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.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+Marigold-main/doc/teaser_collage_transparant.png filter=lfs diff=lfs merge=lfs -text

Marigold-main/.github/workflows/pr_ruff.yml

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+name: Run code quality check with Ruff
+
+run-name: Checking code quality.
+on:
+  pull_request:
+    branches:
+      - main
+  push:
+    branches:
+      - main
+
+concurrency:
+  group: ${{ github.workflow }}-${{ github.head_ref || github.run_id }}
+  cancel-in-progress: true
+
+jobs:
+  check_code_quality:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v3
+      - name: Set up Python
+        uses: actions/setup-python@v4
+        with:
+          python-version: "3.10"
+      - name: Install dependencies
+        run: |
+          python -m pip install --upgrade pip
+          pip install ruff==0.3.7
+      - name: Check quality
+        run: |
+          ruff check .
+          ruff format . --check

Marigold-main/.gitignore

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+
+# ignore these folders
+/checkpoint
+/data
+/input
+/output
+/temp
+/wandb
+/venv
+/cache
+
+**/.ipynb_checkpoints/
+.vscode/
+.idea
+
+# ignore these types
+*.pyc
+*.out
+*.log
+*.mexa64
+*.pdf
+*.tar

Marigold-main/CONTRIBUTING.md

@@ -0,0 +1,15 @@
+## Contributing instructions
+
+We appreciate your interest in contributing. Please follow these guidelines:
+
+1. **Discuss Changes:** Start a GitHub issue to talk about your proposed change before proceeding.
+
+2. **Pull Requests:** Avoid unsolicited PRs. Discussion helps align with project goals.
+
+3. **License Agreement:** By submitting a PR, you accept our LICENSE terms.
+
+4. **Legal Compatibility:** Ensure your change complies with our project's objectives and licensing.
+
+5. **Attribution:** Credit third-party code in your PR if used.
+
+Please, feel free to reach out for questions or assistance. Your contributions are valued, and we're excited to work together to enhance this project!

Marigold-main/LICENSE.txt

@@ -0,0 +1,177 @@
+
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Marigold-main/README.md

@@ -0,0 +1,296 @@
+# Marigold: Repurposing Diffusion-Based Image Generators for Monocular Depth Estimation
+
+**CVPR 2024 (Oral, Best Paper Award Candidate)**
+
+This repository represents the official implementation of the paper titled "Repurposing Diffusion-Based Image Generators for Monocular Depth Estimation".
+
+[![Website](doc/badges/badge-website.svg)](https://marigoldmonodepth.github.io)
+[![Paper](https://img.shields.io/badge/arXiv-PDF-b31b1b)](https://arxiv.org/abs/2312.02145)
+[![Hugging Face (LCM) Space](https://img.shields.io/badge/🤗%20Hugging%20Face%20(LCM)-Space-yellow)](https://huggingface.co/spaces/prs-eth/marigold-lcm)
+[![Hugging Face (LCM) Model](https://img.shields.io/badge/🤗%20Hugging%20Face%20(LCM)-Model-green)](https://huggingface.co/prs-eth/marigold-lcm-v1-0)
+[![Open In Colab](doc/badges/badge-colab.svg)](https://colab.research.google.com/drive/12G8reD13DdpMie5ZQlaFNo2WCGeNUH-u?usp=sharing)
+[![License](https://img.shields.io/badge/License-Apache--2.0-929292)](https://www.apache.org/licenses/LICENSE-2.0)
+<!-- [![Hugging Face Model](https://img.shields.io/badge/🤗%20Hugging%20Face-Model-green)](https://huggingface.co/prs-eth/marigold-v1-0) -->
+<!-- [![Website](https://img.shields.io/badge/Project-Website-1081c2)](https://arxiv.org/abs/2312.02145) -->
+<!-- [![GitHub](https://img.shields.io/github/stars/prs-eth/Marigold?style=default&label=GitHub%20★&logo=github)](https://github.com/prs-eth/Marigold) -->
+<!-- [![HF Space](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Space-blue)]() -->
+<!-- [![Docker](doc/badges/badge-docker.svg)]() -->
+
+[Bingxin Ke](http://www.kebingxin.com/),
+[Anton Obukhov](https://www.obukhov.ai/),
+[Shengyu Huang](https://shengyuh.github.io/),
+[Nando Metzger](https://nandometzger.github.io/),
+[Rodrigo Caye Daudt](https://rcdaudt.github.io/),
+[Konrad Schindler](https://scholar.google.com/citations?user=FZuNgqIAAAAJ&hl=en )
+
+We present Marigold, a diffusion model, and associated fine-tuning protocol for monocular depth estimation. Its core principle is to leverage the rich visual knowledge stored in modern generative image models. Our model, derived from Stable Diffusion and fine-tuned with synthetic data, can zero-shot transfer to unseen data, offering state-of-the-art monocular depth estimation results.
+
+![teaser](doc/teaser_collage_transparant.png)
+
+## 📢 News
+2024-05-28: Training code is released.<br>
+2024-03-23: Added [LCM v1.0](https://huggingface.co/prs-eth/marigold-lcm-v1-0) for faster inference - try it out at <a href="https://huggingface.co/spaces/prs-eth/marigold-lcm"><img src="https://img.shields.io/badge/🤗%20Hugging%20Face%20(LCM)-Space-yellow" height="16"></a><br>
+2024-03-04: Accepted to CVPR 2024. <br>
+2023-12-22: Contributed to Diffusers [community pipeline](https://github.com/huggingface/diffusers/tree/main/examples/community#marigold-depth-estimation). <br>
+2023-12-19: Updated [license](LICENSE.txt) to Apache License, Version 2.0.<br>
+2023-12-08: Added
+<a href="https://huggingface.co/spaces/toshas/marigold"><img src="https://img.shields.io/badge/🤗%20Hugging%20Face-Space-yellow" height="16"></a> - try it out with your images for free!<br>
+2023-12-05: Added <a href="https://colab.research.google.com/drive/12G8reD13DdpMie5ZQlaFNo2WCGeNUH-u?usp=sharing"><img src="doc/badges/badge-colab.svg" height="16"></a> - dive deeper into our inference pipeline!<br>
+2023-12-04: Added <a href="https://arxiv.org/abs/2312.02145"><img src="https://img.shields.io/badge/arXiv-PDF-b31b1b" height="16"></a>
+paper and inference code (this repository).
+
+## 🚀 Usage
+
+**We offer several ways to interact with Marigold**:
+
+1. We integrated [Marigold Pipelines into diffusers 🧨](https://huggingface.co/docs/diffusers/api/pipelines/marigold). Check out many exciting usage scenarios in [this diffusers tutorial](https://huggingface.co/docs/diffusers/using-diffusers/marigold_usage).
+
+1. A free online interactive demo is available here: <a href="https://huggingface.co/spaces/prs-eth/marigold-lcm"><img src="https://img.shields.io/badge/🤗%20Hugging%20Face%20(LCM)-Space-yellow" height="16"></a> (kudos to the HF team for the GPU grant)
+
+1. Run the demo locally (requires a GPU and an `nvidia-docker2`, see [Installation Guide](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/install-guide.html)):
+    1. Paper version: `docker run -it -p 7860:7860 --platform=linux/amd64 --gpus all registry.hf.space/toshas-marigold:latest python app.py`
+    1. LCM version: `docker run -it -p 7860:7860 --platform=linux/amd64 --gpus all registry.hf.space/prs-eth-marigold-lcm:latest python app.py`
+
+1. Extended demo on a Google Colab: <a href="https://colab.research.google.com/drive/12G8reD13DdpMie5ZQlaFNo2WCGeNUH-u?usp=sharing"><img src="doc/badges/badge-colab.svg" height="16"></a>
+
+1. If you just want to see the examples, visit our gallery: <a href="https://marigoldmonodepth.github.io"><img src="doc/badges/badge-website.svg" height="16"></a>
+
+1. Finally, local development instructions with this codebase are given below.
+
+## 🛠️ Setup
+
+The inference code was tested on:
+
+- Ubuntu 22.04 LTS, Python 3.10.12,  CUDA 11.7, GeForce RTX 3090 (pip, Mamba)
+- CentOS Linux 7, Python 3.10.4, CUDA 11.7, GeForce RTX 4090 (pip)
+- Windows 11 22H2, Python 3.10.12, CUDA 12.3, GeForce RTX 3080 (Mamba)
+- MacOS 14.2, Python 3.10.12, M1 16G (pip)
+
+### 🪧 A Note for Windows users
+
+We recommend running the code in WSL2:
+
+1. Install WSL following [installation guide](https://learn.microsoft.com/en-us/windows/wsl/install#install-wsl-command).
+1. Install CUDA support for WSL following [installation guide](https://docs.nvidia.com/cuda/wsl-user-guide/index.html#cuda-support-for-wsl-2).
+1. Find your drives in `/mnt/<drive letter>/`; check [WSL FAQ](https://learn.microsoft.com/en-us/windows/wsl/faq#how-do-i-access-my-c--drive-) for more details. Navigate to the working directory of choice. 
+
+### 📦 Repository
+
+Clone the repository (requires git):
+
+```bash
+git clone https://github.com/prs-eth/Marigold.git
+cd Marigold
+```
+
+### 💻 Dependencies
+
+We provide several ways to install the dependencies.
+
+1. **Using [Mamba](https://github.com/mamba-org/mamba)**, which can installed together with [Miniforge3](https://github.com/conda-forge/miniforge?tab=readme-ov-file#miniforge3). 
+
+    Windows users: Install the Linux version into the WSL.
+
+    After the installation, Miniforge needs to be activated first: `source /home/$USER/miniforge3/bin/activate`.
+
+    Create the environment and install dependencies into it:
+
+    ```bash
+    mamba env create -n marigold --file environment.yaml
+    conda activate marigold
+    ```
+
+2. **Using pip:** 
+    Alternatively, create a Python native virtual environment and install dependencies into it:
+
+    ```bash
+    python -m venv venv/marigold
+    source venv/marigold/bin/activate
+    pip install -r requirements.txt
+    ```
+
+Keep the environment activated before running the inference script. 
+Activate the environment again after restarting the terminal session.
+
+## 🏃 Testing on your images
+
+### 📷 Prepare images
+
+1. Use selected images from our paper:
+
+    ```bash
+    bash script/download_sample_data.sh
+    ```
+
+1. Or place your images in a directory, for example, under `input/in-the-wild_example`, and run the following inference command.
+
+### 🚀 Run inference with LCM (faster)
+
+The [LCM checkpoint](https://huggingface.co/prs-eth/marigold-lcm-v1-0) is distilled from our original checkpoint towards faster inference speed (by reducing inference steps). The inference steps can be as few as 1 (default) to 4. Run with default LCM setting:
+
+```bash
+ python run.py \
+     --input_rgb_dir input/in-the-wild_example \
+     --output_dir output/in-the-wild_example_lcm
+ ```
+
+### 🎮 Run inference with DDIM (paper setting)
+
+This setting corresponds to our paper. For academic comparison, please run with this setting.
+
+```bash
+python run.py \
+    --checkpoint prs-eth/marigold-v1-0 \
+    --denoise_steps 50 \
+    --ensemble_size 10 \
+    --input_rgb_dir input/in-the-wild_example \
+    --output_dir output/in-the-wild_example
+```
+
+You can find all results in `output/in-the-wild_example`. Enjoy!
+
+### ⚙️ Inference settings
+
+The default settings are optimized for the best result. However, the behavior of the code can be customized:
+
+- Trade-offs between the **accuracy** and **speed** (for both options, larger values result in better accuracy at the cost of slower inference.)
+  - `--ensemble_size`: Number of inference passes in the ensemble. For LCM `ensemble_size` is more important than `denoise_steps`. Default: ~~10~~ 5 (for LCM).
+  - `--denoise_steps`: Number of denoising steps of each inference pass. For the original (DDIM) version, it's recommended to use 10-50 steps, while for LCM 1-4 steps. When unassigned (`None`), will read default setting from model config. Default: ~~10 4 (for LCM)~~ `None`.
+
+- By default, the inference script resizes input images to the *processing resolution*, and then resizes the prediction back to the original resolution. This gives the best quality, as Stable Diffusion, from which Marigold is derived, performs best at 768x768 resolution.  
+  
+  - `--processing_res`: the processing resolution; set as 0 to process the input resolution directly. When unassigned (`None`), will read default setting from model config. Default: ~~768~~ `None`.
+  - `--output_processing_res`: produce output at the processing resolution instead of upsampling it to the input resolution. Default: False.
+  - `--resample_method`: the resampling method used to resize images and depth predictions. This can be one of `bilinear`, `bicubic`, or `nearest`. Default: `bilinear`.
+
+- `--half_precision` or `--fp16`: Run with half-precision (16-bit float) to have faster speed and reduced VRAM usage, but might lead to suboptimal results.
+- `--seed`: Random seed can be set to ensure additional reproducibility. Default: None (unseeded). Note: forcing `--batch_size 1` helps to increase reproducibility. To ensure full reproducibility, [deterministic mode](https://pytorch.org/docs/stable/notes/randomness.html#avoiding-nondeterministic-algorithms) needs to be used.
+- `--batch_size`: Batch size of repeated inference. Default: 0 (best value determined automatically).
+- `--color_map`: [Colormap](https://matplotlib.org/stable/users/explain/colors/colormaps.html) used to colorize the depth prediction. Default: Spectral. Set to `None` to skip colored depth map generation.
+- `--apple_silicon`: Use Apple Silicon MPS acceleration.
+
+### ⬇ Checkpoint cache
+
+By default, the [checkpoint](https://huggingface.co/prs-eth/marigold-v1-0) is stored in the Hugging Face cache.
+The `HF_HOME` environment variable defines its location and can be overridden, e.g.:
+
+```bash
+export HF_HOME=$(pwd)/cache
+```
+
+Alternatively, use the following script to download the checkpoint weights locally:
+
+```bash
+bash script/download_weights.sh marigold-v1-0
+# or LCM checkpoint
+bash script/download_weights.sh marigold-lcm-v1-0
+```
+
+At inference, specify the checkpoint path:
+
+```bash
+python run.py \
+    --checkpoint checkpoint/marigold-v1-0 \
+    --denoise_steps 50 \
+    --ensemble_size 10 \
+    --input_rgb_dir input/in-the-wild_example\
+    --output_dir output/in-the-wild_example
+```
+
+## 🦿 Evaluation on test datasets <a name="evaluation"></a>
+
+Install additional dependencies:
+
+```bash
+pip install -r requirements+.txt -r requirements.txt
+```
+
+Set data directory variable (also needed in evaluation scripts) and download [evaluation datasets](https://share.phys.ethz.ch/~pf/bingkedata/marigold/evaluation_dataset) into corresponding subfolders:
+
+```bash
+export BASE_DATA_DIR=<YOUR_DATA_DIR>  # Set target data directory
+
+wget -r -np -nH --cut-dirs=4 -R "index.html*" -P ${BASE_DATA_DIR} https://share.phys.ethz.ch/~pf/bingkedata/marigold/evaluation_dataset/
+```
+
+Run inference and evaluation scripts, for example:
+
+```bash
+# Run inference
+bash script/eval/11_infer_nyu.sh
+
+# Evaluate predictions
+bash script/eval/12_eval_nyu.sh
+```
+
+Note: although the seed has been set, the results might still be slightly different on different hardware.
+
+## 🏋️ Training
+
+Based on the previously created environment, install extended requirements:
+
+```bash
+pip install -r requirements++.txt -r requirements+.txt -r requirements.txt
+```
+
+Set environment parameters for the data directory:
+
+```bash
+export BASE_DATA_DIR=YOUR_DATA_DIR  # directory of training data
+export BASE_CKPT_DIR=YOUR_CHECKPOINT_DIR  # directory of pretrained checkpoint
+```
+
+Download Stable Diffusion v2 [checkpoint](https://huggingface.co/stabilityai/stable-diffusion-2) into `${BASE_CKPT_DIR}`
+
+Prepare for [Hypersim](https://github.com/apple/ml-hypersim) and [Virtual KITTI 2](https://europe.naverlabs.com/research/computer-vision/proxy-virtual-worlds-vkitti-2/) datasets and save into `${BASE_DATA_DIR}`. Please refer to [this README](script/dataset_preprocess/hypersim/README.md) for Hypersim preprocessing.
+
+Run training script
+
+```bash
+python train.py --config config/train_marigold.yaml
+```
+
+Resume from a checkpoint, e.g.
+
+```bash
+python train.py --resume_run output/marigold_base/checkpoint/latest
+```
+
+Evaluating results
+
+Only the U-Net is updated and saved during training. To use the inference pipeline with your training result, replace `unet` folder in Marigold checkpoints with that in the `checkpoint` output folder. Then refer to [this section](#evaluation) for evaluation.
+
+**Note**: Although random seeds have been set, the training result might be slightly different on different hardwares. It's recommended to train without interruption.
+
+## ✏️ Contributing
+
+Please refer to [this](CONTRIBUTING.md) instruction.
+
+## 🤔 Troubleshooting
+
+| Problem                                                                                                                                      | Solution                                                       |
+|----------------------------------------------------------------------------------------------------------------------------------------------|----------------------------------------------------------------|
+| (Windows) Invalid DOS bash script on WSL                                                                                                     | Run `dos2unix <script_name>` to convert script format          |
+| (Windows) error on WSL: `Could not load library libcudnn_cnn_infer.so.8. Error: libcuda.so: cannot open shared object file: No such file or directory` | Run `export LD_LIBRARY_PATH=/usr/lib/wsl/lib:$LD_LIBRARY_PATH` |
+
+
+## 🎓 Citation
+
+Please cite our paper:
+
+```bibtex
+@InProceedings{ke2023repurposing,
+      title={Repurposing Diffusion-Based Image Generators for Monocular Depth Estimation},
+      author={Bingxin Ke and Anton Obukhov and Shengyu Huang and Nando Metzger and Rodrigo Caye Daudt and Konrad Schindler},
+      booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
+      year={2024}
+}
+```
+
+## 🎫 License
+
+This work is licensed under the Apache License, Version 2.0 (as defined in the [LICENSE](LICENSE.txt)).
+
+By downloading and using the code and model you agree to the terms in the  [LICENSE](LICENSE.txt).
+
+[![License](https://img.shields.io/badge/License-Apache--2.0-929292)](https://www.apache.org/licenses/LICENSE-2.0)

Marigold-main/config/dataset/data_diode_all.yaml

@@ -0,0 +1,4 @@
+name: diode
+disp_name: diode_val_all
+dir: diode/diode_val.tar
+filenames: data_split/diode/diode_val_all_filename_list.txt 

Marigold-main/config/dataset/data_eth3d.yaml

@@ -0,0 +1,4 @@
+name: eth3d
+disp_name: eth3d_full
+dir: eth3d/eth3d.tar
+filenames: data_split/eth3d/eth3d_filename_list.txt

Marigold-main/config/dataset/data_hypersim_train.yaml

@@ -0,0 +1,4 @@
+name: hypersim
+disp_name: hypersim_train
+dir: hypersim/hypersim_processed_train.tar
+filenames: data_split/hypersim/filename_list_train_filtered.txt

Marigold-main/config/dataset/data_hypersim_val.yaml

@@ -0,0 +1,4 @@
+name: hypersim
+disp_name: hypersim_val
+dir: hypersim/hypersim_processed_val.tar
+filenames: data_split/hypersim/filename_list_val_filtered.txt

Marigold-main/config/dataset/data_kitti_eigen_test.yaml

@@ -0,0 +1,6 @@
+name: kitti
+disp_name: kitti_eigen_test_full
+dir: kitti/kitti_eigen_split_test.tar
+filenames: data_split/kitti/eigen_test_files_with_gt.txt
+kitti_bm_crop: true
+valid_mask_crop: eigen

Marigold-main/config/dataset/data_kitti_val.yaml

@@ -0,0 +1,6 @@
+name: kitti
+disp_name: kitti_val800_from_eigen_train
+dir: kitti/kitti_sampled_val_800.tar
+filenames: data_split/kitti/eigen_val_from_train_800.txt
+kitti_bm_crop: true
+valid_mask_crop: eigen

Marigold-main/config/dataset/data_nyu_test.yaml

@@ -0,0 +1,5 @@
+name: nyu_v2
+disp_name: nyu_test_full
+dir: nyuv2/nyu_labeled_extracted.tar
+filenames: data_split/nyu/labeled/filename_list_test.txt
+eigen_valid_mask: true

Marigold-main/config/dataset/data_nyu_train.yaml

@@ -0,0 +1,5 @@
+name: nyu_v2
+disp_name: nyu_train_full
+dir: nyuv2/nyu_labeled_extracted.tar
+filenames: data_split/nyu/labeled/filename_list_train.txt
+eigen_valid_mask: true

Marigold-main/config/dataset/data_scannet_val.yaml

@@ -0,0 +1,4 @@
+name: scannet
+disp_name: scannet_val_800_1
+dir: scannet/scannet_val_sampled_800_1.tar
+filenames: data_split/scannet/scannet_val_sampled_list_800_1.txt

Marigold-main/config/dataset/data_vkitti_train.yaml

@@ -0,0 +1,6 @@
+name: vkitti
+disp_name: vkitti_train
+dir: vkitti/vkitti.tar
+filenames: data_split/vkitti/vkitti_train.txt
+kitti_bm_crop: true
+valid_mask_crop: null  # no valid_mask_crop for training

Marigold-main/config/dataset/data_vkitti_val.yaml

@@ -0,0 +1,6 @@
+name: vkitti
+disp_name: vkitti_val
+dir: vkitti/vkitti.tar
+filenames: data_split/vkitti/vkitti_val.txt
+kitti_bm_crop: true
+valid_mask_crop: eigen

Marigold-main/config/dataset/dataset_train.yaml

@@ -0,0 +1,18 @@
+dataset:
+  train:
+    name: mixed
+    prob_ls: [0.9, 0.1]
+    dataset_list:
+    - name: hypersim
+      disp_name: hypersim_train
+      dir: hypersim/hypersim_processed_train.tar
+      filenames: data_split/hypersim/filename_list_train_filtered.txt
+      resize_to_hw:
+      - 480
+      - 640
+    - name: vkitti
+      disp_name: vkitti_train
+      dir: vkitti/vkitti.tar
+      filenames: data_split/vkitti/vkitti_train.txt
+      kitti_bm_crop: true
+      valid_mask_crop: null

Marigold-main/config/dataset/dataset_val.yaml

@@ -0,0 +1,45 @@
+dataset:
+  val:
+  # - name: hypersim
+  #   disp_name: hypersim_val
+  #   dir: hypersim/hypersim_processed_val.tar
+  #   filenames: data_split/hypersim/filename_list_val_filtered.txt
+  #   resize_to_hw:
+  #   - 480
+  #   - 640
+
+  # - name: nyu_v2
+  #   disp_name: nyu_train_full
+  #   dir: nyuv2/nyu_labeled_extracted.tar
+  #   filenames: data_split/nyu/labeled/filename_list_train.txt
+  #   eigen_valid_mask: true
+
+  # - name: kitti
+  #   disp_name: kitti_val800_from_eigen_train
+  #   dir: kitti/kitti_sampled_val_800.tar
+  #   filenames: data_split/kitti/eigen_val_from_train_800.txt
+  #   kitti_bm_crop: true
+  #   valid_mask_crop: eigen
+
+  # Smaller subsets for faster validation during training
+  # The first dataset is used to calculate main eval metric.
+  - name: hypersim
+    disp_name: hypersim_val_small_80
+    dir: hypersim/hypersim_processed_val.tar
+    filenames: data_split/hypersim/filename_list_val_filtered_small_80.txt
+    resize_to_hw:
+    - 480
+    - 640
+
+  - name: nyu_v2
+    disp_name: nyu_train_small_100
+    dir: nyuv2/nyu_labeled_extracted.tar
+    filenames: data_split/nyu/labeled/filename_list_train_small_100.txt
+    eigen_valid_mask: true
+
+  - name: kitti
+    disp_name: kitti_val_from_train_sub_100
+    dir: kitti/kitti_sampled_val_800.tar
+    filenames: data_split/kitti/eigen_val_from_train_sub_100.txt
+    kitti_bm_crop: true
+    valid_mask_crop: eigen

Marigold-main/config/dataset/dataset_vis.yaml

@@ -0,0 +1,9 @@
+dataset:
+  vis:
+  - name: hypersim
+    disp_name: hypersim_vis
+    dir: hypersim/hypersim_processed_val.tar
+    filenames: data_split/hypersim/selected_vis_sample.txt
+    resize_to_hw:
+    - 480
+    - 640

Marigold-main/config/logging.yaml

@@ -0,0 +1,5 @@
+logging:
+  filename: logging.log
+  format: ' %(asctime)s - %(levelname)s -%(filename)s - %(funcName)s >> %(message)s'
+  console_level: 20
+  file_level: 10

Marigold-main/config/model_sdv2.yaml

@@ -0,0 +1,4 @@
+model:
+  name: marigold_pipeline
+  pretrained_path: stable-diffusion-2
+  latent_scale_factor: 0.18215

Marigold-main/config/train_debug.yaml

@@ -0,0 +1,12 @@
+base_config:
+- config/train_marigold.yaml
+
+
+# Training settings
+trainer:
+  save_period: 5
+  backup_period: 10
+  validation_period: 5
+  visualization_period: 5
+
+max_iter: 50

Marigold-main/config/train_marigold.yaml

@@ -0,0 +1,94 @@
+base_config:
+- config/logging.yaml
+- config/wandb.yaml
+- config/dataset/dataset_train.yaml
+- config/dataset/dataset_val.yaml
+- config/dataset/dataset_vis.yaml
+- config/model_sdv2.yaml
+
+
+pipeline:
+  name: MarigoldPipeline
+  kwargs:
+    scale_invariant: true
+    shift_invariant: true
+
+depth_normalization:
+  type: scale_shift_depth
+  clip: true
+  norm_min: -1.0
+  norm_max: 1.0
+  min_max_quantile: 0.02
+
+augmentation:
+  lr_flip_p: 0.5
+
+dataloader:
+  num_workers: 2
+  effective_batch_size: 32
+  max_train_batch_size: 2
+  seed: 2024  # to ensure continuity when resuming from checkpoint
+
+# Training settings
+trainer:
+  name: MarigoldTrainer
+  training_noise_scheduler:
+    pretrained_path: stable-diffusion-2
+  init_seed: 2024  # use null to train w/o seeding
+  save_period: 50
+  backup_period: 2000
+  validation_period: 2000
+  visualization_period: 2000
+
+multi_res_noise:
+  strength: 0.9
+  annealed: true
+  downscale_strategy: original
+
+gt_depth_type: depth_raw_norm
+gt_mask_type: valid_mask_raw
+
+max_epoch: 10000  # a large enough number
+max_iter: 30000  # usually converges at around 20k
+
+optimizer:
+  name: Adam
+
+loss:
+  name: mse_loss
+  kwargs:
+    reduction: mean
+
+lr: 3.0e-05
+lr_scheduler:
+  name: IterExponential
+  kwargs:
+    total_iter: 25000
+    final_ratio: 0.01
+    warmup_steps: 100
+
+# Validation (and visualization) settings
+validation:
+  denoising_steps: 50
+  ensemble_size: 1  # simplified setting for on-training validation
+  processing_res: 0
+  match_input_res: false
+  resample_method: bilinear
+  main_val_metric: abs_relative_difference
+  main_val_metric_goal: minimize
+  init_seed: 2024
+
+eval:
+  alignment: least_square
+  align_max_res: null
+  eval_metrics:
+  - abs_relative_difference
+  - squared_relative_difference
+  - rmse_linear
+  - rmse_log
+  - log10
+  - delta1_acc
+  - delta2_acc
+  - delta3_acc
+  - i_rmse
+  - silog_rmse

Marigold-main/config/wandb.yaml

@@ -0,0 +1,3 @@
+wandb:
+  # entity: your_entity
+  project: marigold

Marigold-main/data_split/diode/diode_val_indoor_filename_list.txt

@@ -0,0 +1,325 @@
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Marigold-main/data_split/hypersim/filename_list_val_filtered_small_80.txt

@@ -0,0 +1,80 @@
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Marigold-main/data_split/hypersim/selected_vis_sample.txt

@@ -0,0 +1,3 @@
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Marigold-main/data_split/kitti/eigen_val_from_train_sub_100.txt

@@ -0,0 +1,100 @@
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Marigold-main/environment.yaml

@@ -0,0 +1,16 @@
+name: marigold
+channels:
+  - conda-forge
+  - nvidia
+  - pytorch
+dependencies:
+  - accelerate>=0.22.0
+  - diffusers>=0.25.0
+  - matplotlib>=3.8.2
+  - scipy>=1.11.4
+  - python=3.10.12
+  - pytorch=2.2.1
+  - pytorch-cuda=11.7
+  - torchvision=0.15.2
+  - pip:
+    - transformers==4.32.1

Marigold-main/eval.py

@@ -0,0 +1,248 @@
+# Last modified: 2024-03-11
+# Copyright 2023 Bingxin Ke, ETH Zurich. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# --------------------------------------------------------------------------
+# If you find this code useful, we kindly ask you to cite our paper in your work.
+# Please find bibtex at: https://github.com/prs-eth/Marigold#-citation
+# More information about the method can be found at https://marigoldmonodepth.github.io
+# --------------------------------------------------------------------------
+
+import argparse
+import logging
+import os
+
+import numpy as np
+import torch
+from omegaconf import OmegaConf
+from tabulate import tabulate
+from torch.utils.data import DataLoader
+from tqdm.auto import tqdm
+
+from src.dataset import (
+    BaseDepthDataset,
+    DatasetMode,
+    get_dataset,
+    get_pred_name,
+)
+from src.util import metric
+from src.util.alignment import (
+    align_depth_least_square,
+    depth2disparity,
+    disparity2depth,
+)
+from src.util.metric import MetricTracker
+
+eval_metrics = [
+    "abs_relative_difference",
+    "squared_relative_difference",
+    "rmse_linear",
+    "rmse_log",
+    "log10",
+    "delta1_acc",
+    "delta2_acc",
+    "delta3_acc",
+    "i_rmse",
+    "silog_rmse",
+]
+
+if "__main__" == __name__:
+    parser = argparse.ArgumentParser()
+    # input
+    parser.add_argument(
+        "--prediction_dir",
+        type=str,
+        required=True,
+        help="Directory of depth predictions",
+    )
+    parser.add_argument(
+        "--output_dir", type=str, required=True, help="Output directory."
+    )
+
+    # dataset setting
+    parser.add_argument(
+        "--dataset_config",
+        type=str,
+        required=True,
+        help="Path to config file of evaluation dataset.",
+    )
+    parser.add_argument(
+        "--base_data_dir",
+        type=str,
+        required=True,
+        help="Path to base data directory.",
+    )
+
+    # LS depth alignment
+    parser.add_argument(
+        "--alignment",
+        choices=[None, "least_square", "least_square_disparity"],
+        default=None,
+        help="Method to estimate scale and shift between predictions and ground truth.",
+    )
+    parser.add_argument(
+        "--alignment_max_res",
+        type=int,
+        default=None,
+        help="Max operating resolution used for LS alignment",
+    )
+
+    parser.add_argument("--no_cuda", action="store_true", help="Run without cuda")
+
+    args = parser.parse_args()
+
+    prediction_dir = args.prediction_dir
+    output_dir = args.output_dir
+
+    dataset_config = args.dataset_config
+    base_data_dir = args.base_data_dir
+
+    alignment = args.alignment
+    alignment_max_res = args.alignment_max_res
+
+    no_cuda = args.no_cuda
+    pred_suffix = ".npy"
+
+    os.makedirs(output_dir, exist_ok=True)
+
+    # -------------------- Device --------------------
+    cuda_avail = torch.cuda.is_available() and not no_cuda
+    device = torch.device("cuda" if cuda_avail else "cpu")
+    logging.info(f"Device: {device}")
+
+    # -------------------- Data --------------------
+    cfg_data = OmegaConf.load(dataset_config)
+
+    dataset: BaseDepthDataset = get_dataset(
+        cfg_data, base_data_dir=base_data_dir, mode=DatasetMode.EVAL
+    )
+
+    dataloader = DataLoader(dataset, batch_size=1, num_workers=0)
+
+    # -------------------- Eval metrics --------------------
+    metric_funcs = [getattr(metric, _met) for _met in eval_metrics]
+
+    metric_tracker = MetricTracker(*[m.__name__ for m in metric_funcs])
+    metric_tracker.reset()
+
+    # -------------------- Per-sample metric file head --------------------
+    per_sample_filename = os.path.join(output_dir, "per_sample_metrics.csv")
+    # write title
+    with open(per_sample_filename, "w+") as f:
+        f.write("filename,")
+        f.write(",".join([m.__name__ for m in metric_funcs]))
+        f.write("\n")
+
+    # -------------------- Evaluate --------------------
+    for data in tqdm(dataloader, desc="Evaluating"):
+        # GT data
+        depth_raw_ts = data["depth_raw_linear"].squeeze()
+        valid_mask_ts = data["valid_mask_raw"].squeeze()
+        rgb_name = data["rgb_relative_path"][0]
+
+        depth_raw = depth_raw_ts.numpy()
+        valid_mask = valid_mask_ts.numpy()
+
+        depth_raw_ts = depth_raw_ts.to(device)
+        valid_mask_ts = valid_mask_ts.to(device)
+
+        # Load predictions
+        rgb_basename = os.path.basename(rgb_name)
+        pred_basename = get_pred_name(
+            rgb_basename, dataset.name_mode, suffix=pred_suffix
+        )
+        pred_name = os.path.join(os.path.dirname(rgb_name), pred_basename)
+        pred_path = os.path.join(prediction_dir, pred_name)
+        depth_pred = np.load(pred_path)
+
+        if not os.path.exists(pred_path):
+            logging.warn(f"Can't find prediction: {pred_path}")
+            continue
+
+        # Align with GT using least square
+        if "least_square" == alignment:
+            depth_pred, scale, shift = align_depth_least_square(
+                gt_arr=depth_raw,
+                pred_arr=depth_pred,
+                valid_mask_arr=valid_mask,
+                return_scale_shift=True,
+                max_resolution=alignment_max_res,
+            )
+        elif "least_square_disparity" == alignment:
+            # convert GT depth -> GT disparity
+            gt_disparity, gt_non_neg_mask = depth2disparity(
+                depth=depth_raw, return_mask=True
+            )
+            # LS alignment in disparity space
+            pred_non_neg_mask = depth_pred > 0
+            valid_nonnegative_mask = valid_mask & gt_non_neg_mask & pred_non_neg_mask
+
+            disparity_pred, scale, shift = align_depth_least_square(
+                gt_arr=gt_disparity,
+                pred_arr=depth_pred,
+                valid_mask_arr=valid_nonnegative_mask,
+                return_scale_shift=True,
+                max_resolution=alignment_max_res,
+            )
+            # convert to depth
+            disparity_pred = np.clip(
+                disparity_pred, a_min=1e-3, a_max=None
+            )  # avoid 0 disparity
+            depth_pred = disparity2depth(disparity_pred)
+
+        # Clip to dataset min max
+        depth_pred = np.clip(
+            depth_pred, a_min=dataset.min_depth, a_max=dataset.max_depth
+        )
+
+        # clip to d > 0 for evaluation
+        depth_pred = np.clip(depth_pred, a_min=1e-6, a_max=None)
+
+        # Evaluate (using CUDA if available)
+        sample_metric = []
+        depth_pred_ts = torch.from_numpy(depth_pred).to(device)
+
+        for met_func in metric_funcs:
+            _metric_name = met_func.__name__
+            _metric = met_func(depth_pred_ts, depth_raw_ts, valid_mask_ts).item()
+            sample_metric.append(_metric.__str__())
+            metric_tracker.update(_metric_name, _metric)
+
+        # Save per-sample metric
+        with open(per_sample_filename, "a+") as f:
+            f.write(pred_name + ",")
+            f.write(",".join(sample_metric))
+            f.write("\n")
+
+    # -------------------- Save metrics to file --------------------
+    eval_text = f"Evaluation metrics:\n\
+    of predictions: {prediction_dir}\n\
+    on dataset: {dataset.disp_name}\n\
+    with samples in: {dataset.filename_ls_path}\n"
+
+    eval_text += f"min_depth = {dataset.min_depth}\n"
+    eval_text += f"max_depth = {dataset.max_depth}\n"
+
+    eval_text += tabulate(
+        [metric_tracker.result().keys(), metric_tracker.result().values()]
+    )
+
+    metrics_filename = "eval_metrics"
+    if alignment:
+        metrics_filename += f"-{alignment}"
+    metrics_filename += ".txt"
+
+    _save_to = os.path.join(output_dir, metrics_filename)
+    with open(_save_to, "w+") as f:
+        f.write(eval_text)
+        logging.info(f"Evaluation metrics saved to {_save_to}")

Marigold-main/infer.py

@@ -0,0 +1,258 @@
+# Last modified: 2024-05-24
+# Copyright 2023 Bingxin Ke, ETH Zurich. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# --------------------------------------------------------------------------
+# If you find this code useful, we kindly ask you to cite our paper in your work.
+# Please find bibtex at: https://github.com/prs-eth/Marigold#-citation
+# More information about the method can be found at https://marigoldmonodepth.github.io
+# --------------------------------------------------------------------------
+
+
+import argparse
+import logging
+import os
+
+import numpy as np
+import torch
+from omegaconf import OmegaConf
+from PIL import Image
+from torch.utils.data import DataLoader
+from tqdm.auto import tqdm
+
+from marigold import MarigoldPipeline
+from src.util.seeding import seed_all
+from src.dataset import (
+    BaseDepthDataset,
+    DatasetMode,
+    get_dataset,
+    get_pred_name,
+)
+
+if "__main__" == __name__:
+    logging.basicConfig(level=logging.INFO)
+
+    # -------------------- Arguments --------------------
+    parser = argparse.ArgumentParser(
+        description="Run single-image depth estimation using Marigold."
+    )
+    parser.add_argument(
+        "--checkpoint",
+        type=str,
+        default="prs-eth/marigold-v1-0",
+        help="Checkpoint path or hub name.",
+    )
+
+    # dataset setting
+    parser.add_argument(
+        "--dataset_config",
+        type=str,
+        required=True,
+        help="Path to config file of evaluation dataset.",
+    )
+    parser.add_argument(
+        "--base_data_dir",
+        type=str,
+        required=True,
+        help="Path to base data directory.",
+    )
+
+    parser.add_argument(
+        "--output_dir", type=str, required=True, help="Output directory."
+    )
+
+    # inference setting
+    parser.add_argument(
+        "--denoise_steps",
+        type=int,
+        default=50,  # quantitative evaluation uses 50 steps
+        help="Diffusion denoising steps, more steps results in higher accuracy but slower inference speed.",
+    )
+    parser.add_argument(
+        "--ensemble_size",
+        type=int,
+        default=10,
+        help="Number of predictions to be ensembled, more inference gives better results but runs slower.",
+    )
+    parser.add_argument(
+        "--half_precision",
+        "--fp16",
+        action="store_true",
+        help="Run with half-precision (16-bit float), might lead to suboptimal result.",
+    )
+
+    # resolution setting
+    parser.add_argument(
+        "--processing_res",
+        type=int,
+        default=0,
+        help="Maximum resolution of processing. 0 for using input image resolution. Default: 0.",
+    )
+    parser.add_argument(
+        "--output_processing_res",
+        action="store_true",
+        help="When input is resized, out put depth at resized operating resolution. Default: False.",
+    )
+    parser.add_argument(
+        "--resample_method",
+        type=str,
+        default="bilinear",
+        help="Resampling method used to resize images. This can be one of 'bilinear' or 'nearest'.",
+    )
+
+    parser.add_argument("--seed", type=int, default=None, help="Random seed.")
+
+    args = parser.parse_args()
+
+    checkpoint_path = args.checkpoint
+    dataset_config = args.dataset_config
+    base_data_dir = args.base_data_dir
+    output_dir = args.output_dir
+
+    denoise_steps = args.denoise_steps
+    ensemble_size = args.ensemble_size
+    if ensemble_size > 15:
+        logging.warning("Running with large ensemble size will be slow.")
+    half_precision = args.half_precision
+
+    processing_res = args.processing_res
+    match_input_res = not args.output_processing_res
+    if 0 == processing_res and match_input_res is False:
+        logging.warning(
+            "Processing at native resolution without resizing output might NOT lead to exactly the same resolution, due to the padding and pooling properties of conv layers."
+        )
+    resample_method = args.resample_method
+
+    seed = args.seed
+
+    print(f"arguments: {args}")
+
+    # -------------------- Preparation --------------------
+    # Print out config
+    logging.info(
+        f"Inference settings: checkpoint = `{checkpoint_path}`, "
+        f"with denoise_steps = {denoise_steps}, ensemble_size = {ensemble_size}, "
+        f"processing resolution = {processing_res}, seed = {seed}; "
+        f"dataset config = `{dataset_config}`."
+    )
+
+    # Random seed
+    if seed is None:
+        import time
+
+        seed = int(time.time())
+    seed_all(seed)
+
+    def check_directory(directory):
+        if os.path.exists(directory):
+            response = (
+                input(
+                    f"The directory '{directory}' already exists. Are you sure to continue? (y/n): "
+                )
+                .strip()
+                .lower()
+            )
+            if "y" == response:
+                pass
+            elif "n" == response:
+                print("Exiting...")
+                exit()
+            else:
+                print("Invalid input. Please enter 'y' (for Yes) or 'n' (for No).")
+                check_directory(directory)  # Recursive call to ask again
+
+    check_directory(output_dir)
+    os.makedirs(output_dir, exist_ok=True)
+    logging.info(f"output dir = {output_dir}")
+
+    # -------------------- Device --------------------
+    if torch.cuda.is_available():
+        device = torch.device("cuda")
+    else:
+        device = torch.device("cpu")
+        logging.warning("CUDA is not available. Running on CPU will be slow.")
+    logging.info(f"device = {device}")
+
+    # -------------------- Data --------------------
+    cfg_data = OmegaConf.load(dataset_config)
+
+    dataset: BaseDepthDataset = get_dataset(
+        cfg_data, base_data_dir=base_data_dir, mode=DatasetMode.RGB_ONLY
+    )
+
+    dataloader = DataLoader(dataset, batch_size=1, num_workers=0)
+
+    # -------------------- Model --------------------
+    if half_precision:
+        dtype = torch.float16
+        variant = "fp16"
+        logging.warning(
+            f"Running with half precision ({dtype}), might lead to suboptimal result."
+        )
+    else:
+        dtype = torch.float32
+        variant = None
+
+    pipe = MarigoldPipeline.from_pretrained(
+        checkpoint_path, variant=variant, torch_dtype=dtype
+    )
+
+    try:
+        pipe.enable_xformers_memory_efficient_attention()
+    except ImportError:
+        logging.debug("run without xformers")
+
+    pipe = pipe.to(device)
+    logging.info(
+        f"scale_invariant: {pipe.scale_invariant}, shift_invariant: {pipe.shift_invariant}"
+    )
+
+    # -------------------- Inference and saving --------------------
+    with torch.no_grad():
+        for batch in tqdm(
+            dataloader, desc=f"Inferencing on {dataset.disp_name}", leave=True
+        ):
+            # Read input image
+            rgb_int = batch["rgb_int"].squeeze().numpy().astype(np.uint8)  # [3, H, W]
+            rgb_int = np.moveaxis(rgb_int, 0, -1)  # [H, W, 3]
+            input_image = Image.fromarray(rgb_int)
+
+            # Predict depth
+            pipe_out = pipe(
+                input_image,
+                denoising_steps=denoise_steps,
+                ensemble_size=ensemble_size,
+                processing_res=processing_res,
+                match_input_res=match_input_res,
+                batch_size=0,
+                color_map=None,
+                show_progress_bar=False,
+                resample_method=resample_method,
+            )
+
+            depth_pred: np.ndarray = pipe_out.depth_np
+
+            # Save predictions
+            rgb_filename = batch["rgb_relative_path"][0]
+            rgb_basename = os.path.basename(rgb_filename)
+            scene_dir = os.path.join(output_dir, os.path.dirname(rgb_filename))
+            if not os.path.exists(scene_dir):
+                os.makedirs(scene_dir)
+            pred_basename = get_pred_name(
+                rgb_basename, dataset.name_mode, suffix=".npy"
+            )
+            save_to = os.path.join(scene_dir, pred_basename)
+            if os.path.exists(save_to):
+                logging.warning(f"Existing file: '{save_to}' will be overwritten")
+
+            np.save(save_to, depth_pred)