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.gitignore

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+
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+
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+# Paket dependency manager
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+# FAKE - F# Make
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+# CodeRush personal settings
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+# Python Tools for Visual Studio (PTVS)
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+# Cake - Uncomment if you are using it
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+# Tabs Studio
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+*.xsd.cs
+
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+
+# MSBuild Binary and Structured Log
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+# MFractors (Xamarin productivity tool) working folder
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+# Local History for Visual Studio
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+
+# BeatPulse healthcheck temp database
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+
+# Backup folder for Package Reference Convert tool in Visual Studio 2017
+MigrationBackup/
+
+# Ionide (cross platform F# VS Code tools) working folder
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+
+# Fody - auto-generated XML schema
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+
+# VS Code files for those working on multiple tools
+.vscode/*
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+*.code-workspace
+
+# Local History for Visual Studio Code
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+
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+*.msm
+*.msp
+
+# JetBrains Rider
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+
+# Python
+*.egg-info/
+/build
+
+# MoGe 
+/data*
+/download
+/extract
+/debug
+/workspace
+/mlruns
+/infer_output
+/video_output
+/eval_output
+/.blobcache
+/test_images
+/test_videos
+/vis
+/videos
+/blobmnt
+/eval_dump
+/pretrained
+/.gradio
+/tmp

CHANGELOG.md

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+## 2024-11-28
+### Added
+- Supported user-provided camera FOV. See [scripts/infer.py](scripts/infer.py) --fov_x. 
+  - Related issues: [#25](https://github.com/microsoft/MoGe/issues/25) and [#24](https://github.com/microsoft/MoGe/issues/24).
+- Added inference scripts for panorama images. See [scripts/infer_panorama.py](scripts/infer_panorama.py).
+  - Related issue: [#19](https://github.com/microsoft/MoGe/issues/19).
+
+### Fixed
+- Suppressed unnecessary numpy runtime warnings.
+- Specified recommended versions of requirements.
+  - Related issue: [#21](https://github.com/microsoft/MoGe/issues/21).
+
+### Changed
+- Moved `app.py` and `infer.py` to [scripts/](scripts/)
+- Improved edge removal. 
+
+## 2025-03-18
+### Added
+- Training and evaluation code. See [docs/train.md](docs/train.md) and [docs/eval.md](docs/eval.md).
+- Supported installation via pip. Thanks to @fabiencastan and @jgoueslard
+ for commits in the [#47](https://github.com/microsoft/MoGe/pull/47)
+- Supported command-line usage when installed.
+
+### Changed
+- Moved `scripts/` into `moge/` for package installation and command-line usage.
+- Renamed `moge.model.moge_model` to `moge.model.v1` for version management. 
+  Now you can import the model class through `from moge.model.v1 import MoGeModel` or `from moge.model import import_model_class_by_version; MoGeModel = import_model_class_by_version('v1')`.
+- Exposed `num_tokens` parameter in MoGe model.
+
+## 2025-06-10
+### Added
+- Released MoGe-2. 
+
+## 2025-10-16
+### Added
+- Update training code for MoGe-2.
+
+### Changed
+- Refactored training dataloader code for better readability.
+- Removed Git LFS for convenience.

CODE_OF_CONDUCT.md

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+# Microsoft Open Source Code of Conduct
+
+This project has adopted the [Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct/).
+
+Resources:
+
+- [Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct/)
+- [Microsoft Code of Conduct FAQ](https://opensource.microsoft.com/codeofconduct/faq/)
+- Contact [opencode@microsoft.com](mailto:opencode@microsoft.com) with questions or concerns

LICENSE

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README.md

@@ -0,0 +1,295 @@
+# MoGe: Accurate Monocular Geometry Estimation
+
+MoGe is a powerful model for recovering 3D geometry from monocular open-domain images, including metric point maps, metric depth maps, normal maps and camera FOV. ***Check our websites ([MoGe-1](https://wangrc.site/MoGePage), [MoGe-2](https://wangrc.site/MoGe2Page)) for videos and interactive results!***
+
+## 📖 Publications
+
+### MoGe-2: Accurate Monocular Geometry with Metric Scale and Sharp Details
+
+<div align="center">
+  <a href="https://arxiv.org/abs/2507.02546"><img src='https://img.shields.io/badge/arXiv-Paper-red?logo=arxiv&logoColor=white' alt='arXiv'></a>
+  <a href='https://wangrc.site/MoGe2Page/'><img src='https://img.shields.io/badge/Project_Page-Website-green?logo=googlechrome&logoColor=white' alt='Project Page'></a>
+  <a href='https://huggingface.co/spaces/Ruicheng/MoGe-2'><img src='https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Demo_(MoGe_v2)-blue'></a>
+
+https://github.com/user-attachments/assets/8f9ae680-659d-4f7f-82e2-b9ed9d6b988a
+
+</div>
+
+### MoGe: Unlocking Accurate Monocular Geometry Estimation for Open-Domain Images with Optimal Training Supervision
+
+<div align="center">
+  <a href="https://arxiv.org/abs/2410.19115"><img src='https://img.shields.io/badge/arXiv-Paper-red?logo=arxiv&logoColor=white' alt='arXiv'></a>
+  <a href='https://wangrc.site/MoGePage/'><img src='https://img.shields.io/badge/Project_Page-Website-green?logo=googlechrome&logoColor=white' alt='Project Page'></a>
+  <a href='https://huggingface.co/spaces/Ruicheng/MoGe'><img src='https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Demo_(MoGe_v1)-blue'></a>
+</div>
+
+<img src="./assets/overview_simplified.png" width="100%" alt="Method overview" align="center">
+
+
+## 🌟 Features
+
+* **Accurate 3D geometry estimation**: Estimate point maps & depth maps & [normal maps](docs/normal.md) from open-domain single images with high precision -- all capabilities in one model, one forward pass.
+* **Optional ground-truth FOV input**: Enhance model accuracy further by providing the true field of view.
+* **Flexible resolution support**: Works seamlessly with various resolutions and aspect ratios, from 2:1 to 1:2.
+* **Optimized for speed**: Achieves 60ms latency per image (A100 or RTX3090, FP16, ViT-L). Adjustable inference resolution for even faster speed.
+
+## ✨ News
+
+***(2025-10-16)***
+* Updated training code for MoGe-2.
+
+***(2025-06-10)***
+
+* ❗**Released MoGe-2**, a state-of-the-art model for monocular geometry, with these new capabilities in one unified model:
+  * point map prediction in **metric scale**;
+  * comparable and even better performance over MoGe-1;
+  * significant improvement of **visual sharpness**;
+  * high-quality [**normal map** estimation](docs/normal.md);
+  * lower inference latency.
+
+## 📦 Installation
+
+### Install via pip
+  
+```bash
+pip install git+https://github.com/microsoft/MoGe.git
+```
+
+### Or clone this repository
+
+```bash
+git clone https://github.com/microsoft/MoGe.git
+cd MoGe
+pip install -r requirements.txt   # install the requirements
+```
+
+Note: MoGe should be compatible with most requirements versions. Please check the `requirements.txt` for more details if you encounter any dependency issues.
+
+## 🤗 Pretrained Models
+
+Our pretrained models are available on the huggingface hub:
+
+<table>
+  <thead>
+    <tr>
+      <th>Version</th>
+      <th>Hugging Face Model</th>
+      <th>Metric scale</th>
+      <th>Normal</th>
+      <th>#Params</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <td>MoGe-1</td>
+      <td><a href="https://huggingface.co/Ruicheng/moge-vitl" target="_blank"><code>Ruicheng/moge-vitl</code><a></td>
+      <td>-</td>
+      <td>-</td>
+      <td>314M</td>
+    </tr>
+    <tr>
+      <td rowspan="4">MoGe-2</td>
+      <td><a href="https://huggingface.co/Ruicheng/moge-2-vitl" target="_blank"><code>Ruicheng/moge-2-vitl</code></a></td>
+      <td>✅</td>
+      <td>-</td>
+      <td>326M</td>
+    </tr>
+    <tr>
+      <td><a href="https://huggingface.co/Ruicheng/moge-2-vitl-normal" target="_blank"><code>Ruicheng/moge-2-vitl-normal</code></a></td>
+      <td>✅</td>
+      <td>✅</td>
+      <td>331M</td>
+    </tr>
+    <tr>
+      <td><a href="https://huggingface.co/Ruicheng/moge-2-vitb-normal" target="_blank"><code>Ruicheng/moge-2-vitb-normal</code></a></td>
+      <td>✅</td>
+      <td>✅</td>
+      <td>104M</td>
+    </tr>
+    <tr>
+      <td><a href="https://huggingface.co/Ruicheng/moge-2-vits-normal" target="_blank"><code>Ruicheng/moge-2-vits-normal</code></a></td>
+      <td>✅</td>
+      <td>✅</td>
+      <td>35M</td>
+    </tr>
+  </tbody>
+</table>
+
+
+> NOTE: `moge-2-vitl-normal` has full capabilities, with almost the same level of performance as `moge-2-vitl` plus extra normal map estimation.
+
+You may import the `MoGeModel` class of the matched version, then load the pretrained weights via `MoGeModel.from_pretrained("HUGGING_FACE_MODEL_REPO_NAME")` with automatic downloading.
+If loading a local checkpoint, replace the model name with the local path.
+
+For ONNX support, please refer to [docs/onnx.md](docs/onnx.md).
+
+## 💡 Minimal Code Example 
+
+Here is a minimal example for loading the model and inferring on a single image. 
+
+```python
+import cv2
+import torch
+# from moge.model.v1 import MoGeModel
+from moge.model.v2 import MoGeModel # Let's try MoGe-2
+
+device = torch.device("cuda")
+
+# Load the model from huggingface hub (or load from local).
+model = MoGeModel.from_pretrained("Ruicheng/moge-2-vitl-normal").to(device)                             
+
+# Read the input image and convert to tensor (3, H, W) with RGB values normalized to [0, 1]
+input_image = cv2.cvtColor(cv2.imread("PATH_TO_IMAGE.jpg"), cv2.COLOR_BGR2RGB)                       
+input_image = torch.tensor(input_image / 255, dtype=torch.float32, device=device).permute(2, 0, 1)    
+
+# Infer 
+output = model.infer(input_image)
+"""
+`output` has keys "points", "depth", "mask", "normal" (optional) and "intrinsics",
+The maps are in the same size as the input image. 
+{
+    "points": (H, W, 3),    # point map in OpenCV camera coordinate system (x right, y down, z forward). For MoGe-2, the point map is in metric scale.
+    "depth": (H, W),        # depth map
+    "normal": (H, W, 3)     # normal map in OpenCV camera coordinate system. (available for MoGe-2-normal)
+    "mask": (H, W),         # a binary mask for valid pixels. 
+    "intrinsics": (3, 3),   # normalized camera intrinsics
+}
+"""
+```
+For more usage details, see the `MoGeModel.infer()` docstring.
+
+## 💡 Usage
+
+### Gradio demo | `moge app`
+
+> The demo for MoGe-1 is also available at our [Hugging Face Space](https://huggingface.co/spaces/Ruicheng/MoGe).
+
+```bash
+# Using the command line tool
+moge app        # will run MoGe-2 demo by default.
+
+# In this repo
+python moge/scripts/app.py   # --share for Gradio public sharing
+```
+
+See also [`moge/scripts/app.py`](moge/scripts/app.py) 
+
+
+### Inference | `moge infer`
+
+Run the script `moge/scripts/infer.py` via the following command:
+
+```bash
+# Save the output [maps], [glb] and [ply] files
+moge infer -i IMAGES_FOLDER_OR_IMAGE_PATH --o OUTPUT_FOLDER --maps --glb --ply
+
+# Show the result in a window (requires pyglet < 2.0, e.g. pip install pyglet==1.5.29)
+moge infer -i IMAGES_FOLDER_OR_IMAGE_PATH --o OUTPUT_FOLDER --show
+```
+
+For detailed options, run `moge infer --help`:
+
+```
+Usage: moge infer [OPTIONS]
+
+  Inference script
+
+Options:
+  -i, --input PATH            Input image or folder path. "jpg" and "png" are
+                              supported.
+  --fov_x FLOAT               If camera parameters are known, set the
+                              horizontal field of view in degrees. Otherwise,
+                              MoGe will estimate it.
+  -o, --output PATH           Output folder path
+  --pretrained TEXT           Pretrained model name or path. If not provided,
+                              the corresponding default model will be chosen.
+  --version [v1|v2]           Model version. Defaults to "v2"
+  --device TEXT               Device name (e.g. "cuda", "cuda:0", "cpu").
+                              Defaults to "cuda"
+  --fp16                      Use fp16 precision for much faster inference.
+  --resize INTEGER            Resize the image(s) & output maps to a specific
+                              size. Defaults to None (no resizing).
+  --resolution_level INTEGER  An integer [0-9] for the resolution level for
+                              inference. Higher value means more tokens and
+                              the finer details will be captured, but
+                              inference can be slower. Defaults to 9. Note
+                              that it is irrelevant to the output size, which
+                              is always the same as the input size.
+                              `resolution_level` actually controls
+                              `num_tokens`. See `num_tokens` for more details.
+  --num_tokens INTEGER        number of tokens used for inference. A integer
+                              in the (suggested) range of `[1200, 2500]`.
+                              `resolution_level` will be ignored if
+                              `num_tokens` is provided. Default: None
+  --threshold FLOAT           Threshold for removing edges. Defaults to 0.01.
+                              Smaller value removes more edges. "inf" means no
+                              thresholding.
+  --maps                      Whether to save the output maps (image, point
+                              map, depth map, normal map, mask) and fov.
+  --glb                       Whether to save the output as a.glb file. The
+                              color will be saved as a texture.
+  --ply                       Whether to save the output as a.ply file. The
+                              color will be saved as vertex colors.
+  --show                      Whether show the output in a window. Note that
+                              this requires pyglet<2 installed as required by
+                              trimesh.
+  --help                      Show this message and exit.
+```
+
+See also [`moge/scripts/infer.py`](moge/scripts/infer.py)
+
+### 360° panorama images | `moge infer_panorama` 
+
+> *NOTE: This is an experimental extension of MoGe.*
+
+The script will split the 360-degree panorama image into multiple perspective views and infer on each view separately. 
+The output maps will be combined to produce a panorama depth map and point map. 
+
+Note that the panorama image must have spherical parameterization (e.g., environment maps or equirectangular images). Other formats must be converted to spherical format before using this script. Run `moge infer_panorama --help` for detailed options.
+
+
+<div align="center">
+  <img src="./assets/panorama_pipeline.png" width="80%">
+
+The photo is from [this URL](https://commons.wikimedia.org/wiki/Category:360%C2%B0_panoramas_with_equirectangular_projection#/media/File:Braunschweig_Sankt-%C3%84gidien_Panorama_02.jpg)
+</div>
+
+See also [`moge/scripts/infer_panorama.py`](moge/scripts/infer_panorama.py)
+
+## 🏋️‍♂️ Training & Finetuning
+
+See [docs/train.md](docs/train.md)
+
+## 🧪 Evaluation
+
+See [docs/eval.md](docs/eval.md)
+
+## ⚖️ License
+
+MoGe code is released under the MIT license, except for DINOv2 code in `moge/model/dinov2` which is released by Meta AI under the Apache 2.0 license. 
+See [LICENSE](LICENSE) for more details.
+
+
+## 📜 Citation
+
+If you find our work useful in your research, we gratefully request that you consider citing our paper:
+
+```
+@inproceedings{wang2025moge,
+  title={Moge: Unlocking accurate monocular geometry estimation for open-domain images with optimal training supervision},
+  author={Wang, Ruicheng and Xu, Sicheng and Dai, Cassie and Xiang, Jianfeng and Deng, Yu and Tong, Xin and Yang, Jiaolong},
+  booktitle={Proceedings of the Computer Vision and Pattern Recognition Conference},
+  pages={5261--5271},
+  year={2025}
+}
+
+@misc{wang2025moge2,
+      title={MoGe-2: Accurate Monocular Geometry with Metric Scale and Sharp Details}, 
+      author={Ruicheng Wang and Sicheng Xu and Yue Dong and Yu Deng and Jianfeng Xiang and Zelong Lv and Guangzhong Sun and Xin Tong and Jiaolong Yang},
+      year={2025},
+      eprint={2507.02546},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV},
+      url={https://arxiv.org/abs/2507.02546}, 
+}
+```

SECURITY.md

@@ -0,0 +1,41 @@
+<!-- BEGIN MICROSOFT SECURITY.MD V0.0.9 BLOCK -->
+
+## Security
+
+Microsoft takes the security of our software products and services seriously, which includes all source code repositories managed through our GitHub organizations, which include [Microsoft](https://github.com/Microsoft), [Azure](https://github.com/Azure), [DotNet](https://github.com/dotnet), [AspNet](https://github.com/aspnet) and [Xamarin](https://github.com/xamarin).
+
+If you believe you have found a security vulnerability in any Microsoft-owned repository that meets [Microsoft's definition of a security vulnerability](https://aka.ms/security.md/definition), please report it to us as described below.
+
+## Reporting Security Issues
+
+**Please do not report security vulnerabilities through public GitHub issues.**
+
+Instead, please report them to the Microsoft Security Response Center (MSRC) at [https://msrc.microsoft.com/create-report](https://aka.ms/security.md/msrc/create-report).
+
+If you prefer to submit without logging in, send email to [secure@microsoft.com](mailto:secure@microsoft.com).  If possible, encrypt your message with our PGP key; please download it from the [Microsoft Security Response Center PGP Key page](https://aka.ms/security.md/msrc/pgp).
+
+You should receive a response within 24 hours. If for some reason you do not, please follow up via email to ensure we received your original message. Additional information can be found at [microsoft.com/msrc](https://www.microsoft.com/msrc). 
+
+Please include the requested information listed below (as much as you can provide) to help us better understand the nature and scope of the possible issue:
+
+  * Type of issue (e.g. buffer overflow, SQL injection, cross-site scripting, etc.)
+  * Full paths of source file(s) related to the manifestation of the issue
+  * The location of the affected source code (tag/branch/commit or direct URL)
+  * Any special configuration required to reproduce the issue
+  * Step-by-step instructions to reproduce the issue
+  * Proof-of-concept or exploit code (if possible)
+  * Impact of the issue, including how an attacker might exploit the issue
+
+This information will help us triage your report more quickly.
+
+If you are reporting for a bug bounty, more complete reports can contribute to a higher bounty award. Please visit our [Microsoft Bug Bounty Program](https://aka.ms/security.md/msrc/bounty) page for more details about our active programs.
+
+## Preferred Languages
+
+We prefer all communications to be in English.
+
+## Policy
+
+Microsoft follows the principle of [Coordinated Vulnerability Disclosure](https://aka.ms/security.md/cvd).
+
+<!-- END MICROSOFT SECURITY.MD BLOCK -->

SUPPORT.md

@@ -0,0 +1,25 @@
+# TODO: The maintainer of this repo has not yet edited this file
+
+**REPO OWNER**: Do you want Customer Service & Support (CSS) support for this product/project?
+
+- **No CSS support:** Fill out this template with information about how to file issues and get help.
+- **Yes CSS support:** Fill out an intake form at [aka.ms/onboardsupport](https://aka.ms/onboardsupport). CSS will work with/help you to determine next steps.
+- **Not sure?** Fill out an intake as though the answer were "Yes". CSS will help you decide.
+
+*Then remove this first heading from this SUPPORT.MD file before publishing your repo.*
+
+# Support
+
+## How to file issues and get help  
+
+This project uses GitHub Issues to track bugs and feature requests. Please search the existing 
+issues before filing new issues to avoid duplicates.  For new issues, file your bug or 
+feature request as a new Issue.
+
+For help and questions about using this project, please **REPO MAINTAINER: INSERT INSTRUCTIONS HERE 
+FOR HOW TO ENGAGE REPO OWNERS OR COMMUNITY FOR HELP. COULD BE A STACK OVERFLOW TAG OR OTHER
+CHANNEL. WHERE WILL YOU HELP PEOPLE?**.
+
+## Microsoft Support Policy  
+
+Support for this **PROJECT or PRODUCT** is limited to the resources listed above.

baselines/da2_custom.py

@@ -0,0 +1,125 @@
+# DAv2 with custom trained DPT/SDT checkpoint
+import os
+import sys
+from typing import *
+from pathlib import Path
+
+import click
+import torch
+import torch.nn.functional as F
+import torchvision.transforms as T
+import torchvision.transforms.functional as TF
+
+from moge.test.baseline import MGEBaselineInterface
+
+
+class Baseline(MGEBaselineInterface):
+    def __init__(self, repo_path: str, checkpoint: str, encoder: str, decoder: str, num_tokens: int, device: Union[torch.device, str]):
+        # Create from repo
+        repo_path = os.path.abspath(repo_path)
+        training_path = os.path.join(repo_path, 'training')
+        # Add both repo root (for depth_anything_v2) and training (for sdt)
+        if repo_path not in sys.path:
+            sys.path.insert(0, repo_path)
+        if training_path not in sys.path:
+            sys.path.insert(0, training_path)
+        if not Path(repo_path).exists():
+            raise FileNotFoundError(f'Cannot find the Depth-Anything-V2 repository at {repo_path}.')
+
+        device = torch.device(device)
+
+        # Model configurations (same as training)
+        model_configs = {
+            'vits': {'encoder': 'vits', 'features': 64, 'out_channels': [48, 96, 192, 384]},
+            'vitb': {'encoder': 'vitb', 'features': 128, 'out_channels': [96, 192, 384, 768]},
+            'vitl': {'encoder': 'vitl', 'features': 256, 'out_channels': [256, 512, 1024, 1024]},
+            'vitg': {'encoder': 'vitg', 'features': 384, 'out_channels': [1536, 1536, 1536, 1536]}
+        }
+
+        # Build model based on decoder type
+        if decoder == 'dpt':
+            from depth_anything_v2.dpt import DepthAnythingV2
+            model = DepthAnythingV2(**model_configs[encoder])
+        elif decoder == 'sdt':
+            from depth_anything_v2.sdt import DepthAnythingV2SDT
+            model = DepthAnythingV2SDT(
+                encoder=encoder,
+                features=model_configs[encoder]['features'],
+                out_channels=model_configs[encoder]['out_channels'],
+                use_clstoken=True,
+                upsampler='dysample'
+            )
+        else:
+            raise ValueError(f"Unknown decoder: {decoder}")
+
+        # Load checkpoint
+        if not os.path.exists(checkpoint):
+            raise FileNotFoundError(f'Cannot find checkpoint at {checkpoint}')
+
+        ckpt = torch.load(checkpoint, map_location='cpu')
+        if 'model' in ckpt:
+            state_dict = ckpt['model']
+        else:
+            state_dict = ckpt
+
+        # Remove 'module.' prefix if present
+        state_dict = {k.replace('module.', ''): v for k, v in state_dict.items()}
+
+        missing, unexpected = model.load_state_dict(state_dict, strict=False)
+        print(f"Loaded checkpoint from {checkpoint}")
+        if missing:
+            print(f"Missing keys: {len(missing)}")
+        if unexpected:
+            print(f"Unexpected keys: {len(unexpected)}")
+
+        model.to(device).eval()
+        self.model = model
+        self.num_tokens = num_tokens
+        self.device = device
+
+    @click.command()
+    @click.option('--repo', 'repo_path', type=click.Path(), default='/home/ywan0794/Depth-Anything-V2', help='Path to the Depth-Anything-V2 repository.')
+    @click.option('--checkpoint', type=click.Path(), required=True, help='Path to trained checkpoint.')
+    @click.option('--encoder', type=click.Choice(['vits', 'vitb', 'vitl']), default='vitb', help='Encoder architecture.')
+    @click.option('--decoder', type=click.Choice(['dpt', 'sdt']), default='dpt', help='Decoder type.')
+    @click.option('--num_tokens', type=int, default=None, help='Number of tokens to use for the input image.')
+    @click.option('--device', type=str, default='cuda', help='Device to use for inference.')
+    @staticmethod
+    def load(repo_path: str, checkpoint: str, encoder: str, decoder: str, num_tokens: int, device: torch.device = 'cuda'):
+        return Baseline(repo_path, checkpoint, encoder, decoder, num_tokens, device)
+
+    @torch.inference_mode()
+    def infer(self, image: torch.Tensor, intrinsics: Optional[torch.Tensor] = None) -> Dict[str, torch.Tensor]:
+        original_height, original_width = image.shape[-2:]
+
+        if image.ndim == 3:
+            image = image.unsqueeze(0)
+            omit_batch_dim = True
+        else:
+            omit_batch_dim = False
+
+        if self.num_tokens is None:
+            resize_factor = 518 / min(original_height, original_width)
+            expected_width = round(original_width * resize_factor / 14) * 14
+            expected_height = round(original_height * resize_factor / 14) * 14
+        else:
+            aspect_ratio = original_width / original_height
+            tokens_rows = round((self.num_tokens * aspect_ratio) ** 0.5)
+            tokens_cols = round((self.num_tokens / aspect_ratio) ** 0.5)
+            expected_width = tokens_cols * 14
+            expected_height = tokens_rows * 14
+
+        image = TF.resize(image, (expected_height, expected_width), interpolation=T.InterpolationMode.BICUBIC, antialias=True)
+        image = TF.normalize(image, mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
+        image = image.to(self.device)
+
+        disparity = self.model(image)
+
+        disparity = F.interpolate(disparity[:, None], size=(original_height, original_width), mode='bilinear', align_corners=False, antialias=False)[:, 0]
+
+        if omit_batch_dim:
+            disparity = disparity.squeeze(0)
+
+        return {
+            'disparity_affine_invariant': disparity
+        }

baselines/da3.py

@@ -0,0 +1,92 @@
+# Reference: https://github.com/ByteDance-Seed/Depth-Anything-3
+import os
+import sys
+from typing import *
+from pathlib import Path
+
+import click
+import torch
+import torch.nn.functional as F
+import torchvision.transforms as T
+import torchvision.transforms.functional as TF
+
+from moge.test.baseline import MGEBaselineInterface
+
+
+class Baseline(MGEBaselineInterface):
+    def __init__(self, repo_path: str, model_name: str, num_tokens: int, device: Union[torch.device, str]):
+        # Create from repo
+        repo_path = os.path.abspath(repo_path)
+        if repo_path not in sys.path:
+            sys.path.insert(0, os.path.join(repo_path, 'src'))
+        if not Path(repo_path).exists():
+            raise FileNotFoundError(f'Cannot find the Depth-Anything-3 repository at {repo_path}. Please clone the repository and provide the path to it using the --repo option.')
+
+        from depth_anything_3.api import DepthAnything3
+
+        device = torch.device(device)
+
+        # Instantiate model
+        model = DepthAnything3.from_pretrained(f"ByteDance-Seed/{model_name}")
+
+        model.to(device).eval()
+        self.model = model
+        self.num_tokens = num_tokens
+        self.device = device
+
+    @click.command()
+    @click.option('--repo', 'repo_path', type=click.Path(), default='../Depth-Anything-3', help='Path to the Depth-Anything-3 repository.')
+    @click.option('--model_name', type=click.Choice(['da3-base', 'da3-large', 'da3-giant']), default='da3-large', help='Model name.')
+    @click.option('--num_tokens', type=int, default=None, help='Number of tokens to use for the input image.')
+    @click.option('--device', type=str, default='cuda', help='Device to use for inference.')
+    @staticmethod
+    def load(repo_path: str, model_name: str, num_tokens: int, device: torch.device = 'cuda'):
+        return Baseline(repo_path, model_name, num_tokens, device)
+
+    @torch.inference_mode()
+    def infer(self, image: torch.Tensor, intrinsics: Optional[torch.Tensor] = None) -> Dict[str, torch.Tensor]:
+        original_height, original_width = image.shape[-2:]
+
+        assert intrinsics is None, "Depth-Anything-3 does not support camera intrinsics input in this baseline"
+
+        if image.ndim == 3:
+            image = image.unsqueeze(0)
+            omit_batch_dim = True
+        else:
+            omit_batch_dim = False
+
+        if self.num_tokens is None:
+            resize_factor = 518 / min(original_height, original_width)
+            expected_width = round(original_width * resize_factor / 14) * 14
+            expected_height = round(original_height * resize_factor / 14) * 14
+        else:
+            aspect_ratio = original_width / original_height
+            tokens_rows = round((self.num_tokens * aspect_ratio) ** 0.5)
+            tokens_cols = round((self.num_tokens / aspect_ratio) ** 0.5)
+            expected_width = tokens_cols * 14
+            expected_height = tokens_rows * 14
+
+        image = TF.resize(image, (expected_height, expected_width), interpolation=T.InterpolationMode.BICUBIC, antialias=True)
+        image = TF.normalize(image, mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
+
+        # DA3 expects [B, N, 3, H, W] where N is number of views
+        image = image.unsqueeze(1)  # [B, 1, 3, H, W]
+
+        # Forward pass
+        output = self.model(image)
+
+        # Extract depth prediction
+        # Output shape: [B, N, H, W]
+        depth = output['depth'][:, 0]  # [B, H, W]
+
+        # Convert depth to disparity (inverse depth)
+        disparity = 1.0 / (depth + 1e-6)
+
+        disparity = F.interpolate(disparity[:, None], size=(original_height, original_width), mode='bilinear', align_corners=False, antialias=False)[:, 0]
+
+        if omit_batch_dim:
+            disparity = disparity.squeeze(0)
+
+        return {
+            'disparity_affine_invariant': disparity
+        }

baselines/da3_custom.py

@@ -0,0 +1,137 @@
+# DA3 with custom trained DPT/DualDPT/SDT checkpoint
+import os
+import sys
+from typing import *
+from pathlib import Path
+
+import click
+import torch
+import torch.nn.functional as F
+import torchvision.transforms as T
+import torchvision.transforms.functional as TF
+
+from moge.test.baseline import MGEBaselineInterface
+
+
+# DA3 Wrapper (same as training)
+class DA3Wrapper(torch.nn.Module):
+    def __init__(self, model):
+        super().__init__()
+        self.model = model
+
+    def forward(self, x):
+        # x: [B, 3, H, W]
+        # DA3 expects [B, N, 3, H, W] where N is number of views
+        x = x.unsqueeze(1)  # [B, 1, 3, H, W]
+        output = self.model(x)
+        # output.depth shape: [B, 1, H, W]
+        depth = output.depth.squeeze(1)  # [B, H, W]
+        return depth
+
+
+class Baseline(MGEBaselineInterface):
+    def __init__(self, repo_path: str, checkpoint: str, decoder: str, num_tokens: int, device: Union[torch.device, str]):
+        # Create from repo
+        repo_path = os.path.abspath(repo_path)
+        src_path = os.path.join(repo_path, 'src')
+        training_path = os.path.join(repo_path, 'training')
+        # Add src path for depth_anything_3
+        if src_path not in sys.path:
+            sys.path.insert(0, src_path)
+        if training_path not in sys.path:
+            sys.path.insert(0, training_path)
+        if not Path(repo_path).exists():
+            raise FileNotFoundError(f'Cannot find the Depth-Anything-3 repository at {repo_path}.')
+
+        device = torch.device(device)
+
+        # Config paths
+        config_dir = os.path.join(repo_path, 'src', 'depth_anything_3', 'configs')
+        if decoder == 'dpt':
+            config_path = os.path.join(config_dir, 'da3dpt-large.yaml')
+        elif decoder == 'dualdpt':
+            config_path = os.path.join(config_dir, 'da3dualdpt-large.yaml')
+        elif decoder == 'sdt':
+            config_path = os.path.join(config_dir, 'da3sdt-large.yaml')
+        else:
+            raise ValueError(f"Unknown decoder: {decoder}")
+
+        from depth_anything_3.cfg import load_config, create_object
+
+        # Build model
+        cfg = load_config(config_path)
+        base_model = create_object(cfg)
+        model = DA3Wrapper(base_model)
+
+        # Load checkpoint
+        if not os.path.exists(checkpoint):
+            raise FileNotFoundError(f'Cannot find checkpoint at {checkpoint}')
+
+        ckpt = torch.load(checkpoint, map_location='cpu')
+        if 'model' in ckpt:
+            state_dict = ckpt['model']
+        else:
+            state_dict = ckpt
+
+        # Remove 'module.' prefix if present
+        state_dict = {k.replace('module.', ''): v for k, v in state_dict.items()}
+
+        missing, unexpected = model.load_state_dict(state_dict, strict=False)
+        print(f"Loaded checkpoint from {checkpoint}")
+        if missing:
+            print(f"Missing keys: {len(missing)}")
+        if unexpected:
+            print(f"Unexpected keys: {len(unexpected)}")
+
+        model.to(device).eval()
+        self.model = model
+        self.num_tokens = num_tokens
+        self.device = device
+
+    @click.command()
+    @click.option('--repo', 'repo_path', type=click.Path(), default='/home/ywan0794/Depth-Anything-3', help='Path to the Depth-Anything-3 repository.')
+    @click.option('--checkpoint', type=click.Path(), required=True, help='Path to trained checkpoint.')
+    @click.option('--decoder', type=click.Choice(['dpt', 'dualdpt', 'sdt']), default='dpt', help='Decoder type.')
+    @click.option('--num_tokens', type=int, default=None, help='Number of tokens to use for the input image.')
+    @click.option('--device', type=str, default='cuda', help='Device to use for inference.')
+    @staticmethod
+    def load(repo_path: str, checkpoint: str, decoder: str, num_tokens: int, device: torch.device = 'cuda'):
+        return Baseline(repo_path, checkpoint, decoder, num_tokens, device)
+
+    @torch.inference_mode()
+    def infer(self, image: torch.Tensor, intrinsics: Optional[torch.Tensor] = None) -> Dict[str, torch.Tensor]:
+        original_height, original_width = image.shape[-2:]
+
+        if image.ndim == 3:
+            image = image.unsqueeze(0)
+            omit_batch_dim = True
+        else:
+            omit_batch_dim = False
+
+        if self.num_tokens is None:
+            resize_factor = 518 / min(original_height, original_width)
+            expected_width = round(original_width * resize_factor / 14) * 14
+            expected_height = round(original_height * resize_factor / 14) * 14
+        else:
+            aspect_ratio = original_width / original_height
+            tokens_rows = round((self.num_tokens * aspect_ratio) ** 0.5)
+            tokens_cols = round((self.num_tokens / aspect_ratio) ** 0.5)
+            expected_width = tokens_cols * 14
+            expected_height = tokens_rows * 14
+
+        image = TF.resize(image, (expected_height, expected_width), interpolation=T.InterpolationMode.BICUBIC, antialias=True)
+        image = TF.normalize(image, mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
+        image = image.to(self.device)
+
+        # DA3 model forward - outputs normalized disparity (NOT depth!)
+        with torch.cuda.amp.autocast(dtype=torch.bfloat16):
+            disparity = self.model(image)
+
+        disparity = F.interpolate(disparity[:, None], size=(original_height, original_width), mode='bilinear', align_corners=False, antialias=False)[:, 0]
+
+        if omit_batch_dim:
+            disparity = disparity.squeeze(0)
+
+        return {
+            'disparity_affine_invariant': disparity
+        }

baselines/da_v2.py

@@ -0,0 +1,88 @@
+# Reference: https://github.com/DepthAnything/Depth-Anything-V2
+import os
+import sys
+from typing import *
+from pathlib import Path
+
+import click
+import torch
+import torch.nn.functional as F
+import torchvision.transforms as T
+import torchvision.transforms.functional as TF
+
+from moge.test.baseline import MGEBaselineInterface
+
+
+class Baseline(MGEBaselineInterface):
+    def __init__(self, repo_path: str, backbone: str, num_tokens: int, device: Union[torch.device, str]):
+        # Create from repo
+        repo_path = os.path.abspath(repo_path)
+        if repo_path not in sys.path:
+            sys.path.append(repo_path)
+        if not Path(repo_path).exists():
+            raise FileNotFoundError(f'Cannot find the Depth-Anything repository at {repo_path}. Please clone the repository and provide the path to it using the --repo option.')
+        from depth_anything_v2.dpt import DepthAnythingV2
+
+        device = torch.device(device)
+
+        # Instantiate model
+        model = DepthAnythingV2(encoder=backbone, features=256, out_channels=[256, 512, 1024, 1024])
+
+        # Load checkpoint
+        checkpoint_path = os.path.join(repo_path, f'checkpoints/depth_anything_v2_{backbone}.pth')
+        if not os.path.exists(checkpoint_path):
+            raise FileNotFoundError(f'Cannot find the checkpoint file at {checkpoint_path}. Please download the checkpoint file and place it in the checkpoints directory.')
+        checkpoint = torch.load(checkpoint_path, map_location='cpu', weights_only=True)
+        model.load_state_dict(checkpoint)
+        
+        model.to(device).eval()
+        self.model = model
+        self.num_tokens = num_tokens
+        self.device = device
+
+    @click.command()
+    @click.option('--repo', 'repo_path', type=click.Path(), default='../Depth-Anything-V2', help='Path to the Depth-Anything repository.')
+    @click.option('--backbone', type=click.Choice(['vits', 'vitb', 'vitl']), default='vitl', help='Encoder architecture.')
+    @click.option('--num_tokens', type=int, default=None, help='Number of tokens to use for the input image.')
+    @click.option('--device', type=str, default='cuda', help='Device to use for inference.')
+    @staticmethod
+    def load(repo_path: str, backbone, num_tokens: int, device: torch.device = 'cuda'):
+        return Baseline(repo_path, backbone, num_tokens, device)
+
+    @torch.inference_mode()
+    def infer(self, image: torch.Tensor, intrinsics: Optional[torch.Tensor] = None) -> Dict[str, torch.Tensor]:
+        original_height, original_width = image.shape[-2:]
+
+        assert intrinsics is None, "Depth-Anything-V2 does not support camera intrinsics input"
+
+        if image.ndim == 3:
+            image = image.unsqueeze(0)
+            omit_batch_dim = True
+        else:
+            omit_batch_dim = False
+        
+        if self.num_tokens is None:
+            resize_factor = 518 / min(original_height, original_width)
+            expected_width = round(original_width * resize_factor / 14) * 14
+            expected_height = round(original_height * resize_factor / 14) * 14
+        else:
+            aspect_ratio = original_width / original_height
+            tokens_rows = round((self.num_tokens * aspect_ratio) ** 0.5)
+            tokens_cols = round((self.num_tokens / aspect_ratio) ** 0.5)
+            expected_width = tokens_cols * 14
+            expected_height = tokens_rows * 14
+        image = TF.resize(image, (expected_height, expected_width), interpolation=T.InterpolationMode.BICUBIC, antialias=True)
+    
+        image = TF.normalize(image, mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
+
+        disparity = self.model(image)
+
+        disparity = F.interpolate(disparity[:, None], size=(original_height, original_width), mode='bilinear', align_corners=False, antialias=False)[:, 0]
+
+        if omit_batch_dim:
+            disparity = disparity.squeeze(0)
+
+        return {
+            'disparity_affine_invariant': disparity
+        }
+            

baselines/da_v2_metric.py

@@ -0,0 +1,99 @@
+# Reference https://github.com/DepthAnything/Depth-Anything-V2/metric_depth
+import os
+import sys
+from typing import *
+from pathlib import Path
+
+import click
+import torch
+import torch.nn.functional as F
+import torchvision.transforms as T
+import torchvision.transforms.functional as TF
+import cv2
+
+from moge.test.baseline import MGEBaselineInterface
+
+
+class Baseline(MGEBaselineInterface):
+
+    def __init__(self, repo_path: str, backbone: str, domain: str, num_tokens: int, device: str):
+        device = torch.device(device)
+        repo_path = os.path.abspath(repo_path)
+        if not Path(repo_path).exists():
+            raise FileNotFoundError(f'Cannot find the Depth-Anything repository at {repo_path}. Please clone the repository and provide the path to it using the --repo option.')
+        sys.path.append(os.path.join(repo_path, 'metric_depth'))
+        from depth_anything_v2.dpt import DepthAnythingV2
+
+        model_configs = {
+            'vits': {'encoder': 'vits', 'features': 64, 'out_channels': [48, 96, 192, 384]},
+            'vitb': {'encoder': 'vitb', 'features': 128, 'out_channels': [96, 192, 384, 768]},
+            'vitl': {'encoder': 'vitl', 'features': 256, 'out_channels': [256, 512, 1024, 1024]}
+        }
+
+        if domain == 'indoor':
+            dataset = 'hypersim'
+            max_depth = 20
+        elif domain == 'outdoor':
+            dataset = 'vkitti'
+            max_depth = 80
+        else:
+            raise ValueError(f"Invalid domain: {domain}")
+
+        model = DepthAnythingV2(**model_configs[backbone], max_depth=max_depth)
+        checkpoint_path = os.path.join(repo_path, f'checkpoints/depth_anything_v2_metric_{dataset}_{backbone}.pth')
+        if not os.path.exists(checkpoint_path):
+            raise FileNotFoundError(f'Cannot find the checkpoint file at {checkpoint_path}. Please download the checkpoint file and place it in the checkpoints directory.')
+        model.load_state_dict(torch.load(checkpoint_path, map_location='cpu', weights_only=True))
+        model.eval().to(device)
+                
+        self.model = model
+        self.num_tokens = num_tokens
+        self.device = device
+
+    @click.command()
+    @click.option('--repo', 'repo_path', type=click.Path(), default='../Depth-Anything-V2', help='Path to the Depth-Anything repository.')
+    @click.option('--backbone', type=click.Choice(['vits', 'vitb', 'vitl']), default='vitl', help='Backbone architecture.')
+    @click.option('--domain', type=click.Choice(['indoor', 'outdoor']), help='Domain of the dataset.')
+    @click.option('--num_tokens', type=int, default=None, help='Number of tokens for the ViT model')
+    @click.option('--device', type=str, default='cuda', help='Device to use for inference.')
+    @staticmethod
+    def load(repo_path: str, backbone: str, domain: str, num_tokens: int, device: str):  
+        return Baseline(repo_path, backbone, domain, num_tokens, device)
+
+    @torch.inference_mode()
+    def infer(self, image: torch.Tensor, intrinsics: Optional[torch.Tensor] = None) -> Dict[str, torch.Tensor]:
+        original_height, original_width = image.shape[-2:]
+
+        assert intrinsics is None, "Depth-Anything-V2 does not support camera intrinsics input"
+
+        if image.ndim == 3:
+            image = image.unsqueeze(0)
+            omit_batch_dim = True
+        else:
+            omit_batch_dim = False
+
+        if self.num_tokens is None:
+            resize_factor = 518 / min(original_height, original_width)
+            expected_width = round(original_width * resize_factor / 14) * 14
+            expected_height = round(original_height * resize_factor / 14) * 14
+        else:
+            aspect_ratio = original_width / original_height
+            tokens_rows = round((self.num_tokens * aspect_ratio) ** 0.5)
+            tokens_cols = round((self.num_tokens / aspect_ratio) ** 0.5)
+            expected_width = tokens_cols * 14
+            expected_height = tokens_rows * 14
+        image = TF.resize(image, (expected_height, expected_width), interpolation=T.InterpolationMode.BICUBIC, antialias=True)
+    
+        image = TF.normalize(image, mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
+
+        depth = self.model(image)
+
+        depth = F.interpolate(depth[:, None], size=(original_height, original_width), mode='bilinear', align_corners=False, antialias=False)[:, 0]
+
+        if omit_batch_dim:
+            depth = depth.squeeze(0)
+
+        return {
+            'depth_metric': depth
+        }
+

baselines/depth_pro.py

@@ -0,0 +1,115 @@
+# Reference: https://github.com/apple/ml-depth-pro
+# Strictly follows official README Python API:
+#   model, transform = depth_pro.create_model_and_transforms()
+#   prediction = model.infer(image, f_px=f_px)
+#   depth = prediction["depth"]  # in [m]
+#   focallength_px = prediction["focallength_px"]
+#
+# Depth Pro outputs *metric* depth. Returns key `depth_metric` plus `intrinsics`
+# when focal length is recovered, so MoGe's compute_metrics can use the metric path.
+
+import os
+import sys
+from typing import *
+from pathlib import Path
+
+import click
+import torch
+import torch.nn.functional as F
+
+from moge.test.baseline import MGEBaselineInterface
+
+
+class Baseline(MGEBaselineInterface):
+    def __init__(self, repo_path: str, checkpoint_path: str, precision: str, device: Union[torch.device, str]):
+        repo_path = os.path.abspath(repo_path)
+        if not Path(repo_path).exists():
+            raise FileNotFoundError(
+                f"Cannot find Depth Pro repo at {repo_path}. Clone https://github.com/apple/ml-depth-pro "
+                f"and pass --repo <path>."
+            )
+        src_path = os.path.join(repo_path, "src")
+        if src_path not in sys.path:
+            sys.path.insert(0, src_path)
+
+        import depth_pro
+        from depth_pro.depth_pro import DepthProConfig, DEFAULT_MONODEPTH_CONFIG_DICT
+
+        if not os.path.isabs(checkpoint_path):
+            checkpoint_path = os.path.join(repo_path, checkpoint_path)
+        if not os.path.exists(checkpoint_path):
+            raise FileNotFoundError(
+                f"Cannot find Depth Pro checkpoint at {checkpoint_path}. "
+                f"Run `source get_pretrained_models.sh` inside the ml-depth-pro repo to download it."
+            )
+
+        device = torch.device(device)
+        precision_dtype = {"fp32": torch.float32, "fp16": torch.float16}[precision]
+
+        config = DepthProConfig(
+            patch_encoder_preset=DEFAULT_MONODEPTH_CONFIG_DICT.patch_encoder_preset,
+            image_encoder_preset=DEFAULT_MONODEPTH_CONFIG_DICT.image_encoder_preset,
+            decoder_features=DEFAULT_MONODEPTH_CONFIG_DICT.decoder_features,
+            checkpoint_uri=checkpoint_path,
+            use_fov_head=DEFAULT_MONODEPTH_CONFIG_DICT.use_fov_head,
+            fov_encoder_preset=DEFAULT_MONODEPTH_CONFIG_DICT.fov_encoder_preset,
+        )
+        model, _ = depth_pro.create_model_and_transforms(config=config, device=device, precision=precision_dtype)
+        model.eval()
+
+        self.model = model
+        self.device = device
+        self.precision_dtype = precision_dtype
+
+    @click.command()
+    @click.option('--repo', 'repo_path', type=click.Path(), default='../ml-depth-pro',
+                  help='Path to the apple/ml-depth-pro repository.')
+    @click.option('--checkpoint', 'checkpoint_path', type=click.Path(),
+                  default='checkpoints/depth_pro.pt',
+                  help='Checkpoint path; relative paths are resolved against --repo.')
+    @click.option('--precision', type=click.Choice(['fp32', 'fp16']), default='fp32')
+    @click.option('--device', type=str, default='cuda')
+    @staticmethod
+    def load(repo_path: str, checkpoint_path: str, precision: str, device: str = 'cuda'):
+        return Baseline(repo_path, checkpoint_path, precision, device)
+
+    @torch.inference_mode()
+    def infer(self, image: torch.Tensor, intrinsics: Optional[torch.Tensor] = None) -> Dict[str, torch.Tensor]:
+        omit_batch = image.ndim == 3
+        if omit_batch:
+            image = image.unsqueeze(0)
+        assert image.shape[0] == 1, "Depth Pro baseline only supports batch size 1"
+        _, _, H, W = image.shape
+
+        # Depth Pro transform: torchvision.Normalize([0.5]*3, [0.5]*3) maps [0,1] -> [-1,1].
+        x = (image.to(self.device, dtype=self.precision_dtype) - 0.5) / 0.5
+
+        # Convert normalized intrinsics (fx, fy in image-relative units) to pixel focal length if provided.
+        f_px = None
+        if intrinsics is not None:
+            intr = intrinsics.to(self.device)
+            if intr.ndim == 3:
+                intr = intr[0]
+            f_px = intr[0, 0] * W  # MoGe normalized intrinsics: fx in [0, 1] of width
+
+        prediction = self.model.infer(x, f_px=f_px)
+        depth = prediction["depth"]              # [H, W] in meters (squeezed by Depth Pro)
+        focallength_px = prediction["focallength_px"]  # scalar tensor (pixels)
+
+        out: Dict[str, torch.Tensor] = {"depth_metric": depth}
+
+        # Build normalized intrinsics (fx, fy in fraction of image width / height).
+        fx_norm = (focallength_px / W).reshape(())
+        fy_norm = (focallength_px / H).reshape(())
+        K = torch.eye(3, device=depth.device, dtype=depth.dtype)
+        K[0, 0] = fx_norm
+        K[1, 1] = fy_norm
+        K[0, 2] = 0.5
+        K[1, 2] = 0.5
+        out["intrinsics"] = K
+
+        if not omit_batch:
+            out["depth_metric"] = out["depth_metric"].unsqueeze(0)
+            out["intrinsics"] = out["intrinsics"].unsqueeze(0)
+
+        return out

baselines/marigold.py

@@ -0,0 +1,119 @@
+# Reference: https://github.com/prs-eth/Marigold
+# Strictly follows official `script/depth/run.py`:
+#   from marigold import MarigoldDepthPipeline
+#   pipe = MarigoldDepthPipeline.from_pretrained(checkpoint, torch_dtype=dtype)
+#   pipe_out = pipe(input_pil_image, denoise_steps, ensemble_size, processing_res,
+#                   match_input_res, batch_size, resample_method, ...)
+#   depth_pred: np.ndarray = pipe_out.depth_np   # normalized affine-invariant depth
+#
+# Marigold reports its outputs as affine-invariant depth (Marigold paper, CVPR 2024).
+# Returns key `depth_affine_invariant`.
+
+import os
+import sys
+from typing import *
+from pathlib import Path
+
+import click
+import torch
+import torch.nn.functional as F
+import numpy as np
+from PIL import Image
+
+from moge.test.baseline import MGEBaselineInterface
+
+
+class Baseline(MGEBaselineInterface):
+    def __init__(self, repo_path: str, checkpoint: str, denoise_steps: Optional[int],
+                 ensemble_size: int, processing_res: Optional[int], half_precision: bool,
+                 device: Union[torch.device, str]):
+        repo_path = os.path.abspath(repo_path)
+        if not Path(repo_path).exists():
+            raise FileNotFoundError(
+                f"Cannot find Marigold repo at {repo_path}. Clone https://github.com/prs-eth/Marigold."
+            )
+        if repo_path not in sys.path:
+            sys.path.insert(0, repo_path)
+
+        from marigold import MarigoldDepthPipeline
+
+        device = torch.device(device)
+        dtype = torch.float16 if half_precision else torch.float32
+        variant = "fp16" if half_precision else None
+
+        pipe = MarigoldDepthPipeline.from_pretrained(checkpoint, variant=variant, torch_dtype=dtype)
+        try:
+            pipe.enable_xformers_memory_efficient_attention()
+        except ImportError:
+            pass
+        pipe = pipe.to(device)
+        pipe.set_progress_bar_config(disable=True)
+
+        self.pipe = pipe
+        self.device = device
+        self.denoise_steps = denoise_steps
+        self.ensemble_size = ensemble_size
+        self.processing_res = processing_res
+
+    @click.command()
+    @click.option('--repo', 'repo_path', type=click.Path(), default='../Marigold',
+                  help='Path to the prs-eth/Marigold repository.')
+    @click.option('--checkpoint', type=str, default='prs-eth/marigold-depth-v1-1',
+                  help='HuggingFace ckpt name or local dir (run.py default).')
+    @click.option('--denoise_steps', type=int, default=None,
+                  help='Diffusion denoising steps. None -> default in ckpt.')
+    @click.option('--ensemble_size', type=int, default=1,
+                  help='Ensemble size. run.py default = 1.')
+    @click.option('--processing_res', type=int, default=None,
+                  help='Processing resolution. None -> default in ckpt.')
+    @click.option('--fp16', 'half_precision', is_flag=True, help='Run in half precision.')
+    @click.option('--device', type=str, default='cuda')
+    @staticmethod
+    def load(repo_path: str, checkpoint: str, denoise_steps: Optional[int],
+             ensemble_size: int, processing_res: Optional[int], half_precision: bool,
+             device: str = 'cuda'):
+        return Baseline(repo_path, checkpoint, denoise_steps, ensemble_size,
+                        processing_res, half_precision, device)
+
+    @torch.inference_mode()
+    def infer(self, image: torch.Tensor, intrinsics: Optional[torch.Tensor] = None) -> Dict[str, torch.Tensor]:
+        assert intrinsics is None or True, "Marigold does not consume intrinsics; argument ignored."
+        omit_batch = image.ndim == 3
+        if omit_batch:
+            image = image.unsqueeze(0)
+        assert image.shape[0] == 1, "Marigold baseline only supports batch size 1"
+        _, _, H, W = image.shape
+
+        # MoGe pipeline supplies image as float tensor in [0, 1]. Marigold pipe takes PIL.Image (run.py uses PIL).
+        arr = (image[0].cpu().permute(1, 2, 0).clamp(0, 1).numpy() * 255).astype(np.uint8)
+        pil = Image.fromarray(arr)
+
+        kwargs: Dict[str, Any] = dict(
+            ensemble_size=self.ensemble_size,
+            match_input_res=True,
+            batch_size=0,
+            resample_method='bilinear',
+            show_progress_bar=False,
+        )
+        if self.denoise_steps is not None:
+            kwargs['denoising_steps'] = self.denoise_steps  # pipeline kwarg is "denoising_steps"
+        if self.processing_res is not None:
+            kwargs['processing_res'] = self.processing_res
+
+        out = self.pipe(pil, **kwargs)
+
+        # MarigoldDepthOutput.depth_np: HxW np.float32 in [0, 1]. Marigold paper:
+        # affine-invariant depth (linear monotone with true depth, scale+shift free).
+        depth_np = out.depth_np
+        depth = torch.from_numpy(np.ascontiguousarray(depth_np)).to(self.device).float()
+
+        # Resize back if pipeline yielded a different size (it shouldn't with match_input_res=True).
+        if depth.shape[-2:] != (H, W):
+            depth = F.interpolate(depth[None, None], size=(H, W), mode='bilinear', align_corners=False)[0, 0]
+
+        # Marigold predicts affine-invariant depth (Marigold paper, CVPR 2024). Emit only
+        # this physical key. MoGe compute_metrics reports `depth_affine_invariant` metric.
+        result = {'depth_affine_invariant': depth}
+        if not omit_batch:
+            result['depth_affine_invariant'] = result['depth_affine_invariant'].unsqueeze(0)
+        return result

baselines/metric3d_v2.py

@@ -0,0 +1,117 @@
+# Reference: https://github.com/YvanYin/Metric3D
+import os
+import sys
+from typing import *
+
+import click
+import torch
+import torch.nn.functional as F
+import cv2
+
+from moge.test.baseline import MGEBaselineInterface
+
+
+class Baseline(MGEBaselineInterface):
+    def __init__(self, backbone: Literal['vits', 'vitl', 'vitg'], device):
+        backbone_map = {
+            'vits': 'metric3d_vit_small',
+            'vitl': 'metric3d_vit_large',
+            'vitg': 'metric3d_vit_giant2'
+        }
+
+        device = torch.device(device)
+        model = torch.hub.load('yvanyin/metric3d', backbone_map[backbone], pretrain=True)
+        model.to(device).eval()
+
+        self.model = model
+        self.device = device
+
+    @click.command()
+    @click.option('--backbone', type=click.Choice(['vits', 'vitl', 'vitg']), default='vitl', help='Encoder architecture.')
+    @click.option('--device', type=str, default='cuda', help='Device to use.')
+    @staticmethod
+    def load(backbone: str = 'vitl', device: torch.device = 'cuda'):
+        return Baseline(backbone, device)
+
+    @torch.inference_mode()
+    def inference_one_image(self, image: torch.Tensor, intrinsics: torch.Tensor = None):
+        # Reference: https://github.com/YvanYin/Metric3D/blob/main/mono/utils/do_test.py
+
+        # rgb_origin: RGB, 0-255, uint8
+        rgb_origin = image.cpu().numpy().transpose((1, 2, 0)) * 255
+
+        # keep ratio resize
+        input_size = (616, 1064) # for vit model
+        h, w = rgb_origin.shape[:2]
+        scale = min(input_size[0] / h, input_size[1] / w)
+        rgb = cv2.resize(rgb_origin, (int(w * scale), int(h * scale)), interpolation=cv2.INTER_LINEAR)
+        if intrinsics is not None:
+            focal = intrinsics[0, 0] * int(w * scale)
+        
+        # padding to input_size
+        padding = [123.675, 116.28, 103.53]
+        h, w = rgb.shape[:2]
+        pad_h = input_size[0] - h
+        pad_w = input_size[1] - w
+        pad_h_half = pad_h // 2
+        pad_w_half = pad_w // 2
+        rgb = cv2.copyMakeBorder(rgb, pad_h_half, pad_h - pad_h_half, pad_w_half, pad_w - pad_w_half, cv2.BORDER_CONSTANT, value=padding)
+        pad_info = [pad_h_half, pad_h - pad_h_half, pad_w_half, pad_w - pad_w_half]
+
+        # normalize rgb
+        mean = torch.tensor([123.675, 116.28, 103.53]).float()[:, None, None]
+        std = torch.tensor([58.395, 57.12, 57.375]).float()[:, None, None]
+        rgb = torch.from_numpy(rgb.transpose((2, 0, 1))).float()
+        rgb = torch.div((rgb - mean), std)
+        rgb = rgb[None, :, :, :].cuda()
+
+        # inference 
+        pred_depth, confidence, output_dict = self.model.inference({'input': rgb})
+
+        # un pad
+        pred_depth = pred_depth.squeeze()
+        pred_depth = pred_depth[pad_info[0] : pred_depth.shape[0] - pad_info[1], pad_info[2] : pred_depth.shape[1] - pad_info[3]]
+        pred_depth = pred_depth.clamp_min(0.5)  # clamp to 0.5m, since metric3d could yield very small depth values, resulting in crashed the scale shift alignment.
+        
+        # upsample to original size
+        pred_depth = F.interpolate(pred_depth[None, None, :, :], image.shape[-2:], mode='bilinear').squeeze()
+        
+        if intrinsics is not None:
+            # de-canonical transform
+            canonical_to_real_scale = focal / 1000.0 # 1000.0 is the focal length of canonical camera
+            pred_depth = pred_depth * canonical_to_real_scale # now the depth is metric
+            pred_depth = torch.clamp(pred_depth, 0, 300)
+
+        pred_normal, normal_confidence = output_dict['prediction_normal'].split([3, 1], dim=1) # see https://arxiv.org/abs/2109.09881 for details
+
+        # un pad and resize to some size if needed
+        pred_normal = pred_normal.squeeze(0)
+        pred_normal = pred_normal[:, pad_info[0] : pred_normal.shape[1] - pad_info[1], pad_info[2] : pred_normal.shape[2] - pad_info[3]]
+
+        # you can now do anything with the normal
+        pred_normal = F.interpolate(pred_normal[None, :, :, :], image.shape[-2:], mode='bilinear').squeeze(0)
+        pred_normal = F.normalize(pred_normal, p=2, dim=0)
+
+        return pred_depth, pred_normal.permute(1, 2, 0)
+
+    @torch.inference_mode()
+    def infer(self, image: torch.Tensor, intrinsics: torch.Tensor = None):    
+        # image: (B, H, W, 3) or (H, W, 3)
+        if image.ndim == 3:
+            pred_depth, pred_normal = self.inference_one_image(image, intrinsics)
+        else:
+            for i in range(image.shape[0]):
+                pred_depth_i, pred_normal_i = self.inference_one_image(image[i], intrinsics[i] if intrinsics is not None else None)
+                pred_depth.append(pred_depth_i)
+                pred_normal.append(pred_normal_i)
+            pred_depth = torch.stack(pred_depth, dim=0)
+            pred_normal = torch.stack(pred_normal, dim=0)
+        
+        if intrinsics is not None:
+            return {
+                "depth_metric": pred_depth,
+            }
+        else:
+            return {
+                "depth_scale_invariant": pred_depth,
+            }

baselines/moge.py

@@ -0,0 +1,83 @@
+import os
+import sys
+from typing import *
+import importlib
+
+import click
+import torch
+import utils3d
+
+from moge.test.baseline import MGEBaselineInterface
+
+
+class Baseline(MGEBaselineInterface):
+
+    def __init__(self, num_tokens: int, resolution_level: int, pretrained_model_name_or_path: str, use_fp16: bool, device: str = 'cuda:0', version: str = 'v1'):
+        super().__init__()
+        from moge.model import import_model_class_by_version
+        MoGeModel = import_model_class_by_version(version)
+        self.version = version
+
+        self.model = MoGeModel.from_pretrained(pretrained_model_name_or_path).to(device).eval()
+        
+        self.device = torch.device(device)
+        self.num_tokens = num_tokens
+        self.resolution_level = resolution_level
+        self.use_fp16 = use_fp16
+    
+    @click.command()
+    @click.option('--num_tokens', type=int, default=None)
+    @click.option('--resolution_level', type=int, default=9)
+    @click.option('--pretrained', 'pretrained_model_name_or_path', type=str, default='Ruicheng/moge-vitl')
+    @click.option('--fp16', 'use_fp16', is_flag=True)
+    @click.option('--device', type=str, default='cuda:0')
+    @click.option('--version', type=str, default='v1')
+    @staticmethod
+    def load(num_tokens: int, resolution_level: int, pretrained_model_name_or_path: str, use_fp16: bool, device: str = 'cuda:0', version: str = 'v1'):
+        return Baseline(num_tokens, resolution_level, pretrained_model_name_or_path, use_fp16, device, version)
+
+    # Implementation for inference
+    @torch.inference_mode()
+    def infer(self, image: torch.FloatTensor, intrinsics: Optional[torch.FloatTensor] = None):
+        if intrinsics is not None:
+            fov_x, _ = utils3d.pt.intrinsics_to_fov(intrinsics)
+            fov_x = torch.rad2deg(fov_x)
+        else:
+            fov_x = None
+        output = self.model.infer(image, fov_x=fov_x, apply_mask=True, num_tokens=self.num_tokens)
+        
+        if self.version == 'v1':
+            return {
+                'points_scale_invariant': output['points'],
+                'depth_scale_invariant': output['depth'],
+                'intrinsics': output['intrinsics'],
+            }
+        else:
+            return {
+                'points_metric': output['points'],
+                'depth_metric': output['depth'],
+                'intrinsics': output['intrinsics'],
+            }
+
+    @torch.inference_mode()
+    def infer_for_evaluation(self, image: torch.FloatTensor, intrinsics: torch.FloatTensor = None):
+        if intrinsics is not None:
+            fov_x, _ = utils3d.pt.intrinsics_to_fov(intrinsics)
+            fov_x = torch.rad2deg(fov_x)
+        else:
+            fov_x = None
+        output = self.model.infer(image, fov_x=fov_x, apply_mask=False, num_tokens=self.num_tokens, use_fp16=self.use_fp16)
+        
+        if self.version == 'v1':
+            return {
+                'points_scale_invariant': output['points'],
+                'depth_scale_invariant': output['depth'],
+                'intrinsics': output['intrinsics'],
+            }
+        else:
+            return {
+                'points_metric': output['points'],
+                'depth_metric': output['depth'],
+                'intrinsics': output['intrinsics'],
+            }
+        

baselines/rae_depth.py

@@ -0,0 +1,157 @@
+import os
+import sys
+from typing import *
+import math
+
+import click
+import torch
+import torch.nn.functional as F
+
+from moge.test.baseline import MGEBaselineInterface
+
+
+class Baseline(MGEBaselineInterface):
+
+    def __init__(self, repo_path: str, config_path: str, checkpoint_path: str,
+                 image_size: int, num_steps: int, use_fp16: bool, device: str = 'cuda:0'):
+        super().__init__()
+        repo_path = os.path.abspath(repo_path)
+        src_path = os.path.join(repo_path, 'src')
+        if src_path not in sys.path:
+            sys.path.insert(0, src_path)
+
+        from omegaconf import OmegaConf
+        from stage2.transport import create_transport, Sampler
+        from utils.model_utils import instantiate_from_config
+
+        # Load config
+        full_cfg = OmegaConf.load(config_path)
+        rae_config = full_cfg.get("stage_1", None)
+        model_config = full_cfg.get("stage_2", None)
+        transport_config = full_cfg.get("transport", {})
+        sampler_config = full_cfg.get("sampler", {})
+        misc_config = full_cfg.get("misc", {})
+
+        transport_cfg = OmegaConf.to_container(transport_config, resolve=True) if transport_config else {}
+        sampler_cfg = OmegaConf.to_container(sampler_config, resolve=True) if sampler_config else {}
+        misc = OmegaConf.to_container(misc_config, resolve=True) if misc_config else {}
+
+        latent_size = tuple(int(dim) for dim in misc.get("latent_size", (768, 32, 32)))
+        shift_dim = misc.get("time_dist_shift_dim", math.prod(latent_size))
+        shift_base = misc.get("time_dist_shift_base", 4096)
+        time_dist_shift = math.sqrt(shift_dim / shift_base)
+
+        # Load RAE (DepthRAE)
+        rae = instantiate_from_config(rae_config).to(device)
+        rae.eval()
+
+        # Load Stage-2 model
+        model = instantiate_from_config(model_config).to(device)
+
+        # Load checkpoint
+        checkpoint = torch.load(checkpoint_path, map_location='cpu', weights_only=False)
+        if 'ema' in checkpoint:
+            state_dict = checkpoint['ema']
+        elif 'model' in checkpoint:
+            state_dict = checkpoint['model']
+        else:
+            state_dict = checkpoint
+        model.load_state_dict(state_dict)
+        model.eval()
+
+        # Create transport sampler
+        transport_params = dict(transport_cfg.get("params", {}))
+        transport_params.pop("time_dist_shift", None)
+        transport = create_transport(**transport_params, time_dist_shift=time_dist_shift)
+        transport_sampler = Sampler(transport)
+
+        sampler_mode = sampler_cfg.get("mode", "ODE").upper()
+        sampler_params = dict(sampler_cfg.get("params", {}))
+        sampler_params['num_steps'] = num_steps
+
+        if sampler_mode == "ODE":
+            eval_sampler = transport_sampler.sample_ode(**sampler_params)
+        else:
+            eval_sampler = transport_sampler.sample_sde(**sampler_params)
+
+        self.rae = rae
+        self.model = model
+        self.eval_sampler = eval_sampler
+        self.latent_size = latent_size
+        self.image_size = image_size
+        self.device = torch.device(device)
+        self.use_fp16 = use_fp16
+
+    @click.command()
+    @click.option('--repo', 'repo_path', type=str, default='/home/ywan0794/RAE')
+    @click.option('--rae_config', 'config_path', type=str, required=True)
+    @click.option('--checkpoint', 'checkpoint_path', type=str, required=True)
+    @click.option('--image_size', type=int, default=512)
+    @click.option('--num_steps', type=int, default=2)
+    @click.option('--fp16', 'use_fp16', is_flag=True)
+    @click.option('--device', type=str, default='cuda:0')
+    @staticmethod
+    def load(repo_path, config_path, checkpoint_path, image_size, num_steps, use_fp16, device):
+        return Baseline(repo_path, config_path, checkpoint_path, image_size, num_steps, use_fp16, device)
+
+    def _predict_depth(self, image: torch.FloatTensor):
+        original_height, original_width = image.shape[-2:]
+
+        if image.ndim == 3:
+            image = image.unsqueeze(0)
+            omit_batch_dim = True
+        else:
+            omit_batch_dim = False
+
+        b = image.shape[0]
+
+        # Resize to model input size
+        image_resized = F.interpolate(
+            image, size=(self.image_size, self.image_size),
+            mode='bilinear', align_corners=False, antialias=True,
+        )
+
+        # Encode RGB
+        z_rgb = self.rae.encode(image_resized)
+
+        # Sample depth from noise
+        z_noise = torch.randn(b, *self.latent_size, device=self.device)
+        y = torch.zeros(b, dtype=torch.long, device=self.device)
+
+        # Marigold-style: concat z_rgb with xt before passing to model
+        def model_fn(xt, t, y):
+            x_input = torch.cat([xt, z_rgb], dim=1)
+            return self.model(x_input, t, y)
+
+        # Run diffusion sampling
+        z_pred = self.eval_sampler(z_noise, model_fn, y=y)[-1]
+
+        # Decode to depth (pass z_rgb for conditioning)
+        depth_pred = self.rae.decode(z_pred.float(), z_rgb)
+        depth_pred = depth_pred.mean(dim=1)  # (B, H, W)
+
+        # Resize back to original size
+        depth_pred = F.interpolate(
+            depth_pred.unsqueeze(1), size=(original_height, original_width),
+            mode='bilinear', align_corners=False,
+        )[:, 0]
+
+        if omit_batch_dim:
+            depth_pred = depth_pred.squeeze(0)
+
+        return depth_pred
+
+    @torch.inference_mode()
+    def infer(self, image: torch.FloatTensor, intrinsics: Optional[torch.FloatTensor] = None):
+        depth_pred = self._predict_depth(image)
+        return {
+            'depth_affine_invariant': depth_pred,
+        }
+
+    @torch.inference_mode()
+    def infer_for_evaluation(self, image: torch.FloatTensor, intrinsics: torch.FloatTensor = None):
+        with torch.cuda.amp.autocast(enabled=self.use_fp16, dtype=torch.float16):
+            depth_pred = self._predict_depth(image)
+        return {
+            'depth_affine_invariant': depth_pred,
+        }

baselines/vggt_custom.py

@@ -0,0 +1,139 @@
+# VGGT with custom trained DPT/SDT checkpoint (LoRA)
+import os
+import sys
+from typing import *
+from pathlib import Path
+
+import click
+import torch
+import torch.nn.functional as F
+import torchvision.transforms as T
+import torchvision.transforms.functional as TF
+
+from moge.test.baseline import MGEBaselineInterface
+
+
+class Baseline(MGEBaselineInterface):
+    def __init__(self, repo_path: str, checkpoint: str, decoder: str, lora_rank: int, lora_alpha: int, num_tokens: int, device: Union[torch.device, str]):
+        # Create from repo
+        repo_path = os.path.abspath(repo_path)
+        training_path = os.path.join(repo_path, 'training')
+        if training_path not in sys.path:
+            sys.path.insert(0, training_path)
+        if repo_path not in sys.path:
+            sys.path.insert(0, repo_path)
+        if not Path(repo_path).exists():
+            raise FileNotFoundError(f'Cannot find the VGGT repository at {repo_path}.')
+
+        device = torch.device(device)
+
+        # Build model based on decoder type
+        if decoder == 'dpt':
+            from vggt.models.vggt import VGGT
+            model = VGGT(
+                enable_camera=True,
+                enable_depth=True,
+                enable_point=False,
+                enable_track=False,
+            )
+        elif decoder == 'sdt':
+            from vggt.models.vggt_sdt import VGGT_SDT
+            model = VGGT_SDT(
+                enable_camera=True,
+                enable_depth=True,
+                enable_point=False,
+                enable_track=False,
+            )
+        else:
+            raise ValueError(f"Unknown decoder: {decoder}")
+
+        # Apply LoRA
+        from lora import apply_lora
+        model = apply_lora(model, rank=lora_rank, alpha=lora_alpha)
+        print(f"Applied LoRA (rank={lora_rank}, alpha={lora_alpha})")
+
+        # Load checkpoint
+        if not os.path.exists(checkpoint):
+            raise FileNotFoundError(f'Cannot find checkpoint at {checkpoint}')
+
+        ckpt = torch.load(checkpoint, map_location='cpu')
+        if 'model' in ckpt:
+            state_dict = ckpt['model']
+        else:
+            state_dict = ckpt
+
+        # Remove 'module.' prefix if present
+        state_dict = {k.replace('module.', ''): v for k, v in state_dict.items()}
+
+        missing, unexpected = model.load_state_dict(state_dict, strict=False)
+        print(f"Loaded checkpoint from {checkpoint}")
+        if missing:
+            print(f"Missing keys: {len(missing)}")
+        if unexpected:
+            print(f"Unexpected keys: {len(unexpected)}")
+
+        model.to(device).eval()
+        self.model = model
+        self.num_tokens = num_tokens
+        self.device = device
+
+    @click.command()
+    @click.option('--repo', 'repo_path', type=click.Path(), default='/home/ywan0794/vggt', help='Path to the VGGT repository.')
+    @click.option('--checkpoint', type=click.Path(), required=True, help='Path to trained checkpoint.')
+    @click.option('--decoder', type=click.Choice(['dpt', 'sdt']), default='dpt', help='Decoder type.')
+    @click.option('--lora_rank', type=int, default=8, help='LoRA rank.')
+    @click.option('--lora_alpha', type=int, default=16, help='LoRA alpha.')
+    @click.option('--num_tokens', type=int, default=None, help='Number of tokens to use for the input image.')
+    @click.option('--device', type=str, default='cuda', help='Device to use for inference.')
+    @staticmethod
+    def load(repo_path: str, checkpoint: str, decoder: str, lora_rank: int, lora_alpha: int, num_tokens: int, device: torch.device = 'cuda'):
+        return Baseline(repo_path, checkpoint, decoder, lora_rank, lora_alpha, num_tokens, device)
+
+    @torch.inference_mode()
+    def infer(self, image: torch.Tensor, intrinsics: Optional[torch.Tensor] = None) -> Dict[str, torch.Tensor]:
+        original_height, original_width = image.shape[-2:]
+
+        if image.ndim == 3:
+            image = image.unsqueeze(0)
+            omit_batch_dim = True
+        else:
+            omit_batch_dim = False
+
+        if self.num_tokens is None:
+            resize_factor = 518 / min(original_height, original_width)
+            expected_width = round(original_width * resize_factor / 14) * 14
+            expected_height = round(original_height * resize_factor / 14) * 14
+        else:
+            aspect_ratio = original_width / original_height
+            tokens_rows = round((self.num_tokens * aspect_ratio) ** 0.5)
+            tokens_cols = round((self.num_tokens / aspect_ratio) ** 0.5)
+            expected_width = tokens_cols * 14
+            expected_height = tokens_rows * 14
+
+        image = TF.resize(image, (expected_height, expected_width), interpolation=T.InterpolationMode.BICUBIC, antialias=True)
+
+        # VGGT expects [0, 1] range, not ImageNet normalized
+        image = image.to(self.device)
+
+        # VGGT expects sequence of images: [B, S, 3, H, W]
+        rgb_seq = image.unsqueeze(1).repeat(1, 2, 1, 1, 1)
+
+        # Forward pass
+        with torch.cuda.amp.autocast(dtype=torch.bfloat16):
+            output = self.model(images=rgb_seq)
+
+        # Extract depth from prediction
+        # pred["depth"] shape: [B, S, H, W, 1]
+        depth = output["depth"][0, 0, :, :, 0]
+
+        # Convert depth to disparity
+        disparity = 1.0 / (depth + 1e-6)
+
+        disparity = F.interpolate(disparity[None, None], size=(original_height, original_width), mode='bilinear', align_corners=False, antialias=False)[0, 0]
+
+        if omit_batch_dim:
+            pass  # already squeezed
+
+        return {
+            'disparity_affine_invariant': disparity
+        }

baselines/vggt_metric.py

@@ -0,0 +1,137 @@
+# VGGT with custom trained DPT/SDT checkpoint (LoRA) - Metric Depth Output
+import os
+import sys
+from typing import *
+from pathlib import Path
+
+import click
+import torch
+import torch.nn.functional as F
+import torchvision.transforms as T
+import torchvision.transforms.functional as TF
+
+from moge.test.baseline import MGEBaselineInterface
+
+
+class Baseline(MGEBaselineInterface):
+    def __init__(self, repo_path: str, checkpoint: str, decoder: str, lora_rank: int, lora_alpha: int, num_tokens: int, device: Union[torch.device, str]):
+        # Create from repo
+        repo_path = os.path.abspath(repo_path)
+        training_path = os.path.join(repo_path, 'training')
+        if training_path not in sys.path:
+            sys.path.insert(0, training_path)
+        if repo_path not in sys.path:
+            sys.path.insert(0, repo_path)
+        if not Path(repo_path).exists():
+            raise FileNotFoundError(f'Cannot find the VGGT repository at {repo_path}.')
+
+        device = torch.device(device)
+
+        # Build model based on decoder type
+        if decoder == 'dpt':
+            from vggt.models.vggt import VGGT
+            model = VGGT(
+                enable_camera=True,
+                enable_depth=True,
+                enable_point=False,
+                enable_track=False,
+            )
+        elif decoder == 'sdt':
+            from vggt.models.vggt_sdt import VGGT_SDT
+            model = VGGT_SDT(
+                enable_camera=True,
+                enable_depth=True,
+                enable_point=False,
+                enable_track=False,
+            )
+        else:
+            raise ValueError(f"Unknown decoder: {decoder}")
+
+        # Apply LoRA
+        from lora import apply_lora
+        model = apply_lora(model, rank=lora_rank, alpha=lora_alpha)
+        print(f"Applied LoRA (rank={lora_rank}, alpha={lora_alpha})")
+
+        # Load checkpoint
+        if not os.path.exists(checkpoint):
+            raise FileNotFoundError(f'Cannot find checkpoint at {checkpoint}')
+
+        ckpt = torch.load(checkpoint, map_location='cpu')
+        if 'model' in ckpt:
+            state_dict = ckpt['model']
+        else:
+            state_dict = ckpt
+
+        # Remove 'module.' prefix if present
+        state_dict = {k.replace('module.', ''): v for k, v in state_dict.items()}
+
+        missing, unexpected = model.load_state_dict(state_dict, strict=False)
+        print(f"Loaded checkpoint from {checkpoint}")
+        if missing:
+            print(f"Missing keys: {len(missing)}")
+        if unexpected:
+            print(f"Unexpected keys: {len(unexpected)}")
+
+        model.to(device).eval()
+        self.model = model
+        self.num_tokens = num_tokens
+        self.device = device
+
+    @click.command()
+    @click.option('--repo', 'repo_path', type=click.Path(), default='/home/ywan0794/vggt', help='Path to the VGGT repository.')
+    @click.option('--checkpoint', type=click.Path(), required=True, help='Path to trained checkpoint.')
+    @click.option('--decoder', type=click.Choice(['dpt', 'sdt']), default='dpt', help='Decoder type.')
+    @click.option('--lora_rank', type=int, default=8, help='LoRA rank.')
+    @click.option('--lora_alpha', type=int, default=16, help='LoRA alpha.')
+    @click.option('--num_tokens', type=int, default=None, help='Number of tokens to use for the input image.')
+    @click.option('--device', type=str, default='cuda', help='Device to use for inference.')
+    @staticmethod
+    def load(repo_path: str, checkpoint: str, decoder: str, lora_rank: int, lora_alpha: int, num_tokens: int, device: torch.device = 'cuda'):
+        return Baseline(repo_path, checkpoint, decoder, lora_rank, lora_alpha, num_tokens, device)
+
+    @torch.inference_mode()
+    def infer(self, image: torch.Tensor, intrinsics: Optional[torch.Tensor] = None) -> Dict[str, torch.Tensor]:
+        original_height, original_width = image.shape[-2:]
+
+        if image.ndim == 3:
+            image = image.unsqueeze(0)
+            omit_batch_dim = True
+        else:
+            omit_batch_dim = False
+
+        if self.num_tokens is None:
+            resize_factor = 518 / min(original_height, original_width)
+            expected_width = round(original_width * resize_factor / 14) * 14
+            expected_height = round(original_height * resize_factor / 14) * 14
+        else:
+            aspect_ratio = original_width / original_height
+            tokens_rows = round((self.num_tokens * aspect_ratio) ** 0.5)
+            tokens_cols = round((self.num_tokens / aspect_ratio) ** 0.5)
+            expected_width = tokens_cols * 14
+            expected_height = tokens_rows * 14
+
+        image = TF.resize(image, (expected_height, expected_width), interpolation=T.InterpolationMode.BICUBIC, antialias=True)
+
+        # VGGT expects [0, 1] range, not ImageNet normalized
+        image = image.to(self.device)
+
+        # VGGT expects sequence of images: [B, S, 3, H, W]
+        rgb_seq = image.unsqueeze(1).repeat(1, 2, 1, 1, 1)
+
+        # Forward pass
+        with torch.cuda.amp.autocast(dtype=torch.bfloat16):
+            output = self.model(images=rgb_seq)
+
+        # Extract depth from prediction
+        # pred["depth"] shape: [B, S, H, W, 1]
+        depth = output["depth"][0, 0, :, :, 0]
+
+        # Output metric depth directly (no 1/depth conversion)
+        depth = F.interpolate(depth[None, None], size=(original_height, original_width), mode='bilinear', align_corners=False, antialias=False)[0, 0]
+
+        if omit_batch_dim:
+            pass  # already squeezed
+
+        return {
+            'depth_metric': depth
+        }

eval_all_12110.log

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+============================================
+eval-all started at Thu May 14 04:58:17 AM AEST 2026
+Config (main): /home/ywan0794/MoGe/configs/eval/all_benchmarks.json
+Config (fe2e): /home/ywan0794/MoGe/configs/eval/fe2e_all_benchmarks.json
+TIMESTAMP: 20260514_045817
+Summary file: eval_output/_eval_all_20260514_045817.summary.txt
+============================================
+Thu May 14 04:58:17 2026       
++-----------------------------------------------------------------------------------------+
+| NVIDIA-SMI 550.163.01             Driver Version: 550.163.01     CUDA Version: 12.4     |
+|-----------------------------------------+------------------------+----------------------+
+| GPU  Name                 Persistence-M | Bus-Id          Disp.A | Volatile Uncorr. ECC |
+| Fan  Temp   Perf          Pwr:Usage/Cap |           Memory-Usage | GPU-Util  Compute M. |
+|                                         |                        |               MIG M. |
+|=========================================+========================+======================|
+|   0  NVIDIA H100 NVL                Off |   00000000:E1:00.0 Off |                    0 |
+| N/A   35C    P0             60W /  400W |      14MiB /  95830MiB |      0%      Default |
+|                                         |                        |             Disabled |
++-----------------------------------------+------------------------+----------------------+
+                                                                                         
++-----------------------------------------------------------------------------------------+
+| Processes:                                                                              |
+|  GPU   GI   CI        PID   Type   Process name                              GPU Memory |
+|        ID   ID                                                               Usage      |
+|=========================================================================================|
+|    0   N/A  N/A      4274      G   /usr/lib/xorg/Xorg                              4MiB |
++-----------------------------------------------------------------------------------------+
+
+============================================
+[marigold] starting at Thu May 14 04:58:17 AM AEST 2026  (conda env: marigold)
+============================================
+Active env: marigold
+CUDA: True NVIDIA H100 NVL
+The config attributes {'prediction_type': 'depth'} were passed to MarigoldDepthPipeline, but are not expected and will be ignored. Please verify your model_index.json configuration file.
+Keyword arguments {'prediction_type': 'depth'} are not expected by MarigoldDepthPipeline and will be ignored.
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moge_da2_dpt_subset_12087.log

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+============================================
+Activated conda environment: da2
+CUDA_HOME: /home/ywan0794/miniconda3/envs/da2
+============================================
+=== GPU Info ===
+Tue May 12 18:06:35 2026       
++-----------------------------------------------------------------------------------------+
+| NVIDIA-SMI 550.163.01             Driver Version: 550.163.01     CUDA Version: 12.4     |
+|-----------------------------------------+------------------------+----------------------+
+| GPU  Name                 Persistence-M | Bus-Id          Disp.A | Volatile Uncorr. ECC |
+| Fan  Temp   Perf          Pwr:Usage/Cap |           Memory-Usage | GPU-Util  Compute M. |
+|                                         |                        |               MIG M. |
+|=========================================+========================+======================|
+|   0  NVIDIA H100 NVL                Off |   00000000:E1:00.0 Off |                    0 |
+| N/A   36C    P0             60W /  400W |      14MiB /  95830MiB |      0%      Default |
+|                                         |                        |             Disabled |
++-----------------------------------------+------------------------+----------------------+
+                                                                                         
++-----------------------------------------------------------------------------------------+
+| Processes:                                                                              |
+|  GPU   GI   CI        PID   Type   Process name                              GPU Memory |
+|        ID   ID                                                               Usage      |
+|=========================================================================================|
+|    0   N/A  N/A      4274      G   /usr/lib/xorg/Xorg                              4MiB |
++-----------------------------------------------------------------------------------------+
+CUDA available: True
+GPU count: 1
+GPU name: NVIDIA H100 NVL
+============================================
+Starting MoGe Subset Sanity Eval (DA2 public vitb) at Tue May 12 06:06:55 PM AEST 2026
+Config: /home/ywan0794/MoGe/configs/eval/subset_benchmarks.json
+Output: eval_output/da2_public_vitb_subset_20260512_180655.json
+============================================
+xFormers not available
+xFormers not available
+Traceback (most recent call last):
+  File "/home/ywan0794/MoGe/moge/scripts/eval_baseline.py", line 165, in <module>
+    main()
+  File "/home/ywan0794/miniconda3/envs/da2/lib/python3.10/site-packages/click/core.py", line 1485, in __call__
+    return self.main(*args, **kwargs)
+  File "/home/ywan0794/miniconda3/envs/da2/lib/python3.10/site-packages/click/core.py", line 1406, in main
+    rv = self.invoke(ctx)
+  File "/home/ywan0794/miniconda3/envs/da2/lib/python3.10/site-packages/click/core.py", line 1269, in invoke
+    return ctx.invoke(self.callback, **ctx.params)
+  File "/home/ywan0794/miniconda3/envs/da2/lib/python3.10/site-packages/click/core.py", line 824, in invoke
+    return callback(*args, **kwargs)
+  File "/home/ywan0794/miniconda3/envs/da2/lib/python3.10/site-packages/click/decorators.py", line 34, in new_func
+    return f(get_current_context(), *args, **kwargs)
+  File "/home/ywan0794/MoGe/moge/scripts/eval_baseline.py", line 42, in main
+    baseline : MGEBaselineInterface = baseline_cls.load.main(ctx.args, standalone_mode=False)
+  File "/home/ywan0794/miniconda3/envs/da2/lib/python3.10/site-packages/click/core.py", line 1406, in main
+    rv = self.invoke(ctx)
+  File "/home/ywan0794/miniconda3/envs/da2/lib/python3.10/site-packages/click/core.py", line 1269, in invoke
+    return ctx.invoke(self.callback, **ctx.params)
+  File "/home/ywan0794/miniconda3/envs/da2/lib/python3.10/site-packages/click/core.py", line 824, in invoke
+    return callback(*args, **kwargs)
+  File "/home/ywan0794/MoGe/baselines/da_v2.py", line 50, in load
+    return Baseline(repo_path, backbone, num_tokens, device)
+  File "/home/ywan0794/MoGe/baselines/da_v2.py", line 36, in __init__
+    model.load_state_dict(checkpoint)
+  File "/home/ywan0794/miniconda3/envs/da2/lib/python3.10/site-packages/torch/nn/modules/module.py", line 2629, in load_state_dict
+    raise RuntimeError(
+RuntimeError: Error(s) in loading state_dict for DepthAnythingV2:
+	size mismatch for depth_head.projects.0.weight: copying a param with shape torch.Size([96, 768, 1, 1]) from checkpoint, the shape in current model is torch.Size([256, 768, 1, 1]).
+	size mismatch for depth_head.projects.0.bias: copying a param with shape torch.Size([96]) from checkpoint, the shape in current model is torch.Size([256]).
+	size mismatch for depth_head.projects.1.weight: copying a param with shape torch.Size([192, 768, 1, 1]) from checkpoint, the shape in current model is torch.Size([512, 768, 1, 1]).
+	size mismatch for depth_head.projects.1.bias: copying a param with shape torch.Size([192]) from checkpoint, the shape in current model is torch.Size([512]).
+	size mismatch for depth_head.projects.2.weight: copying a param with shape torch.Size([384, 768, 1, 1]) from checkpoint, the shape in current model is torch.Size([1024, 768, 1, 1]).
+	size mismatch for depth_head.projects.2.bias: copying a param with shape torch.Size([384]) from checkpoint, the shape in current model is torch.Size([1024]).
+	size mismatch for depth_head.projects.3.weight: copying a param with shape torch.Size([768, 768, 1, 1]) from checkpoint, the shape in current model is torch.Size([1024, 768, 1, 1]).
+	size mismatch for depth_head.projects.3.bias: copying a param with shape torch.Size([768]) from checkpoint, the shape in current model is torch.Size([1024]).
+	size mismatch for depth_head.resize_layers.0.weight: copying a param with shape torch.Size([96, 96, 4, 4]) from checkpoint, the shape in current model is torch.Size([256, 256, 4, 4]).
+	size mismatch for depth_head.resize_layers.0.bias: copying a param with shape torch.Size([96]) from checkpoint, the shape in current model is torch.Size([256]).
+	size mismatch for depth_head.resize_layers.1.weight: copying a param with shape torch.Size([192, 192, 2, 2]) from checkpoint, the shape in current model is torch.Size([512, 512, 2, 2]).
+	size mismatch for depth_head.resize_layers.1.bias: copying a param with shape torch.Size([192]) from checkpoint, the shape in current model is torch.Size([512]).
+	size mismatch for depth_head.resize_layers.3.weight: copying a param with shape torch.Size([768, 768, 3, 3]) from checkpoint, the shape in current model is torch.Size([1024, 1024, 3, 3]).
+	size mismatch for depth_head.resize_layers.3.bias: copying a param with shape torch.Size([768]) from checkpoint, the shape in current model is torch.Size([1024]).
+	size mismatch for depth_head.scratch.layer1_rn.weight: copying a param with shape torch.Size([128, 96, 3, 3]) from checkpoint, the shape in current model is torch.Size([256, 256, 3, 3]).
+	size mismatch for depth_head.scratch.layer2_rn.weight: copying a param with shape torch.Size([128, 192, 3, 3]) from checkpoint, the shape in current model is torch.Size([256, 512, 3, 3]).
+	size mismatch for depth_head.scratch.layer3_rn.weight: copying a param with shape torch.Size([128, 384, 3, 3]) from checkpoint, the shape in current model is torch.Size([256, 1024, 3, 3]).
+	size mismatch for depth_head.scratch.layer4_rn.weight: copying a param with shape torch.Size([128, 768, 3, 3]) from checkpoint, the shape in current model is torch.Size([256, 1024, 3, 3]).
+	size mismatch for depth_head.scratch.refinenet1.out_conv.weight: copying a param with shape torch.Size([128, 128, 1, 1]) from checkpoint, the shape in current model is torch.Size([256, 256, 1, 1]).
+	size mismatch for depth_head.scratch.refinenet1.out_conv.bias: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([256]).
+	size mismatch for depth_head.scratch.refinenet1.resConfUnit1.conv1.weight: copying a param with shape torch.Size([128, 128, 3, 3]) from checkpoint, the shape in current model is torch.Size([256, 256, 3, 3]).
+	size mismatch for depth_head.scratch.refinenet1.resConfUnit1.conv1.bias: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([256]).
+	size mismatch for depth_head.scratch.refinenet1.resConfUnit1.conv2.weight: copying a param with shape torch.Size([128, 128, 3, 3]) from checkpoint, the shape in current model is torch.Size([256, 256, 3, 3]).
+	size mismatch for depth_head.scratch.refinenet1.resConfUnit1.conv2.bias: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([256]).
+	size mismatch for depth_head.scratch.refinenet1.resConfUnit2.conv1.weight: copying a param with shape torch.Size([128, 128, 3, 3]) from checkpoint, the shape in current model is torch.Size([256, 256, 3, 3]).
+	size mismatch for depth_head.scratch.refinenet1.resConfUnit2.conv1.bias: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([256]).
+	size mismatch for depth_head.scratch.refinenet1.resConfUnit2.conv2.weight: copying a param with shape torch.Size([128, 128, 3, 3]) from checkpoint, the shape in current model is torch.Size([256, 256, 3, 3]).
+	size mismatch for depth_head.scratch.refinenet1.resConfUnit2.conv2.bias: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([256]).
+	size mismatch for depth_head.scratch.refinenet2.out_conv.weight: copying a param with shape torch.Size([128, 128, 1, 1]) from checkpoint, the shape in current model is torch.Size([256, 256, 1, 1]).
+	size mismatch for depth_head.scratch.refinenet2.out_conv.bias: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([256]).
+	size mismatch for depth_head.scratch.refinenet2.resConfUnit1.conv1.weight: copying a param with shape torch.Size([128, 128, 3, 3]) from checkpoint, the shape in current model is torch.Size([256, 256, 3, 3]).
+	size mismatch for depth_head.scratch.refinenet2.resConfUnit1.conv1.bias: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([256]).
+	size mismatch for depth_head.scratch.refinenet2.resConfUnit1.conv2.weight: copying a param with shape torch.Size([128, 128, 3, 3]) from checkpoint, the shape in current model is torch.Size([256, 256, 3, 3]).
+	size mismatch for depth_head.scratch.refinenet2.resConfUnit1.conv2.bias: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([256]).
+	size mismatch for depth_head.scratch.refinenet2.resConfUnit2.conv1.weight: copying a param with shape torch.Size([128, 128, 3, 3]) from checkpoint, the shape in current model is torch.Size([256, 256, 3, 3]).
+	size mismatch for depth_head.scratch.refinenet2.resConfUnit2.conv1.bias: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([256]).
+	size mismatch for depth_head.scratch.refinenet2.resConfUnit2.conv2.weight: copying a param with shape torch.Size([128, 128, 3, 3]) from checkpoint, the shape in current model is torch.Size([256, 256, 3, 3]).
+	size mismatch for depth_head.scratch.refinenet2.resConfUnit2.conv2.bias: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([256]).
+	size mismatch for depth_head.scratch.refinenet3.out_conv.weight: copying a param with shape torch.Size([128, 128, 1, 1]) from checkpoint, the shape in current model is torch.Size([256, 256, 1, 1]).
+	size mismatch for depth_head.scratch.refinenet3.out_conv.bias: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([256]).
+	size mismatch for depth_head.scratch.refinenet3.resConfUnit1.conv1.weight: copying a param with shape torch.Size([128, 128, 3, 3]) from checkpoint, the shape in current model is torch.Size([256, 256, 3, 3]).
+	size mismatch for depth_head.scratch.refinenet3.resConfUnit1.conv1.bias: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([256]).
+	size mismatch for depth_head.scratch.refinenet3.resConfUnit1.conv2.weight: copying a param with shape torch.Size([128, 128, 3, 3]) from checkpoint, the shape in current model is torch.Size([256, 256, 3, 3]).
+	size mismatch for depth_head.scratch.refinenet3.resConfUnit1.conv2.bias: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([256]).
+	size mismatch for depth_head.scratch.refinenet3.resConfUnit2.conv1.weight: copying a param with shape torch.Size([128, 128, 3, 3]) from checkpoint, the shape in current model is torch.Size([256, 256, 3, 3]).
+	size mismatch for depth_head.scratch.refinenet3.resConfUnit2.conv1.bias: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([256]).
+	size mismatch for depth_head.scratch.refinenet3.resConfUnit2.conv2.weight: copying a param with shape torch.Size([128, 128, 3, 3]) from checkpoint, the shape in current model is torch.Size([256, 256, 3, 3]).
+	size mismatch for depth_head.scratch.refinenet3.resConfUnit2.conv2.bias: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([256]).
+	size mismatch for depth_head.scratch.refinenet4.out_conv.weight: copying a param with shape torch.Size([128, 128, 1, 1]) from checkpoint, the shape in current model is torch.Size([256, 256, 1, 1]).
+	size mismatch for depth_head.scratch.refinenet4.out_conv.bias: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([256]).
+	size mismatch for depth_head.scratch.refinenet4.resConfUnit1.conv1.weight: copying a param with shape torch.Size([128, 128, 3, 3]) from checkpoint, the shape in current model is torch.Size([256, 256, 3, 3]).
+	size mismatch for depth_head.scratch.refinenet4.resConfUnit1.conv1.bias: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([256]).
+	size mismatch for depth_head.scratch.refinenet4.resConfUnit1.conv2.weight: copying a param with shape torch.Size([128, 128, 3, 3]) from checkpoint, the shape in current model is torch.Size([256, 256, 3, 3]).
+	size mismatch for depth_head.scratch.refinenet4.resConfUnit1.conv2.bias: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([256]).
+	size mismatch for depth_head.scratch.refinenet4.resConfUnit2.conv1.weight: copying a param with shape torch.Size([128, 128, 3, 3]) from checkpoint, the shape in current model is torch.Size([256, 256, 3, 3]).
+	size mismatch for depth_head.scratch.refinenet4.resConfUnit2.conv1.bias: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([256]).
+	size mismatch for depth_head.scratch.refinenet4.resConfUnit2.conv2.weight: copying a param with shape torch.Size([128, 128, 3, 3]) from checkpoint, the shape in current model is torch.Size([256, 256, 3, 3]).
+	size mismatch for depth_head.scratch.refinenet4.resConfUnit2.conv2.bias: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([256]).
+	size mismatch for depth_head.scratch.output_conv1.weight: copying a param with shape torch.Size([64, 128, 3, 3]) from checkpoint, the shape in current model is torch.Size([128, 256, 3, 3]).
+	size mismatch for depth_head.scratch.output_conv1.bias: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([128]).
+	size mismatch for depth_head.scratch.output_conv2.0.weight: copying a param with shape torch.Size([32, 64, 3, 3]) from checkpoint, the shape in current model is torch.Size([32, 128, 3, 3]).
+============================================
+Evaluation completed at Tue May 12 06:07:22 PM AEST 2026
+============================================

moge_da2_dpt_subset_12088.log

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+============================================
+Activated conda environment: da2
+CUDA_HOME: /home/ywan0794/miniconda3/envs/da2
+============================================
+=== GPU Info ===
+Tue May 12 18:08:32 2026       
++-----------------------------------------------------------------------------------------+
+| NVIDIA-SMI 550.163.01             Driver Version: 550.163.01     CUDA Version: 12.4     |
+|-----------------------------------------+------------------------+----------------------+
+| GPU  Name                 Persistence-M | Bus-Id          Disp.A | Volatile Uncorr. ECC |
+| Fan  Temp   Perf          Pwr:Usage/Cap |           Memory-Usage | GPU-Util  Compute M. |
+|                                         |                        |               MIG M. |
+|=========================================+========================+======================|
+|   0  NVIDIA H100 NVL                Off |   00000000:E1:00.0 Off |                    0 |
+| N/A   35C    P0             60W /  400W |      14MiB /  95830MiB |      0%      Default |
+|                                         |                        |             Disabled |
++-----------------------------------------+------------------------+----------------------+
+                                                                                         
++-----------------------------------------------------------------------------------------+
+| Processes:                                                                              |
+|  GPU   GI   CI        PID   Type   Process name                              GPU Memory |
+|        ID   ID                                                               Usage      |
+|=========================================================================================|
+|    0   N/A  N/A      4274      G   /usr/lib/xorg/Xorg                              4MiB |
++-----------------------------------------------------------------------------------------+
+CUDA available: True
+GPU count: 1
+GPU name: NVIDIA H100 NVL
+============================================
+Starting MoGe Subset Sanity Eval (DA2 public vitl) at Tue May 12 06:08:34 PM AEST 2026
+Config: /home/ywan0794/MoGe/configs/eval/subset_benchmarks.json
+Output: eval_output/da2_public_vitl_subset_20260512_180834.json
+============================================
+xFormers not available
+xFormers not available
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+
                                                          
+============================================
+Evaluation completed at Tue May 12 06:11:17 PM AEST 2026
+============================================

pyproject.toml

@@ -0,0 +1,36 @@
+[build-system]
+requires = ["setuptools>=61.0", "wheel"]
+build-backend = "setuptools.build_meta"
+
+[project]
+name = "moge"
+version = "2.0.0"
+description = "MoGe: Unlocking Accurate Monocular Geometry Estimation for Open-Domain Images with Optimal Training Supervision"
+readme = "README.md"
+license = {text = "MIT"}
+dependencies = [
+    "click",
+    "opencv-python",
+    "scipy",
+    "matplotlib",
+    "trimesh",
+    "pillow",
+    "huggingface_hub",
+    "numpy",
+    "torch>=2.0.0",
+    "torchvision",
+    "gradio",
+    "utils3d @ git+https://github.com/EasternJournalist/utils3d.git@3fab839f0be9931dac7c8488eb0e1600c236e183",
+    "pipeline @ git+https://github.com/EasternJournalist/pipeline.git@866f059d2a05cde05e4a52211ec5051fd5f276d6"
+]
+requires-python = ">=3.9"
+
+[project.urls]
+Homepage = "https://github.com/microsoft/MoGe"
+
+[tool.setuptools.packages.find]
+where = ["."]
+include = ["moge*"]
+
+[project.scripts]
+moge = "moge.scripts.cli:main"

pyrightconfig.json

@@ -0,0 +1,10 @@
+{   
+    "include": [
+        "moge", 
+        "scripts",
+        "baselines"
+    ],
+    "ignore": [
+        "**"
+    ]
+}

requirements.txt

@@ -0,0 +1,14 @@
+# The versions are not specified since MoGe should be compatible with most versions of the packages. 
+# If incompatibilities are found, consider upgrading to latest versions or installing the following recommended version of the package.
+torch           # >= 2.0.0
+torchvision
+gradio          # ==2.8.13
+click           # ==8.1.7
+opencv-python   # ==4.10.0.84
+scipy           # ==1.14.1
+matplotlib      # ==3.9.2
+trimesh         # ==4.5.1
+pillow          # ==10.4.0
+huggingface_hub # ==0.25.2
+git+https://github.com/EasternJournalist/utils3d.git@3fab839f0be9931dac7c8488eb0e1600c236e183
+git+https://github.com/EasternJournalist/pipeline.git@866f059d2a05cde05e4a52211ec5051fd5f276d6

sanity_all_12094.log

@@ -0,0 +1,328 @@
+============================================
+sanity-all started at Wed May 13 02:31:53 AM AEST 2026
+Config: /home/ywan0794/MoGe/configs/eval/sanity_benchmarks.json
+TIMESTAMP: 20260513_023153
+Summary file: sanity_output/_sanity_all_20260513_023153.summary.txt
+============================================
+Wed May 13 02:31:53 2026       
++-----------------------------------------------------------------------------------------+
+| NVIDIA-SMI 550.163.01             Driver Version: 550.163.01     CUDA Version: 12.4     |
+|-----------------------------------------+------------------------+----------------------+
+| GPU  Name                 Persistence-M | Bus-Id          Disp.A | Volatile Uncorr. ECC |
+| Fan  Temp   Perf          Pwr:Usage/Cap |           Memory-Usage | GPU-Util  Compute M. |
+|                                         |                        |               MIG M. |
+|=========================================+========================+======================|
+|   0  NVIDIA H100 NVL                Off |   00000000:E1:00.0 Off |                    0 |
+| N/A   35C    P0             60W /  400W |      14MiB /  95830MiB |      0%      Default |
+|                                         |                        |             Disabled |
++-----------------------------------------+------------------------+----------------------+
+                                                                                         
++-----------------------------------------------------------------------------------------+
+| Processes:                                                                              |
+|  GPU   GI   CI        PID   Type   Process name                              GPU Memory |
+|        ID   ID                                                               Usage      |
+|=========================================================================================|
+|    0   N/A  N/A      4274      G   /usr/lib/xorg/Xorg                              4MiB |
++-----------------------------------------------------------------------------------------+
+
+============================================
+[marigold] starting at Wed May 13 02:31:53 AM AEST 2026  (conda env: marigold)
+============================================
+Active env: marigold
+CUDA: True NVIDIA H100 NVL
+Traceback (most recent call last):
+  File "/home/ywan0794/miniconda3/envs/marigold/lib/python3.10/site-packages/diffusers/utils/import_utils.py", line 1015, in _get_module
+    return importlib.import_module("." + module_name, self.__name__)
+  File "/home/ywan0794/miniconda3/envs/marigold/lib/python3.10/importlib/__init__.py", line 126, in import_module
+    return _bootstrap._gcd_import(name[level:], package, level)
+  File "<frozen importlib._bootstrap>", line 1050, in _gcd_import
+  File "<frozen importlib._bootstrap>", line 1027, in _find_and_load
+  File "<frozen importlib._bootstrap>", line 992, in _find_and_load_unlocked
+  File "<frozen importlib._bootstrap>", line 241, in _call_with_frames_removed
+  File "<frozen importlib._bootstrap>", line 1050, in _gcd_import
+  File "<frozen importlib._bootstrap>", line 1027, in _find_and_load
+  File "<frozen importlib._bootstrap>", line 1006, in _find_and_load_unlocked
+  File "<frozen importlib._bootstrap>", line 688, in _load_unlocked
+  File "<frozen importlib._bootstrap_external>", line 883, in exec_module
+  File "<frozen importlib._bootstrap>", line 241, in _call_with_frames_removed
+  File "/home/ywan0794/miniconda3/envs/marigold/lib/python3.10/site-packages/diffusers/models/autoencoders/__init__.py", line 1, in <module>
+    from .autoencoder_asym_kl import AsymmetricAutoencoderKL
+  File "/home/ywan0794/miniconda3/envs/marigold/lib/python3.10/site-packages/diffusers/models/autoencoders/autoencoder_asym_kl.py", line 21, in <module>
+    from .vae import AutoencoderMixin, DecoderOutput, DiagonalGaussianDistribution, Encoder, MaskConditionDecoder
+  File "/home/ywan0794/miniconda3/envs/marigold/lib/python3.10/site-packages/diffusers/models/autoencoders/vae.py", line 24, in <module>
+    from ..unets.unet_2d_blocks import (
+  File "/home/ywan0794/miniconda3/envs/marigold/lib/python3.10/site-packages/diffusers/models/unets/__init__.py", line 6, in <module>
+    from .unet_2d import UNet2DModel
+  File "/home/ywan0794/miniconda3/envs/marigold/lib/python3.10/site-packages/diffusers/models/unets/unet_2d.py", line 23, in <module>
+    from .unet_2d_blocks import UNetMidBlock2D, get_down_block, get_up_block
+  File "/home/ywan0794/miniconda3/envs/marigold/lib/python3.10/site-packages/diffusers/models/unets/unet_2d_blocks.py", line 36, in <module>
+    from ..transformers.dual_transformer_2d import DualTransformer2DModel
+  File "/home/ywan0794/miniconda3/envs/marigold/lib/python3.10/site-packages/diffusers/models/transformers/__init__.py", line 5, in <module>
+    from .ace_step_transformer import AceStepTransformer1DModel
+  File "/home/ywan0794/miniconda3/envs/marigold/lib/python3.10/site-packages/diffusers/models/transformers/ace_step_transformer.py", line 26, in <module>
+    from ..attention_dispatch import (
+  File "/home/ywan0794/miniconda3/envs/marigold/lib/python3.10/site-packages/diffusers/models/attention_dispatch.py", line 740, in <module>
+    def _wrapped_flash_attn_3(
+  File "/home/ywan0794/miniconda3/envs/marigold/lib/python3.10/site-packages/torch/_library/custom_ops.py", line 119, in inner
+    schema_str = torch._custom_op.impl.infer_schema(fn, mutates_args)
+  File "/home/ywan0794/miniconda3/envs/marigold/lib/python3.10/site-packages/torch/_library/infer_schema.py", line 42, in infer_schema
+    error_fn(
+  File "/home/ywan0794/miniconda3/envs/marigold/lib/python3.10/site-packages/torch/_library/infer_schema.py", line 21, in error_fn
+    raise ValueError(
+ValueError: infer_schema(func): Parameter q has unsupported type torch.Tensor. The valid types are: dict_keys([<class 'torch.Tensor'>, typing.Optional[torch.Tensor], typing.Sequence[torch.Tensor], typing.List[torch.Tensor], typing.Sequence[typing.Optional[torch.Tensor]], typing.List[typing.Optional[torch.Tensor]], <class 'int'>, typing.Optional[int], typing.Sequence[int], typing.List[int], typing.Optional[typing.Sequence[int]], typing.Optional[typing.List[int]], <class 'float'>, typing.Optional[float], typing.Sequence[float], typing.List[float], typing.Optional[typing.Sequence[float]], typing.Optional[typing.List[float]], <class 'bool'>, typing.Optional[bool], typing.Sequence[bool], typing.List[bool], typing.Optional[typing.Sequence[bool]], typing.Optional[typing.List[bool]], <class 'str'>, typing.Optional[str], typing.Union[int, float, bool], typing.Union[int, float, bool, NoneType], typing.Sequence[typing.Union[int, float, bool]], typing.List[typing.Union[int, float, bool]], <class 'torch.dtype'>, typing.Optional[torch.dtype], <class 'torch.device'>, typing.Optional[torch.device]]). Got func with signature (q: 'torch.Tensor', k: 'torch.Tensor', v: 'torch.Tensor', softmax_scale: 'float | None' = None, causal: 'bool' = False, qv: 'torch.Tensor | None' = None, q_descale: 'torch.Tensor | None' = None, k_descale: 'torch.Tensor | None' = None, v_descale: 'torch.Tensor | None' = None, attention_chunk: 'int' = 0, softcap: 'float' = 0.0, num_splits: 'int' = 1, pack_gqa: 'bool | None' = None, deterministic: 'bool' = False, sm_margin: 'int' = 0) -> 'tuple[torch.Tensor, torch.Tensor]')
+
+The above exception was the direct cause of the following exception:
+
+Traceback (most recent call last):
+  File "/home/ywan0794/MoGe/moge/scripts/eval_baseline.py", line 165, in <module>
+    main()
+  File "/home/ywan0794/miniconda3/envs/marigold/lib/python3.10/site-packages/click/core.py", line 1514, in __call__
+    return self.main(*args, **kwargs)
+  File "/home/ywan0794/miniconda3/envs/marigold/lib/python3.10/site-packages/click/core.py", line 1435, in main
+    rv = self.invoke(ctx)
+  File "/home/ywan0794/miniconda3/envs/marigold/lib/python3.10/site-packages/click/core.py", line 1298, in invoke
+    return ctx.invoke(self.callback, **ctx.params)
+  File "/home/ywan0794/miniconda3/envs/marigold/lib/python3.10/site-packages/click/core.py", line 853, in invoke
+    return callback(*args, **kwargs)
+  File "/home/ywan0794/miniconda3/envs/marigold/lib/python3.10/site-packages/click/decorators.py", line 34, in new_func
+    return f(get_current_context(), *args, **kwargs)
+  File "/home/ywan0794/MoGe/moge/scripts/eval_baseline.py", line 42, in main
+    baseline : MGEBaselineInterface = baseline_cls.load.main(ctx.args, standalone_mode=False)
+  File "/home/ywan0794/miniconda3/envs/marigold/lib/python3.10/site-packages/click/core.py", line 1435, in main
+    rv = self.invoke(ctx)
+  File "/home/ywan0794/miniconda3/envs/marigold/lib/python3.10/site-packages/click/core.py", line 1298, in invoke
+    return ctx.invoke(self.callback, **ctx.params)
+  File "/home/ywan0794/miniconda3/envs/marigold/lib/python3.10/site-packages/click/core.py", line 853, in invoke
+    return callback(*args, **kwargs)
+  File "/home/ywan0794/MoGe/baselines/marigold.py", line 75, in load
+    return Baseline(repo_path, checkpoint, denoise_steps, ensemble_size,
+  File "/home/ywan0794/MoGe/baselines/marigold.py", line 38, in __init__
+    from marigold import MarigoldDepthPipeline
+  File "/home/ywan0794/EvalMDE/Marigold/marigold/__init__.py", line 31, in <module>
+    from .marigold_depth_pipeline import (
+  File "/home/ywan0794/EvalMDE/Marigold/marigold/marigold_depth_pipeline.py", line 35, in <module>
+    from diffusers import (
+  File "<frozen importlib._bootstrap>", line 1075, in _handle_fromlist
+  File "/home/ywan0794/miniconda3/envs/marigold/lib/python3.10/site-packages/diffusers/utils/import_utils.py", line 1006, in __getattr__
+    value = getattr(module, name)
+  File "/home/ywan0794/miniconda3/envs/marigold/lib/python3.10/site-packages/diffusers/utils/import_utils.py", line 1005, in __getattr__
+    module = self._get_module(self._class_to_module[name])
+  File "/home/ywan0794/miniconda3/envs/marigold/lib/python3.10/site-packages/diffusers/utils/import_utils.py", line 1017, in _get_module
+    raise RuntimeError(
+RuntimeError: Failed to import diffusers.models.autoencoders.autoencoder_kl because of the following error (look up to see its traceback):
+infer_schema(func): Parameter q has unsupported type torch.Tensor. The valid types are: dict_keys([<class 'torch.Tensor'>, typing.Optional[torch.Tensor], typing.Sequence[torch.Tensor], typing.List[torch.Tensor], typing.Sequence[typing.Optional[torch.Tensor]], typing.List[typing.Optional[torch.Tensor]], <class 'int'>, typing.Optional[int], typing.Sequence[int], typing.List[int], typing.Optional[typing.Sequence[int]], typing.Optional[typing.List[int]], <class 'float'>, typing.Optional[float], typing.Sequence[float], typing.List[float], typing.Optional[typing.Sequence[float]], typing.Optional[typing.List[float]], <class 'bool'>, typing.Optional[bool], typing.Sequence[bool], typing.List[bool], typing.Optional[typing.Sequence[bool]], typing.Optional[typing.List[bool]], <class 'str'>, typing.Optional[str], typing.Union[int, float, bool], typing.Union[int, float, bool, NoneType], typing.Sequence[typing.Union[int, float, bool]], typing.List[typing.Union[int, float, bool]], <class 'torch.dtype'>, typing.Optional[torch.dtype], <class 'torch.device'>, typing.Optional[torch.device]]). Got func with signature (q: 'torch.Tensor', k: 'torch.Tensor', v: 'torch.Tensor', softmax_scale: 'float | None' = None, causal: 'bool' = False, qv: 'torch.Tensor | None' = None, q_descale: 'torch.Tensor | None' = None, k_descale: 'torch.Tensor | None' = None, v_descale: 'torch.Tensor | None' = None, attention_chunk: 'int' = 0, softcap: 'float' = 0.0, num_splits: 'int' = 1, pack_gqa: 'bool | None' = None, deterministic: 'bool' = False, sm_margin: 'int' = 0) -> 'tuple[torch.Tensor, torch.Tensor]')
+[FAIL rc=1] marigold
+
+============================================
+[lotus] starting at Wed May 13 02:32:24 AM AEST 2026  (conda env: lotus)
+============================================
+Active env: lotus
+CUDA: True NVIDIA H100 NVL
+Traceback (most recent call last):
+  File "/home/ywan0794/MoGe/moge/scripts/eval_baseline.py", line 165, in <module>
+    main()
+  File "/home/ywan0794/miniconda3/envs/lotus/lib/python3.10/site-packages/click/core.py", line 1514, in __call__
+    return self.main(*args, **kwargs)
+  File "/home/ywan0794/miniconda3/envs/lotus/lib/python3.10/site-packages/click/core.py", line 1435, in main
+    rv = self.invoke(ctx)
+  File "/home/ywan0794/miniconda3/envs/lotus/lib/python3.10/site-packages/click/core.py", line 1298, in invoke
+    return ctx.invoke(self.callback, **ctx.params)
+  File "/home/ywan0794/miniconda3/envs/lotus/lib/python3.10/site-packages/click/core.py", line 853, in invoke
+    return callback(*args, **kwargs)
+  File "/home/ywan0794/miniconda3/envs/lotus/lib/python3.10/site-packages/click/decorators.py", line 34, in new_func
+    return f(get_current_context(), *args, **kwargs)
+  File "/home/ywan0794/MoGe/moge/scripts/eval_baseline.py", line 42, in main
+    baseline : MGEBaselineInterface = baseline_cls.load.main(ctx.args, standalone_mode=False)
+  File "/home/ywan0794/miniconda3/envs/lotus/lib/python3.10/site-packages/click/core.py", line 1435, in main
+    rv = self.invoke(ctx)
+  File "/home/ywan0794/miniconda3/envs/lotus/lib/python3.10/site-packages/click/core.py", line 1298, in invoke
+    return ctx.invoke(self.callback, **ctx.params)
+  File "/home/ywan0794/miniconda3/envs/lotus/lib/python3.10/site-packages/click/core.py", line 853, in invoke
+    return callback(*args, **kwargs)
+  File "/home/ywan0794/MoGe/baselines/lotus.py", line 90, in load
+    return Baseline(repo_path, pretrained, mode, task_name, disparity, timestep,
+  File "/home/ywan0794/MoGe/baselines/lotus.py", line 48, in __init__
+    from pipeline import LotusGPipeline, LotusDPipeline
+ImportError: cannot import name 'LotusGPipeline' from 'pipeline' (/home/ywan0794/miniconda3/envs/lotus/lib/python3.10/site-packages/pipeline/__init__.py)
+[FAIL rc=1] lotus
+
+============================================
+[depthmaster] starting at Wed May 13 02:32:41 AM AEST 2026  (conda env: depthmaster)
+============================================
+Active env: depthmaster
+CUDA: True NVIDIA H100 NVL
+/home/ywan0794/miniconda3/envs/depthmaster/lib/python3.10/site-packages/diffusers/models/transformers/transformer_2d.py:34: FutureWarning: `Transformer2DModelOutput` is deprecated and will be removed in version 1.0.0. Importing `Transformer2DModelOutput` from `diffusers.models.transformer_2d` is deprecated and this will be removed in a future version. Please use `from diffusers.models.modeling_outputs import Transformer2DModelOutput`, instead.
+  deprecate("Transformer2DModelOutput", "1.0.0", deprecation_message)
+Traceback (most recent call last):
+  File "/home/ywan0794/miniconda3/envs/depthmaster/lib/python3.10/site-packages/diffusers/utils/import_utils.py", line 808, in _get_module
+    return importlib.import_module("." + module_name, self.__name__)
+  File "/home/ywan0794/miniconda3/envs/depthmaster/lib/python3.10/importlib/__init__.py", line 126, in import_module
+    return _bootstrap._gcd_import(name[level:], package, level)
+  File "<frozen importlib._bootstrap>", line 1050, in _gcd_import
+  File "<frozen importlib._bootstrap>", line 1027, in _find_and_load
+  File "<frozen importlib._bootstrap>", line 1006, in _find_and_load_unlocked
+  File "<frozen importlib._bootstrap>", line 688, in _load_unlocked
+  File "<frozen importlib._bootstrap_external>", line 883, in exec_module
+  File "<frozen importlib._bootstrap>", line 241, in _call_with_frames_removed
+  File "/home/ywan0794/miniconda3/envs/depthmaster/lib/python3.10/site-packages/diffusers/pipelines/pipeline_utils.py", line 69, in <module>
+    from .pipeline_loading_utils import (
+  File "/home/ywan0794/miniconda3/envs/depthmaster/lib/python3.10/site-packages/diffusers/pipelines/pipeline_loading_utils.py", line 48, in <module>
+    from transformers.utils import FLAX_WEIGHTS_NAME as TRANSFORMERS_FLAX_WEIGHTS_NAME
+ImportError: cannot import name 'FLAX_WEIGHTS_NAME' from 'transformers.utils' (/home/ywan0794/miniconda3/envs/depthmaster/lib/python3.10/site-packages/transformers/utils/__init__.py)
+
+The above exception was the direct cause of the following exception:
+
+Traceback (most recent call last):
+  File "/home/ywan0794/MoGe/moge/scripts/eval_baseline.py", line 165, in <module>
+    main()
+  File "/home/ywan0794/miniconda3/envs/depthmaster/lib/python3.10/site-packages/click/core.py", line 1514, in __call__
+    return self.main(*args, **kwargs)
+  File "/home/ywan0794/miniconda3/envs/depthmaster/lib/python3.10/site-packages/click/core.py", line 1435, in main
+    rv = self.invoke(ctx)
+  File "/home/ywan0794/miniconda3/envs/depthmaster/lib/python3.10/site-packages/click/core.py", line 1298, in invoke
+    return ctx.invoke(self.callback, **ctx.params)
+  File "/home/ywan0794/miniconda3/envs/depthmaster/lib/python3.10/site-packages/click/core.py", line 853, in invoke
+    return callback(*args, **kwargs)
+  File "/home/ywan0794/miniconda3/envs/depthmaster/lib/python3.10/site-packages/click/decorators.py", line 34, in new_func
+    return f(get_current_context(), *args, **kwargs)
+  File "/home/ywan0794/MoGe/moge/scripts/eval_baseline.py", line 42, in main
+    baseline : MGEBaselineInterface = baseline_cls.load.main(ctx.args, standalone_mode=False)
+  File "/home/ywan0794/miniconda3/envs/depthmaster/lib/python3.10/site-packages/click/core.py", line 1435, in main
+    rv = self.invoke(ctx)
+  File "/home/ywan0794/miniconda3/envs/depthmaster/lib/python3.10/site-packages/click/core.py", line 1298, in invoke
+    return ctx.invoke(self.callback, **ctx.params)
+  File "/home/ywan0794/miniconda3/envs/depthmaster/lib/python3.10/site-packages/click/core.py", line 853, in invoke
+    return callback(*args, **kwargs)
+  File "/home/ywan0794/MoGe/baselines/depthmaster.py", line 71, in load
+    return Baseline(repo_path, checkpoint, processing_res, half_precision, device)
+  File "/home/ywan0794/MoGe/baselines/depthmaster.py", line 38, in __init__
+    from depthmaster import DepthMasterPipeline
+  File "/home/ywan0794/EvalMDE/DepthMaster/depthmaster/__init__.py", line 26, in <module>
+    from .depthmaster_pipeline import DepthMasterPipeline, DepthMasterDepthOutput  # noqa: F401
+  File "/home/ywan0794/EvalMDE/DepthMaster/depthmaster/depthmaster_pipeline.py", line 31, in <module>
+    from diffusers import (
+  File "<frozen importlib._bootstrap>", line 1075, in _handle_fromlist
+  File "/home/ywan0794/miniconda3/envs/depthmaster/lib/python3.10/site-packages/diffusers/utils/import_utils.py", line 799, in __getattr__
+    value = getattr(module, name)
+  File "/home/ywan0794/miniconda3/envs/depthmaster/lib/python3.10/site-packages/diffusers/utils/import_utils.py", line 798, in __getattr__
+    module = self._get_module(self._class_to_module[name])
+  File "/home/ywan0794/miniconda3/envs/depthmaster/lib/python3.10/site-packages/diffusers/utils/import_utils.py", line 810, in _get_module
+    raise RuntimeError(
+RuntimeError: Failed to import diffusers.pipelines.pipeline_utils because of the following error (look up to see its traceback):
+cannot import name 'FLAX_WEIGHTS_NAME' from 'transformers.utils' (/home/ywan0794/miniconda3/envs/depthmaster/lib/python3.10/site-packages/transformers/utils/__init__.py)
+[FAIL rc=1] depthmaster
+
+============================================
+[ppd] starting at Wed May 13 02:33:15 AM AEST 2026  (conda env: ppd)
+============================================
+Active env: ppd
+CUDA: True NVIDIA H100 NVL
+Traceback (most recent call last):
+  File "/home/ywan0794/MoGe/moge/scripts/eval_baseline.py", line 165, in <module>
+    main()
+  File "/home/ywan0794/miniconda3/envs/ppd/lib/python3.10/site-packages/click/core.py", line 1514, in __call__
+    return self.main(*args, **kwargs)
+  File "/home/ywan0794/miniconda3/envs/ppd/lib/python3.10/site-packages/click/core.py", line 1435, in main
+    rv = self.invoke(ctx)
+  File "/home/ywan0794/miniconda3/envs/ppd/lib/python3.10/site-packages/click/core.py", line 1298, in invoke
+    return ctx.invoke(self.callback, **ctx.params)
+  File "/home/ywan0794/miniconda3/envs/ppd/lib/python3.10/site-packages/click/core.py", line 853, in invoke
+    return callback(*args, **kwargs)
+  File "/home/ywan0794/miniconda3/envs/ppd/lib/python3.10/site-packages/click/decorators.py", line 34, in new_func
+    return f(get_current_context(), *args, **kwargs)
+  File "/home/ywan0794/MoGe/moge/scripts/eval_baseline.py", line 42, in main
+    baseline : MGEBaselineInterface = baseline_cls.load.main(ctx.args, standalone_mode=False)
+  File "/home/ywan0794/miniconda3/envs/ppd/lib/python3.10/site-packages/click/core.py", line 1435, in main
+    rv = self.invoke(ctx)
+  File "/home/ywan0794/miniconda3/envs/ppd/lib/python3.10/site-packages/click/core.py", line 1298, in invoke
+    return ctx.invoke(self.callback, **ctx.params)
+  File "/home/ywan0794/miniconda3/envs/ppd/lib/python3.10/site-packages/click/core.py", line 853, in invoke
+    return callback(*args, **kwargs)
+  File "/home/ywan0794/MoGe/baselines/ppd.py", line 79, in load
+    return Baseline(repo_path, semantics_model, semantics_pth, model_pth, sampling_steps, device)
+  File "/home/ywan0794/MoGe/baselines/ppd.py", line 37, in __init__
+    from ppd.models.ppd import PixelPerfectDepth
+  File "/home/ywan0794/EvalMDE/Pixel-Perfect-Depth/ppd/models/ppd.py", line 9, in <module>
+    from omegaconf import DictConfig
+ModuleNotFoundError: No module named 'omegaconf'
+[FAIL rc=1] ppd
+
+============================================
+[da3_mono] starting at Wed May 13 02:33:45 AM AEST 2026  (conda env: da3)
+============================================
+Active env: da3
+CUDA: True NVIDIA H100 NVL
+[WARN ] Dependency `gsplat` is required for rendering 3DGS. Install via: pip install git+https://github.com/nerfstudio-project/gsplat.git@0b4dddf04cb687367602c01196913cde6a743d70
+[INFO ] using MLP layer as FFN
+
+
+
                                                     
+Traceback (most recent call last):
+  File "/home/ywan0794/MoGe/moge/scripts/eval_baseline.py", line 165, in <module>
+    main()
+  File "/home/ywan0794/miniconda3/envs/da3/lib/python3.10/site-packages/click/core.py", line 1485, in __call__
+    return self.main(*args, **kwargs)
+  File "/home/ywan0794/miniconda3/envs/da3/lib/python3.10/site-packages/click/core.py", line 1406, in main
+    rv = self.invoke(ctx)
+  File "/home/ywan0794/miniconda3/envs/da3/lib/python3.10/site-packages/click/core.py", line 1269, in invoke
+    return ctx.invoke(self.callback, **ctx.params)
+  File "/home/ywan0794/miniconda3/envs/da3/lib/python3.10/site-packages/click/core.py", line 824, in invoke
+    return callback(*args, **kwargs)
+  File "/home/ywan0794/miniconda3/envs/da3/lib/python3.10/site-packages/click/decorators.py", line 34, in new_func
+    return f(get_current_context(), *args, **kwargs)
+  File "/home/ywan0794/MoGe/moge/scripts/eval_baseline.py", line 70, in main
+    pred = baseline.infer_for_evaluation(image)
+  File "/home/ywan0794/MoGe/moge/test/baseline.py", line 43, in infer_for_evaluation
+    return self.infer(image, intrinsics)
+  File "/home/ywan0794/miniconda3/envs/da3/lib/python3.10/site-packages/torch/utils/_contextlib.py", line 116, in decorate_context
+    return func(*args, **kwargs)
+  File "/home/ywan0794/MoGe/baselines/da3_mono.py", line 91, in infer
+    output = self.model(x)
+  File "/home/ywan0794/miniconda3/envs/da3/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1736, in _wrapped_call_impl
+    return self._call_impl(*args, **kwargs)
+  File "/home/ywan0794/miniconda3/envs/da3/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1747, in _call_impl
+    return forward_call(*args, **kwargs)
+  File "/home/ywan0794/miniconda3/envs/da3/lib/python3.10/site-packages/torch/utils/_contextlib.py", line 116, in decorate_context
+    return func(*args, **kwargs)
+  File "/home/ywan0794/EvalMDE/Depth-Anything-3/src/depth_anything_3/api.py", line 129, in forward
+    return self.model(
+  File "/home/ywan0794/miniconda3/envs/da3/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1736, in _wrapped_call_impl
+    return self._call_impl(*args, **kwargs)
+  File "/home/ywan0794/miniconda3/envs/da3/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1747, in _call_impl
+    return forward_call(*args, **kwargs)
+  File "/home/ywan0794/EvalMDE/Depth-Anything-3/src/depth_anything_3/model/da3.py", line 132, in forward
+    feats, aux_feats = self.backbone(
+  File "/home/ywan0794/miniconda3/envs/da3/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1736, in _wrapped_call_impl
+    return self._call_impl(*args, **kwargs)
+  File "/home/ywan0794/miniconda3/envs/da3/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1747, in _call_impl
+    return forward_call(*args, **kwargs)
+  File "/home/ywan0794/EvalMDE/Depth-Anything-3/src/depth_anything_3/model/dinov2/dinov2.py", line 60, in forward
+    return self.pretrained.get_intermediate_layers(
+  File "/home/ywan0794/EvalMDE/Depth-Anything-3/src/depth_anything_3/model/dinov2/vision_transformer.py", line 379, in get_intermediate_layers
+    outputs, aux_outputs = self._get_intermediate_layers_not_chunked(
+  File "/home/ywan0794/EvalMDE/Depth-Anything-3/src/depth_anything_3/model/dinov2/vision_transformer.py", line 347, in _get_intermediate_layers_not_chunked
+    if i in export_feat_layers:
+TypeError: argument of type 'NoneType' is not iterable
+[FAIL rc=1] da3_mono
+
+============================================
+[fe2e] starting at Wed May 13 02:34:42 AM AEST 2026  (conda env: fe2e)
+============================================
+Active env: fe2e
+CUDA: True NVIDIA H100 NVL
+Traceback (most recent call last):
+  File "/home/ywan0794/MoGe/moge/scripts/eval_baseline.py", line 11, in <module>
+    import click
+ModuleNotFoundError: No module named 'click'
+[FAIL rc=1] fe2e
+
+============================================
+sanity-all finished at Wed May 13 02:34:58 AM AEST 2026
+============================================
+=== Summary ===
+[FAIL rc=1] marigold
+[FAIL rc=1] lotus
+[FAIL rc=1] depthmaster
+[FAIL rc=1] ppd
+[FAIL rc=1] da3_mono
+[FAIL rc=1] fe2e

sanity_all_12095.log

@@ -0,0 +1,259 @@
+============================================
+sanity-all started at Wed May 13 02:45:10 AM AEST 2026
+Config: /home/ywan0794/MoGe/configs/eval/sanity_benchmarks.json
+TIMESTAMP: 20260513_024510
+Summary file: sanity_output/_sanity_all_20260513_024510.summary.txt
+============================================
+Wed May 13 02:45:10 2026       
++-----------------------------------------------------------------------------------------+
+| NVIDIA-SMI 550.163.01             Driver Version: 550.163.01     CUDA Version: 12.4     |
+|-----------------------------------------+------------------------+----------------------+
+| GPU  Name                 Persistence-M | Bus-Id          Disp.A | Volatile Uncorr. ECC |
+| Fan  Temp   Perf          Pwr:Usage/Cap |           Memory-Usage | GPU-Util  Compute M. |
+|                                         |                        |               MIG M. |
+|=========================================+========================+======================|
+|   0  NVIDIA H100 NVL                Off |   00000000:E1:00.0 Off |                    0 |
+| N/A   35C    P0             60W /  400W |      14MiB /  95830MiB |      0%      Default |
+|                                         |                        |             Disabled |
++-----------------------------------------+------------------------+----------------------+
+                                                                                         
++-----------------------------------------------------------------------------------------+
+| Processes:                                                                              |
+|  GPU   GI   CI        PID   Type   Process name                              GPU Memory |
+|        ID   ID                                                               Usage      |
+|=========================================================================================|
+|    0   N/A  N/A      4274      G   /usr/lib/xorg/Xorg                              4MiB |
++-----------------------------------------------------------------------------------------+
+
+============================================
+[marigold] starting at Wed May 13 02:45:10 AM AEST 2026  (conda env: marigold)
+============================================
+Active env: marigold
+CUDA: True NVIDIA H100 NVL
+Traceback (most recent call last):
+  File "/home/ywan0794/miniconda3/envs/marigold/lib/python3.10/site-packages/diffusers/utils/import_utils.py", line 920, in _get_module
+    return importlib.import_module("." + module_name, self.__name__)
+  File "/home/ywan0794/miniconda3/envs/marigold/lib/python3.10/importlib/__init__.py", line 126, in import_module
+    return _bootstrap._gcd_import(name[level:], package, level)
+  File "<frozen importlib._bootstrap>", line 1050, in _gcd_import
+  File "<frozen importlib._bootstrap>", line 1027, in _find_and_load
+  File "<frozen importlib._bootstrap>", line 1006, in _find_and_load_unlocked
+  File "<frozen importlib._bootstrap>", line 688, in _load_unlocked
+  File "<frozen importlib._bootstrap_external>", line 883, in exec_module
+  File "<frozen importlib._bootstrap>", line 241, in _call_with_frames_removed
+  File "/home/ywan0794/miniconda3/envs/marigold/lib/python3.10/site-packages/diffusers/loaders/peft.py", line 40, in <module>
+    from .lora_base import _fetch_state_dict
+  File "/home/ywan0794/miniconda3/envs/marigold/lib/python3.10/site-packages/diffusers/loaders/lora_base.py", line 44, in <module>
+    from transformers import PreTrainedModel
+  File "<frozen importlib._bootstrap>", line 1075, in _handle_fromlist
+  File "/home/ywan0794/miniconda3/envs/marigold/lib/python3.10/site-packages/transformers/utils/import_utils.py", line 2226, in __getattr__
+    module = self._get_module(self._class_to_module[name])
+  File "/home/ywan0794/miniconda3/envs/marigold/lib/python3.10/site-packages/transformers/utils/import_utils.py", line 2460, in _get_module
+    raise e
+  File "/home/ywan0794/miniconda3/envs/marigold/lib/python3.10/site-packages/transformers/utils/import_utils.py", line 2458, in _get_module
+    return importlib.import_module("." + module_name, self.__name__)
+  File "/home/ywan0794/miniconda3/envs/marigold/lib/python3.10/importlib/__init__.py", line 126, in import_module
+    return _bootstrap._gcd_import(name[level:], package, level)
+  File "/home/ywan0794/miniconda3/envs/marigold/lib/python3.10/site-packages/transformers/modeling_utils.py", line 69, in <module>
+    from .integrations.finegrained_fp8 import ALL_FP8_EXPERTS_FUNCTIONS
+  File "/home/ywan0794/miniconda3/envs/marigold/lib/python3.10/site-packages/transformers/integrations/finegrained_fp8.py", line 30, in <module>
+    from .moe import ExpertsInterface, use_experts_implementation
+  File "/home/ywan0794/miniconda3/envs/marigold/lib/python3.10/site-packages/transformers/integrations/moe.py", line 250, in <module>
+    torch.library.custom_op("transformers::grouped_mm_fallback", _grouped_mm_fallback, mutates_args=())
+  File "/home/ywan0794/miniconda3/envs/marigold/lib/python3.10/site-packages/torch/_library/custom_ops.py", line 142, in custom_op
+    return inner(fn)
+  File "/home/ywan0794/miniconda3/envs/marigold/lib/python3.10/site-packages/torch/_library/custom_ops.py", line 119, in inner
+    schema_str = torch._custom_op.impl.infer_schema(fn, mutates_args)
+  File "/home/ywan0794/miniconda3/envs/marigold/lib/python3.10/site-packages/torch/_library/infer_schema.py", line 42, in infer_schema
+    error_fn(
+  File "/home/ywan0794/miniconda3/envs/marigold/lib/python3.10/site-packages/torch/_library/infer_schema.py", line 21, in error_fn
+    raise ValueError(
+ValueError: infer_schema(func): Parameter input has unsupported type torch.Tensor. The valid types are: dict_keys([<class 'torch.Tensor'>, typing.Optional[torch.Tensor], typing.Sequence[torch.Tensor], typing.List[torch.Tensor], typing.Sequence[typing.Optional[torch.Tensor]], typing.List[typing.Optional[torch.Tensor]], <class 'int'>, typing.Optional[int], typing.Sequence[int], typing.List[int], typing.Optional[typing.Sequence[int]], typing.Optional[typing.List[int]], <class 'float'>, typing.Optional[float], typing.Sequence[float], typing.List[float], typing.Optional[typing.Sequence[float]], typing.Optional[typing.List[float]], <class 'bool'>, typing.Optional[bool], typing.Sequence[bool], typing.List[bool], typing.Optional[typing.Sequence[bool]], typing.Optional[typing.List[bool]], <class 'str'>, typing.Optional[str], typing.Union[int, float, bool], typing.Union[int, float, bool, NoneType], typing.Sequence[typing.Union[int, float, bool]], typing.List[typing.Union[int, float, bool]], <class 'torch.dtype'>, typing.Optional[torch.dtype], <class 'torch.device'>, typing.Optional[torch.device]]). Got func with signature (input: 'torch.Tensor', weight: 'torch.Tensor', offs: 'torch.Tensor') -> 'torch.Tensor')
+
+The above exception was the direct cause of the following exception:
+
+Traceback (most recent call last):
+  File "/home/ywan0794/miniconda3/envs/marigold/lib/python3.10/site-packages/diffusers/utils/import_utils.py", line 920, in _get_module
+    return importlib.import_module("." + module_name, self.__name__)
+  File "/home/ywan0794/miniconda3/envs/marigold/lib/python3.10/importlib/__init__.py", line 126, in import_module
+    return _bootstrap._gcd_import(name[level:], package, level)
+  File "<frozen importlib._bootstrap>", line 1050, in _gcd_import
+  File "<frozen importlib._bootstrap>", line 1027, in _find_and_load
+  File "<frozen importlib._bootstrap>", line 992, in _find_and_load_unlocked
+  File "<frozen importlib._bootstrap>", line 241, in _call_with_frames_removed
+  File "<frozen importlib._bootstrap>", line 1050, in _gcd_import
+  File "<frozen importlib._bootstrap>", line 1027, in _find_and_load
+  File "<frozen importlib._bootstrap>", line 1006, in _find_and_load_unlocked
+  File "<frozen importlib._bootstrap>", line 688, in _load_unlocked
+  File "<frozen importlib._bootstrap_external>", line 883, in exec_module
+  File "<frozen importlib._bootstrap>", line 241, in _call_with_frames_removed
+  File "/home/ywan0794/miniconda3/envs/marigold/lib/python3.10/site-packages/diffusers/models/autoencoders/__init__.py", line 1, in <module>
+    from .autoencoder_asym_kl import AsymmetricAutoencoderKL
+  File "/home/ywan0794/miniconda3/envs/marigold/lib/python3.10/site-packages/diffusers/models/autoencoders/autoencoder_asym_kl.py", line 23, in <module>
+    from .vae import DecoderOutput, DiagonalGaussianDistribution, Encoder, MaskConditionDecoder
+  File "/home/ywan0794/miniconda3/envs/marigold/lib/python3.10/site-packages/diffusers/models/autoencoders/vae.py", line 25, in <module>
+    from ..unets.unet_2d_blocks import (
+  File "/home/ywan0794/miniconda3/envs/marigold/lib/python3.10/site-packages/diffusers/models/unets/__init__.py", line 6, in <module>
+    from .unet_2d import UNet2DModel
+  File "/home/ywan0794/miniconda3/envs/marigold/lib/python3.10/site-packages/diffusers/models/unets/unet_2d.py", line 24, in <module>
+    from .unet_2d_blocks import UNetMidBlock2D, get_down_block, get_up_block
+  File "/home/ywan0794/miniconda3/envs/marigold/lib/python3.10/site-packages/diffusers/models/unets/unet_2d_blocks.py", line 36, in <module>
+    from ..transformers.dual_transformer_2d import DualTransformer2DModel
+  File "/home/ywan0794/miniconda3/envs/marigold/lib/python3.10/site-packages/diffusers/models/transformers/__init__.py", line 6, in <module>
+    from .cogvideox_transformer_3d import CogVideoXTransformer3DModel
+  File "/home/ywan0794/miniconda3/envs/marigold/lib/python3.10/site-packages/diffusers/models/transformers/cogvideox_transformer_3d.py", line 22, in <module>
+    from ...loaders import PeftAdapterMixin
+  File "<frozen importlib._bootstrap>", line 1075, in _handle_fromlist
+  File "/home/ywan0794/miniconda3/envs/marigold/lib/python3.10/site-packages/diffusers/utils/import_utils.py", line 910, in __getattr__
+    module = self._get_module(self._class_to_module[name])
+  File "/home/ywan0794/miniconda3/envs/marigold/lib/python3.10/site-packages/diffusers/utils/import_utils.py", line 922, in _get_module
+    raise RuntimeError(
+RuntimeError: Failed to import diffusers.loaders.peft because of the following error (look up to see its traceback):
+infer_schema(func): Parameter input has unsupported type torch.Tensor. The valid types are: dict_keys([<class 'torch.Tensor'>, typing.Optional[torch.Tensor], typing.Sequence[torch.Tensor], typing.List[torch.Tensor], typing.Sequence[typing.Optional[torch.Tensor]], typing.List[typing.Optional[torch.Tensor]], <class 'int'>, typing.Optional[int], typing.Sequence[int], typing.List[int], typing.Optional[typing.Sequence[int]], typing.Optional[typing.List[int]], <class 'float'>, typing.Optional[float], typing.Sequence[float], typing.List[float], typing.Optional[typing.Sequence[float]], typing.Optional[typing.List[float]], <class 'bool'>, typing.Optional[bool], typing.Sequence[bool], typing.List[bool], typing.Optional[typing.Sequence[bool]], typing.Optional[typing.List[bool]], <class 'str'>, typing.Optional[str], typing.Union[int, float, bool], typing.Union[int, float, bool, NoneType], typing.Sequence[typing.Union[int, float, bool]], typing.List[typing.Union[int, float, bool]], <class 'torch.dtype'>, typing.Optional[torch.dtype], <class 'torch.device'>, typing.Optional[torch.device]]). Got func with signature (input: 'torch.Tensor', weight: 'torch.Tensor', offs: 'torch.Tensor') -> 'torch.Tensor')
+
+The above exception was the direct cause of the following exception:
+
+Traceback (most recent call last):
+  File "/home/ywan0794/MoGe/moge/scripts/eval_baseline.py", line 165, in <module>
+    main()
+  File "/home/ywan0794/miniconda3/envs/marigold/lib/python3.10/site-packages/click/core.py", line 1514, in __call__
+    return self.main(*args, **kwargs)
+  File "/home/ywan0794/miniconda3/envs/marigold/lib/python3.10/site-packages/click/core.py", line 1435, in main
+    rv = self.invoke(ctx)
+  File "/home/ywan0794/miniconda3/envs/marigold/lib/python3.10/site-packages/click/core.py", line 1298, in invoke
+    return ctx.invoke(self.callback, **ctx.params)
+  File "/home/ywan0794/miniconda3/envs/marigold/lib/python3.10/site-packages/click/core.py", line 853, in invoke
+    return callback(*args, **kwargs)
+  File "/home/ywan0794/miniconda3/envs/marigold/lib/python3.10/site-packages/click/decorators.py", line 34, in new_func
+    return f(get_current_context(), *args, **kwargs)
+  File "/home/ywan0794/MoGe/moge/scripts/eval_baseline.py", line 42, in main
+    baseline : MGEBaselineInterface = baseline_cls.load.main(ctx.args, standalone_mode=False)
+  File "/home/ywan0794/miniconda3/envs/marigold/lib/python3.10/site-packages/click/core.py", line 1435, in main
+    rv = self.invoke(ctx)
+  File "/home/ywan0794/miniconda3/envs/marigold/lib/python3.10/site-packages/click/core.py", line 1298, in invoke
+    return ctx.invoke(self.callback, **ctx.params)
+  File "/home/ywan0794/miniconda3/envs/marigold/lib/python3.10/site-packages/click/core.py", line 853, in invoke
+    return callback(*args, **kwargs)
+  File "/home/ywan0794/MoGe/baselines/marigold.py", line 75, in load
+    return Baseline(repo_path, checkpoint, denoise_steps, ensemble_size,
+  File "/home/ywan0794/MoGe/baselines/marigold.py", line 38, in __init__
+    from marigold import MarigoldDepthPipeline
+  File "/home/ywan0794/EvalMDE/Marigold/marigold/__init__.py", line 31, in <module>
+    from .marigold_depth_pipeline import (
+  File "/home/ywan0794/EvalMDE/Marigold/marigold/marigold_depth_pipeline.py", line 35, in <module>
+    from diffusers import (
+  File "<frozen importlib._bootstrap>", line 1075, in _handle_fromlist
+  File "/home/ywan0794/miniconda3/envs/marigold/lib/python3.10/site-packages/diffusers/utils/import_utils.py", line 911, in __getattr__
+    value = getattr(module, name)
+  File "/home/ywan0794/miniconda3/envs/marigold/lib/python3.10/site-packages/diffusers/utils/import_utils.py", line 910, in __getattr__
+    module = self._get_module(self._class_to_module[name])
+  File "/home/ywan0794/miniconda3/envs/marigold/lib/python3.10/site-packages/diffusers/utils/import_utils.py", line 922, in _get_module
+    raise RuntimeError(
+RuntimeError: Failed to import diffusers.models.autoencoders.autoencoder_kl because of the following error (look up to see its traceback):
+Failed to import diffusers.loaders.peft because of the following error (look up to see its traceback):
+infer_schema(func): Parameter input has unsupported type torch.Tensor. The valid types are: dict_keys([<class 'torch.Tensor'>, typing.Optional[torch.Tensor], typing.Sequence[torch.Tensor], typing.List[torch.Tensor], typing.Sequence[typing.Optional[torch.Tensor]], typing.List[typing.Optional[torch.Tensor]], <class 'int'>, typing.Optional[int], typing.Sequence[int], typing.List[int], typing.Optional[typing.Sequence[int]], typing.Optional[typing.List[int]], <class 'float'>, typing.Optional[float], typing.Sequence[float], typing.List[float], typing.Optional[typing.Sequence[float]], typing.Optional[typing.List[float]], <class 'bool'>, typing.Optional[bool], typing.Sequence[bool], typing.List[bool], typing.Optional[typing.Sequence[bool]], typing.Optional[typing.List[bool]], <class 'str'>, typing.Optional[str], typing.Union[int, float, bool], typing.Union[int, float, bool, NoneType], typing.Sequence[typing.Union[int, float, bool]], typing.List[typing.Union[int, float, bool]], <class 'torch.dtype'>, typing.Optional[torch.dtype], <class 'torch.device'>, typing.Optional[torch.device]]). Got func with signature (input: 'torch.Tensor', weight: 'torch.Tensor', offs: 'torch.Tensor') -> 'torch.Tensor')
+[FAIL rc=1] marigold
+
+============================================
+[lotus] starting at Wed May 13 02:45:25 AM AEST 2026  (conda env: lotus)
+============================================
+Active env: lotus
+CUDA: True NVIDIA H100 NVL
+
+
+
+Traceback (most recent call last):
+Thread-12 (loop):
+  File "/home/ywan0794/miniconda3/envs/lotus/lib/python3.10/threading.py", line 1016, in _bootstrap_inner
+Traceback (most recent call last):
+  File "/home/ywan0794/miniconda3/envs/lotus/lib/python3.10/threading.py", line 1016, in _bootstrap_inner
+    Exception in thread Thread-14 (loop):
+Traceback (most recent call last):
+  File "/home/ywan0794/miniconda3/envs/lotus/lib/python3.10/threading.py", line 1016, in _bootstrap_inner
+self.run()
+  File "/home/ywan0794/miniconda3/envs/lotus/lib/python3.10/threading.py", line 953, in run
+Exception in thread Thread-16 (loop):
+Traceback (most recent call last):
+  File "/home/ywan0794/miniconda3/envs/lotus/lib/python3.10/threading.py", line 1016, in _bootstrap_inner
+Exception in thread Thread-15 (loop)    self.run()
+  File "/home/ywan0794/miniconda3/envs/lotus/lib/python3.10/threading.py", line 953, in run
+    self.run()
+  File "/home/ywan0794/miniconda3/envs/lotus/lib/python3.10/threading.py", line 953, in run
+:
+        self.run()
+  File "/home/ywan0794/miniconda3/envs/lotus/lib/python3.10/threading.py", line 953, in run
+self._target(*self._args, **self._kwargs)    self._target(*self._args, **self._kwargs)
+  File "/home/ywan0794/miniconda3/envs/lotus/lib/python3.10/site-packages/pipeline/components.py", line 218, in loop
+    self._target(*self._args, **self._kwargs)
+Traceback (most recent call last):
+  File "/home/ywan0794/miniconda3/envs/lotus/lib/python3.10/threading.py", line 1016, in _bootstrap_inner
+
+  File "/home/ywan0794/miniconda3/envs/lotus/lib/python3.10/site-packages/pipeline/components.py", line 218, in loop
+  File "/home/ywan0794/miniconda3/envs/lotus/lib/python3.10/site-packages/pipeline/components.py", line 218, in loop
+    self._target(*self._args, **self._kwargs)
+  File "/home/ywan0794/miniconda3/envs/lotus/lib/python3.10/site-packages/pipeline/components.py", line 218, in loop
+    result = self.work(item)
+  File "/home/ywan0794/miniconda3/envs/lotus/lib/python3.10/site-packages/pipeline/components.py", line 208, in work
+    result = self.work(item)
+  File "/home/ywan0794/miniconda3/envs/lotus/lib/python3.10/site-packages/pipeline/components.py", line 208, in work
+    return self.work_fn(*args, **kwargs)
+  File "/home/ywan0794/MoGe/moge/test/dataloader.py", line 120, in _process_instance
+    return self.work_fn(*args, **kwargs)
+  File "/home/ywan0794/MoGe/moge/test/dataloader.py", line 120, in _process_instance
+    result = self.work(item)
+  File "/home/ywan0794/miniconda3/envs/lotus/lib/python3.10/site-packages/pipeline/components.py", line 208, in work
+    return self.work_fn(*args, **kwargs)
+  File "/home/ywan0794/MoGe/moge/test/dataloader.py", line 120, in _process_instance
+    self.run()
+  File "/home/ywan0794/miniconda3/envs/lotus/lib/python3.10/threading.py", line 953, in run
+    self._target(*self._args, **self._kwargs)
+  File "/home/ywan0794/miniconda3/envs/lotus/lib/python3.10/site-packages/pipeline/components.py", line 218, in loop
+    result = self.work(item)
+  File "/home/ywan0794/miniconda3/envs/lotus/lib/python3.10/site-packages/pipeline/components.py", line 208, in work
+    direction = utils3d.np.unproject_cv(np.array([[cu, cv]], dtype=np.float32), np.array([1.0], dtype=np.float32), intrinsics=intrinsics)[0]
+  File "/home/ywan0794/miniconda3/envs/lotus/lib/python3.10/site-packages/utils3d/helpers.py", line 16, in wrapper
+    return self.work_fn(*args, **kwargs)
+  File "/home/ywan0794/MoGe/moge/test/dataloader.py", line 120, in _process_instance
+    direction = utils3d.np.unproject_cv(np.array([[cu, cv]], dtype=np.float32), np.array([1.0], dtype=np.float32), intrinsics=intrinsics)[0]
+  File "/home/ywan0794/miniconda3/envs/lotus/lib/python3.10/site-packages/utils3d/helpers.py", line 16, in wrapper
+    direction = utils3d.np.unproject_cv(np.array([[cu, cv]], dtype=np.float32), np.array([1.0], dtype=np.float32), intrinsics=intrinsics)[0]
+  File "/home/ywan0794/miniconda3/envs/lotus/lib/python3.10/site-packages/utils3d/helpers.py", line 16, in wrapper
+    result = self.work(item)
+  File "/home/ywan0794/miniconda3/envs/lotus/lib/python3.10/site-packages/pipeline/components.py", line 208, in work
+    return self.work_fn(*args, **kwargs)
+  File "/home/ywan0794/MoGe/moge/test/dataloader.py", line 120, in _process_instance
+    return fn(*args, **kwargs)
+  File "/home/ywan0794/miniconda3/envs/lotus/lib/python3.10/site-packages/utils3d/numpy/helpers.py", line 90, in wrapper
+    direction = utils3d.np.unproject_cv(np.array([[cu, cv]], dtype=np.float32), np.array([1.0], dtype=np.float32), intrinsics=intrinsics)[0]
+  File "/home/ywan0794/miniconda3/envs/lotus/lib/python3.10/site-packages/utils3d/helpers.py", line 16, in wrapper
+    return fn(*args, **kwargs)
+  File "/home/ywan0794/miniconda3/envs/lotus/lib/python3.10/site-packages/utils3d/numpy/helpers.py", line 90, in wrapper
+    direction = utils3d.np.unproject_cv(np.array([[cu, cv]], dtype=np.float32), np.array([1.0], dtype=np.float32), intrinsics=intrinsics)[0]
+  File "/home/ywan0794/miniconda3/envs/lotus/lib/python3.10/site-packages/utils3d/helpers.py", line 16, in wrapper
+    result = func(*args, **kwargs)
+  File "/home/ywan0794/miniconda3/envs/lotus/lib/python3.10/site-packages/utils3d/numpy/transforms.py", line 737, in unproject_cv
+    return fn(*args, **kwargs)
+  File "/home/ywan0794/miniconda3/envs/lotus/lib/python3.10/site-packages/utils3d/numpy/helpers.py", line 90, in wrapper
+    return fn(*args, **kwargs)
+  File "/home/ywan0794/miniconda3/envs/lotus/lib/python3.10/site-packages/utils3d/numpy/helpers.py", line 90, in wrapper
+    return fn(*args, **kwargs)
+  File "/home/ywan0794/miniconda3/envs/lotus/lib/python3.10/site-packages/utils3d/numpy/helpers.py", line 90, in wrapper
+    result = func(*args, **kwargs)
+  File "/home/ywan0794/miniconda3/envs/lotus/lib/python3.10/site-packages/utils3d/numpy/transforms.py", line 737, in unproject_cv
+    result = func(*args, **kwargs)
+  File "/home/ywan0794/miniconda3/envs/lotus/lib/python3.10/site-packages/utils3d/numpy/transforms.py", line 737, in unproject_cv
+    result = func(*args, **kwargs)
+  File "/home/ywan0794/miniconda3/envs/lotus/lib/python3.10/site-packages/utils3d/numpy/transforms.py", line 737, in unproject_cv
+    points = points @ np.linalg.inv(transform).mT
+AttributeError: 'numpy.ndarray' object has no attribute 'mT'. Did you mean: 'T'?
+    points = points @ np.linalg.inv(transform).mT
+AttributeError: 'numpy.ndarray' object has no attribute 'mT'. Did you mean: 'T'?
+    points = points @ np.linalg.inv(transform).mT
+AttributeError: 'numpy.ndarray' object has no attribute 'mT'. Did you mean: 'T'?
+    result = func(*args, **kwargs)
+  File "/home/ywan0794/miniconda3/envs/lotus/lib/python3.10/site-packages/utils3d/numpy/transforms.py", line 737, in unproject_cv
+    points = points @ np.linalg.inv(transform).mT
+AttributeError: 'numpy.ndarray' object has no attribute 'mT'. Did you mean: 'T'?
+    points = points @ np.linalg.inv(transform).mT
+AttributeError: 'numpy.ndarray' object has no attribute 'mT'. Did you mean: 'T'?
+slurmstepd-erinyes: error: *** JOB 12095 ON erinyes CANCELLED AT 2026-05-13T02:49:20 ***

sanity_all_12096.log

@@ -0,0 +1,332 @@
+============================================
+sanity-all started at Wed May 13 02:59:45 AM AEST 2026
+Config: /home/ywan0794/MoGe/configs/eval/sanity_benchmarks.json
+TIMESTAMP: 20260513_025945
+Summary file: sanity_output/_sanity_all_20260513_025945.summary.txt
+============================================
+Wed May 13 02:59:45 2026       
++-----------------------------------------------------------------------------------------+
+| NVIDIA-SMI 550.163.01             Driver Version: 550.163.01     CUDA Version: 12.4     |
+|-----------------------------------------+------------------------+----------------------+
+| GPU  Name                 Persistence-M | Bus-Id          Disp.A | Volatile Uncorr. ECC |
+| Fan  Temp   Perf          Pwr:Usage/Cap |           Memory-Usage | GPU-Util  Compute M. |
+|                                         |                        |               MIG M. |
+|=========================================+========================+======================|
+|   0  NVIDIA H100 NVL                Off |   00000000:E1:00.0 Off |                    0 |
+| N/A   35C    P0             60W /  400W |      14MiB /  95830MiB |      0%      Default |
+|                                         |                        |             Disabled |
++-----------------------------------------+------------------------+----------------------+
+                                                                                         
++-----------------------------------------------------------------------------------------+
+| Processes:                                                                              |
+|  GPU   GI   CI        PID   Type   Process name                              GPU Memory |
+|        ID   ID                                                               Usage      |
+|=========================================================================================|
+|    0   N/A  N/A      4274      G   /usr/lib/xorg/Xorg                              4MiB |
++-----------------------------------------------------------------------------------------+
+
+============================================
+[marigold] starting at Wed May 13 02:59:45 AM AEST 2026  (conda env: marigold)
+============================================
+Active env: marigold
+CUDA: True NVIDIA H100 NVL
+The config attributes {'prediction_type': 'depth'} were passed to MarigoldDepthPipeline, but are not expected and will be ignored. Please verify your model_index.json configuration file.
+Keyword arguments {'prediction_type': 'depth'} are not expected by MarigoldDepthPipeline and will be ignored.
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
                                                             
+[OK] marigold -> sanity_output/sanity_marigold_20260513_025945.json
+
+============================================
+[lotus] starting at Wed May 13 03:01:07 AM AEST 2026  (conda env: lotus)
+============================================
+Active env: lotus
+CUDA: True NVIDIA H100 NVL
+
+A module that was compiled using NumPy 1.x cannot be run in
+NumPy 2.2.6 as it may crash. To support both 1.x and 2.x
+versions of NumPy, modules must be compiled with NumPy 2.0.
+Some module may need to rebuild instead e.g. with 'pybind11>=2.12'.
+
+If you are a user of the module, the easiest solution will be to
+downgrade to 'numpy<2' or try to upgrade the affected module.
+We expect that some modules will need time to support NumPy 2.
+
+Traceback (most recent call last):  File "/home/ywan0794/MoGe/moge/scripts/eval_baseline.py", line 165, in <module>
+    main()
+  File "/home/ywan0794/miniconda3/envs/lotus/lib/python3.10/site-packages/click/core.py", line 1514, in __call__
+    return self.main(*args, **kwargs)
+  File "/home/ywan0794/miniconda3/envs/lotus/lib/python3.10/site-packages/click/core.py", line 1435, in main
+    rv = self.invoke(ctx)
+  File "/home/ywan0794/miniconda3/envs/lotus/lib/python3.10/site-packages/click/core.py", line 1298, in invoke
+    return ctx.invoke(self.callback, **ctx.params)
+  File "/home/ywan0794/miniconda3/envs/lotus/lib/python3.10/site-packages/click/core.py", line 853, in invoke
+    return callback(*args, **kwargs)
+  File "/home/ywan0794/miniconda3/envs/lotus/lib/python3.10/site-packages/click/decorators.py", line 34, in new_func
+    return f(get_current_context(), *args, **kwargs)
+  File "/home/ywan0794/MoGe/moge/scripts/eval_baseline.py", line 25, in main
+    import  cv2
+  File "/home/ywan0794/miniconda3/envs/lotus/lib/python3.10/site-packages/cv2/__init__.py", line 181, in <module>
+    bootstrap()
+  File "/home/ywan0794/miniconda3/envs/lotus/lib/python3.10/site-packages/cv2/__init__.py", line 153, in bootstrap
+    native_module = importlib.import_module("cv2")
+  File "/home/ywan0794/miniconda3/envs/lotus/lib/python3.10/importlib/__init__.py", line 126, in import_module
+    return _bootstrap._gcd_import(name[level:], package, level)
+AttributeError: _ARRAY_API not found
+Traceback (most recent call last):
+  File "/home/ywan0794/MoGe/moge/scripts/eval_baseline.py", line 165, in <module>
+    main()
+  File "/home/ywan0794/miniconda3/envs/lotus/lib/python3.10/site-packages/click/core.py", line 1514, in __call__
+    return self.main(*args, **kwargs)
+  File "/home/ywan0794/miniconda3/envs/lotus/lib/python3.10/site-packages/click/core.py", line 1435, in main
+    rv = self.invoke(ctx)
+  File "/home/ywan0794/miniconda3/envs/lotus/lib/python3.10/site-packages/click/core.py", line 1298, in invoke
+    return ctx.invoke(self.callback, **ctx.params)
+  File "/home/ywan0794/miniconda3/envs/lotus/lib/python3.10/site-packages/click/core.py", line 853, in invoke
+    return callback(*args, **kwargs)
+  File "/home/ywan0794/miniconda3/envs/lotus/lib/python3.10/site-packages/click/decorators.py", line 34, in new_func
+    return f(get_current_context(), *args, **kwargs)
+  File "/home/ywan0794/MoGe/moge/scripts/eval_baseline.py", line 25, in main
+    import  cv2
+  File "/home/ywan0794/miniconda3/envs/lotus/lib/python3.10/site-packages/cv2/__init__.py", line 181, in <module>
+    bootstrap()
+  File "/home/ywan0794/miniconda3/envs/lotus/lib/python3.10/site-packages/cv2/__init__.py", line 153, in bootstrap
+    native_module = importlib.import_module("cv2")
+  File "/home/ywan0794/miniconda3/envs/lotus/lib/python3.10/importlib/__init__.py", line 126, in import_module
+    return _bootstrap._gcd_import(name[level:], package, level)
+ImportError: numpy.core.multiarray failed to import
+[FAIL rc=1] lotus
+
+============================================
+[depthmaster] starting at Wed May 13 03:01:12 AM AEST 2026  (conda env: depthmaster)
+============================================
+Active env: depthmaster
+CUDA: True NVIDIA H100 NVL
+/home/ywan0794/miniconda3/envs/depthmaster/lib/python3.10/site-packages/diffusers/models/transformers/transformer_2d.py:34: FutureWarning: `Transformer2DModelOutput` is deprecated and will be removed in version 1.0.0. Importing `Transformer2DModelOutput` from `diffusers.models.transformer_2d` is deprecated and this will be removed in a future version. Please use `from diffusers.models.modeling_outputs import Transformer2DModelOutput`, instead.
+  deprecate("Transformer2DModelOutput", "1.0.0", deprecation_message)
+The config attributes {'default_denoising_steps': 10, 'scheduler': ['diffusers', 'DDIMScheduler']} were passed to DepthMasterPipeline, but are not expected and will be ignored. Please verify your model_index.json configuration file.
+Keyword arguments {'default_denoising_steps': 10, 'scheduler': ['diffusers', 'DDIMScheduler']} are not expected by DepthMasterPipeline and will be ignored.
+
+Defaulting to unsafe serialization. Pass `allow_pickle=False` to raise an error instead.
+Some weights of the model checkpoint were not used when initializing UNet2DConditionModel: 
+ ['fftblock.norm.weight, fftblock.norm.bias, fftblock.conv_f1.weight, fftblock.conv_f1.bias, fftblock.conv_f2.weight, fftblock.conv_f2.bias, fftblock.conv_f4.weight, fftblock.conv_f4.bias, fftblock.conv_f3.weight, fftblock.conv_f3.bias, fftblock.conv_s1.weight, fftblock.conv_s1.bias, fftblock.conv_s2.weight, fftblock.conv_s2.bias, fftblock.fuse.weight, fftblock.fuse.bias']
+
+Traceback (most recent call last):
+  File "/home/ywan0794/MoGe/moge/scripts/eval_baseline.py", line 165, in <module>
+    main()
+  File "/home/ywan0794/miniconda3/envs/depthmaster/lib/python3.10/site-packages/click/core.py", line 1514, in __call__
+    return self.main(*args, **kwargs)
+  File "/home/ywan0794/miniconda3/envs/depthmaster/lib/python3.10/site-packages/click/core.py", line 1435, in main
+    rv = self.invoke(ctx)
+  File "/home/ywan0794/miniconda3/envs/depthmaster/lib/python3.10/site-packages/click/core.py", line 1298, in invoke
+    return ctx.invoke(self.callback, **ctx.params)
+  File "/home/ywan0794/miniconda3/envs/depthmaster/lib/python3.10/site-packages/click/core.py", line 853, in invoke
+    return callback(*args, **kwargs)
+  File "/home/ywan0794/miniconda3/envs/depthmaster/lib/python3.10/site-packages/click/decorators.py", line 34, in new_func
+    return f(get_current_context(), *args, **kwargs)
+  File "/home/ywan0794/MoGe/moge/scripts/eval_baseline.py", line 42, in main
+    baseline : MGEBaselineInterface = baseline_cls.load.main(ctx.args, standalone_mode=False)
+  File "/home/ywan0794/miniconda3/envs/depthmaster/lib/python3.10/site-packages/click/core.py", line 1435, in main
+    rv = self.invoke(ctx)
+  File "/home/ywan0794/miniconda3/envs/depthmaster/lib/python3.10/site-packages/click/core.py", line 1298, in invoke
+    return ctx.invoke(self.callback, **ctx.params)
+  File "/home/ywan0794/miniconda3/envs/depthmaster/lib/python3.10/site-packages/click/core.py", line 853, in invoke
+    return callback(*args, **kwargs)
+  File "/home/ywan0794/MoGe/baselines/depthmaster.py", line 71, in load
+    return Baseline(repo_path, checkpoint, processing_res, half_precision, device)
+  File "/home/ywan0794/MoGe/baselines/depthmaster.py", line 45, in __init__
+    pipe = DepthMasterPipeline.from_pretrained(checkpoint, variant=variant, torch_dtype=dtype)
+  File "/home/ywan0794/miniconda3/envs/depthmaster/lib/python3.10/site-packages/huggingface_hub/utils/_validators.py", line 114, in _inner_fn
+    return fn(*args, **kwargs)
+  File "/home/ywan0794/miniconda3/envs/depthmaster/lib/python3.10/site-packages/diffusers/pipelines/pipeline_utils.py", line 972, in from_pretrained
+    model = pipeline_class(**init_kwargs)
+  File "/home/ywan0794/EvalMDE/DepthMaster/depthmaster/depthmaster_pipeline.py", line 125, in __init__
+    self.register_modules(
+  File "/home/ywan0794/miniconda3/envs/depthmaster/lib/python3.10/site-packages/diffusers/pipelines/pipeline_utils.py", line 159, in register_modules
+    library, class_name = _fetch_class_library_tuple(module)
+  File "/home/ywan0794/miniconda3/envs/depthmaster/lib/python3.10/site-packages/diffusers/pipelines/pipeline_loading_utils.py", line 733, in _fetch_class_library_tuple
+    not_compiled_module = _unwrap_model(module)
+  File "/home/ywan0794/miniconda3/envs/depthmaster/lib/python3.10/site-packages/diffusers/pipelines/pipeline_loading_utils.py", line 236, in _unwrap_model
+    from peft import PeftModel
+  File "/home/ywan0794/miniconda3/envs/depthmaster/lib/python3.10/site-packages/peft/__init__.py", line 17, in <module>
+    from .auto import (
+  File "/home/ywan0794/miniconda3/envs/depthmaster/lib/python3.10/site-packages/peft/auto.py", line 32, in <module>
+    from .peft_model import (
+  File "/home/ywan0794/miniconda3/envs/depthmaster/lib/python3.10/site-packages/peft/peft_model.py", line 38, in <module>
+    from transformers import Cache, DynamicCache, EncoderDecoderCache, PreTrainedModel
+ImportError: cannot import name 'EncoderDecoderCache' from 'transformers' (/home/ywan0794/miniconda3/envs/depthmaster/lib/python3.10/site-packages/transformers/__init__.py)
+[FAIL rc=1] depthmaster
+
+============================================
+[ppd] starting at Wed May 13 03:02:57 AM AEST 2026  (conda env: ppd)
+============================================
+Active env: ppd
+CUDA: True NVIDIA H100 NVL
+xFormers not available
+xFormers not available
+
+
+
+
+
+
+
+
                                                             
+[OK] ppd -> sanity_output/sanity_ppd_20260513_025945.json
+
+============================================
+[da3_mono] starting at Wed May 13 03:04:28 AM AEST 2026  (conda env: da3)
+============================================
+Active env: da3
+CUDA: True NVIDIA H100 NVL
+[WARN ] Dependency `gsplat` is required for rendering 3DGS. Install via: pip install git+https://github.com/nerfstudio-project/gsplat.git@0b4dddf04cb687367602c01196913cde6a743d70
+[INFO ] using MLP layer as FFN
+
+
+[INFO ] Model Forward Pass Done. Time: 1.5514147281646729 seconds
+[INFO ] Conversion to Prediction Done. Time: 0.003040313720703125 seconds
+
+
+[INFO ] Model Forward Pass Done. Time: 0.019557952880859375 seconds
+[INFO ] Conversion to Prediction Done. Time: 0.0003116130828857422 seconds
+[INFO ] Processed Images Done taking 0.010450124740600586 seconds. Shape:  torch.Size([1, 3, 378, 504])
+[INFO ] Model Forward Pass Done. Time: 0.019212961196899414 seconds
+[INFO ] Conversion to Prediction Done. Time: 0.00028777122497558594 seconds
+
+
+[INFO ] Model Forward Pass Done. Time: 0.38853001594543457 seconds
+[INFO ] Conversion to Prediction Done. Time: 0.002028226852416992 seconds
+[INFO ] Processed Images Done taking 0.0074176788330078125 seconds. Shape:  torch.Size([1, 3, 378, 504])
+[INFO ] Model Forward Pass Done. Time: 0.019327163696289062 seconds
+[INFO ] Conversion to Prediction Done. Time: 0.0002503395080566406 seconds
+
+
+
                                                             
+[OK] da3_mono -> sanity_output/sanity_da3_mono_20260513_025945.json
+
+============================================
+[fe2e] starting at Wed May 13 03:04:51 AM AEST 2026  (conda env: fe2e)
+============================================
+Active env: fe2e
+CUDA: True NVIDIA H100 NVL
+[INFO] prompt_type=empty, 跳过Qwen模型加载
+create LoRA network from weights
+train all blocks only
+create LoRA for DIT all blocks: 304 modules.
+enable LoRA for U-Net
+weights are merged
+
+
+Traceback (most recent call last):
+  File "/home/ywan0794/miniconda3/envs/fe2e/lib/python3.10/threading.py", line 1016, in _bootstrap_inner
+Thread-13 (loop):
+Traceback (most recent call last):
+  File "/home/ywan0794/miniconda3/envs/fe2e/lib/python3.10/threading.py", line 1016, in _bootstrap_inner
+    self.run()
+  File "/home/ywan0794/miniconda3/envs/fe2e/lib/python3.10/threading.py", line 953, in run
+Exception in thread Thread-15 (loop):
+Traceback (most recent call last):
+  File "/home/ywan0794/miniconda3/envs/fe2e/lib/python3.10/threading.py", line 1016, in _bootstrap_inner
+    self._target(*self._args, **self._kwargs)
+  File "/home/ywan0794/miniconda3/envs/fe2e/lib/python3.10/site-packages/pipeline/components.py", line 218, in loop
+Exception in thread Thread-14 (loop):
+Traceback (most recent call last):
+  File "/home/ywan0794/miniconda3/envs/fe2e/lib/python3.10/threading.py", line 1016, in _bootstrap_inner
+    self.run()
+  File "/home/ywan0794/miniconda3/envs/fe2e/lib/python3.10/threading.py", line 953, in run
+    self.run()
+  File "/home/ywan0794/miniconda3/envs/fe2e/lib/python3.10/threading.py", line 953, in run
+    self._target(*self._args, **self._kwargs)
+  File "/home/ywan0794/miniconda3/envs/fe2e/lib/python3.10/site-packages/pipeline/components.py", line 218, in loop
+    self.run()
+  File "/home/ywan0794/miniconda3/envs/fe2e/lib/python3.10/threading.py", line 953, in run
+    self._target(*self._args, **self._kwargs)
+  File "/home/ywan0794/miniconda3/envs/fe2e/lib/python3.10/site-packages/pipeline/components.py", line 218, in loop
+    self._target(*self._args, **self._kwargs)
+  File "/home/ywan0794/miniconda3/envs/fe2e/lib/python3.10/site-packages/pipeline/components.py", line 218, in loop
+    result = self.work(item)
+  File "/home/ywan0794/miniconda3/envs/fe2e/lib/python3.10/site-packages/pipeline/components.py", line 208, in work
+    result = self.work(item)
+  File "/home/ywan0794/miniconda3/envs/fe2e/lib/python3.10/site-packages/pipeline/components.py", line 208, in work
+    result = self.work(item)
+  File "/home/ywan0794/miniconda3/envs/fe2e/lib/python3.10/site-packages/pipeline/components.py", line 208, in work
+    return self.work_fn(*args, **kwargs)
+  File "/home/ywan0794/MoGe/moge/test/dataloader.py", line 120, in _process_instance
+        return self.work_fn(*args, **kwargs)
+  File "/home/ywan0794/MoGe/moge/test/dataloader.py", line 120, in _process_instance
+    direction = utils3d.np.unproject_cv(np.array([[cu, cv]], dtype=np.float32), np.array([1.0], dtype=np.float32), intrinsics=intrinsics)[0]
+  File "/home/ywan0794/miniconda3/envs/fe2e/lib/python3.10/site-packages/utils3d/helpers.py", line 16, in wrapper
+result = self.work(item)
+  File "/home/ywan0794/miniconda3/envs/fe2e/lib/python3.10/site-packages/pipeline/components.py", line 208, in work
+    return self.work_fn(*args, **kwargs)
+  File "/home/ywan0794/MoGe/moge/test/dataloader.py", line 120, in _process_instance
+    direction = utils3d.np.unproject_cv(np.array([[cu, cv]], dtype=np.float32), np.array([1.0], dtype=np.float32), intrinsics=intrinsics)[0]
+  File "/home/ywan0794/miniconda3/envs/fe2e/lib/python3.10/site-packages/utils3d/helpers.py", line 16, in wrapper
+    return self.work_fn(*args, **kwargs)
+  File "/home/ywan0794/MoGe/moge/test/dataloader.py", line 120, in _process_instance
+    direction = utils3d.np.unproject_cv(np.array([[cu, cv]], dtype=np.float32), np.array([1.0], dtype=np.float32), intrinsics=intrinsics)[0]
+  File "/home/ywan0794/miniconda3/envs/fe2e/lib/python3.10/site-packages/utils3d/helpers.py", line 16, in wrapper
+    return fn(*args, **kwargs)
+  File "/home/ywan0794/miniconda3/envs/fe2e/lib/python3.10/site-packages/utils3d/numpy/helpers.py", line 90, in wrapper
+    result = func(*args, **kwargs)
+  File "/home/ywan0794/miniconda3/envs/fe2e/lib/python3.10/site-packages/utils3d/numpy/transforms.py", line 737, in unproject_cv
+    return fn(*args, **kwargs)
+  File "/home/ywan0794/miniconda3/envs/fe2e/lib/python3.10/site-packages/utils3d/numpy/helpers.py", line 90, in wrapper
+    return fn(*args, **kwargs)
+  File "/home/ywan0794/miniconda3/envs/fe2e/lib/python3.10/site-packages/utils3d/numpy/helpers.py", line 90, in wrapper
+    direction = utils3d.np.unproject_cv(np.array([[cu, cv]], dtype=np.float32), np.array([1.0], dtype=np.float32), intrinsics=intrinsics)[0]
+  File "/home/ywan0794/miniconda3/envs/fe2e/lib/python3.10/site-packages/utils3d/helpers.py", line 16, in wrapper
+    return fn(*args, **kwargs)
+  File "/home/ywan0794/miniconda3/envs/fe2e/lib/python3.10/site-packages/utils3d/numpy/helpers.py", line 90, in wrapper
+    result = func(*args, **kwargs)
+  File "/home/ywan0794/miniconda3/envs/fe2e/lib/python3.10/site-packages/utils3d/numpy/transforms.py", line 737, in unproject_cv
+    result = func(*args, **kwargs)
+  File "/home/ywan0794/miniconda3/envs/fe2e/lib/python3.10/site-packages/utils3d/numpy/transforms.py", line 737, in unproject_cv
+    points = points @ np.linalg.inv(transform).mT
+AttributeError: 'numpy.ndarray' object has no attribute 'mT'. Did you mean: 'T'?
+    result = func(*args, **kwargs)
+  File "/home/ywan0794/miniconda3/envs/fe2e/lib/python3.10/site-packages/utils3d/numpy/transforms.py", line 737, in unproject_cv
+    points = points @ np.linalg.inv(transform).mT
+AttributeError: 'numpy.ndarray' object has no attribute 'mT'. Did you mean: 'T'?
+    points = points @ np.linalg.inv(transform).mT
+AttributeError: 'numpy.ndarray' object has no attribute 'mT'. Did you mean: 'T'?
+    points = points @ np.linalg.inv(transform).mT
+AttributeError: 'numpy.ndarray' object has no attribute 'mT'. Did you mean: 'T'?
+Exception in thread Thread-16 (loop):
+Traceback (most recent call last):
+  File "/home/ywan0794/miniconda3/envs/fe2e/lib/python3.10/threading.py", line 1016, in _bootstrap_inner
+    self.run()
+  File "/home/ywan0794/miniconda3/envs/fe2e/lib/python3.10/threading.py", line 953, in run
+    self._target(*self._args, **self._kwargs)
+  File "/home/ywan0794/miniconda3/envs/fe2e/lib/python3.10/site-packages/pipeline/components.py", line 218, in loop
+    result = self.work(item)
+  File "/home/ywan0794/miniconda3/envs/fe2e/lib/python3.10/site-packages/pipeline/components.py", line 208, in work
+    return self.work_fn(*args, **kwargs)
+  File "/home/ywan0794/MoGe/moge/test/dataloader.py", line 120, in _process_instance
+    direction = utils3d.np.unproject_cv(np.array([[cu, cv]], dtype=np.float32), np.array([1.0], dtype=np.float32), intrinsics=intrinsics)[0]
+  File "/home/ywan0794/miniconda3/envs/fe2e/lib/python3.10/site-packages/utils3d/helpers.py", line 16, in wrapper
+    return fn(*args, **kwargs)
+  File "/home/ywan0794/miniconda3/envs/fe2e/lib/python3.10/site-packages/utils3d/numpy/helpers.py", line 90, in wrapper
+    result = func(*args, **kwargs)
+  File "/home/ywan0794/miniconda3/envs/fe2e/lib/python3.10/site-packages/utils3d/numpy/transforms.py", line 737, in unproject_cv
+    points = points @ np.linalg.inv(transform).mT
+AttributeError: 'numpy.ndarray' object has no attribute 'mT'. Did you mean: 'T'?
+slurmstepd-erinyes: error: *** JOB 12096 ON erinyes CANCELLED AT 2026-05-13T03:20:24 ***

sanity_all_12097.log

@@ -0,0 +1,209 @@
+============================================
+sanity-all started at Wed May 13 03:20:26 AM AEST 2026
+Config: /home/ywan0794/MoGe/configs/eval/sanity_benchmarks.json
+TIMESTAMP: 20260513_032026
+Summary file: sanity_output/_sanity_all_20260513_032026.summary.txt
+============================================
+Wed May 13 03:20:26 2026       
++-----------------------------------------------------------------------------------------+
+| NVIDIA-SMI 550.163.01             Driver Version: 550.163.01     CUDA Version: 12.4     |
+|-----------------------------------------+------------------------+----------------------+
+| GPU  Name                 Persistence-M | Bus-Id          Disp.A | Volatile Uncorr. ECC |
+| Fan  Temp   Perf          Pwr:Usage/Cap |           Memory-Usage | GPU-Util  Compute M. |
+|                                         |                        |               MIG M. |
+|=========================================+========================+======================|
+|   0  NVIDIA H100 NVL                Off |   00000000:E1:00.0 Off |                    0 |
+| N/A   38C    P0             88W /  400W |      14MiB /  95830MiB |      0%      Default |
+|                                         |                        |             Disabled |
++-----------------------------------------+------------------------+----------------------+
+                                                                                         
++-----------------------------------------------------------------------------------------+
+| Processes:                                                                              |
+|  GPU   GI   CI        PID   Type   Process name                              GPU Memory |
+|        ID   ID                                                               Usage      |
+|=========================================================================================|
+|    0   N/A  N/A      4274      G   /usr/lib/xorg/Xorg                              4MiB |
++-----------------------------------------------------------------------------------------+
+
+============================================
+[marigold] starting at Wed May 13 03:20:27 AM AEST 2026  (conda env: marigold)
+============================================
+Active env: marigold
+CUDA: True NVIDIA H100 NVL
+The config attributes {'prediction_type': 'depth'} were passed to MarigoldDepthPipeline, but are not expected and will be ignored. Please verify your model_index.json configuration file.
+Keyword arguments {'prediction_type': 'depth'} are not expected by MarigoldDepthPipeline and will be ignored.
+
+
+
+
+
+
+
+
+
+
+
                                                           
+[OK] marigold -> sanity_output/sanity_marigold_20260513_032026.json
+
+============================================
+[lotus] starting at Wed May 13 03:20:47 AM AEST 2026  (conda env: lotus)
+============================================
+Active env: lotus
+CUDA: True NVIDIA H100 NVL
+
+
+
+
+
+
+
+
+
                                                           
+[OK] lotus -> sanity_output/sanity_lotus_20260513_032026.json
+
+============================================
+[depthmaster] starting at Wed May 13 03:22:05 AM AEST 2026  (conda env: depthmaster)
+============================================
+Active env: depthmaster
+CUDA: True NVIDIA H100 NVL
+The config attributes {'default_denoising_steps': 10, 'scheduler': ['diffusers', 'DDIMScheduler']} were passed to DepthMasterPipeline, but are not expected and will be ignored. Please verify your model_index.json configuration file.
+Keyword arguments {'default_denoising_steps': 10, 'scheduler': ['diffusers', 'DDIMScheduler']} are not expected by DepthMasterPipeline and will be ignored.
+
+Defaulting to unsafe serialization. Pass `allow_pickle=False` to raise an error instead.
+Some weights of the model checkpoint at /home/ywan0794/EvalMDE/DepthMaster/ckpt/eval/unet were not used when initializing UNet2DConditionModel: 
+ ['fftblock.conv_f1.weight, fftblock.conv_s2.weight, fftblock.conv_f2.bias, fftblock.conv_f3.bias, fftblock.conv_f4.bias, fftblock.conv_s2.bias, fftblock.fuse.bias, fftblock.conv_f3.weight, fftblock.conv_f2.weight, fftblock.norm.weight, fftblock.conv_s1.bias, fftblock.fuse.weight, fftblock.conv_s1.weight, fftblock.conv_f4.weight, fftblock.conv_f1.bias, fftblock.norm.bias']
+
+
+
+Expected types for unet: (<class 'depthmaster.modules.unet_2d_condition_s2.UNet2DConditionModel'>,), got <class 'diffusers.models.unets.unet_2d_condition.UNet2DConditionModel'>.
+An error occurred while trying to fetch /home/ywan0794/EvalMDE/DepthMaster/ckpt/eval/unet: Error no file named diffusion_pytorch_model.safetensors found in directory /home/ywan0794/EvalMDE/DepthMaster/ckpt/eval/unet.
+Defaulting to unsafe serialization. Pass `allow_pickle=False` to raise an error instead.
+
+
+
+
+
+
+
+
                                                           
+[OK] depthmaster -> sanity_output/sanity_depthmaster_20260513_032026.json
+
+============================================
+[ppd] starting at Wed May 13 03:23:33 AM AEST 2026  (conda env: ppd)
+============================================
+Active env: ppd
+CUDA: True NVIDIA H100 NVL
+xFormers not available
+xFormers not available
+
+
+
+
+
+
+
+
                                                           
+[OK] ppd -> sanity_output/sanity_ppd_20260513_032026.json
+
+============================================
+[da3_mono] starting at Wed May 13 03:24:12 AM AEST 2026  (conda env: da3)
+============================================
+Active env: da3
+CUDA: True NVIDIA H100 NVL
+[WARN ] Dependency `gsplat` is required for rendering 3DGS. Install via: pip install git+https://github.com/nerfstudio-project/gsplat.git@0b4dddf04cb687367602c01196913cde6a743d70
+[INFO ] using MLP layer as FFN
+
+
+[INFO ] Model Forward Pass Done. Time: 1.4963836669921875 seconds
+[INFO ] Conversion to Prediction Done. Time: 0.0010690689086914062 seconds
+
+
+[INFO ] Model Forward Pass Done. Time: 0.019660472869873047 seconds
+[INFO ] Conversion to Prediction Done. Time: 0.00032258033752441406 seconds
+[INFO ] Processed Images Done taking 0.01800370216369629 seconds. Shape:  torch.Size([1, 3, 378, 504])
+[INFO ] Model Forward Pass Done. Time: 0.01959395408630371 seconds
+[INFO ] Conversion to Prediction Done. Time: 0.0003299713134765625 seconds
+
+
+[INFO ] Model Forward Pass Done. Time: 0.019454002380371094 seconds
+[INFO ] Conversion to Prediction Done. Time: 0.0003523826599121094 seconds
+[INFO ] Processed Images Done taking 0.012474536895751953 seconds. Shape:  torch.Size([1, 3, 378, 504])
+[INFO ] Model Forward Pass Done. Time: 0.019382238388061523 seconds
+[INFO ] Conversion to Prediction Done. Time: 0.0003466606140136719 seconds
+
+
+
                                                           
+[OK] da3_mono -> sanity_output/sanity_da3_mono_20260513_032026.json
+
+============================================
+[fe2e] starting at Wed May 13 03:25:00 AM AEST 2026  (conda env: fe2e)
+============================================
+Active env: fe2e
+CUDA: True NVIDIA H100 NVL
+[INFO] prompt_type=empty, 跳过Qwen模型加载
+create LoRA network from weights
+train all blocks only
+create LoRA for DIT all blocks: 304 modules.
+enable LoRA for U-Net
+weights are merged
+
+
+
                                                   
+Traceback (most recent call last):
+  File "/home/ywan0794/MoGe/moge/scripts/eval_baseline.py", line 165, in <module>
+    main()
+  File "/home/ywan0794/miniconda3/envs/fe2e/lib/python3.10/site-packages/click/core.py", line 1514, in __call__
+    return self.main(*args, **kwargs)
+  File "/home/ywan0794/miniconda3/envs/fe2e/lib/python3.10/site-packages/click/core.py", line 1435, in main
+    rv = self.invoke(ctx)
+  File "/home/ywan0794/miniconda3/envs/fe2e/lib/python3.10/site-packages/click/core.py", line 1298, in invoke
+    return ctx.invoke(self.callback, **ctx.params)
+  File "/home/ywan0794/miniconda3/envs/fe2e/lib/python3.10/site-packages/click/core.py", line 853, in invoke
+    return callback(*args, **kwargs)
+  File "/home/ywan0794/miniconda3/envs/fe2e/lib/python3.10/site-packages/click/decorators.py", line 34, in new_func
+    return f(get_current_context(), *args, **kwargs)
+  File "/home/ywan0794/MoGe/moge/scripts/eval_baseline.py", line 70, in main
+    pred = baseline.infer_for_evaluation(image)
+  File "/home/ywan0794/MoGe/moge/test/baseline.py", line 43, in infer_for_evaluation
+    return self.infer(image, intrinsics)
+  File "/home/ywan0794/miniconda3/envs/fe2e/lib/python3.10/site-packages/torch/utils/_contextlib.py", line 116, in decorate_context
+    return func(*args, **kwargs)
+  File "/home/ywan0794/MoGe/baselines/fe2e.py", line 163, in infer
+    images_list, Lpred, Rpred = self.image_gen.generate_image(
+  File "/home/ywan0794/miniconda3/envs/fe2e/lib/python3.10/site-packages/torch/utils/_contextlib.py", line 116, in decorate_context
+    return func(*args, **kwargs)
+  File "/home/ywan0794/EvalMDE/FE2E/infer/inference.py", line 475, in generate_image
+    Lpred,Rpred = self.denoise(**inputs,cfg_guidance=cfg_guidance,timesteps=timesteps,show_progress=show_progress,timesteps_truncate=1.0,)#图像中包括ref image
+  File "/home/ywan0794/EvalMDE/FE2E/infer/inference.py", line 270, in denoise
+    pred, feat = self.dit(
+  File "/home/ywan0794/miniconda3/envs/fe2e/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1739, in _wrapped_call_impl
+    return self._call_impl(*args, **kwargs)
+  File "/home/ywan0794/miniconda3/envs/fe2e/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1750, in _call_impl
+    return forward_call(*args, **kwargs)
+  File "/home/ywan0794/EvalMDE/FE2E/modules/model_edit.py", line 197, in forward
+    img, txt = block(img=img, txt=txt, vec=vec, pe=pe)
+  File "/home/ywan0794/miniconda3/envs/fe2e/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1739, in _wrapped_call_impl
+    return self._call_impl(*args, **kwargs)
+  File "/home/ywan0794/miniconda3/envs/fe2e/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1750, in _call_impl
+    return forward_call(*args, **kwargs)
+  File "/home/ywan0794/EvalMDE/FE2E/modules/layers.py", line 639, in forward
+    return self._forward(img, txt, vec, pe)
+  File "/home/ywan0794/EvalMDE/FE2E/modules/layers.py", line 600, in _forward
+    attn = attention_after_rope(q, k, v, pe=pe)
+  File "/home/ywan0794/EvalMDE/FE2E/modules/layers.py", line 403, in attention_after_rope
+    x = attention(q, k, v, mode="flash")
+  File "/home/ywan0794/EvalMDE/FE2E/modules/attention.py", line 82, in attention
+    assert flash_attn_func is not None, "flash_attn_func未定义"
+AssertionError: flash_attn_func未定义
+[FAIL rc=1] fe2e
+
+============================================
+sanity-all finished at Wed May 13 03:25:36 AM AEST 2026
+============================================
+=== Summary ===
+[OK] marigold -> sanity_output/sanity_marigold_20260513_032026.json
+[OK] lotus -> sanity_output/sanity_lotus_20260513_032026.json
+[OK] depthmaster -> sanity_output/sanity_depthmaster_20260513_032026.json
+[OK] ppd -> sanity_output/sanity_ppd_20260513_032026.json
+[OK] da3_mono -> sanity_output/sanity_da3_mono_20260513_032026.json
+[FAIL rc=1] fe2e

sanity_all_12098.log

@@ -0,0 +1,151 @@
+============================================
+sanity-all started at Wed May 13 03:56:23 AM AEST 2026
+Config: /home/ywan0794/MoGe/configs/eval/sanity_benchmarks.json
+TIMESTAMP: 20260513_035623
+Summary file: sanity_output/_sanity_all_20260513_035623.summary.txt
+============================================
+Wed May 13 03:56:24 2026       
++-----------------------------------------------------------------------------------------+
+| NVIDIA-SMI 550.163.01             Driver Version: 550.163.01     CUDA Version: 12.4     |
+|-----------------------------------------+------------------------+----------------------+
+| GPU  Name                 Persistence-M | Bus-Id          Disp.A | Volatile Uncorr. ECC |
+| Fan  Temp   Perf          Pwr:Usage/Cap |           Memory-Usage | GPU-Util  Compute M. |
+|                                         |                        |               MIG M. |
+|=========================================+========================+======================|
+|   0  NVIDIA H100 NVL                Off |   00000000:E1:00.0 Off |                    0 |
+| N/A   35C    P0             60W /  400W |      14MiB /  95830MiB |      0%      Default |
+|                                         |                        |             Disabled |
++-----------------------------------------+------------------------+----------------------+
+                                                                                         
++-----------------------------------------------------------------------------------------+
+| Processes:                                                                              |
+|  GPU   GI   CI        PID   Type   Process name                              GPU Memory |
+|        ID   ID                                                               Usage      |
+|=========================================================================================|
+|    0   N/A  N/A      4274      G   /usr/lib/xorg/Xorg                              4MiB |
++-----------------------------------------------------------------------------------------+
+
+============================================
+[marigold] starting at Wed May 13 03:56:24 AM AEST 2026  (conda env: marigold)
+============================================
+Active env: marigold
+CUDA: True NVIDIA H100 NVL
+The config attributes {'prediction_type': 'depth'} were passed to MarigoldDepthPipeline, but are not expected and will be ignored. Please verify your model_index.json configuration file.
+Keyword arguments {'prediction_type': 'depth'} are not expected by MarigoldDepthPipeline and will be ignored.
+
+
+
+
+
+
+
+
+
+
+
                                                           
+[OK] marigold -> sanity_output/sanity_marigold_20260513_035623.json
+
+============================================
+[lotus] starting at Wed May 13 03:56:58 AM AEST 2026  (conda env: lotus)
+============================================
+Active env: lotus
+CUDA: True NVIDIA H100 NVL
+
+
+
+
+
+
+
+
+
                                                           
+[OK] lotus -> sanity_output/sanity_lotus_20260513_035623.json
+
+============================================
+[depthmaster] starting at Wed May 13 03:57:21 AM AEST 2026  (conda env: depthmaster)
+============================================
+Active env: depthmaster
+CUDA: True NVIDIA H100 NVL
+The config attributes {'default_denoising_steps': 10, 'scheduler': ['diffusers', 'DDIMScheduler']} were passed to DepthMasterPipeline, but are not expected and will be ignored. Please verify your model_index.json configuration file.
+Keyword arguments {'default_denoising_steps': 10, 'scheduler': ['diffusers', 'DDIMScheduler']} are not expected by DepthMasterPipeline and will be ignored.
+
+Defaulting to unsafe serialization. Pass `allow_pickle=False` to raise an error instead.
+Some weights of the model checkpoint at /home/ywan0794/EvalMDE/DepthMaster/ckpt/eval/unet were not used when initializing UNet2DConditionModel: 
+ ['fftblock.norm.weight, fftblock.conv_f2.bias, fftblock.conv_f4.weight, fftblock.conv_s2.bias, fftblock.conv_f1.bias, fftblock.conv_f2.weight, fftblock.conv_f3.bias, fftblock.fuse.bias, fftblock.conv_f4.bias, fftblock.norm.bias, fftblock.conv_f3.weight, fftblock.fuse.weight, fftblock.conv_s1.weight, fftblock.conv_f1.weight, fftblock.conv_s1.bias, fftblock.conv_s2.weight']
+
+
+
+Expected types for unet: (<class 'depthmaster.modules.unet_2d_condition_s2.UNet2DConditionModel'>,), got <class 'diffusers.models.unets.unet_2d_condition.UNet2DConditionModel'>.
+An error occurred while trying to fetch /home/ywan0794/EvalMDE/DepthMaster/ckpt/eval/unet: Error no file named diffusion_pytorch_model.safetensors found in directory /home/ywan0794/EvalMDE/DepthMaster/ckpt/eval/unet.
+Defaulting to unsafe serialization. Pass `allow_pickle=False` to raise an error instead.
+
+
+
+
+
+
+
+
                                                           
+[OK] depthmaster -> sanity_output/sanity_depthmaster_20260513_035623.json
+
+============================================
+[ppd] starting at Wed May 13 03:58:14 AM AEST 2026  (conda env: ppd)
+============================================
+Active env: ppd
+CUDA: True NVIDIA H100 NVL
+xFormers not available
+xFormers not available
+
+
+
+
+
+
+
+
                                                           
+[OK] ppd -> sanity_output/sanity_ppd_20260513_035623.json
+
+============================================
+[da3_mono] starting at Wed May 13 03:59:10 AM AEST 2026  (conda env: da3)
+============================================
+Active env: da3
+CUDA: True NVIDIA H100 NVL
+[WARN ] Dependency `gsplat` is required for rendering 3DGS. Install via: pip install git+https://github.com/nerfstudio-project/gsplat.git@0b4dddf04cb687367602c01196913cde6a743d70
+
+
+
+
+
                                                           
+[OK] da3_mono -> sanity_output/sanity_da3_mono_20260513_035623.json
+
+============================================
+[fe2e] starting at Wed May 13 03:59:26 AM AEST 2026  (conda env: fe2e)
+============================================
+Active env: fe2e
+CUDA: True NVIDIA H100 NVL
+[INFO] prompt_type=empty, 跳过Qwen模型加载
+create LoRA network from weights
+train all blocks only
+create LoRA for DIT all blocks: 304 modules.
+enable LoRA for U-Net
+weights are merged
+
+
+
+
+
+
+
+
                                                           
+[OK] fe2e -> sanity_output/sanity_fe2e_20260513_035623.json
+
+============================================
+sanity-all finished at Wed May 13 04:00:03 AM AEST 2026
+============================================
+=== Summary ===
+[OK] marigold -> sanity_output/sanity_marigold_20260513_035623.json
+[OK] lotus -> sanity_output/sanity_lotus_20260513_035623.json
+[OK] depthmaster -> sanity_output/sanity_depthmaster_20260513_035623.json
+[OK] ppd -> sanity_output/sanity_ppd_20260513_035623.json
+[OK] da3_mono -> sanity_output/sanity_da3_mono_20260513_035623.json
+[OK] fe2e -> sanity_output/sanity_fe2e_20260513_035623.json

sanity_all_12104.log

@@ -0,0 +1,177 @@
+============================================
+sanity-all started at Thu May 14 12:15:41 AM AEST 2026
+Config: /home/ywan0794/MoGe/configs/eval/sanity_benchmarks.json
+TIMESTAMP: 20260514_001541
+Summary file: sanity_output/_sanity_all_20260514_001541.summary.txt
+============================================
+Thu May 14 00:15:41 2026       
++-----------------------------------------------------------------------------------------+
+| NVIDIA-SMI 550.163.01             Driver Version: 550.163.01     CUDA Version: 12.4     |
+|-----------------------------------------+------------------------+----------------------+
+| GPU  Name                 Persistence-M | Bus-Id          Disp.A | Volatile Uncorr. ECC |
+| Fan  Temp   Perf          Pwr:Usage/Cap |           Memory-Usage | GPU-Util  Compute M. |
+|                                         |                        |               MIG M. |
+|=========================================+========================+======================|
+|   0  NVIDIA H100 NVL                Off |   00000000:E1:00.0 Off |                    0 |
+| N/A   39C    P0             93W /  400W |      14MiB /  95830MiB |      0%      Default |
+|                                         |                        |             Disabled |
++-----------------------------------------+------------------------+----------------------+
+                                                                                         
++-----------------------------------------------------------------------------------------+
+| Processes:                                                                              |
+|  GPU   GI   CI        PID   Type   Process name                              GPU Memory |
+|        ID   ID                                                               Usage      |
+|=========================================================================================|
+|    0   N/A  N/A      4274      G   /usr/lib/xorg/Xorg                              4MiB |
++-----------------------------------------------------------------------------------------+
+
+============================================
+[marigold] starting at Thu May 14 12:15:41 AM AEST 2026  (conda env: marigold)
+============================================
+Active env: marigold
+CUDA: True NVIDIA H100 NVL
+
+The config attributes {'prediction_type': 'depth'} were passed to MarigoldDepthPipeline, but are not expected and will be ignored. Please verify your model_index.json configuration file.
+Keyword arguments {'prediction_type': 'depth'} are not expected by MarigoldDepthPipeline and will be ignored.
+
+
+
+
+
+
+
+
+
+
+
+
                                                   
+Traceback (most recent call last):
+  File "/home/ywan0794/MoGe/moge/scripts/eval_baseline.py", line 165, in <module>
+    main()
+  File "/home/ywan0794/miniconda3/envs/marigold/lib/python3.10/site-packages/click/core.py", line 1514, in __call__
+    return self.main(*args, **kwargs)
+  File "/home/ywan0794/miniconda3/envs/marigold/lib/python3.10/site-packages/click/core.py", line 1435, in main
+    rv = self.invoke(ctx)
+  File "/home/ywan0794/miniconda3/envs/marigold/lib/python3.10/site-packages/click/core.py", line 1298, in invoke
+    return ctx.invoke(self.callback, **ctx.params)
+  File "/home/ywan0794/miniconda3/envs/marigold/lib/python3.10/site-packages/click/core.py", line 853, in invoke
+    return callback(*args, **kwargs)
+  File "/home/ywan0794/miniconda3/envs/marigold/lib/python3.10/site-packages/click/decorators.py", line 34, in new_func
+    return f(get_current_context(), *args, **kwargs)
+  File "/home/ywan0794/MoGe/moge/scripts/eval_baseline.py", line 70, in main
+    pred = baseline.infer_for_evaluation(image)
+  File "/home/ywan0794/MoGe/moge/test/baseline.py", line 43, in infer_for_evaluation
+    return self.infer(image, intrinsics)
+  File "/home/ywan0794/miniconda3/envs/marigold/lib/python3.10/site-packages/torch/utils/_contextlib.py", line 116, in decorate_context
+    return func(*args, **kwargs)
+  File "/home/ywan0794/MoGe/baselines/marigold.py", line 103, in infer
+    out = self.pipe(pil, **kwargs)
+  File "/home/ywan0794/miniconda3/envs/marigold/lib/python3.10/site-packages/torch/utils/_contextlib.py", line 116, in decorate_context
+    return func(*args, **kwargs)
+TypeError: MarigoldDepthPipeline.__call__() got an unexpected keyword argument 'denoise_steps'
+[FAIL rc=1] marigold
+
+============================================
+[lotus] starting at Thu May 14 12:20:01 AM AEST 2026  (conda env: lotus)
+============================================
+Active env: lotus
+CUDA: True NVIDIA H100 NVL
+
+
+
+
+
+
+
+
+
                                                           
+[OK] lotus -> sanity_output/sanity_lotus_20260514_001541.json
+
+============================================
+[depthmaster] starting at Thu May 14 12:21:26 AM AEST 2026  (conda env: depthmaster)
+============================================
+Active env: depthmaster
+CUDA: True NVIDIA H100 NVL
+The config attributes {'default_denoising_steps': 10, 'scheduler': ['diffusers', 'DDIMScheduler']} were passed to DepthMasterPipeline, but are not expected and will be ignored. Please verify your model_index.json configuration file.
+Keyword arguments {'default_denoising_steps': 10, 'scheduler': ['diffusers', 'DDIMScheduler']} are not expected by DepthMasterPipeline and will be ignored.
+
+
+
+Defaulting to unsafe serialization. Pass `allow_pickle=False` to raise an error instead.
+Some weights of the model checkpoint at /home/ywan0794/EvalMDE/DepthMaster/ckpt/eval/unet were not used when initializing UNet2DConditionModel: 
+ ['fftblock.conv_f3.bias, fftblock.conv_f1.weight, fftblock.fuse.weight, fftblock.conv_s1.bias, fftblock.conv_f4.weight, fftblock.norm.weight, fftblock.conv_s1.weight, fftblock.conv_f4.bias, fftblock.conv_f3.weight, fftblock.conv_f2.weight, fftblock.norm.bias, fftblock.conv_f1.bias, fftblock.fuse.bias, fftblock.conv_s2.weight, fftblock.conv_f2.bias, fftblock.conv_s2.bias']
+
+Expected types for unet: (<class 'depthmaster.modules.unet_2d_condition_s2.UNet2DConditionModel'>,), got <class 'diffusers.models.unets.unet_2d_condition.UNet2DConditionModel'>.
+An error occurred while trying to fetch /home/ywan0794/EvalMDE/DepthMaster/ckpt/eval/unet: Error no file named diffusion_pytorch_model.safetensors found in directory /home/ywan0794/EvalMDE/DepthMaster/ckpt/eval/unet.
+Defaulting to unsafe serialization. Pass `allow_pickle=False` to raise an error instead.
+
+
+
+
+
+
+
+
                                                           
+[OK] depthmaster -> sanity_output/sanity_depthmaster_20260514_001541.json
+
+============================================
+[ppd] starting at Thu May 14 12:23:03 AM AEST 2026  (conda env: ppd)
+============================================
+Active env: ppd
+CUDA: True NVIDIA H100 NVL
+xFormers not available
+xFormers not available
+
+
+
+
+
+
+
+
                                                           
+[OK] ppd -> sanity_output/sanity_ppd_20260514_001541.json
+
+============================================
+[da3_mono] starting at Thu May 14 12:24:16 AM AEST 2026  (conda env: da3)
+============================================
+Active env: da3
+CUDA: True NVIDIA H100 NVL
+[WARN ] Dependency `gsplat` is required for rendering 3DGS. Install via: pip install git+https://github.com/nerfstudio-project/gsplat.git@0b4dddf04cb687367602c01196913cde6a743d70
+
+
+
+
+
                                                           
+[OK] da3_mono -> sanity_output/sanity_da3_mono_20260514_001541.json
+
+============================================
+[fe2e] starting at Thu May 14 12:25:14 AM AEST 2026  (conda env: fe2e)
+============================================
+Active env: fe2e
+CUDA: True NVIDIA H100 NVL
+[INFO] prompt_type=empty, 跳过Qwen模型加载
+create LoRA network from weights
+train all blocks only
+create LoRA for DIT all blocks: 304 modules.
+enable LoRA for U-Net
+weights are merged
+
+
+
+
+
+
+
+
                                                           
+[OK] fe2e -> sanity_output/sanity_fe2e_20260514_001541.json
+
+============================================
+sanity-all finished at Thu May 14 12:29:27 AM AEST 2026
+============================================
+=== Summary ===
+[FAIL rc=1] marigold
+[OK] lotus -> sanity_output/sanity_lotus_20260514_001541.json
+[OK] depthmaster -> sanity_output/sanity_depthmaster_20260514_001541.json
+[OK] ppd -> sanity_output/sanity_ppd_20260514_001541.json
+[OK] da3_mono -> sanity_output/sanity_da3_mono_20260514_001541.json
+[OK] fe2e -> sanity_output/sanity_fe2e_20260514_001541.json

sanity_all_12107.log

@@ -0,0 +1,185 @@
+============================================
+sanity-all started at Thu May 14 12:34:57 AM AEST 2026
+Config: /home/ywan0794/MoGe/configs/eval/sanity_benchmarks.json
+TIMESTAMP: 20260514_003457
+Summary file: sanity_output/_sanity_all_20260514_003457.summary.txt
+============================================
+Thu May 14 00:34:57 2026       
++-----------------------------------------------------------------------------------------+
+| NVIDIA-SMI 550.163.01             Driver Version: 550.163.01     CUDA Version: 12.4     |
+|-----------------------------------------+------------------------+----------------------+
+| GPU  Name                 Persistence-M | Bus-Id          Disp.A | Volatile Uncorr. ECC |
+| Fan  Temp   Perf          Pwr:Usage/Cap |           Memory-Usage | GPU-Util  Compute M. |
+|                                         |                        |               MIG M. |
+|=========================================+========================+======================|
+|   0  NVIDIA H100 NVL                Off |   00000000:E1:00.0 Off |                    0 |
+| N/A   36C    P0             60W /  400W |      14MiB /  95830MiB |      0%      Default |
+|                                         |                        |             Disabled |
++-----------------------------------------+------------------------+----------------------+
+                                                                                         
++-----------------------------------------------------------------------------------------+
+| Processes:                                                                              |
+|  GPU   GI   CI        PID   Type   Process name                              GPU Memory |
+|        ID   ID                                                               Usage      |
+|=========================================================================================|
+|    0   N/A  N/A      4274      G   /usr/lib/xorg/Xorg                              4MiB |
++-----------------------------------------------------------------------------------------+
+
+============================================
+[depth_pro] starting at Thu May 14 12:34:57 AM AEST 2026  (conda env: depth-pro)
+============================================
+Active env: depth-pro
+CUDA: True NVIDIA H100 NVL
+
+
+
+
+
+
+
+
                                                           
+[OK] depth_pro -> sanity_output/sanity_depth_pro_20260514_003457.json
+
+============================================
+[marigold] starting at Thu May 14 12:36:47 AM AEST 2026  (conda env: marigold)
+============================================
+Active env: marigold
+CUDA: True NVIDIA H100 NVL
+The config attributes {'prediction_type': 'depth'} were passed to MarigoldDepthPipeline, but are not expected and will be ignored. Please verify your model_index.json configuration file.
+Keyword arguments {'prediction_type': 'depth'} are not expected by MarigoldDepthPipeline and will be ignored.
+
+
+
+
+
+WARNING:root:The loaded `DDIMScheduler` is configured with `rescale_betas_zero_snr=False`; the recommended setting is True. Consider using `prs-eth/marigold-depth-v1-1` for the best experience.
+
+
+WARNING:root:The loaded `DDIMScheduler` is configured with `rescale_betas_zero_snr=False`; the recommended setting is True. Consider using `prs-eth/marigold-depth-v1-1` for the best experience.
+
+
+WARNING:root:The loaded `DDIMScheduler` is configured with `rescale_betas_zero_snr=False`; the recommended setting is True. Consider using `prs-eth/marigold-depth-v1-1` for the best experience.
+
+
+WARNING:root:The loaded `DDIMScheduler` is configured with `rescale_betas_zero_snr=False`; the recommended setting is True. Consider using `prs-eth/marigold-depth-v1-1` for the best experience.
+
+
+WARNING:root:The loaded `DDIMScheduler` is configured with `rescale_betas_zero_snr=False`; the recommended setting is True. Consider using `prs-eth/marigold-depth-v1-1` for the best experience.
+
+
+
                                                           
+[OK] marigold -> sanity_output/sanity_marigold_20260514_003457.json
+
+============================================
+[lotus] starting at Thu May 14 12:39:11 AM AEST 2026  (conda env: lotus)
+============================================
+Active env: lotus
+CUDA: True NVIDIA H100 NVL
+
+
+
+
+
+
+
+
+
                                                           
+[OK] lotus -> sanity_output/sanity_lotus_20260514_003457.json
+
+============================================
+[depthmaster] starting at Thu May 14 12:39:30 AM AEST 2026  (conda env: depthmaster)
+============================================
+Active env: depthmaster
+CUDA: True NVIDIA H100 NVL
+The config attributes {'default_denoising_steps': 10, 'scheduler': ['diffusers', 'DDIMScheduler']} were passed to DepthMasterPipeline, but are not expected and will be ignored. Please verify your model_index.json configuration file.
+Keyword arguments {'default_denoising_steps': 10, 'scheduler': ['diffusers', 'DDIMScheduler']} are not expected by DepthMasterPipeline and will be ignored.
+
+Defaulting to unsafe serialization. Pass `allow_pickle=False` to raise an error instead.
+Some weights of the model checkpoint at /home/ywan0794/EvalMDE/DepthMaster/ckpt/eval/unet were not used when initializing UNet2DConditionModel: 
+ ['fftblock.conv_f1.bias, fftblock.conv_s1.bias, fftblock.conv_f3.weight, fftblock.conv_f1.weight, fftblock.conv_f4.weight, fftblock.norm.bias, fftblock.conv_f3.bias, fftblock.fuse.weight, fftblock.conv_s2.bias, fftblock.conv_f2.weight, fftblock.conv_f4.bias, fftblock.conv_f2.bias, fftblock.norm.weight, fftblock.fuse.bias, fftblock.conv_s1.weight, fftblock.conv_s2.weight']
+
+
+
+
+
+
+
+
+
+
+Expected types for unet: (<class 'depthmaster.modules.unet_2d_condition_s2.UNet2DConditionModel'>,), got <class 'diffusers.models.unets.unet_2d_condition.UNet2DConditionModel'>.
+An error occurred while trying to fetch /home/ywan0794/EvalMDE/DepthMaster/ckpt/eval/unet: Error no file named diffusion_pytorch_model.safetensors found in directory /home/ywan0794/EvalMDE/DepthMaster/ckpt/eval/unet.
+Defaulting to unsafe serialization. Pass `allow_pickle=False` to raise an error instead.
+
+
+
+
+
+
+
+
                                                           
+[OK] depthmaster -> sanity_output/sanity_depthmaster_20260514_003457.json
+
+============================================
+[ppd] starting at Thu May 14 12:41:08 AM AEST 2026  (conda env: ppd)
+============================================
+Active env: ppd
+CUDA: True NVIDIA H100 NVL
+xFormers not available
+xFormers not available
+
+
+
+
+
+
+
+
                                                           
+[OK] ppd -> sanity_output/sanity_ppd_20260514_003457.json
+
+============================================
+[da3_mono] starting at Thu May 14 12:42:38 AM AEST 2026  (conda env: da3)
+============================================
+Active env: da3
+CUDA: True NVIDIA H100 NVL
+[WARN ] Dependency `gsplat` is required for rendering 3DGS. Install via: pip install git+https://github.com/nerfstudio-project/gsplat.git@0b4dddf04cb687367602c01196913cde6a743d70
+
+
+
+
+
+
                                                           
+[OK] da3_mono -> sanity_output/sanity_da3_mono_20260514_003457.json
+
+============================================
+[fe2e] starting at Thu May 14 12:43:07 AM AEST 2026  (conda env: fe2e)
+============================================
+Active env: fe2e
+CUDA: True NVIDIA H100 NVL
+[INFO] prompt_type=empty, 跳过Qwen模型加载
+create LoRA network from weights
+train all blocks only
+create LoRA for DIT all blocks: 304 modules.
+enable LoRA for U-Net
+weights are merged
+
+
+
+
+
+
+
+
                                                           
+[OK] fe2e -> sanity_output/sanity_fe2e_20260514_003457.json
+
+============================================
+sanity-all finished at Thu May 14 12:45:52 AM AEST 2026
+============================================
+=== Summary ===
+[OK] depth_pro -> sanity_output/sanity_depth_pro_20260514_003457.json
+[OK] marigold -> sanity_output/sanity_marigold_20260514_003457.json
+[OK] lotus -> sanity_output/sanity_lotus_20260514_003457.json
+[OK] depthmaster -> sanity_output/sanity_depthmaster_20260514_003457.json
+[OK] ppd -> sanity_output/sanity_ppd_20260514_003457.json
+[OK] da3_mono -> sanity_output/sanity_da3_mono_20260514_003457.json
+[OK] fe2e -> sanity_output/sanity_fe2e_20260514_003457.json

sanity_all_12109.log

@@ -0,0 +1,186 @@
+============================================
+sanity-all started at Thu May 14 04:40:31 AM AEST 2026
+Config: /home/ywan0794/MoGe/configs/eval/sanity_benchmarks.json
+TIMESTAMP: 20260514_044031
+Summary file: sanity_output/_sanity_all_20260514_044031.summary.txt
+============================================
+Thu May 14 04:40:31 2026       
++-----------------------------------------------------------------------------------------+
+| NVIDIA-SMI 550.163.01             Driver Version: 550.163.01     CUDA Version: 12.4     |
+|-----------------------------------------+------------------------+----------------------+
+| GPU  Name                 Persistence-M | Bus-Id          Disp.A | Volatile Uncorr. ECC |
+| Fan  Temp   Perf          Pwr:Usage/Cap |           Memory-Usage | GPU-Util  Compute M. |
+|                                         |                        |               MIG M. |
+|=========================================+========================+======================|
+|   0  NVIDIA H100 NVL                Off |   00000000:E1:00.0 Off |                    0 |
+| N/A   36C    P0             60W /  400W |      14MiB /  95830MiB |      0%      Default |
+|                                         |                        |             Disabled |
++-----------------------------------------+------------------------+----------------------+
+                                                                                         
++-----------------------------------------------------------------------------------------+
+| Processes:                                                                              |
+|  GPU   GI   CI        PID   Type   Process name                              GPU Memory |
+|        ID   ID                                                               Usage      |
+|=========================================================================================|
+|    0   N/A  N/A      4274      G   /usr/lib/xorg/Xorg                              4MiB |
++-----------------------------------------------------------------------------------------+
+
+============================================
+[depth_pro] starting at Thu May 14 04:40:31 AM AEST 2026  (conda env: depth-pro)
+============================================
+Active env: depth-pro
+CUDA: True NVIDIA H100 NVL
+
+
+
+
+
+
+
+
                                                           
+[OK] depth_pro -> sanity_output/sanity_depth_pro_20260514_044031.json
+
+============================================
+[marigold] starting at Thu May 14 04:40:58 AM AEST 2026  (conda env: marigold)
+============================================
+Active env: marigold
+CUDA: True NVIDIA H100 NVL
+The config attributes {'prediction_type': 'depth'} were passed to MarigoldDepthPipeline, but are not expected and will be ignored. Please verify your model_index.json configuration file.
+Keyword arguments {'prediction_type': 'depth'} are not expected by MarigoldDepthPipeline and will be ignored.
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
                                                           
+[OK] marigold -> sanity_output/sanity_marigold_20260514_044031.json
+
+============================================
+[lotus] starting at Thu May 14 04:42:38 AM AEST 2026  (conda env: lotus)
+============================================
+Active env: lotus
+CUDA: True NVIDIA H100 NVL
+
+
+
+
+
+
+
+
+
                                                           
+[OK] lotus -> sanity_output/sanity_lotus_20260514_044031.json
+
+============================================
+[depthmaster] starting at Thu May 14 04:44:16 AM AEST 2026  (conda env: depthmaster)
+============================================
+Active env: depthmaster
+CUDA: True NVIDIA H100 NVL
+The config attributes {'default_denoising_steps': 10, 'scheduler': ['diffusers', 'DDIMScheduler']} were passed to DepthMasterPipeline, but are not expected and will be ignored. Please verify your model_index.json configuration file.
+Keyword arguments {'default_denoising_steps': 10, 'scheduler': ['diffusers', 'DDIMScheduler']} are not expected by DepthMasterPipeline and will be ignored.
+
+
+
+
+
+
+
+
+
+Defaulting to unsafe serialization. Pass `allow_pickle=False` to raise an error instead.
+Some weights of the model checkpoint at /home/ywan0794/EvalMDE/DepthMaster/ckpt/eval/unet were not used when initializing UNet2DConditionModel: 
+ ['fftblock.conv_f4.weight, fftblock.norm.weight, fftblock.norm.bias, fftblock.conv_s2.weight, fftblock.conv_f4.bias, fftblock.conv_s1.weight, fftblock.conv_f1.bias, fftblock.conv_s2.bias, fftblock.fuse.weight, fftblock.conv_f2.bias, fftblock.conv_f1.weight, fftblock.conv_f3.bias, fftblock.fuse.bias, fftblock.conv_f3.weight, fftblock.conv_f2.weight, fftblock.conv_s1.bias']
+
+Expected types for unet: (<class 'depthmaster.modules.unet_2d_condition_s2.UNet2DConditionModel'>,), got <class 'diffusers.models.unets.unet_2d_condition.UNet2DConditionModel'>.
+An error occurred while trying to fetch /home/ywan0794/EvalMDE/DepthMaster/ckpt/eval/unet: Error no file named diffusion_pytorch_model.safetensors found in directory /home/ywan0794/EvalMDE/DepthMaster/ckpt/eval/unet.
+Defaulting to unsafe serialization. Pass `allow_pickle=False` to raise an error instead.
+
+
+
+
+
+
+
+
                                                           
+[OK] depthmaster -> sanity_output/sanity_depthmaster_20260514_044031.json
+
+============================================
+[ppd] starting at Thu May 14 04:45:58 AM AEST 2026  (conda env: ppd)
+============================================
+Active env: ppd
+CUDA: True NVIDIA H100 NVL
+xFormers not available
+xFormers not available
+
+
+
+
+
+
+
+
                                                           
+[OK] ppd -> sanity_output/sanity_ppd_20260514_044031.json
+
+============================================
+[da3_mono] starting at Thu May 14 04:47:15 AM AEST 2026  (conda env: da3)
+============================================
+Active env: da3
+CUDA: True NVIDIA H100 NVL
+[WARN ] Dependency `gsplat` is required for rendering 3DGS. Install via: pip install git+https://github.com/nerfstudio-project/gsplat.git@0b4dddf04cb687367602c01196913cde6a743d70
+
+
+
+
+
                                                           
+[OK] da3_mono -> sanity_output/sanity_da3_mono_20260514_044031.json
+
+============================================
+[fe2e] starting at Thu May 14 04:47:49 AM AEST 2026  (conda env: fe2e)
+============================================
+Active env: fe2e
+CUDA: True NVIDIA H100 NVL
+[INFO] prompt_type=empty, 跳过Qwen模型加载
+create LoRA network from weights
+train all blocks only
+create LoRA for DIT all blocks: 304 modules.
+enable LoRA for U-Net
+weights are merged
+
+
+
+
+
+
+
+
                                                           
+[OK] fe2e -> sanity_output/sanity_fe2e_20260514_044031.json
+
+============================================
+sanity-all finished at Thu May 14 04:49:52 AM AEST 2026
+============================================
+=== Summary ===
+[OK] depth_pro -> sanity_output/sanity_depth_pro_20260514_044031.json
+[OK] marigold -> sanity_output/sanity_marigold_20260514_044031.json
+[OK] lotus -> sanity_output/sanity_lotus_20260514_044031.json
+[OK] depthmaster -> sanity_output/sanity_depthmaster_20260514_044031.json
+[OK] ppd -> sanity_output/sanity_ppd_20260514_044031.json
+[OK] da3_mono -> sanity_output/sanity_da3_mono_20260514_044031.json
+[OK] fe2e -> sanity_output/sanity_fe2e_20260514_044031.json

sanity_depth_pro_12089.log

@@ -0,0 +1,51 @@
+============================================
+Activated conda environment: depth-pro
+CUDA_HOME: /home/ywan0794/miniconda3/envs/depth-pro
+============================================
+=== GPU Info ===
+Wed May 13 01:59:42 2026       
++-----------------------------------------------------------------------------------------+
+| NVIDIA-SMI 550.163.01             Driver Version: 550.163.01     CUDA Version: 12.4     |
+|-----------------------------------------+------------------------+----------------------+
+| GPU  Name                 Persistence-M | Bus-Id          Disp.A | Volatile Uncorr. ECC |
+| Fan  Temp   Perf          Pwr:Usage/Cap |           Memory-Usage | GPU-Util  Compute M. |
+|                                         |                        |               MIG M. |
+|=========================================+========================+======================|
+|   0  NVIDIA H100 NVL                Off |   00000000:E1:00.0 Off |                    0 |
+| N/A   35C    P0             60W /  400W |      14MiB /  95830MiB |      0%      Default |
+|                                         |                        |             Disabled |
++-----------------------------------------+------------------------+----------------------+
+                                                                                         
++-----------------------------------------------------------------------------------------+
+| Processes:                                                                              |
+|  GPU   GI   CI        PID   Type   Process name                              GPU Memory |
+|        ID   ID                                                               Usage      |
+|=========================================================================================|
+|    0   N/A  N/A      4274      G   /usr/lib/xorg/Xorg                              4MiB |
++-----------------------------------------------------------------------------------------+
+CUDA: True NVIDIA H100 NVL
+============================================
+Starting MoGe Eval for Depth Pro at Wed May 13 02:00:06 AM AEST 2026
+Repo: /home/ywan0794/EvalMDE/ml-depth-pro
+Checkpoint: /home/ywan0794/EvalMDE/ml-depth-pro/checkpoints/depth_pro.pt
+Config: /home/ywan0794/MoGe/configs/eval/sanity_benchmarks.json
+============================================
+Traceback (most recent call last):
+  File "/home/ywan0794/MoGe/moge/scripts/eval_baseline.py", line 165, in <module>
+    main()
+  File "/home/ywan0794/miniconda3/envs/depth-pro/lib/python3.9/site-packages/click/core.py", line 1161, in __call__
+    return self.main(*args, **kwargs)
+  File "/home/ywan0794/miniconda3/envs/depth-pro/lib/python3.9/site-packages/click/core.py", line 1082, in main
+    rv = self.invoke(ctx)
+  File "/home/ywan0794/miniconda3/envs/depth-pro/lib/python3.9/site-packages/click/core.py", line 1443, in invoke
+    return ctx.invoke(self.callback, **ctx.params)
+  File "/home/ywan0794/miniconda3/envs/depth-pro/lib/python3.9/site-packages/click/core.py", line 788, in invoke
+    return __callback(*args, **kwargs)
+  File "/home/ywan0794/miniconda3/envs/depth-pro/lib/python3.9/site-packages/click/decorators.py", line 33, in new_func
+    return f(get_current_context(), *args, **kwargs)
+  File "/home/ywan0794/MoGe/moge/scripts/eval_baseline.py", line 25, in main
+    import  cv2
+ModuleNotFoundError: No module named 'cv2'
+============================================
+Evaluation completed at Wed May 13 02:00:07 AM AEST 2026
+============================================

sanity_depth_pro_12090.log

@@ -0,0 +1,57 @@
+============================================
+Activated conda environment: depth-pro
+CUDA_HOME: /home/ywan0794/miniconda3/envs/depth-pro
+============================================
+=== GPU Info ===
+Wed May 13 02:05:28 2026       
++-----------------------------------------------------------------------------------------+
+| NVIDIA-SMI 550.163.01             Driver Version: 550.163.01     CUDA Version: 12.4     |
+|-----------------------------------------+------------------------+----------------------+
+| GPU  Name                 Persistence-M | Bus-Id          Disp.A | Volatile Uncorr. ECC |
+| Fan  Temp   Perf          Pwr:Usage/Cap |           Memory-Usage | GPU-Util  Compute M. |
+|                                         |                        |               MIG M. |
+|=========================================+========================+======================|
+|   0  NVIDIA H100 NVL                Off |   00000000:E1:00.0 Off |                    0 |
+| N/A   35C    P0             60W /  400W |      14MiB /  95830MiB |      0%      Default |
+|                                         |                        |             Disabled |
++-----------------------------------------+------------------------+----------------------+
+                                                                                         
++-----------------------------------------------------------------------------------------+
+| Processes:                                                                              |
+|  GPU   GI   CI        PID   Type   Process name                              GPU Memory |
+|        ID   ID                                                               Usage      |
+|=========================================================================================|
+|    0   N/A  N/A      4274      G   /usr/lib/xorg/Xorg                              4MiB |
++-----------------------------------------------------------------------------------------+
+CUDA: True NVIDIA H100 NVL
+============================================
+Starting MoGe Eval for Depth Pro at Wed May 13 02:05:30 AM AEST 2026
+Repo: /home/ywan0794/EvalMDE/ml-depth-pro
+Checkpoint: /home/ywan0794/EvalMDE/ml-depth-pro/checkpoints/depth_pro.pt
+Config: /home/ywan0794/MoGe/configs/eval/sanity_benchmarks.json
+============================================
+Traceback (most recent call last):
+  File "/home/ywan0794/MoGe/moge/scripts/eval_baseline.py", line 165, in <module>
+    main()
+  File "/home/ywan0794/miniconda3/envs/depth-pro/lib/python3.9/site-packages/click/core.py", line 1161, in __call__
+    return self.main(*args, **kwargs)
+  File "/home/ywan0794/miniconda3/envs/depth-pro/lib/python3.9/site-packages/click/core.py", line 1082, in main
+    rv = self.invoke(ctx)
+  File "/home/ywan0794/miniconda3/envs/depth-pro/lib/python3.9/site-packages/click/core.py", line 1443, in invoke
+    return ctx.invoke(self.callback, **ctx.params)
+  File "/home/ywan0794/miniconda3/envs/depth-pro/lib/python3.9/site-packages/click/core.py", line 788, in invoke
+    return __callback(*args, **kwargs)
+  File "/home/ywan0794/miniconda3/envs/depth-pro/lib/python3.9/site-packages/click/decorators.py", line 33, in new_func
+    return f(get_current_context(), *args, **kwargs)
+  File "/home/ywan0794/MoGe/moge/scripts/eval_baseline.py", line 32, in main
+    from moge.test.baseline import MGEBaselineInterface
+  File "/home/ywan0794/MoGe/moge/test/baseline.py", line 7, in <module>
+    class MGEBaselineInterface:
+  File "/home/ywan0794/MoGe/moge/test/baseline.py", line 15, in MGEBaselineInterface
+    def load(*args, **kwargs) -> "MGEBaselineInterface":
+  File "/home/ywan0794/miniconda3/envs/depth-pro/lib/python3.9/site-packages/click/decorators.py", line 235, in decorator
+    name=name or f.__name__.lower().replace("_", "-"),
+AttributeError: 'staticmethod' object has no attribute '__name__'
+============================================
+Evaluation completed at Wed May 13 02:05:32 AM AEST 2026
+============================================

sanity_depth_pro_12091.log

@@ -0,0 +1,80 @@
+============================================
+Activated conda environment: depth-pro
+CUDA_HOME: /home/ywan0794/miniconda3/envs/depth-pro
+============================================
+=== GPU Info ===
+Wed May 13 02:11:06 2026       
++-----------------------------------------------------------------------------------------+
+| NVIDIA-SMI 550.163.01             Driver Version: 550.163.01     CUDA Version: 12.4     |
+|-----------------------------------------+------------------------+----------------------+
+| GPU  Name                 Persistence-M | Bus-Id          Disp.A | Volatile Uncorr. ECC |
+| Fan  Temp   Perf          Pwr:Usage/Cap |           Memory-Usage | GPU-Util  Compute M. |
+|                                         |                        |               MIG M. |
+|=========================================+========================+======================|
+|   0  NVIDIA H100 NVL                Off |   00000000:E1:00.0 Off |                    0 |
+| N/A   35C    P0             60W /  400W |      14MiB /  95830MiB |      0%      Default |
+|                                         |                        |             Disabled |
++-----------------------------------------+------------------------+----------------------+
+                                                                                         
++-----------------------------------------------------------------------------------------+
+| Processes:                                                                              |
+|  GPU   GI   CI        PID   Type   Process name                              GPU Memory |
+|        ID   ID                                                               Usage      |
+|=========================================================================================|
+|    0   N/A  N/A      4274      G   /usr/lib/xorg/Xorg                              4MiB |
++-----------------------------------------------------------------------------------------+
+/home/ywan0794/miniconda3/envs/depth-pro/lib/python3.10/site-packages/torch/cuda/__init__.py:180: UserWarning: CUDA initialization: The NVIDIA driver on your system is too old (found version 12040). Please update your GPU driver by downloading and installing a new version from the URL: http://www.nvidia.com/Download/index.aspx Alternatively, go to: https://pytorch.org to install a PyTorch version that has been compiled with your version of the CUDA driver. (Triggered internally at /pytorch/c10/cuda/CUDAFunctions.cpp:119.)
+  return torch._C._cuda_getDeviceCount() > 0
+CUDA: False 
+============================================
+Starting MoGe Eval for Depth Pro at Wed May 13 02:11:09 AM AEST 2026
+Repo: /home/ywan0794/EvalMDE/ml-depth-pro
+Checkpoint: /home/ywan0794/EvalMDE/ml-depth-pro/checkpoints/depth_pro.pt
+Config: /home/ywan0794/MoGe/configs/eval/sanity_benchmarks.json
+============================================
+Traceback (most recent call last):
+  File "/home/ywan0794/MoGe/moge/scripts/eval_baseline.py", line 165, in <module>
+    main()
+  File "/home/ywan0794/miniconda3/envs/depth-pro/lib/python3.10/site-packages/click/core.py", line 1514, in __call__
+    return self.main(*args, **kwargs)
+  File "/home/ywan0794/miniconda3/envs/depth-pro/lib/python3.10/site-packages/click/core.py", line 1435, in main
+    rv = self.invoke(ctx)
+  File "/home/ywan0794/miniconda3/envs/depth-pro/lib/python3.10/site-packages/click/core.py", line 1298, in invoke
+    return ctx.invoke(self.callback, **ctx.params)
+  File "/home/ywan0794/miniconda3/envs/depth-pro/lib/python3.10/site-packages/click/core.py", line 853, in invoke
+    return callback(*args, **kwargs)
+  File "/home/ywan0794/miniconda3/envs/depth-pro/lib/python3.10/site-packages/click/decorators.py", line 34, in new_func
+    return f(get_current_context(), *args, **kwargs)
+  File "/home/ywan0794/MoGe/moge/scripts/eval_baseline.py", line 42, in main
+    baseline : MGEBaselineInterface = baseline_cls.load.main(ctx.args, standalone_mode=False)
+  File "/home/ywan0794/miniconda3/envs/depth-pro/lib/python3.10/site-packages/click/core.py", line 1435, in main
+    rv = self.invoke(ctx)
+  File "/home/ywan0794/miniconda3/envs/depth-pro/lib/python3.10/site-packages/click/core.py", line 1298, in invoke
+    return ctx.invoke(self.callback, **ctx.params)
+  File "/home/ywan0794/miniconda3/envs/depth-pro/lib/python3.10/site-packages/click/core.py", line 853, in invoke
+    return callback(*args, **kwargs)
+  File "/home/ywan0794/MoGe/baselines/depth_pro.py", line 74, in load
+    return Baseline(repo_path, checkpoint_path, precision, device)
+  File "/home/ywan0794/MoGe/baselines/depth_pro.py", line 57, in __init__
+    model, _ = depth_pro.create_model_and_transforms(config=config, device=device, precision=precision_dtype)
+  File "/home/ywan0794/EvalMDE/ml-depth-pro/src/depth_pro/depth_pro.py", line 120, in create_model_and_transforms
+    ).to(device)
+  File "/home/ywan0794/miniconda3/envs/depth-pro/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1384, in to
+    return self._apply(convert)
+  File "/home/ywan0794/miniconda3/envs/depth-pro/lib/python3.10/site-packages/torch/nn/modules/module.py", line 934, in _apply
+    module._apply(fn)
+  File "/home/ywan0794/miniconda3/envs/depth-pro/lib/python3.10/site-packages/torch/nn/modules/module.py", line 934, in _apply
+    module._apply(fn)
+  File "/home/ywan0794/miniconda3/envs/depth-pro/lib/python3.10/site-packages/torch/nn/modules/module.py", line 934, in _apply
+    module._apply(fn)
+  [Previous line repeated 1 more time]
+  File "/home/ywan0794/miniconda3/envs/depth-pro/lib/python3.10/site-packages/torch/nn/modules/module.py", line 965, in _apply
+    param_applied = fn(param)
+  File "/home/ywan0794/miniconda3/envs/depth-pro/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1370, in convert
+    return t.to(
+  File "/home/ywan0794/miniconda3/envs/depth-pro/lib/python3.10/site-packages/torch/cuda/__init__.py", line 478, in _lazy_init
+    torch._C._cuda_init()
+RuntimeError: The NVIDIA driver on your system is too old (found version 12040). Please update your GPU driver by downloading and installing a new version from the URL: http://www.nvidia.com/Download/index.aspx Alternatively, go to: https://pytorch.org to install a PyTorch version that has been compiled with your version of the CUDA driver.
+============================================
+Evaluation completed at Wed May 13 02:11:24 AM AEST 2026
+============================================

sanity_depth_pro_12092.log

@@ -0,0 +1,43 @@
+============================================
+Activated conda environment: depth-pro
+CUDA_HOME: /home/ywan0794/miniconda3/envs/depth-pro
+============================================
+=== GPU Info ===
+Wed May 13 02:18:58 2026       
++-----------------------------------------------------------------------------------------+
+| NVIDIA-SMI 550.163.01             Driver Version: 550.163.01     CUDA Version: 12.4     |
+|-----------------------------------------+------------------------+----------------------+
+| GPU  Name                 Persistence-M | Bus-Id          Disp.A | Volatile Uncorr. ECC |
+| Fan  Temp   Perf          Pwr:Usage/Cap |           Memory-Usage | GPU-Util  Compute M. |
+|                                         |                        |               MIG M. |
+|=========================================+========================+======================|
+|   0  NVIDIA H100 NVL                Off |   00000000:E1:00.0 Off |                    0 |
+| N/A   35C    P0             60W /  400W |      14MiB /  95830MiB |      0%      Default |
+|                                         |                        |             Disabled |
++-----------------------------------------+------------------------+----------------------+
+                                                                                         
++-----------------------------------------------------------------------------------------+
+| Processes:                                                                              |
+|  GPU   GI   CI        PID   Type   Process name                              GPU Memory |
+|        ID   ID                                                               Usage      |
+|=========================================================================================|
+|    0   N/A  N/A      4274      G   /usr/lib/xorg/Xorg                              4MiB |
++-----------------------------------------------------------------------------------------+
+CUDA: True NVIDIA H100 NVL
+============================================
+Starting MoGe Eval for Depth Pro at Wed May 13 02:19:02 AM AEST 2026
+Repo: /home/ywan0794/EvalMDE/ml-depth-pro
+Checkpoint: /home/ywan0794/EvalMDE/ml-depth-pro/checkpoints/depth_pro.pt
+Config: /home/ywan0794/MoGe/configs/eval/sanity_benchmarks.json
+============================================
+
+
+
+
+
+
+
+
                                                             
+============================================
+Evaluation completed at Wed May 13 02:20:04 AM AEST 2026
+============================================

vis_depth_8709.log

@@ -0,0 +1,11 @@
+Loading models...
+  Loading DA2-DPT...
+Traceback (most recent call last):
+  File "/home/ywan0794/MoGe/visualize_depth.py", line 328, in <module>
+    main()
+  File "/home/ywan0794/MoGe/visualize_depth.py", line 209, in main
+    da2_dpt = load_da2_model(CHECKPOINTS['da2_dpt'], 'dpt')
+  File "/home/ywan0794/MoGe/visualize_depth.py", line 46, in load_da2_model
+    model = DepthAnythingV2(**model_configs, decoder=decoder_type)
+TypeError: DepthAnythingV2.__init__() got an unexpected keyword argument 'decoder'
+Visualization completed!

vis_depth_8711.log

@@ -0,0 +1,54 @@
+/home/ywan0794/MoGe/visualize_depth.py:73: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.
+  ckpt = torch.load(checkpoint_path, map_location='cpu')
+/home/ywan0794/MoGe/visualize_depth.py:135: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.
+  ckpt = torch.load(checkpoint_path, map_location='cpu')
+/home/ywan0794/MoGe/visualize_depth.py:178: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with torch.cuda.amp.autocast(dtype=torch.bfloat16):
+Loading models...
+  Loading DA2-DPT...
+Loaded DA2 dpt from /home/ywan0794/Depth-Anything-V2/training/exp/dpt_vitb_both/epoch_007.pth
+  Loading DA2-SDT...
+Loaded DA2 sdt from /home/ywan0794/Depth-Anything-V2/training/exp/sdt_vitb_both/epoch_008.pth
+  Loading DA3-DPT...
+[INFO ] using MLP layer as FFN
+Loaded DA3 dpt from /home/ywan0794/Depth-Anything-3/training/exp/da3_dpt_vitl_both/epoch_010.pth
+  Loading DA3-SDT...
+[INFO ] using MLP layer as FFN
+Loaded DA3 sdt from /home/ywan0794/Depth-Anything-3/training/exp/da3_sdt_vitl_both/epoch_010.pth
+  Loading DA3-DualDPT...
+[INFO ] using MLP layer as FFN
+Loaded DA3 dualdpt from /home/ywan0794/Depth-Anything-3/training/exp/da3_dualdpt_vitl_both/epoch_010.pth
+All models loaded!
+
+Processing 10 KITTI samples...
+  [1/10] 2011_09_26_drive_0059_0000000154
+  [2/10] 2011_09_26_drive_0029_0000000296
+  [3/10] 2011_09_26_drive_0029_0000000154
+  [4/10] 2011_09_26_drive_0096_0000000171
+  [5/10] 2011_10_03_drive_0027_0000000362
+  [6/10] 2011_09_26_drive_0064_0000000462
+  [7/10] 2011_09_26_drive_0002_0000000051
+  [8/10] 2011_09_26_drive_0048_0000000016
+  [9/10] 2011_09_30_drive_0016_0000000110
+  [10/10] 2011_09_26_drive_0059_0000000098
+
+Processing 10 DDAD samples...
+  [1/10] 000508_CAMERA_05
+  [2/10] 001971_CAMERA_09
+  [3/10] 003267_CAMERA_06
+  [4/10] 001726_CAMERA_09
+  [5/10] 002738_CAMERA_05
+  [6/10] 000339_CAMERA_01
+  [7/10] 000104_CAMERA_05
+  [8/10] 001069_CAMERA_06
+  [9/10] 003710_CAMERA_06
+  [10/10] 003376_CAMERA_05
+
+Done! Results saved to /home/ywan0794/MoGe/vis_output
+Structure:
+  /home/ywan0794/MoGe/vis_output/
+    KITTI/
+      rgb/, gt/, da2_dpt/, da2_sdt/, da3_dpt/, da3_sdt/, da3_dualdpt/
+    DDAD/
+      rgb/, gt/, da2_dpt/, da2_sdt/, da3_dpt/, da3_sdt/, da3_dualdpt/
+Visualization completed!

vis_depth_8712.log

@@ -0,0 +1,7 @@
+/home/ywan0794/MoGe/visualize_depth.py:73: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.
+  ckpt = torch.load(checkpoint_path, map_location='cpu')
+/home/ywan0794/MoGe/visualize_depth.py:135: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.
+  ckpt = torch.load(checkpoint_path, map_location='cpu')
+/home/ywan0794/MoGe/visualize_depth.py:178: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with torch.cuda.amp.autocast(dtype=torch.bfloat16):
+slurmstepd-hades: error: *** JOB 8712 ON hades CANCELLED AT 2026-01-14T23:06:30 ***

vis_depth_8714.log

@@ -0,0 +1,434 @@
+/home/ywan0794/MoGe/visualize_depth.py:73: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.
+  ckpt = torch.load(checkpoint_path, map_location='cpu')
+/home/ywan0794/MoGe/visualize_depth.py:135: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.
+  ckpt = torch.load(checkpoint_path, map_location='cpu')
+/home/ywan0794/MoGe/visualize_depth.py:178: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with torch.cuda.amp.autocast(dtype=torch.bfloat16):
+Loading models...
+  Loading DA2-DPT...
+Loaded DA2 dpt from /home/ywan0794/Depth-Anything-V2/training/exp/dpt_vitb_both/epoch_007.pth
+  Loading DA2-SDT...
+Loaded DA2 sdt from /home/ywan0794/Depth-Anything-V2/training/exp/sdt_vitb_both/epoch_008.pth
+  Loading DA3-DPT...
+[INFO ] using MLP layer as FFN
+Loaded DA3 dpt from /home/ywan0794/Depth-Anything-3/training/exp/da3_dpt_vitl_both/epoch_010.pth
+  Loading DA3-SDT...
+[INFO ] using MLP layer as FFN
+Loaded DA3 sdt from /home/ywan0794/Depth-Anything-3/training/exp/da3_sdt_vitl_both/epoch_010.pth
+  Loading DA3-DualDPT...
+[INFO ] using MLP layer as FFN
+Loaded DA3 dualdpt from /home/ywan0794/Depth-Anything-3/training/exp/da3_dualdpt_vitl_both/epoch_010.pth
+All models loaded!
+
+Processing 200 KITTI samples...
+  [1/200] 2011_09_26_drive_0059_0000000154
+  [2/200] 2011_09_26_drive_0029_0000000296
+  [3/200] 2011_09_26_drive_0029_0000000154
+  [4/200] 2011_09_26_drive_0096_0000000171
+  [5/200] 2011_10_03_drive_0027_0000000362
+  [6/200] 2011_09_26_drive_0064_0000000462
+  [7/200] 2011_09_26_drive_0002_0000000051
+  [8/200] 2011_09_26_drive_0048_0000000016
+  [9/200] 2011_09_30_drive_0016_0000000110
+  [10/200] 2011_09_26_drive_0059_0000000098
+  [11/200] 2011_09_26_drive_0009_0000000032
+  [12/200] 2011_09_26_drive_0027_0000000147
+  [13/200] 2011_09_26_drive_0086_0000000277
+  [14/200] 2011_10_03_drive_0027_0000002001
+  [15/200] 2011_09_30_drive_0016_0000000121
+  [16/200] 2011_09_29_drive_0071_0000000252
+  [17/200] 2011_09_26_drive_0059_0000000070
+  [18/200] 2011_09_26_drive_0023_0000000198
+  [19/200] 2011_09_26_drive_0046_0000000110
+  [20/200] 2011_09_26_drive_0093_0000000176
+  [21/200] 2011_09_26_drive_0027_0000000014
+  [22/200] 2011_09_26_drive_0046_0000000080
+  [23/200] 2011_09_26_drive_0056_0000000275
+  [24/200] 2011_09_26_drive_0046_0000000035
+  [25/200] 2011_09_30_drive_0027_0000000123
+  [26/200] 2011_09_26_drive_0009_0000000176
+  [27/200] 2011_09_26_drive_0096_0000000437
+  [28/200] 2011_09_26_drive_0084_0000000296
+  [29/200] 2011_09_26_drive_0020_0000000054
+  [30/200] 2011_09_26_drive_0117_0000000208
+  [31/200] 2011_09_26_drive_0029_0000000112
+  [32/200] 2011_09_26_drive_0046_0000000040
+  [33/200] 2011_09_30_drive_0018_0000002033
+  [34/200] 2011_09_26_drive_0023_0000000450
+  [35/200] 2011_09_30_drive_0027_0000000835
+  [36/200] 2011_09_26_drive_0013_0000000050
+  [37/200] 2011_09_26_drive_0106_0000000147
+  [38/200] 2011_09_26_drive_0013_0000000045
+  [39/200] 2011_09_26_drive_0013_0000000060
+  [40/200] 2011_09_30_drive_0018_0000000214
+  [41/200] 2011_09_30_drive_0018_0000001070
+  [42/200] 2011_09_26_drive_0009_0000000276
+  [43/200] 2011_09_26_drive_0096_0000000361
+  [44/200] 2011_10_03_drive_0027_0000001096
+  [45/200] 2011_09_26_drive_0086_0000000250
+  [46/200] 2011_09_26_drive_0093_0000000048
+  [47/200] 2011_09_26_drive_0059_0000000224
+  [48/200] 2011_09_26_drive_0020_0000000012
+  [49/200] 2011_09_26_drive_0064_0000000396
+  [50/200] 2011_09_26_drive_0084_0000000140
+  [51/200] 2011_09_26_drive_0059_0000000302
+  [52/200] 2011_10_03_drive_0027_0000003811
+  [53/200] 2011_09_30_drive_0016_0000000143
+  [54/200] 2011_09_26_drive_0036_0000000768
+  [55/200] 2011_09_26_drive_0117_0000000182
+  [56/200] 2011_09_28_drive_0002_0000000045
+  [57/200] 2011_09_30_drive_0018_0000002247
+  [58/200] 2011_09_26_drive_0056_0000000011
+  [59/200] 2011_09_26_drive_0117_0000000572
+  [60/200] 2011_09_26_drive_0059_0000000056
+  [61/200] 2011_10_03_drive_0027_0000001458
+  [62/200] 2011_09_26_drive_0013_0000000085
+  [63/200] 2011_09_26_drive_0106_0000000075
+  [64/200] 2011_09_26_drive_0064_0000000044
+  [65/200] 2011_09_29_drive_0071_0000000915
+  [66/200] 2011_09_26_drive_0056_0000000242
+  [67/200] 2011_09_29_drive_0071_0000000288
+  [68/200] 2011_09_26_drive_0020_0000000063
+  [69/200] 2011_09_30_drive_0018_0000000856
+  [70/200] 2011_09_26_drive_0096_0000000190
+  [71/200] 2011_09_26_drive_0046_0000000090
+  [72/200] 2011_10_03_drive_0047_0000000192
+  [73/200] 2011_09_26_drive_0046_0000000010
+  [74/200] 2011_09_26_drive_0029_0000000338
+  [75/200] 2011_09_26_drive_0056_0000000154
+  [76/200] 2011_09_26_drive_0117_0000000416
+  [77/200] 2011_09_26_drive_0013_0000000070
+  [78/200] 2011_09_26_drive_0052_0000000006
+  [79/200] 2011_09_26_drive_0093_0000000112
+  [80/200] 2011_09_26_drive_0027_0000000049
+  [81/200] 2011_09_26_drive_0023_0000000270
+  [82/200] 2011_09_26_drive_0020_0000000015
+  [83/200] 2011_09_26_drive_0084_0000000179
+  [84/200] 2011_09_26_drive_0013_0000000115
+  [85/200] 2011_09_26_drive_0023_0000000252
+  [86/200] 2011_09_26_drive_0052_0000000030
+  [87/200] 2011_10_03_drive_0027_0000003087
+  [88/200] 2011_09_26_drive_0029_0000000140
+  [89/200] 2011_09_26_drive_0064_0000000418
+  [90/200] 2011_09_26_drive_0027_0000000175
+  [91/200] 2011_09_26_drive_0106_0000000139
+  [92/200] 2011_09_26_drive_0101_0000000658
+  [93/200] 2011_09_26_drive_0117_0000000468
+  [94/200] 2011_09_28_drive_0002_0000000030
+  [95/200] 2011_09_26_drive_0002_0000000036
+  [96/200] 2011_09_26_drive_0046_0000000045
+  [97/200] 2011_09_26_drive_0059_0000000316
+  [98/200] 2011_09_26_drive_0009_0000000080
+  [99/200] 2011_09_26_drive_0009_0000000016
+  [100/200] 2011_09_26_drive_0101_0000000556
+  [101/200] 2011_09_26_drive_0013_0000000010
+  [102/200] 2011_09_26_drive_0106_0000000131
+  [103/200] 2011_09_26_drive_0117_0000000026
+  [104/200] 2011_09_26_drive_0106_0000000211
+  [105/200] 2011_09_26_drive_0101_0000000114
+  [106/200] 2011_09_26_drive_0096_0000000038
+  [107/200] 2011_09_26_drive_0084_0000000309
+  [108/200] 2011_10_03_drive_0027_0000002363
+  [109/200] 2011_09_26_drive_0064_0000000440
+  [110/200] 2011_09_30_drive_0018_0000001284
+  [111/200] 2011_10_03_drive_0027_0000001277
+  [112/200] 2011_09_28_drive_0002_0000000051
+  [113/200] 2011_09_26_drive_0046_0000000115
+  [114/200] 2011_09_26_drive_0002_0000000012
+  [115/200] 2011_09_26_drive_0013_0000000110
+  [116/200] 2011_09_26_drive_0086_0000000331
+  [117/200] 2011_09_26_drive_0101_0000000080
+  [118/200] 2011_09_26_drive_0046_0000000065
+  [119/200] 2011_09_30_drive_0027_0000000164
+  [120/200] 2011_09_26_drive_0013_0000000040
+  [121/200] 2011_09_26_drive_0056_0000000187
+  [122/200] 2011_09_26_drive_0086_0000000196
+  [123/200] 2011_09_26_drive_0020_0000000057
+  [124/200] 2011_09_26_drive_0101_0000000182
+  [125/200] 2011_09_26_drive_0023_0000000306
+  [126/200] 2011_09_26_drive_0056_0000000088
+  [127/200] 2011_10_03_drive_0047_0000000320
+  [128/200] 2011_09_26_drive_0086_0000000115
+  [129/200] 2011_09_26_drive_0023_0000000360
+  [130/200] 2011_09_26_drive_0020_0000000036
+  [131/200] 2011_09_26_drive_0056_0000000176
+  [132/200] 2011_09_26_drive_0117_0000000156
+  [133/200] 2011_09_26_drive_0036_0000000256
+  [134/200] 2011_09_29_drive_0071_0000000771
+  [135/200] 2011_09_26_drive_0052_0000000046
+  [136/200] 2011_09_28_drive_0002_0000000006
+  [137/200] 2011_10_03_drive_0047_0000000480
+  [138/200] 2011_09_26_drive_0027_0000000007
+  [139/200] 2011_09_26_drive_0056_0000000077
+  [140/200] 2011_09_28_drive_0002_0000000036
+  [141/200] 2011_09_26_drive_0009_0000000308
+  [142/200] 2011_09_26_drive_0056_0000000022
+  [143/200] 2011_09_26_drive_0056_0000000165
+  [144/200] 2011_09_26_drive_0086_0000000088
+  [145/200] 2011_09_26_drive_0020_0000000018
+  [146/200] 2011_09_26_drive_0029_0000000098
+  [147/200] 2011_10_03_drive_0047_0000000512
+  [148/200] 2011_09_26_drive_0084_0000000127
+  [149/200] 2011_09_30_drive_0027_0000000041
+  [150/200] 2011_09_29_drive_0071_0000000576
+  [151/200] 2011_09_26_drive_0106_0000000099
+  [152/200] 2011_09_26_drive_0106_0000000179
+  [153/200] 2011_09_26_drive_0101_0000000896
+  [154/200] 2011_09_26_drive_0036_0000000480
+  [155/200] 2011_09_26_drive_0093_0000000128
+  [156/200] 2011_09_26_drive_0029_0000000014
+  [157/200] 2011_09_26_drive_0064_0000000242
+  [158/200] 2011_09_26_drive_0056_0000000209
+  [159/200] 2011_09_26_drive_0027_0000000098
+  [160/200] 2011_09_26_drive_0056_0000000121
+  [161/200] 2011_09_26_drive_0086_0000000358
+  [162/200] 2011_09_26_drive_0009_0000000292
+  [163/200] 2011_09_26_drive_0101_0000000386
+  [164/200] 2011_09_28_drive_0002_0000000084
+  [165/200] 2011_09_26_drive_0117_0000000546
+  [166/200] 2011_09_26_drive_0117_0000000494
+  [167/200] 2011_10_03_drive_0027_0000000543
+  [168/200] 2011_10_03_drive_0047_0000000064
+  [169/200] 2011_09_26_drive_0020_0000000042
+  [170/200] 2011_09_26_drive_0046_0000000095
+  [171/200] 2011_09_26_drive_0093_0000000192
+  [172/200] 2011_09_26_drive_0059_0000000344
+  [173/200] 2011_09_28_drive_0002_0000000078
+  [174/200] 2011_09_28_drive_0002_0000000087
+  [175/200] 2011_09_26_drive_0023_0000000468
+  [176/200] 2011_09_26_drive_0029_0000000268
+  [177/200] 2011_10_03_drive_0047_0000000032
+  [178/200] 2011_09_30_drive_0018_0000002419
+  [179/200] 2011_09_28_drive_0002_0000000057
+  [180/200] 2011_10_03_drive_0047_0000000672
+  [181/200] 2011_10_03_drive_0027_0000002544
+  [182/200] 2011_09_26_drive_0002_0000000015
+  [183/200] 2011_09_26_drive_0027_0000000182
+  [184/200] 2011_09_26_drive_0084_0000000218
+  [185/200] 2011_10_03_drive_0027_0000001639
+  [186/200] 2011_09_26_drive_0093_0000000417
+  [187/200] 2011_09_26_drive_0096_0000000456
+  [188/200] 2011_10_03_drive_0047_0000000416
+  [189/200] 2011_09_26_drive_0086_0000000034
+  [190/200] 2011_09_26_drive_0096_0000000247
+  [191/200] 2011_09_26_drive_0096_0000000209
+  [192/200] 2011_09_29_drive_0071_0000000144
+  [193/200] 2011_09_26_drive_0084_0000000270
+  [194/200] 2011_09_26_drive_0101_0000000284
+  [195/200] 2011_09_29_drive_0071_0000000036
+  [196/200] 2011_09_29_drive_0071_0000000360
+  [197/200] 2011_09_26_drive_0086_0000000304
+  [198/200] 2011_09_26_drive_0013_0000000065
+  [199/200] 2011_09_26_drive_0093_0000000160
+  [200/200] 2011_09_26_drive_0036_0000000064
+
+Processing 200 DDAD samples...
+  [1/200] 000508_CAMERA_05
+  [2/200] 001971_CAMERA_09
+  [3/200] 003267_CAMERA_06
+  [4/200] 001726_CAMERA_09
+  [5/200] 002738_CAMERA_05
+  [6/200] 000339_CAMERA_01
+  [7/200] 000104_CAMERA_05
+  [8/200] 001069_CAMERA_06
+  [9/200] 003710_CAMERA_06
+  [10/200] 003376_CAMERA_05
+  [11/200] 000864_CAMERA_09
+  [12/200] 003894_CAMERA_06
+  [13/200] 002730_CAMERA_01
+  [14/200] 000125_CAMERA_05
+  [15/200] 002151_CAMERA_05
+  [16/200] 002147_CAMERA_09
+  [17/200] 003924_CAMERA_09
+  [18/200] 002818_CAMERA_01
+  [19/200] 003451_CAMERA_09
+  [20/200] 001686_CAMERA_05
+  [21/200] 002310_CAMERA_01
+  [22/200] 003416_CAMERA_05
+  [23/200] 003797_CAMERA_06
+  [24/200] 001782_CAMERA_05
+  [25/200] 002078_CAMERA_09
+  [26/200] 001568_CAMERA_05
+  [27/200] 002371_CAMERA_06
+  [28/200] 001397_CAMERA_06
+  [29/200] 000278_CAMERA_05
+  [30/200] 000101_CAMERA_09
+  [31/200] 001674_CAMERA_09
+  [32/200] 001627_CAMERA_01
+  [33/200] 002721_CAMERA_05
+  [34/200] 002251_CAMERA_01
+  [35/200] 000127_CAMERA_06
+  [36/200] 000470_CAMERA_05
+  [37/200] 000865_CAMERA_05
+  [38/200] 002088_CAMERA_01
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+
+Done! Results saved to /home/ywan0794/MoGe/vis_output
+Structure:
+  /home/ywan0794/MoGe/vis_output/
+    KITTI/
+      rgb/, gt/, da2_dpt/, da2_sdt/, da3_dpt/, da3_sdt/, da3_dualdpt/
+    DDAD/
+      rgb/, gt/, da2_dpt/, da2_sdt/, da3_dpt/, da3_sdt/, da3_dualdpt/
+Visualization completed!

vis_depth_8787.log

@@ -0,0 +1,1034 @@
+/home/ywan0794/MoGe/visualize_depth.py:73: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.
+  ckpt = torch.load(checkpoint_path, map_location='cpu')
+/home/ywan0794/MoGe/visualize_depth.py:135: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.
+  ckpt = torch.load(checkpoint_path, map_location='cpu')
+/home/ywan0794/MoGe/visualize_depth.py:178: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+  with torch.cuda.amp.autocast(dtype=torch.bfloat16):
+Loading models...
+  Loading DA2-DPT...
+Loaded DA2 dpt from /home/ywan0794/Depth-Anything-V2/training/exp/dpt_vitb_both/epoch_007.pth
+  Loading DA2-SDT...
+Loaded DA2 sdt from /home/ywan0794/Depth-Anything-V2/training/exp/sdt_vitb_both/epoch_008.pth
+  Loading DA3-DPT...
+[INFO ] using MLP layer as FFN
+Loaded DA3 dpt from /home/ywan0794/Depth-Anything-3/training/exp/da3_dpt_vitl_both/epoch_010.pth
+  Loading DA3-SDT...
+[INFO ] using MLP layer as FFN
+Loaded DA3 sdt from /home/ywan0794/Depth-Anything-3/training/exp/da3_sdt_vitl_both/epoch_010.pth
+  Loading DA3-DualDPT...
+[INFO ] using MLP layer as FFN
+Loaded DA3 dualdpt from /home/ywan0794/Depth-Anything-3/training/exp/da3_dualdpt_vitl_both/epoch_010.pth
+All models loaded!
+
+Processing 500 KITTI samples...
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+  [200/500] 2011_09_26_drive_0036_0000000064
+  [201/500] 2011_09_26_drive_0036_0000000160
+  [202/500] 2011_09_26_drive_0027_0000000042
+  [203/500] 2011_09_26_drive_0059_0000000126
+  [204/500] 2011_09_26_drive_0002_0000000060
+  [205/500] 2011_10_03_drive_0027_0000002725
+  [206/500] 2011_09_26_drive_0036_0000000096
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+  [304/500] 2011_09_26_drive_0101_0000000862
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+  [307/500] 2011_09_26_drive_0009_0000000228
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+  [329/500] 2011_09_26_drive_0020_0000000060
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+  [362/500] 2011_09_30_drive_0027_0000000753
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+  [398/500] 2011_09_26_drive_0117_0000000338
+  [399/500] 2011_09_26_drive_0101_0000000352
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+  [499/500] 2011_09_28_drive_0002_0000000021
+  [500/500] 2011_09_30_drive_0018_0000000963
+
+Processing 500 DDAD samples...
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+  [500/500] 002774_CAMERA_09
+
+Done! Results saved to /home/ywan0794/MoGe/vis_output
+Structure:
+  /home/ywan0794/MoGe/vis_output/
+    KITTI/
+      rgb/, gt/, gt_reverse/, da2_dpt/, da2_sdt/, da3_dpt/, da3_sdt/, da3_dualdpt/
+    DDAD/
+      rgb/, gt/, gt_reverse/, da2_dpt/, da2_sdt/, da3_dpt/, da3_sdt/, da3_dualdpt/
+Visualization completed!

vis_gt_8719.log

@@ -0,0 +1,12 @@
+Processing KITTI GT...
+  KITTI: 50/200
+  KITTI: 100/200
+  KITTI: 150/200
+  KITTI: 200/200
+Processing DDAD GT...
+  DDAD: 50/200
+  DDAD: 100/200
+  DDAD: 150/200
+  DDAD: 200/200
+Done!
+GT visualization completed!

vis_gt_8722.log

@@ -0,0 +1,22 @@
+Starting GT visualization at Wed Jan 14 11:22:04 PM AEDT 2026
+==================================================
+GT Depth Visualization (both versions)
+==================================================
+
+Collecting KITTI samples...
+Processing 200 KITTI GT samples...
+
+
+Collecting DDAD samples...
+Processing 200 DDAD GT samples...
+
+
+==================================================
+Done!
+Output:
+  /home/ywan0794/MoGe/vis_output/KITTI/gt/         (non-reverse)
+  /home/ywan0794/MoGe/vis_output/KITTI/gt_reverse/ (reverse)
+  /home/ywan0794/MoGe/vis_output/DDAD/gt/          (non-reverse)
+  /home/ywan0794/MoGe/vis_output/DDAD/gt_reverse/  (reverse)
+==================================================
+GT visualization completed at Wed Jan 14 11:23:23 PM AEDT 2026

vis_gt_8725.log

@@ -0,0 +1,22 @@
+Starting GT visualization at Thu Jan 15 01:58:52 AM AEDT 2026
+==================================================
+GT Depth Visualization (both versions)
+==================================================
+
+Collecting KITTI samples...
+Processing 10 KITTI GT samples...
+
+
+Collecting DDAD samples...
+Processing 10 DDAD GT samples...
+
+
+==================================================
+Done!
+Output:
+  /home/ywan0794/MoGe/vis_output/KITTI/gt/         (non-reverse)
+  /home/ywan0794/MoGe/vis_output/KITTI/gt_reverse/ (reverse)
+  /home/ywan0794/MoGe/vis_output/DDAD/gt/          (non-reverse)
+  /home/ywan0794/MoGe/vis_output/DDAD/gt_reverse/  (reverse)
+==================================================
+GT visualization completed at Thu Jan 15 01:59:22 AM AEDT 2026

visualize_depth.py

@@ -0,0 +1,387 @@
+"""
+Visualize depth predictions from different decoders on KITTI and DDAD datasets
+"""
+import os
+import sys
+import argparse
+import numpy as np
+import torch
+import torch.nn.functional as F
+import torchvision.transforms as T
+import torchvision.transforms.functional as TF
+from PIL import Image
+import matplotlib.pyplot as plt
+import matplotlib
+matplotlib.use('Agg')
+
+# Paths
+DA2_REPO = '/home/ywan0794/Depth-Anything-V2'
+DA3_REPO = '/home/ywan0794/Depth-Anything-3'
+
+# Checkpoints
+CHECKPOINTS = {
+    'da2_dpt': '/home/ywan0794/Depth-Anything-V2/training/exp/dpt_vitb_both/epoch_007.pth',
+    'da2_sdt': '/home/ywan0794/Depth-Anything-V2/training/exp/sdt_vitb_both/epoch_008.pth',
+    'da3_dpt': '/home/ywan0794/Depth-Anything-3/training/exp/da3_dpt_vitl_both/epoch_010.pth',
+    'da3_sdt': '/home/ywan0794/Depth-Anything-3/training/exp/da3_sdt_vitl_both/epoch_010.pth',
+    'da3_dualdpt': '/home/ywan0794/Depth-Anything-3/training/exp/da3_dualdpt_vitl_both/epoch_010.pth',
+}
+
+# Dataset paths
+KITTI_BASE = '/home/ywan0794/datasets/eval/moge_style_eval/KITTI'
+DDAD_BASE = '/home/ywan0794/datasets/eval/moge_style_eval/DDAD/val'
+
+
+# ============================================
+# DA2 Model Loading (same as da2_custom.py)
+# ============================================
+def load_da2_model(checkpoint_path, encoder='vitb', decoder='dpt'):
+    """Load DA2 model with DPT or SDT decoder"""
+    repo_path = DA2_REPO
+    training_path = os.path.join(repo_path, 'training')
+
+    if repo_path not in sys.path:
+        sys.path.insert(0, repo_path)
+    if training_path not in sys.path:
+        sys.path.insert(0, training_path)
+
+    # Model configurations (same as training)
+    model_configs = {
+        'vits': {'encoder': 'vits', 'features': 64, 'out_channels': [48, 96, 192, 384]},
+        'vitb': {'encoder': 'vitb', 'features': 128, 'out_channels': [96, 192, 384, 768]},
+        'vitl': {'encoder': 'vitl', 'features': 256, 'out_channels': [256, 512, 1024, 1024]},
+        'vitg': {'encoder': 'vitg', 'features': 384, 'out_channels': [1536, 1536, 1536, 1536]}
+    }
+
+    # Build model based on decoder type
+    if decoder == 'dpt':
+        from depth_anything_v2.dpt import DepthAnythingV2
+        model = DepthAnythingV2(**model_configs[encoder])
+    elif decoder == 'sdt':
+        from depth_anything_v2.sdt import DepthAnythingV2SDT
+        model = DepthAnythingV2SDT(
+            encoder=encoder,
+            features=model_configs[encoder]['features'],
+            out_channels=model_configs[encoder]['out_channels'],
+            use_clstoken=True,
+            upsampler='dysample'
+        )
+    else:
+        raise ValueError(f"Unknown decoder: {decoder}")
+
+    # Load checkpoint
+    ckpt = torch.load(checkpoint_path, map_location='cpu')
+    if 'model' in ckpt:
+        state_dict = ckpt['model']
+    else:
+        state_dict = ckpt
+    state_dict = {k.replace('module.', ''): v for k, v in state_dict.items()}
+    missing, unexpected = model.load_state_dict(state_dict, strict=False)
+    print(f"Loaded DA2 {decoder} from {checkpoint_path}")
+    if missing:
+        print(f"  Missing keys: {len(missing)}")
+    if unexpected:
+        print(f"  Unexpected keys: {len(unexpected)}")
+
+    return model
+
+
+# ============================================
+# DA3 Model Loading (same as da3_custom.py)
+# ============================================
+class DA3Wrapper(torch.nn.Module):
+    def __init__(self, model):
+        super().__init__()
+        self.model = model
+
+    def forward(self, x):
+        # x: [B, 3, H, W]
+        x = x.unsqueeze(1)  # [B, 1, 3, H, W]
+        output = self.model(x)
+        depth = output.depth.squeeze(1)  # [B, H, W]
+        return depth
+
+
+def load_da3_model(checkpoint_path, decoder='dpt'):
+    """Load DA3 model with DPT, SDT, or DualDPT decoder"""
+    repo_path = DA3_REPO
+    src_path = os.path.join(repo_path, 'src')
+    training_path = os.path.join(repo_path, 'training')
+
+    if src_path not in sys.path:
+        sys.path.insert(0, src_path)
+    if training_path not in sys.path:
+        sys.path.insert(0, training_path)
+
+    # Config paths
+    config_dir = os.path.join(repo_path, 'src', 'depth_anything_3', 'configs')
+    if decoder == 'dpt':
+        config_path = os.path.join(config_dir, 'da3dpt-large.yaml')
+    elif decoder == 'sdt':
+        config_path = os.path.join(config_dir, 'da3sdt-large.yaml')
+    elif decoder == 'dualdpt':
+        config_path = os.path.join(config_dir, 'da3dualdpt-large.yaml')
+    else:
+        raise ValueError(f"Unknown decoder: {decoder}")
+
+    from depth_anything_3.cfg import load_config, create_object
+
+    # Build model
+    cfg = load_config(config_path)
+    base_model = create_object(cfg)
+    model = DA3Wrapper(base_model)
+
+    # Load checkpoint
+    ckpt = torch.load(checkpoint_path, map_location='cpu')
+    if 'model' in ckpt:
+        state_dict = ckpt['model']
+    else:
+        state_dict = ckpt
+    state_dict = {k.replace('module.', ''): v for k, v in state_dict.items()}
+    missing, unexpected = model.load_state_dict(state_dict, strict=False)
+    print(f"Loaded DA3 {decoder} from {checkpoint_path}")
+    if missing:
+        print(f"  Missing keys: {len(missing)}")
+    if unexpected:
+        print(f"  Unexpected keys: {len(unexpected)}")
+
+    return model
+
+
+# ============================================
+# Inference Wrapper
+# ============================================
+class ModelWrapper:
+    def __init__(self, model, device, use_amp=True):
+        self.model = model.to(device).eval()
+        self.device = device
+        self.use_amp = use_amp
+
+    @torch.inference_mode()
+    def predict(self, image):
+        """image: PIL Image, returns disparity numpy array"""
+        # Convert to tensor
+        img = TF.to_tensor(image).unsqueeze(0)  # [1, 3, H, W]
+        original_height, original_width = img.shape[-2:]
+
+        # Resize to multiple of 14
+        resize_factor = 518 / min(original_height, original_width)
+        expected_width = round(original_width * resize_factor / 14) * 14
+        expected_height = round(original_height * resize_factor / 14) * 14
+
+        img = TF.resize(img, (expected_height, expected_width), interpolation=T.InterpolationMode.BICUBIC, antialias=True)
+        img = TF.normalize(img, mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
+        img = img.to(self.device)
+
+        # Forward
+        if self.use_amp:
+            with torch.cuda.amp.autocast(dtype=torch.bfloat16):
+                disp = self.model(img)
+        else:
+            disp = self.model(img)
+
+        # Resize back
+        disp = F.interpolate(disp[:, None], size=(original_height, original_width), mode='bilinear', align_corners=False)[:, 0]
+        disp = disp.squeeze().cpu().numpy()
+
+        return disp
+
+
+def colorize_depth(depth, cmap='Spectral', reverse=False, mask_invalid=False):
+    """Convert depth/disparity to colorized image using Spectral colormap"""
+    depth = depth.copy()
+
+    # Create mask for invalid (zero) regions
+    if mask_invalid:
+        invalid_mask = depth <= 0
+
+    # Only use valid values for percentile calculation
+    if mask_invalid:
+        valid_depth = depth[~invalid_mask]
+        if len(valid_depth) > 0:
+            vmin = np.percentile(valid_depth, 2)
+            vmax = np.percentile(valid_depth, 98)
+        else:
+            vmin, vmax = 0, 1
+    else:
+        vmin = np.percentile(depth, 2)
+        vmax = np.percentile(depth, 98)
+
+    depth = (depth - vmin) / (vmax - vmin + 1e-8)
+    depth = np.clip(depth, 0, 1)
+
+    # Reverse if needed
+    if reverse:
+        depth = 1 - depth
+
+    cm = plt.get_cmap(cmap)
+    colored = cm(depth)[:, :, :3]
+    colored = (colored * 255).astype(np.uint8)
+
+    # Set invalid regions to black
+    if mask_invalid:
+        colored[invalid_mask] = 0
+
+    return colored
+
+
+def load_gt_depth(depth_path):
+    """Load ground truth depth from PNG"""
+    depth = np.array(Image.open(depth_path))
+    if depth.dtype == np.uint16:
+        depth = depth.astype(np.float32) / 256.0
+    elif depth.dtype == np.uint8:
+        depth = depth.astype(np.float32)
+    return depth
+
+
+def main():
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--output-dir', type=str, default='/home/ywan0794/MoGe/vis_output')
+    parser.add_argument('--num-samples', type=int, default=10)
+    parser.add_argument('--device', type=str, default='cuda')
+    args = parser.parse_args()
+
+    device = torch.device(args.device)
+
+    # Create output directories
+    datasets = ['KITTI', 'DDAD']
+    subfolders = ['rgb', 'gt', 'gt_reverse', 'da2_dpt', 'da2_sdt', 'da3_dpt', 'da3_sdt', 'da3_dualdpt']
+
+    for dataset in datasets:
+        for subfolder in subfolders:
+            os.makedirs(os.path.join(args.output_dir, dataset, subfolder), exist_ok=True)
+
+    print("Loading models...")
+    models = {}
+
+    # Load DA2 models
+    print("  Loading DA2-DPT...")
+    da2_dpt = load_da2_model(CHECKPOINTS['da2_dpt'], encoder='vitb', decoder='dpt')
+    models['da2_dpt'] = ModelWrapper(da2_dpt, device, use_amp=False)
+
+    print("  Loading DA2-SDT...")
+    da2_sdt = load_da2_model(CHECKPOINTS['da2_sdt'], encoder='vitb', decoder='sdt')
+    models['da2_sdt'] = ModelWrapper(da2_sdt, device, use_amp=False)
+
+    # Load DA3 models
+    print("  Loading DA3-DPT...")
+    da3_dpt = load_da3_model(CHECKPOINTS['da3_dpt'], decoder='dpt')
+    models['da3_dpt'] = ModelWrapper(da3_dpt, device, use_amp=True)
+
+    print("  Loading DA3-SDT...")
+    da3_sdt = load_da3_model(CHECKPOINTS['da3_sdt'], decoder='sdt')
+    models['da3_sdt'] = ModelWrapper(da3_sdt, device, use_amp=True)
+
+    print("  Loading DA3-DualDPT...")
+    da3_dualdpt = load_da3_model(CHECKPOINTS['da3_dualdpt'], decoder='dualdpt')
+    models['da3_dualdpt'] = ModelWrapper(da3_dualdpt, device, use_amp=True)
+
+    print("All models loaded!")
+
+    # Get KITTI samples
+    kitti_samples = []
+    for drive in os.listdir(KITTI_BASE):
+        drive_path = os.path.join(KITTI_BASE, drive, 'image_02')
+        if os.path.isdir(drive_path):
+            for frame in sorted(os.listdir(drive_path)):
+                sample_dir = os.path.join(drive_path, frame)
+                img_path = os.path.join(sample_dir, 'image.jpg')
+                gt_path = os.path.join(sample_dir, 'depth.png')
+                if os.path.exists(img_path) and os.path.exists(gt_path):
+                    kitti_samples.append({
+                        'image': img_path,
+                        'gt': gt_path,
+                        'name': f"{drive}_{frame}"
+                    })
+
+    # Get DDAD samples
+    ddad_samples = []
+    for scene in sorted(os.listdir(DDAD_BASE)):
+        scene_path = os.path.join(DDAD_BASE, scene)
+        if os.path.isdir(scene_path):
+            for cam in sorted(os.listdir(scene_path)):
+                sample_dir = os.path.join(scene_path, cam)
+                img_path = os.path.join(sample_dir, 'image.jpg')
+                gt_path = os.path.join(sample_dir, 'depth.png')
+                if os.path.exists(img_path) and os.path.exists(gt_path):
+                    ddad_samples.append({
+                        'image': img_path,
+                        'gt': gt_path,
+                        'name': f"{scene}_{cam}"
+                    })
+
+    # Select random samples
+    np.random.seed(42)
+    kitti_selected = np.random.choice(len(kitti_samples), min(args.num_samples, len(kitti_samples)), replace=False)
+    ddad_selected = np.random.choice(len(ddad_samples), min(args.num_samples, len(ddad_samples)), replace=False)
+
+    # Process KITTI
+    print(f"\nProcessing {len(kitti_selected)} KITTI samples...")
+    for idx, i in enumerate(kitti_selected):
+        sample = kitti_samples[i]
+        print(f"  [{idx+1}/{len(kitti_selected)}] {sample['name']}")
+
+        # Load image
+        image = Image.open(sample['image']).convert('RGB')
+
+        # Save RGB
+        image.save(os.path.join(args.output_dir, 'KITTI', 'rgb', f"{idx:03d}.png"))
+
+        # Load and save GT (both versions)
+        gt_depth = load_gt_depth(sample['gt'])
+        gt_colored = colorize_depth(gt_depth, reverse=False, mask_invalid=True)
+        gt_colored_rev = colorize_depth(gt_depth, reverse=True, mask_invalid=True)
+        Image.fromarray(gt_colored).save(os.path.join(args.output_dir, 'KITTI', 'gt', f"{idx:03d}.png"))
+        Image.fromarray(gt_colored_rev).save(os.path.join(args.output_dir, 'KITTI', 'gt_reverse', f"{idx:03d}.png"))
+
+        # Predict and save for each model
+        for model_name, wrapper in models.items():
+            pred = wrapper.predict(image)
+            # DA3 DPT needs reverse
+            need_reverse = (model_name == 'da3_dpt')
+            pred_colored = colorize_depth(pred, reverse=need_reverse)
+            Image.fromarray(pred_colored).save(
+                os.path.join(args.output_dir, 'KITTI', model_name, f"{idx:03d}.png")
+            )
+
+    # Process DDAD
+    print(f"\nProcessing {len(ddad_selected)} DDAD samples...")
+    for idx, i in enumerate(ddad_selected):
+        sample = ddad_samples[i]
+        print(f"  [{idx+1}/{len(ddad_selected)}] {sample['name']}")
+
+        # Load image
+        image = Image.open(sample['image']).convert('RGB')
+
+        # Save RGB
+        image.save(os.path.join(args.output_dir, 'DDAD', 'rgb', f"{idx:03d}.png"))
+
+        # Load and save GT (both versions)
+        gt_depth = load_gt_depth(sample['gt'])
+        gt_colored = colorize_depth(gt_depth, reverse=False, mask_invalid=True)
+        gt_colored_rev = colorize_depth(gt_depth, reverse=True, mask_invalid=True)
+        Image.fromarray(gt_colored).save(os.path.join(args.output_dir, 'DDAD', 'gt', f"{idx:03d}.png"))
+        Image.fromarray(gt_colored_rev).save(os.path.join(args.output_dir, 'DDAD', 'gt_reverse', f"{idx:03d}.png"))
+
+        # Predict and save for each model
+        for model_name, wrapper in models.items():
+            pred = wrapper.predict(image)
+            # DA3 DPT needs reverse
+            need_reverse = (model_name == 'da3_dpt')
+            pred_colored = colorize_depth(pred, reverse=need_reverse)
+            Image.fromarray(pred_colored).save(
+                os.path.join(args.output_dir, 'DDAD', model_name, f"{idx:03d}.png")
+            )
+
+    print(f"\nDone! Results saved to {args.output_dir}")
+    print(f"Structure:")
+    print(f"  {args.output_dir}/")
+    print(f"    KITTI/")
+    print(f"      rgb/, gt/, gt_reverse/, da2_dpt/, da2_sdt/, da3_dpt/, da3_sdt/, da3_dualdpt/")
+    print(f"    DDAD/")
+    print(f"      rgb/, gt/, gt_reverse/, da2_dpt/, da2_sdt/, da3_dpt/, da3_sdt/, da3_dualdpt/")
+
+
+if __name__ == '__main__':
+    main()

visualize_gt_only.py

@@ -0,0 +1,156 @@
+"""
+Visualize GT depth - both reverse and non-reverse versions
+Must match exactly with visualize_depth.py sample selection
+"""
+import os
+import sys
+import numpy as np
+from PIL import Image
+import matplotlib.pyplot as plt
+import matplotlib
+matplotlib.use('Agg')
+from tqdm import tqdm
+
+# Dataset paths - MUST match visualize_depth.py
+KITTI_BASE = '/home/ywan0794/datasets/eval/moge_style_eval/KITTI'
+DDAD_BASE = '/home/ywan0794/datasets/eval/moge_style_eval/DDAD/val'
+OUTPUT_DIR = '/home/ywan0794/MoGe/vis_output'
+
+
+def colorize_depth(depth, cmap='Spectral', reverse=False, mask_invalid=False):
+    depth = depth.copy()
+    if mask_invalid:
+        invalid_mask = depth <= 0
+        valid_depth = depth[~invalid_mask]
+        if len(valid_depth) > 0:
+            vmin = np.percentile(valid_depth, 2)
+            vmax = np.percentile(valid_depth, 98)
+        else:
+            vmin, vmax = 0, 1
+    else:
+        vmin = np.percentile(depth, 2)
+        vmax = np.percentile(depth, 98)
+
+    depth = (depth - vmin) / (vmax - vmin + 1e-8)
+    depth = np.clip(depth, 0, 1)
+
+    if reverse:
+        depth = 1 - depth
+
+    cm = plt.get_cmap(cmap)
+    colored = cm(depth)[:, :, :3]
+    colored = (colored * 255).astype(np.uint8)
+
+    if mask_invalid:
+        colored[invalid_mask] = 0
+
+    return colored
+
+
+def load_gt_depth(depth_path):
+    depth = np.array(Image.open(depth_path))
+    if depth.dtype == np.uint16:
+        depth = depth.astype(np.float32) / 256.0
+    elif depth.dtype == np.uint8:
+        depth = depth.astype(np.float32)
+    return depth
+
+
+def main():
+    print("=" * 50, flush=True)
+    print("GT Depth Visualization (both versions)", flush=True)
+    print("=" * 50, flush=True)
+
+    # Create output directories
+    for dataset in ['KITTI', 'DDAD']:
+        os.makedirs(os.path.join(OUTPUT_DIR, dataset, 'gt'), exist_ok=True)
+        os.makedirs(os.path.join(OUTPUT_DIR, dataset, 'gt_reverse'), exist_ok=True)
+
+    # ============================================
+    # KITTI - MUST match visualize_depth.py exactly
+    # ============================================
+    print("\nCollecting KITTI samples...", flush=True)
+    kitti_samples = []
+    for drive in os.listdir(KITTI_BASE):
+        drive_path = os.path.join(KITTI_BASE, drive, 'image_02')
+        if os.path.isdir(drive_path):
+            for frame in sorted(os.listdir(drive_path)):
+                sample_dir = os.path.join(drive_path, frame)
+                img_path = os.path.join(sample_dir, 'image.jpg')
+                gt_path = os.path.join(sample_dir, 'depth.png')
+                # MUST check both img and gt exist - same as visualize_depth.py
+                if os.path.exists(img_path) and os.path.exists(gt_path):
+                    kitti_samples.append({
+                        'image': img_path,
+                        'gt': gt_path,
+                        'name': f"{drive}_{frame}"
+                    })
+
+    # Same random seed and selection as visualize_depth.py
+    np.random.seed(42)
+    num_samples = 10  # Must match --num-samples in visualize_depth.py
+    kitti_selected = np.random.choice(len(kitti_samples), min(num_samples, len(kitti_samples)), replace=False)
+
+    print(f"Processing {len(kitti_selected)} KITTI GT samples...", flush=True)
+    for idx, i in tqdm(enumerate(kitti_selected), total=len(kitti_selected), desc="KITTI GT", file=sys.stdout):
+        sample = kitti_samples[i]
+        gt_depth = load_gt_depth(sample['gt'])
+
+        # Non-reverse version
+        gt_colored = colorize_depth(gt_depth, reverse=False, mask_invalid=True)
+        Image.fromarray(gt_colored).save(os.path.join(OUTPUT_DIR, 'KITTI', 'gt', f"{idx:03d}.png"))
+
+        # Reverse version
+        gt_colored_rev = colorize_depth(gt_depth, reverse=True, mask_invalid=True)
+        Image.fromarray(gt_colored_rev).save(os.path.join(OUTPUT_DIR, 'KITTI', 'gt_reverse', f"{idx:03d}.png"))
+
+    # ============================================
+    # DDAD - MUST match visualize_depth.py exactly
+    # ============================================
+    print("\nCollecting DDAD samples...", flush=True)
+    ddad_samples = []
+    for scene in sorted(os.listdir(DDAD_BASE)):
+        scene_path = os.path.join(DDAD_BASE, scene)
+        if os.path.isdir(scene_path):
+            for cam in sorted(os.listdir(scene_path)):
+                sample_dir = os.path.join(scene_path, cam)
+                img_path = os.path.join(sample_dir, 'image.jpg')
+                gt_path = os.path.join(sample_dir, 'depth.png')
+                # MUST check both img and gt exist - same as visualize_depth.py
+                if os.path.exists(img_path) and os.path.exists(gt_path):
+                    ddad_samples.append({
+                        'image': img_path,
+                        'gt': gt_path,
+                        'name': f"{scene}_{cam}"
+                    })
+
+    # Same random seed and selection as visualize_depth.py
+    # Note: seed was already set to 42 above, and kitti_selected consumed some random numbers
+    # We need to match the exact sequence
+    ddad_selected = np.random.choice(len(ddad_samples), min(num_samples, len(ddad_samples)), replace=False)
+
+    print(f"Processing {len(ddad_selected)} DDAD GT samples...", flush=True)
+    for idx, i in tqdm(enumerate(ddad_selected), total=len(ddad_selected), desc="DDAD GT", file=sys.stdout):
+        sample = ddad_samples[i]
+        gt_depth = load_gt_depth(sample['gt'])
+
+        # Non-reverse version
+        gt_colored = colorize_depth(gt_depth, reverse=False, mask_invalid=True)
+        Image.fromarray(gt_colored).save(os.path.join(OUTPUT_DIR, 'DDAD', 'gt', f"{idx:03d}.png"))
+
+        # Reverse version
+        gt_colored_rev = colorize_depth(gt_depth, reverse=True, mask_invalid=True)
+        Image.fromarray(gt_colored_rev).save(os.path.join(OUTPUT_DIR, 'DDAD', 'gt_reverse', f"{idx:03d}.png"))
+
+    print("\n" + "=" * 50, flush=True)
+    print("Done!", flush=True)
+    print("Output:", flush=True)
+    print(f"  {OUTPUT_DIR}/KITTI/gt/         (non-reverse)", flush=True)
+    print(f"  {OUTPUT_DIR}/KITTI/gt_reverse/ (reverse)", flush=True)
+    print(f"  {OUTPUT_DIR}/DDAD/gt/          (non-reverse)", flush=True)
+    print(f"  {OUTPUT_DIR}/DDAD/gt_reverse/  (reverse)", flush=True)
+    print("=" * 50, flush=True)
+
+
+if __name__ == '__main__':
+    main()

visualize_gt_slurm.sh

@@ -0,0 +1,22 @@
+#!/bin/bash
+#SBATCH --job-name=vis-gt
+#SBATCH --output=vis_gt_%j.log
+#SBATCH --error=vis_gt_%j.log
+#SBATCH --open-mode=append
+#SBATCH --ntasks=1
+#SBATCH --cpus-per-task=4
+#SBATCH --time=0:30:00
+#SBATCH --mem=16G
+
+# 禁用Python输出缓冲
+export PYTHONUNBUFFERED=1
+
+# Initialize conda
+source /home/ywan0794/miniconda3/etc/profile.d/conda.sh
+conda activate da3
+
+cd /home/ywan0794/MoGe
+
+echo "Starting GT visualization at $(date)"
+python visualize_gt_only.py
+echo "GT visualization completed at $(date)"

visualize_slurm.sh

@@ -0,0 +1,22 @@
+#!/bin/bash
+#SBATCH --job-name=vis-depth
+#SBATCH --output=vis_depth_%j.log
+#SBATCH --error=vis_depth_%j.log
+#SBATCH --ntasks=1
+#SBATCH --cpus-per-task=8
+#SBATCH --gres=gpu:1
+#SBATCH --time=1:00:00
+#SBATCH --mem=40G
+
+# Initialize conda
+source /home/ywan0794/miniconda3/etc/profile.d/conda.sh
+conda activate da3
+
+cd /home/ywan0794/MoGe
+
+python visualize_depth.py \
+    --output-dir /home/ywan0794/MoGe/vis_output \
+    --num-samples 500 \
+    --device cuda
+
+echo "Visualization completed!"