ViT-B/14 backbone with registers (optionally) pretrained on the LVD-142M dataset and trained on ImageNet-1k.
+    """
+    return _make_dinov2_linear_classifier(
+        arch_name="vit_base",
+        layers=layers,
+        pretrained=pretrained,
+        weights=weights,
+        num_register_tokens=4,
+        interpolate_antialias=True,
+        interpolate_offset=0.0,
+        **kwargs,
+    )
+
+
+def dinov2_vitl14_reg_lc(
+    *, layers: int = 4, pretrained: bool = True, weights: Union[Weights, str] = Weights.IMAGENET1K, **kwargs
+):
+    """
+    Linear classifier (1 or 4 layers) on top of a DINOv2 ViT-L/14 backbone with registers (optionally) pretrained on the LVD-142M dataset and trained on ImageNet-1k.
+    """
+    return _make_dinov2_linear_classifier(
+        arch_name="vit_large",
+        layers=layers,
+        pretrained=pretrained,
+        weights=weights,
+        num_register_tokens=4,
+        interpolate_antialias=True,
+        interpolate_offset=0.0,
+        **kwargs,
+    )
+
+
+def dinov2_vitg14_reg_lc(
+    *, layers: int = 4, pretrained: bool = True, weights: Union[Weights, str] = Weights.IMAGENET1K, **kwargs
+):
+    """
+    Linear classifier (1 or 4 layers) on top of a DINOv2 ViT-g/14 backbone with registers (optionally) pretrained on the LVD-142M dataset and trained on ImageNet-1k.
+    """
+    return _make_dinov2_linear_classifier(
+        arch_name="vit_giant2",
+        layers=layers,
+        ffn_layer="swiglufused",
+        pretrained=pretrained,
+        weights=weights,
+        num_register_tokens=4,
+        interpolate_antialias=True,
+        interpolate_offset=0.0,
+        **kwargs,
+    )

openlrm/models/encoders/dinov2/hub/depth/__init__.py

@@ -0,0 +1,7 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+#
+# This source code is licensed under the Apache License, Version 2.0
+# found in the LICENSE file in the root directory of this source tree.
+
+from .decode_heads import BNHead, DPTHead
+from .encoder_decoder import DepthEncoderDecoder

openlrm/models/encoders/dinov2/hub/depth/decode_heads.py

@@ -0,0 +1,747 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+#
+# This source code is licensed under the Apache License, Version 2.0
+# found in the LICENSE file in the root directory of this source tree.
+
+import copy
+from functools import partial
+import math
+import warnings
+
+import torch
+import torch.nn as nn
+
+from .ops import resize
+
+
+# XXX: (Untested) replacement for mmcv.imdenormalize()
+def _imdenormalize(img, mean, std, to_bgr=True):
+    import numpy as np
+
+    mean = mean.reshape(1, -1).astype(np.float64)
+    std = std.reshape(1, -1).astype(np.float64)
+    img = (img * std) + mean
+    if to_bgr:
+        img = img[::-1]
+    return img
+
+
+class DepthBaseDecodeHead(nn.Module):
+    """Base class for BaseDecodeHead.
+
+    Args:
+        in_channels (List): Input channels.
+        channels (int): Channels after modules, before conv_depth.
+        conv_layer (nn.Module): Conv layers. Default: None.
+        act_layer (nn.Module): Activation layers. Default: nn.ReLU.
+        loss_decode (dict): Config of decode loss.
+            Default: ().
+        sampler (dict|None): The config of depth map sampler.
+            Default: None.
+        align_corners (bool): align_corners argument of F.interpolate.
+            Default: False.
+        min_depth (int): Min depth in dataset setting.
+            Default: 1e-3.
+        max_depth (int): Max depth in dataset setting.
+            Default: None.
+        norm_layer (dict|None): Norm layers.
+            Default: None.
+        classify (bool): Whether predict depth in a cls.-reg. manner.
+            Default: False.
+        n_bins (int): The number of bins used in cls. step.
+            Default: 256.
+        bins_strategy (str): The discrete strategy used in cls. step.
+            Default: 'UD'.
+        norm_strategy (str): The norm strategy on cls. probability
+            distribution. Default: 'linear'
+        scale_up (str): Whether predict depth in a scale-up manner.
+            Default: False.
+    """
+
+    def __init__(
+        self,
+        in_channels,
+        conv_layer=None,
+        act_layer=nn.ReLU,
+        channels=96,
+        loss_decode=(),
+        sampler=None,
+        align_corners=False,
+        min_depth=1e-3,
+        max_depth=None,
+        norm_layer=None,
+        classify=False,
+        n_bins=256,
+        bins_strategy="UD",
+        norm_strategy="linear",
+        scale_up=False,
+    ):
+        super(DepthBaseDecodeHead, self).__init__()
+
+        self.in_channels = in_channels
+        self.channels = channels
+        self.conf_layer = conv_layer
+        self.act_layer = act_layer
+        self.loss_decode = loss_decode
+        self.align_corners = align_corners
+        self.min_depth = min_depth
+        self.max_depth = max_depth
+        self.norm_layer = norm_layer
+        self.classify = classify
+        self.n_bins = n_bins
+        self.scale_up = scale_up
+
+        if self.classify:
+            assert bins_strategy in ["UD", "SID"], "Support bins_strategy: UD, SID"
+            assert norm_strategy in ["linear", "softmax", "sigmoid"], "Support norm_strategy: linear, softmax, sigmoid"
+
+            self.bins_strategy = bins_strategy
+            self.norm_strategy = norm_strategy
+            self.softmax = nn.Softmax(dim=1)
+            self.conv_depth = nn.Conv2d(channels, n_bins, kernel_size=3, padding=1, stride=1)
+        else:
+            self.conv_depth = nn.Conv2d(channels, 1, kernel_size=3, padding=1, stride=1)
+
+        self.relu = nn.ReLU()
+        self.sigmoid = nn.Sigmoid()
+
+    def forward(self, inputs, img_metas):
+        """Placeholder of forward function."""
+        pass
+
+    def forward_train(self, img, inputs, img_metas, depth_gt):
+        """Forward function for training.
+        Args:
+            inputs (list[Tensor]): List of multi-level img features.
+            img_metas (list[dict]): List of image info dict where each dict
+                has: 'img_shape', 'scale_factor', 'flip', and may also contain
+                'filename', 'ori_shape', 'pad_shape', and 'img_norm_cfg'.
+                For details on the values of these keys see
+                `depth/datasets/pipelines/formatting.py:Collect`.
+            depth_gt (Tensor): GT depth
+
+        Returns:
+            dict[str, Tensor]: a dictionary of loss components
+        """
+        depth_pred = self.forward(inputs, img_metas)
+        losses = self.losses(depth_pred, depth_gt)
+
+        log_imgs = self.log_images(img[0], depth_pred[0], depth_gt[0], img_metas[0])
+        losses.update(**log_imgs)
+
+        return losses
+
+    def forward_test(self, inputs, img_metas):
+        """Forward function for testing.
+        Args:
+            inputs (list[Tensor]): List of multi-level img features.
+            img_metas (list[dict]): List of image info dict where each dict
+                has: 'img_shape', 'scale_factor', 'flip', and may also contain
+                'filename', 'ori_shape', 'pad_shape', and 'img_norm_cfg'.
+                For details on the values of these keys see
+                `depth/datasets/pipelines/formatting.py:Collect`.
+
+        Returns:
+            Tensor: Output depth map.
+        """
+        return self.forward(inputs, img_metas)
+
+    def depth_pred(self, feat):
+        """Prediction each pixel."""
+        if self.classify:
+            logit = self.conv_depth(feat)
+
+            if self.bins_strategy == "UD":
+                bins = torch.linspace(self.min_depth, self.max_depth, self.n_bins, device=feat.device)
+            elif self.bins_strategy == "SID":
+                bins = torch.logspace(self.min_depth, self.max_depth, self.n_bins, device=feat.device)
+
+            # following Adabins, default linear
+            if self.norm_strategy == "linear":
+                logit = torch.relu(logit)
+                eps = 0.1
+                logit = logit + eps
+                logit = logit / logit.sum(dim=1, keepdim=True)
+            elif self.norm_strategy == "softmax":
+                logit = torch.softmax(logit, dim=1)
+            elif self.norm_strategy == "sigmoid":
+                logit = torch.sigmoid(logit)
+                logit = logit / logit.sum(dim=1, keepdim=True)
+
+            output = torch.einsum("ikmn,k->imn", [logit, bins]).unsqueeze(dim=1)
+
+        else:
+            if self.scale_up:
+                output = self.sigmoid(self.conv_depth(feat)) * self.max_depth
+            else:
+                output = self.relu(self.conv_depth(feat)) + self.min_depth
+        return output
+
+    def losses(self, depth_pred, depth_gt):
+        """Compute depth loss."""
+        loss = dict()
+        depth_pred = resize(
+            input=depth_pred, size=depth_gt.shape[2:], mode="bilinear", align_corners=self.align_corners, warning=False
+        )
+        if not isinstance(self.loss_decode, nn.ModuleList):
+            losses_decode = [self.loss_decode]
+        else:
+            losses_decode = self.loss_decode
+        for loss_decode in losses_decode:
+            if loss_decode.loss_name not in loss:
+                loss[loss_decode.loss_name] = loss_decode(depth_pred, depth_gt)
+            else:
+                loss[loss_decode.loss_name] += loss_decode(depth_pred, depth_gt)
+        return loss
+
+    def log_images(self, img_path, depth_pred, depth_gt, img_meta):
+        import numpy as np
+
+        show_img = copy.deepcopy(img_path.detach().cpu().permute(1, 2, 0))
+        show_img = show_img.numpy().astype(np.float32)
+        show_img = _imdenormalize(
+            show_img,
+            img_meta["img_norm_cfg"]["mean"],
+            img_meta["img_norm_cfg"]["std"],
+            img_meta["img_norm_cfg"]["to_rgb"],
+        )
+        show_img = np.clip(show_img, 0, 255)
+        show_img = show_img.astype(np.uint8)
+        show_img = show_img[:, :, ::-1]
+        show_img = show_img.transpose(0, 2, 1)
+        show_img = show_img.transpose(1, 0, 2)
+
+        depth_pred = depth_pred / torch.max(depth_pred)
+        depth_gt = depth_gt / torch.max(depth_gt)
+
+        depth_pred_color = copy.deepcopy(depth_pred.detach().cpu())
+        depth_gt_color = copy.deepcopy(depth_gt.detach().cpu())
+
+        return {"img_rgb": show_img, "img_depth_pred": depth_pred_color, "img_depth_gt": depth_gt_color}
+
+
+class BNHead(DepthBaseDecodeHead):
+    """Just a batchnorm."""
+
+    def __init__(self, input_transform="resize_concat", in_index=(0, 1, 2, 3), upsample=1, **kwargs):
+        super().__init__(**kwargs)
+        self.input_transform = input_transform
+        self.in_index = in_index
+        self.upsample = upsample
+        # self.bn = nn.SyncBatchNorm(self.in_channels)
+        if self.classify:
+            self.conv_depth = nn.Conv2d(self.channels, self.n_bins, kernel_size=1, padding=0, stride=1)
+        else:
+            self.conv_depth = nn.Conv2d(self.channels, 1, kernel_size=1, padding=0, stride=1)
+
+    def _transform_inputs(self, inputs):
+        """Transform inputs for decoder.
+        Args:
+            inputs (list[Tensor]): List of multi-level img features.
+        Returns:
+            Tensor: The transformed inputs
+        """
+
+        if "concat" in self.input_transform:
+            inputs = [inputs[i] for i in self.in_index]
+            if "resize" in self.input_transform:
+                inputs = [
+                    resize(
+                        input=x,
+                        size=[s * self.upsample for s in inputs[0].shape[2:]],
+                        mode="bilinear",
+                        align_corners=self.align_corners,
+                    )
+                    for x in inputs
+                ]
+            inputs = torch.cat(inputs, dim=1)
+        elif self.input_transform == "multiple_select":
+            inputs = [inputs[i] for i in self.in_index]
+        else:
+            inputs = inputs[self.in_index]
+
+        return inputs
+
+    def _forward_feature(self, inputs, img_metas=None, **kwargs):
+        """Forward function for feature maps before classifying each pixel with
+        ``self.cls_seg`` fc.
+        Args:
+            inputs (list[Tensor]): List of multi-level img features.
+        Returns:
+            feats (Tensor): A tensor of shape (batch_size, self.channels,
+                H, W) which is feature map for last layer of decoder head.
+        """
+        # accept lists (for cls token)
+        inputs = list(inputs)
+        for i, x in enumerate(inputs):
+            if len(x) == 2:
+                x, cls_token = x[0], x[1]
+                if len(x.shape) == 2:
+                    x = x[:, :, None, None]
+                cls_token = cls_token[:, :, None, None].expand_as(x)
+                inputs[i] = torch.cat((x, cls_token), 1)
+            else:
+                x = x[0]
+                if len(x.shape) == 2:
+                    x = x[:, :, None, None]
+                inputs[i] = x
+        x = self._transform_inputs(inputs)
+        # feats = self.bn(x)
+        return x
+
+    def forward(self, inputs, img_metas=None, **kwargs):
+        """Forward function."""
+        output = self._forward_feature(inputs, img_metas=img_metas, **kwargs)
+        output = self.depth_pred(output)
+        return output
+
+
+class ConvModule(nn.Module):
+    """A conv block that bundles conv/norm/activation layers.
+
+    This block simplifies the usage of convolution layers, which are commonly
+    used with a norm layer (e.g., BatchNorm) and activation layer (e.g., ReLU).
+    It is based upon three build methods: `build_conv_layer()`,
+    `build_norm_layer()` and `build_activation_layer()`.
+
+    Besides, we add some additional features in this module.
+    1. Automatically set `bias` of the conv layer.
+    2. Spectral norm is supported.
+    3. More padding modes are supported. Before PyTorch 1.5, nn.Conv2d only
+    supports zero and circular padding, and we add "reflect" padding mode.
+
+    Args:
+        in_channels (int): Number of channels in the input feature map.
+            Same as that in ``nn._ConvNd``.
+        out_channels (int): Number of channels produced by the convolution.
+            Same as that in ``nn._ConvNd``.
+        kernel_size (int | tuple[int]): Size of the convolving kernel.
+            Same as that in ``nn._ConvNd``.
+        stride (int | tuple[int]): Stride of the convolution.
+            Same as that in ``nn._ConvNd``.
+        padding (int | tuple[int]): Zero-padding added to both sides of
+            the input. Same as that in ``nn._ConvNd``.
+        dilation (int | tuple[int]): Spacing between kernel elements.
+            Same as that in ``nn._ConvNd``.
+        groups (int): Number of blocked connections from input channels to
+            output channels. Same as that in ``nn._ConvNd``.
+        bias (bool | str): If specified as `auto`, it will be decided by the
+            norm_layer. Bias will be set as True if `norm_layer` is None, otherwise
+            False. Default: "auto".
+        conv_layer (nn.Module): Convolution layer. Default: None,
+            which means using conv2d.
+        norm_layer (nn.Module): Normalization layer. Default: None.
+        act_layer (nn.Module): Activation layer. Default: nn.ReLU.
+        inplace (bool): Whether to use inplace mode for activation.
+            Default: True.
+        with_spectral_norm (bool): Whether use spectral norm in conv module.
+            Default: False.
+        padding_mode (str): If the `padding_mode` has not been supported by
+            current `Conv2d` in PyTorch, we will use our own padding layer
+            instead. Currently, we support ['zeros', 'circular'] with official
+            implementation and ['reflect'] with our own implementation.
+            Default: 'zeros'.
+        order (tuple[str]): The order of conv/norm/activation layers. It is a
+            sequence of "conv", "norm" and "act". Common examples are
+            ("conv", "norm", "act") and ("act", "conv", "norm").
+            Default: ('conv', 'norm', 'act').
+    """
+
+    _abbr_ = "conv_block"
+
+    def __init__(
+        self,
+        in_channels,
+        out_channels,
+        kernel_size,
+        stride=1,
+        padding=0,
+        dilation=1,
+        groups=1,
+        bias="auto",
+        conv_layer=nn.Conv2d,
+        norm_layer=None,
+        act_layer=nn.ReLU,
+        inplace=True,
+        with_spectral_norm=False,
+        padding_mode="zeros",
+        order=("conv", "norm", "act"),
+    ):
+        super(ConvModule, self).__init__()
+        official_padding_mode = ["zeros", "circular"]
+        self.conv_layer = conv_layer
+        self.norm_layer = norm_layer
+        self.act_layer = act_layer
+        self.inplace = inplace
+        self.with_spectral_norm = with_spectral_norm
+        self.with_explicit_padding = padding_mode not in official_padding_mode
+        self.order = order
+        assert isinstance(self.order, tuple) and len(self.order) == 3
+        assert set(order) == set(["conv", "norm", "act"])
+
+        self.with_norm = norm_layer is not None
+        self.with_activation = act_layer is not None
+        # if the conv layer is before a norm layer, bias is unnecessary.
+        if bias == "auto":
+            bias = not self.with_norm
+        self.with_bias = bias
+
+        if self.with_explicit_padding:
+            if padding_mode == "zeros":
+                padding_layer = nn.ZeroPad2d
+            else:
+                raise AssertionError(f"Unsupported padding mode: {padding_mode}")
+            self.pad = padding_layer(padding)
+
+        # reset padding to 0 for conv module
+        conv_padding = 0 if self.with_explicit_padding else padding
+        # build convolution layer
+        self.conv = self.conv_layer(
+            in_channels,
+            out_channels,
+            kernel_size,
+            stride=stride,
+            padding=conv_padding,
+            dilation=dilation,
+            groups=groups,
+            bias=bias,
+        )
+        # export the attributes of self.conv to a higher level for convenience
+        self.in_channels = self.conv.in_channels
+        self.out_channels = self.conv.out_channels
+        self.kernel_size = self.conv.kernel_size
+        self.stride = self.conv.stride
+        self.padding = padding
+        self.dilation = self.conv.dilation
+        self.transposed = self.conv.transposed
+        self.output_padding = self.conv.output_padding
+        self.groups = self.conv.groups
+
+        if self.with_spectral_norm:
+            self.conv = nn.utils.spectral_norm(self.conv)
+
+        # build normalization layers
+        if self.with_norm:
+            # norm layer is after conv layer
+            if order.index("norm") > order.index("conv"):
+                norm_channels = out_channels
+            else:
+                norm_channels = in_channels
+            norm = partial(norm_layer, num_features=norm_channels)
+            self.add_module("norm", norm)
+            if self.with_bias:
+                from torch.nnModules.batchnorm import _BatchNorm
+                from torch.nnModules.instancenorm import _InstanceNorm
+
+                if isinstance(norm, (_BatchNorm, _InstanceNorm)):
+                    warnings.warn("Unnecessary conv bias before batch/instance norm")
+        else:
+            self.norm_name = None
+
+        # build activation layer
+        if self.with_activation:
+            # nn.Tanh has no 'inplace' argument
+            # (nn.Tanh, nn.PReLU, nn.Sigmoid, nn.HSigmoid, nn.Swish, nn.GELU)
+            if not isinstance(act_layer, (nn.Tanh, nn.PReLU, nn.Sigmoid, nn.GELU)):
+                act_layer = partial(act_layer, inplace=inplace)
+            self.activate = act_layer()
+
+        # Use msra init by default
+        self.init_weights()
+
+    @property
+    def norm(self):
+        if self.norm_name:
+            return getattr(self, self.norm_name)
+        else:
+            return None
+
+    def init_weights(self):
+        # 1. It is mainly for customized conv layers with their own
+        #    initialization manners by calling their own ``init_weights()``,
+        #    and we do not want ConvModule to override the initialization.
+        # 2. For customized conv layers without their own initialization
+        #    manners (that is, they don't have their own ``init_weights()``)
+        #    and PyTorch's conv layers, they will be initialized by
+        #    this method with default ``kaiming_init``.
+        # Note: For PyTorch's conv layers, they will be overwritten by our
+        #    initialization implementation using default ``kaiming_init``.
+        if not hasattr(self.conv, "init_weights"):
+            if self.with_activation and isinstance(self.act_layer, nn.LeakyReLU):
+                nonlinearity = "leaky_relu"
+                a = 0.01  # XXX: default negative_slope
+            else:
+                nonlinearity = "relu"
+                a = 0
+            if hasattr(self.conv, "weight") and self.conv.weight is not None:
+                nn.init.kaiming_normal_(self.conv.weight, a=a, mode="fan_out", nonlinearity=nonlinearity)
+            if hasattr(self.conv, "bias") and self.conv.bias is not None:
+                nn.init.constant_(self.conv.bias, 0)
+        if self.with_norm:
+            if hasattr(self.norm, "weight") and self.norm.weight is not None:
+                nn.init.constant_(self.norm.weight, 1)
+            if hasattr(self.norm, "bias") and self.norm.bias is not None:
+                nn.init.constant_(self.norm.bias, 0)
+
+    def forward(self, x, activate=True, norm=True):
+        for layer in self.order:
+            if layer == "conv":
+                if self.with_explicit_padding:
+                    x = self.pad(x)
+                x = self.conv(x)
+            elif layer == "norm" and norm and self.with_norm:
+                x = self.norm(x)
+            elif layer == "act" and activate and self.with_activation:
+                x = self.activate(x)
+        return x
+
+
+class Interpolate(nn.Module):
+    def __init__(self, scale_factor, mode, align_corners=False):
+        super(Interpolate, self).__init__()
+        self.interp = nn.functional.interpolate
+        self.scale_factor = scale_factor
+        self.mode = mode
+        self.align_corners = align_corners
+
+    def forward(self, x):
+        x = self.interp(x, scale_factor=self.scale_factor, mode=self.mode, align_corners=self.align_corners)
+        return x
+
+
+class HeadDepth(nn.Module):
+    def __init__(self, features):
+        super(HeadDepth, self).__init__()
+        self.head = nn.Sequential(
+            nn.Conv2d(features, features // 2, kernel_size=3, stride=1, padding=1),
+            Interpolate(scale_factor=2, mode="bilinear", align_corners=True),
+            nn.Conv2d(features // 2, 32, kernel_size=3, stride=1, padding=1),
+            nn.ReLU(),
+            nn.Conv2d(32, 1, kernel_size=1, stride=1, padding=0),
+        )
+
+    def forward(self, x):
+        x = self.head(x)
+        return x
+
+
+class ReassembleBlocks(nn.Module):
+    """ViTPostProcessBlock, process cls_token in ViT backbone output and
+    rearrange the feature vector to feature map.
+    Args:
+        in_channels (int): ViT feature channels. Default: 768.
+        out_channels (List): output channels of each stage.
+            Default: [96, 192, 384, 768].
+        readout_type (str): Type of readout operation. Default: 'ignore'.
+        patch_size (int): The patch size. Default: 16.
+    """
+
+    def __init__(self, in_channels=768, out_channels=[96, 192, 384, 768], readout_type="ignore", patch_size=16):
+        super(ReassembleBlocks, self).__init__()
+
+        assert readout_type in ["ignore", "add", "project"]
+        self.readout_type = readout_type
+        self.patch_size = patch_size
+
+        self.projects = nn.ModuleList(
+            [
+                ConvModule(
+                    in_channels=in_channels,
+                    out_channels=out_channel,
+                    kernel_size=1,
+                    act_layer=None,
+                )
+                for out_channel in out_channels
+            ]
+        )
+
+        self.resize_layers = nn.ModuleList(
+            [
+                nn.ConvTranspose2d(
+                    in_channels=out_channels[0], out_channels=out_channels[0], kernel_size=4, stride=4, padding=0
+                ),
+                nn.ConvTranspose2d(
+                    in_channels=out_channels[1], out_channels=out_channels[1], kernel_size=2, stride=2, padding=0
+                ),
+                nn.Identity(),
+                nn.Conv2d(
+                    in_channels=out_channels[3], out_channels=out_channels[3], kernel_size=3, stride=2, padding=1
+                ),
+            ]
+        )
+        if self.readout_type == "project":
+            self.readout_projects = nn.ModuleList()
+            for _ in range(len(self.projects)):
+                self.readout_projects.append(nn.Sequential(nn.Linear(2 * in_channels, in_channels), nn.GELU()))
+
+    def forward(self, inputs):
+        assert isinstance(inputs, list)
+        out = []
+        for i, x in enumerate(inputs):
+            assert len(x) == 2
+            x, cls_token = x[0], x[1]
+            feature_shape = x.shape
+            if self.readout_type == "project":
+                x = x.flatten(2).permute((0, 2, 1))
+                readout = cls_token.unsqueeze(1).expand_as(x)
+                x = self.readout_projects[i](torch.cat((x, readout), -1))
+                x = x.permute(0, 2, 1).reshape(feature_shape)
+            elif self.readout_type == "add":
+                x = x.flatten(2) + cls_token.unsqueeze(-1)
+                x = x.reshape(feature_shape)
+            else:
+                pass
+            x = self.projects[i](x)
+            x = self.resize_layers[i](x)
+            out.append(x)
+        return out
+
+
+class PreActResidualConvUnit(nn.Module):
+    """ResidualConvUnit, pre-activate residual unit.
+    Args:
+        in_channels (int): number of channels in the input feature map.
+        act_layer (nn.Module): activation layer.
+        norm_layer (nn.Module): norm layer.
+        stride (int): stride of the first block. Default: 1
+        dilation (int): dilation rate for convs layers. Default: 1.
+    """
+
+    def __init__(self, in_channels, act_layer, norm_layer, stride=1, dilation=1):
+        super(PreActResidualConvUnit, self).__init__()
+
+        self.conv1 = ConvModule(
+            in_channels,
+            in_channels,
+            3,
+            stride=stride,
+            padding=dilation,
+            dilation=dilation,
+            norm_layer=norm_layer,
+            act_layer=act_layer,
+            bias=False,
+            order=("act", "conv", "norm"),
+        )
+
+        self.conv2 = ConvModule(
+            in_channels,
+            in_channels,
+            3,
+            padding=1,
+            norm_layer=norm_layer,
+            act_layer=act_layer,
+            bias=False,
+            order=("act", "conv", "norm"),
+        )
+
+    def forward(self, inputs):
+        inputs_ = inputs.clone()
+        x = self.conv1(inputs)
+        x = self.conv2(x)
+        return x + inputs_
+
+
+class FeatureFusionBlock(nn.Module):
+    """FeatureFusionBlock, merge feature map from different stages.
+    Args:
+        in_channels (int): Input channels.
+        act_layer (nn.Module): activation layer for ResidualConvUnit.
+        norm_layer (nn.Module): normalization layer.
+        expand (bool): Whether expand the channels in post process block.
+            Default: False.
+        align_corners (bool): align_corner setting for bilinear upsample.
+            Default: True.
+    """
+
+    def __init__(self, in_channels, act_layer, norm_layer, expand=False, align_corners=True):
+        super(FeatureFusionBlock, self).__init__()
+
+        self.in_channels = in_channels
+        self.expand = expand
+        self.align_corners = align_corners
+
+        self.out_channels = in_channels
+        if self.expand:
+            self.out_channels = in_channels // 2
+
+        self.project = ConvModule(self.in_channels, self.out_channels, kernel_size=1, act_layer=None, bias=True)
+
+        self.res_conv_unit1 = PreActResidualConvUnit(
+            in_channels=self.in_channels, act_layer=act_layer, norm_layer=norm_layer
+        )
+        self.res_conv_unit2 = PreActResidualConvUnit(
+            in_channels=self.in_channels, act_layer=act_layer, norm_layer=norm_layer
+        )
+
+    def forward(self, *inputs):
+        x = inputs[0]
+        if len(inputs) == 2:
+            if x.shape != inputs[1].shape:
+                res = resize(inputs[1], size=(x.shape[2], x.shape[3]), mode="bilinear", align_corners=False)
+            else:
+                res = inputs[1]
+            x = x + self.res_conv_unit1(res)
+        x = self.res_conv_unit2(x)
+        x = resize(x, scale_factor=2, mode="bilinear", align_corners=self.align_corners)
+        x = self.project(x)
+        return x
+
+
+class DPTHead(DepthBaseDecodeHead):
+    """Vision Transformers for Dense Prediction.
+    This head is implemented of `DPT <https://arxiv.org/abs/2103.13413>`_.
+    Args:
+        embed_dims (int): The embed dimension of the ViT backbone.
+            Default: 768.
+        post_process_channels (List): Out channels of post process conv
+            layers. Default: [96, 192, 384, 768].
+        readout_type (str): Type of readout operation. Default: 'ignore'.
+        patch_size (int): The patch size. Default: 16.
+        expand_channels (bool): Whether expand the channels in post process
+            block. Default: False.
+    """
+
+    def __init__(
+        self,
+        embed_dims=768,
+        post_process_channels=[96, 192, 384, 768],
+        readout_type="ignore",
+        patch_size=16,
+        expand_channels=False,
+        **kwargs,
+    ):
+        super(DPTHead, self).__init__(**kwargs)
+
+        self.in_channels = self.in_channels
+        self.expand_channels = expand_channels
+        self.reassemble_blocks = ReassembleBlocks(embed_dims, post_process_channels, readout_type, patch_size)
+
+        self.post_process_channels = [
+            channel * math.pow(2, i) if expand_channels else channel for i, channel in enumerate(post_process_channels)
+        ]
+        self.convs = nn.ModuleList()
+        for channel in self.post_process_channels:
+            self.convs.append(ConvModule(channel, self.channels, kernel_size=3, padding=1, act_layer=None, bias=False))
+        self.fusion_blocks = nn.ModuleList()
+        for _ in range(len(self.convs)):
+            self.fusion_blocks.append(FeatureFusionBlock(self.channels, self.act_layer, self.norm_layer))
+        self.fusion_blocks[0].res_conv_unit1 = None
+        self.project = ConvModule(self.channels, self.channels, kernel_size=3, padding=1, norm_layer=self.norm_layer)
+        self.num_fusion_blocks = len(self.fusion_blocks)
+        self.num_reassemble_blocks = len(self.reassemble_blocks.resize_layers)
+        self.num_post_process_channels = len(self.post_process_channels)
+        assert self.num_fusion_blocks == self.num_reassemble_blocks
+        assert self.num_reassemble_blocks == self.num_post_process_channels
+        self.conv_depth = HeadDepth(self.channels)
+
+    def forward(self, inputs, img_metas):
+        assert len(inputs) == self.num_reassemble_blocks
+        x = [inp for inp in inputs]
+        x = self.reassemble_blocks(x)
+        x = [self.convs[i](feature) for i, feature in enumerate(x)]
+        out = self.fusion_blocks[0](x[-1])
+        for i in range(1, len(self.fusion_blocks)):
+            out = self.fusion_blocks[i](out, x[-(i + 1)])
+        out = self.project(out)
+        out = self.depth_pred(out)
+        return out

openlrm/models/encoders/dinov2/hub/depth/encoder_decoder.py

@@ -0,0 +1,351 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+#
+# This source code is licensed under the Apache License, Version 2.0
+# found in the LICENSE file in the root directory of this source tree.
+
+from collections import OrderedDict
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from .ops import resize
+
+
+def add_prefix(inputs, prefix):
+    """Add prefix for dict.
+
+    Args:
+        inputs (dict): The input dict with str keys.
+        prefix (str): The prefix to add.
+
+    Returns:
+
+        dict: The dict with keys updated with ``prefix``.
+    """
+
+    outputs = dict()
+    for name, value in inputs.items():
+        outputs[f"{prefix}.{name}"] = value
+
+    return outputs
+
+
+class DepthEncoderDecoder(nn.Module):
+    """Encoder Decoder depther.
+
+    EncoderDecoder typically consists of backbone and decode_head.
+    """
+
+    def __init__(self, backbone, decode_head):
+        super(DepthEncoderDecoder, self).__init__()
+
+        self.backbone = backbone
+        self.decode_head = decode_head
+        self.align_corners = self.decode_head.align_corners
+
+    def extract_feat(self, img):
+        """Extract features from images."""
+        return self.backbone(img)
+
+    def encode_decode(self, img, img_metas, rescale=True, size=None):
+        """Encode images with backbone and decode into a depth estimation
+        map of the same size as input."""
+        x = self.extract_feat(img)
+        out = self._decode_head_forward_test(x, img_metas)
+        # crop the pred depth to the certain range.
+        out = torch.clamp(out, min=self.decode_head.min_depth, max=self.decode_head.max_depth)
+        if rescale:
+            if size is None:
+                if img_metas is not None:
+                    size = img_metas[0]["ori_shape"][:2]
+                else:
+                    size = img.shape[2:]
+            out = resize(input=out, size=size, mode="bilinear", align_corners=self.align_corners)
+        return out
+
+    def _decode_head_forward_train(self, img, x, img_metas, depth_gt, **kwargs):
+        """Run forward function and calculate loss for decode head in
+        training."""
+        losses = dict()
+        loss_decode = self.decode_head.forward_train(img, x, img_metas, depth_gt, **kwargs)
+        losses.update(add_prefix(loss_decode, "decode"))
+        return losses
+
+    def _decode_head_forward_test(self, x, img_metas):
+        """Run forward function and calculate loss for decode head in
+        inference."""
+        depth_pred = self.decode_head.forward_test(x, img_metas)
+        return depth_pred
+
+    def forward_dummy(self, img):
+        """Dummy forward function."""
+        depth = self.encode_decode(img, None)
+
+        return depth
+
+    def forward_train(self, img, img_metas, depth_gt, **kwargs):
+        """Forward function for training.
+
+        Args:
+            img (Tensor): Input images.
+            img_metas (list[dict]): List of image info dict where each dict
+                has: 'img_shape', 'scale_factor', 'flip', and may also contain
+                'filename', 'ori_shape', 'pad_shape', and 'img_norm_cfg'.
+                For details on the values of these keys see
+                `depth/datasets/pipelines/formatting.py:Collect`.
+            depth_gt (Tensor): Depth gt
+                used if the architecture supports depth estimation task.
+
+        Returns:
+            dict[str, Tensor]: a dictionary of loss components
+        """
+
+        x = self.extract_feat(img)
+
+        losses = dict()
+
+        # the last of x saves the info from neck
+        loss_decode = self._decode_head_forward_train(img, x, img_metas, depth_gt, **kwargs)
+
+        losses.update(loss_decode)
+
+        return losses
+
+    def whole_inference(self, img, img_meta, rescale, size=None):
+        """Inference with full image."""
+        return self.encode_decode(img, img_meta, rescale, size=size)
+
+    def slide_inference(self, img, img_meta, rescale, stride, crop_size):
+        """Inference by sliding-window with overlap.
+
+        If h_crop > h_img or w_crop > w_img, the small patch will be used to
+        decode without padding.
+        """
+
+        h_stride, w_stride = stride
+        h_crop, w_crop = crop_size
+        batch_size, _, h_img, w_img = img.size()
+        h_grids = max(h_img - h_crop + h_stride - 1, 0) // h_stride + 1
+        w_grids = max(w_img - w_crop + w_stride - 1, 0) // w_stride + 1
+        preds = img.new_zeros((batch_size, 1, h_img, w_img))
+        count_mat = img.new_zeros((batch_size, 1, h_img, w_img))
+        for h_idx in range(h_grids):
+            for w_idx in range(w_grids):
+                y1 = h_idx * h_stride
+                x1 = w_idx * w_stride
+                y2 = min(y1 + h_crop, h_img)
+                x2 = min(x1 + w_crop, w_img)
+                y1 = max(y2 - h_crop, 0)
+                x1 = max(x2 - w_crop, 0)
+                crop_img = img[:, :, y1:y2, x1:x2]
+                depth_pred = self.encode_decode(crop_img, img_meta, rescale)
+                preds += F.pad(depth_pred, (int(x1), int(preds.shape[3] - x2), int(y1), int(preds.shape[2] - y2)))
+
+                count_mat[:, :, y1:y2, x1:x2] += 1
+        assert (count_mat == 0).sum() == 0
+        if torch.onnx.is_in_onnx_export():
+            # cast count_mat to constant while exporting to ONNX
+            count_mat = torch.from_numpy(count_mat.cpu().detach().numpy()).to(device=img.device)
+        preds = preds / count_mat
+        return preds
+
+    def inference(self, img, img_meta, rescale, size=None, mode="whole"):
+        """Inference with slide/whole style.
+
+        Args:
+            img (Tensor): The input image of shape (N, 3, H, W).
+            img_meta (dict): Image info dict where each dict has: 'img_shape',
+                'scale_factor', 'flip', and may also contain
+                'filename', 'ori_shape', 'pad_shape', and 'img_norm_cfg'.
+                For details on the values of these keys see
+                `depth/datasets/pipelines/formatting.py:Collect`.
+            rescale (bool): Whether rescale back to original shape.
+
+        Returns:
+            Tensor: The output depth map.
+        """
+
+        assert mode in ["slide", "whole"]
+        ori_shape = img_meta[0]["ori_shape"]
+        assert all(_["ori_shape"] == ori_shape for _ in img_meta)
+        if mode == "slide":
+            depth_pred = self.slide_inference(img, img_meta, rescale)
+        else:
+            depth_pred = self.whole_inference(img, img_meta, rescale, size=size)
+        output = depth_pred
+        flip = img_meta[0]["flip"]
+        if flip:
+            flip_direction = img_meta[0]["flip_direction"]
+            assert flip_direction in ["horizontal", "vertical"]
+            if flip_direction == "horizontal":
+                output = output.flip(dims=(3,))
+            elif flip_direction == "vertical":
+                output = output.flip(dims=(2,))
+
+        return output
+
+    def simple_test(self, img, img_meta, rescale=True):
+        """Simple test with single image."""
+        depth_pred = self.inference(img, img_meta, rescale)
+        if torch.onnx.is_in_onnx_export():
+            # our inference backend only support 4D output
+            depth_pred = depth_pred.unsqueeze(0)
+            return depth_pred
+        depth_pred = depth_pred.cpu().numpy()
+        # unravel batch dim
+        depth_pred = list(depth_pred)
+        return depth_pred
+
+    def aug_test(self, imgs, img_metas, rescale=True):
+        """Test with augmentations.
+
+        Only rescale=True is supported.
+        """
+        # aug_test rescale all imgs back to ori_shape for now
+        assert rescale
+        # to save memory, we get augmented depth logit inplace
+        depth_pred = self.inference(imgs[0], img_metas[0], rescale)
+        for i in range(1, len(imgs)):
+            cur_depth_pred = self.inference(imgs[i], img_metas[i], rescale, size=depth_pred.shape[-2:])
+            depth_pred += cur_depth_pred
+        depth_pred /= len(imgs)
+        depth_pred = depth_pred.cpu().numpy()
+        # unravel batch dim
+        depth_pred = list(depth_pred)
+        return depth_pred
+
+    def forward_test(self, imgs, img_metas, **kwargs):
+        """
+        Args:
+            imgs (List[Tensor]): the outer list indicates test-time
+                augmentations and inner Tensor should have a shape NxCxHxW,
+                which contains all images in the batch.
+            img_metas (List[List[dict]]): the outer list indicates test-time
+                augs (multiscale, flip, etc.) and the inner list indicates
+                images in a batch.
+        """
+        for var, name in [(imgs, "imgs"), (img_metas, "img_metas")]:
+            if not isinstance(var, list):
+                raise TypeError(f"{name} must be a list, but got " f"{type(var)}")
+        num_augs = len(imgs)
+        if num_augs != len(img_metas):
+            raise ValueError(f"num of augmentations ({len(imgs)}) != " f"num of image meta ({len(img_metas)})")
+        # all images in the same aug batch all of the same ori_shape and pad
+        # shape
+        for img_meta in img_metas:
+            ori_shapes = [_["ori_shape"] for _ in img_meta]
+            assert all(shape == ori_shapes[0] for shape in ori_shapes)
+            img_shapes = [_["img_shape"] for _ in img_meta]
+            assert all(shape == img_shapes[0] for shape in img_shapes)
+            pad_shapes = [_["pad_shape"] for _ in img_meta]
+            assert all(shape == pad_shapes[0] for shape in pad_shapes)
+
+        if num_augs == 1:
+            return self.simple_test(imgs[0], img_metas[0], **kwargs)
+        else:
+            return self.aug_test(imgs, img_metas, **kwargs)
+
+    def forward(self, img, img_metas, return_loss=True, **kwargs):
+        """Calls either :func:`forward_train` or :func:`forward_test` depending
+        on whether ``return_loss`` is ``True``.
+
+        Note this setting will change the expected inputs. When
+        ``return_loss=True``, img and img_meta are single-nested (i.e. Tensor
+        and List[dict]), and when ``resturn_loss=False``, img and img_meta
+        should be double nested (i.e.  List[Tensor], List[List[dict]]), with
+        the outer list indicating test time augmentations.
+        """
+        if return_loss:
+            return self.forward_train(img, img_metas, **kwargs)
+        else:
+            return self.forward_test(img, img_metas, **kwargs)
+
+    def train_step(self, data_batch, optimizer, **kwargs):
+        """The iteration step during training.
+
+        This method defines an iteration step during training, except for the
+        back propagation and optimizer updating, which are done in an optimizer
+        hook. Note that in some complicated cases or models, the whole process
+        including back propagation and optimizer updating is also defined in
+        this method, such as GAN.
+
+        Args:
+            data (dict): The output of dataloader.
+            optimizer (:obj:`torch.optim.Optimizer` | dict): The optimizer of
+                runner is passed to ``train_step()``. This argument is unused
+                and reserved.
+
+        Returns:
+            dict: It should contain at least 3 keys: ``loss``, ``log_vars``,
+                ``num_samples``.
+                ``loss`` is a tensor for back propagation, which can be a
+                weighted sum of multiple losses.
+                ``log_vars`` contains all the variables to be sent to the
+                logger.
+                ``num_samples`` indicates the batch size (when the model is
+                DDP, it means the batch size on each GPU), which is used for
+                averaging the logs.
+        """
+        losses = self(**data_batch)
+
+        # split losses and images
+        real_losses = {}
+        log_imgs = {}
+        for k, v in losses.items():
+            if "img" in k:
+                log_imgs[k] = v
+            else:
+                real_losses[k] = v
+
+        loss, log_vars = self._parse_losses(real_losses)
+
+        outputs = dict(loss=loss, log_vars=log_vars, num_samples=len(data_batch["img_metas"]), log_imgs=log_imgs)
+
+        return outputs
+
+    def val_step(self, data_batch, **kwargs):
+        """The iteration step during validation.
+
+        This method shares the same signature as :func:`train_step`, but used
+        during val epochs. Note that the evaluation after training epochs is
+        not implemented with this method, but an evaluation hook.
+        """
+        output = self(**data_batch, **kwargs)
+        return output
+
+    @staticmethod
+    def _parse_losses(losses):
+        import torch.distributed as dist
+
+        """Parse the raw outputs (losses) of the network.
+
+        Args:
+            losses (dict): Raw output of the network, which usually contain
+                losses and other necessary information.
+
+        Returns:
+            tuple[Tensor, dict]: (loss, log_vars), loss is the loss tensor
+                which may be a weighted sum of all losses, log_vars contains
+                all the variables to be sent to the logger.
+        """
+        log_vars = OrderedDict()
+        for loss_name, loss_value in losses.items():
+            if isinstance(loss_value, torch.Tensor):
+                log_vars[loss_name] = loss_value.mean()
+            elif isinstance(loss_value, list):
+                log_vars[loss_name] = sum(_loss.mean() for _loss in loss_value)
+            else:
+                raise TypeError(f"{loss_name} is not a tensor or list of tensors")
+
+        loss = sum(_value for _key, _value in log_vars.items() if "loss" in _key)
+
+        log_vars["loss"] = loss
+        for loss_name, loss_value in log_vars.items():
+            # reduce loss when distributed training
+            if dist.is_available() and dist.is_initialized():
+                loss_value = loss_value.data.clone()
+                dist.all_reduce(loss_value.div_(dist.get_world_size()))
+            log_vars[loss_name] = loss_value.item()
+
+        return loss, log_vars