Merge TeFlow into codebase

README.md

@@ -14,7 +14,7 @@ It is also an official implementation of the following papers (sorted by the tim
 - **TeFlow: Enabling Multi-frame Supervision for Self-Supervised Feed-forward Scene Flow Estimation**   
 *Qingwen Zhang, Chenhan Jiang, Xiaomeng Zhu, Yunqi Miao, Yushan Zhang, Olov Andersson, Patric Jensfelt*  
 Conference on Computer Vision and Pattern Recognition (**CVPR**) 2026  
-[ Strategy ] [ Self-Supervised ] - [ [arXiv](https://arxiv.org/abs/2602.19053) ] [ [Project]() ]
+[ Strategy ] [ Self-Supervised ] - [ [arXiv](https://arxiv.org/abs/2602.19053) ] [ [Project](https://github.com/Kin-Zhang/TeFlow) ]→ [here](#teflow)
 
 - **DeltaFlow: An Efficient Multi-frame Scene Flow Estimation Method**   
 *Qingwen Zhang, Xiaomeng Zhu, Yushan Zhang, Yixi Cai, Olov Andersson, Patric Jensfelt*  
@@ -96,10 +96,10 @@ You always can choose [Docker](https://en.wikipedia.org/wiki/Docker_(software))
 
 ```bash
 # option 1: pull from docker hub
-docker pull zhangkin/opensf
+docker pull zhangkin/opensf:full
 
 # run container
-docker run -it --net=host --gpus all -v /dev/shm:/dev/shm -v /home/kin/data:/home/kin/data --name opensf zhangkin/opensf /bin/zsh
+docker run -it --net=host --gpus all -v /dev/shm:/dev/shm -v /home/kin/data:/home/kin/data --name opensf zhangkin/opensf:full /bin/zsh
 
 # and better to read your own gpu device info to compile the cuda extension again:
 cd /home/kin/workspace/OpenSceneFlow && git pull
@@ -149,7 +149,9 @@ Train DeltaFlow with the leaderboard submit config. [Runtime: Around 18 hours in
 
 ```bash
 # total bz then it's 10x2 under above training setup.
-python train.py model=deltaFlow optimizer.lr=2e-3 epochs=20 batch_size=2 num_frames=5 loss_fn=deflowLoss train_aug=True "voxel_size=[0.15, 0.15, 0.15]" "point_cloud_range=[-38.4, -38.4, -3, 38.4, 38.4, 3]" +optimizer.scheduler.name=WarmupCosLR +optimizer.scheduler.max_lr=2e-3 +optimizer.scheduler.total_steps=20000
+python train.py model=deltaflow optimizer.lr=2e-3 epochs=20 batch_size=2 num_frames=5 \
+  loss_fn=deflowLoss train_aug=True "voxel_size=[0.15, 0.15, 0.15]" "point_cloud_range=[-38.4, -38.4, -3, 38.4, 38.4, 3]" \
+  optimizer.lr=2e-4 +optimizer.scheduler.name=WarmupCosLR +optimizer.scheduler.max_lr=2e-3 +optimizer.scheduler.warmup_epochs=2
 
 # Pretrained weight can be downloaded through (av2), check all other datasets in the same folder.
 wget https://huggingface.co/kin-zhang/OpenSceneFlow/resolve/main/deltaflow/deltaflow-av2.ckpt
@@ -206,6 +208,19 @@ Train Feed-forward SSL methods (e.g. SeFlow/SeFlow++/VoteFlow etc), we needed to
 1) process auto-label process for training. Check [dataprocess/README.md#self-supervised-process](dataprocess/README.md#self-supervised-process) for more details. We provide these inside the demo dataset already.
 2) specify the loss function, we set the config here for our best model in the leaderboard.
 
+#### TeFlow
+
+```bash
+# [Runtime: Around 20 hours in 10x3080Ti GPUs.]
+python train.py model=deltaflow epochs=15 batch_size=2 num_frames=5 train_aug=True \
+  loss_fn=teflowLoss "voxel_size=[0.15, 0.15, 0.15]" "point_cloud_range=[-38.4, -38.4, -3, 38.4, 38.4, 3]" \
+  +ssl_label=seflow_auto "+add_seloss={chamfer_dis: 1.0, static_flow_loss: 1.0, dynamic_chamfer_dis: 1.0, cluster_based_pc0pc1: 1.0}" \
+  optimizer.name=Adam optimizer.lr=2e-3 +optimizer.scheduler.name=StepLR +optimizer.scheduler.step_size=9 +optimizer.scheduler.gamma=0.5
+
+# Pretrained weight can be downloaded through (av2), check all other datasets in the same folder.
+wget https://huggingface.co/kin-zhang/OpenSceneFlow/resolve/main/teflow/teflow-av2.ckpt
+```
+
 #### SeFlow
 
 ```bash
@@ -217,6 +232,7 @@ wget https://huggingface.co/kin-zhang/OpenSceneFlow/resolve/main/seflow_best.ckp
 ```
 
 #### VoteFlow
+
 Extra pakcges needed for VoteFlow, [pytorch3d](https://pytorch3d.org/) (prefer 0.7.7) and [torch-scatter](https://github.com/rusty1s/pytorch_scatter?tab=readme-ov-file) (prefer 2.1.2):
 
 ```bash
@@ -280,6 +296,7 @@ python eval.py checkpoint=/home/kin/seflow_best.ckpt data_mode=test leaderboard_
 ```
 
 ### **📊 Range-Wise Metric (New!)**
+
 In [SSF paper](https://arxiv.org/abs/2501.17821), we introduce a new distance-based evaluation metric for scene flow estimation. Below is an example output for SSF with point_cloud_range to 204.8m and voxel_size=0.2m. Check more long-range result in [SSF paper](https://arxiv.org/abs/2501.17821).
 
 | Distance  | Static    | Dynamic  | NumPointsStatic | NumPointsDynamic |
@@ -293,6 +310,7 @@ In [SSF paper](https://arxiv.org/abs/2501.17821), we introduce a new distance-ba
 
 
 ### Submit result to public leaderboard
+
 To submit your result to the public Leaderboard, if you select `data_mode=test`, it should be a zip file for you to submit to the leaderboard.
 Note: The leaderboard result in DeFlow&SeFlow main paper is [version 1](https://eval.ai/web/challenges/challenge-page/2010/evaluation), as [version 2](https://eval.ai/web/challenges/challenge-page/2210/overview) is updated after DeFlow&SeFlow.
 
@@ -337,13 +355,13 @@ For exporting easy comparsion with ground truth and other methods, we also provi
 python tools/visualization.py vis --res_name "['flow', 'seflow_best']" --data_dir /home/kin/data/av2/preprocess_v2/sensor/vis
 ```
 
-**Tips**: To quickly create qualitative results for all methods, you can use multiple results comparison mode, select a good viewpoint and then save screenshots for all frames by pressing `P` key. You will found all methods' results are saved in the output folder (default is `logs/imgs`). Enjoy it!
+**Tips**: To quickly create qualitative results for all methods, you can use multiple results comparison mode, select a good viewpoint and then save screenshots for all frames by pressing the `P` key. You will find all methods' results saved in the output folder (default: `logs/imgs`). [IrfanView](https://www.irfanview.com/) can help you easily crop the images in batch. Enjoy!
 
 
-_Rerun_: Another way to interact with [rerun](https://github.com/rerun-io/rerun) but please only vis scene by scene, not all at once.
+_Rerun_: Another way to interact with [rerun](https://github.com/rerun-io/rerun), here we vis scene by scene, you can also specify the result name to compare with GT or other methods.
 
 ```bash
-python tools/visualization_rerun.py --data_dir /home/kin/data/av2/h5py/demo/train --res_name "['flow', 'deflow']"
+python tools/visualization_rerun.py --scene_file /home/kin/data/av2/h5py/demo/val/25e5c600-36fe-3245-9cc0-40ef91620c22.h5 --res_name "['flow', 'deflow']"
 ```
 
 https://github.com/user-attachments/assets/07e8d430-a867-42b7-900a-11755949de21

assets/README.md

@@ -51,7 +51,30 @@ Then follow [this stackoverflow answers](https://stackoverflow.com/questions/596
    ```bash
    cd OpenSceneFlow && docker build -f Dockerfile -t zhangkin/opensf .
    ```
-
+
+### To Apptainer container
+
+If you want to build a **minimal** training env for Apptainer container, you can use the following command:
+```bash
+apptainer build opensf.sif assets/opensf.def
+# zhangkin/opensf:full is created by Dockerfile
+```
+
+Then run as a Python env with:
+```bash
+PYTHON="apptainer run --nv --writable-tmpfs opensf.sif"
+$PYTHON train.py
+```
+
+<!-- 
+In case the compile package not working for your CUDA cability, add following code to the `assets/opensf.def` file before `exec`:
+```bash
+echo "Running pip install for local CUDA modules..."
+/opt/conda/bin/pip install /workspace/assets/cuda/chamfer3D
+/opt/conda/bin/pip install /workspace/assets/cuda/mmcv
+``` -->
+
+
 ## Installation
 
 We will use conda to manage the environment with mamba for faster package installation.
@@ -77,10 +100,11 @@ Checking important packages in our environment now:
 ```bash
 mamba activate opensf
 python -c "import torch; print(torch.__version__); print(torch.cuda.is_available()); print(torch.version.cuda)"
-python -c "import lightning.pytorch as pl; print(pl.__version__)"
+python -c "import lightning.pytorch as pl; print('pl version:', pl.__version__)"
+python -c "import spconv.pytorch as spconv; print('spconv import successfully')"
 python -c "from assets.cuda.mmcv import Voxelization, DynamicScatter;print('successfully import on our lite mmcv package')"
 python -c "from assets.cuda.chamfer3D import nnChamferDis;print('successfully import on our chamfer3D package')"
-python -c "from av2.utils.io import read_feather; print('av2 package ok')"
+python -c "from av2.utils.io import read_feather; print('av2 package ok') "
 ```
 
 
@@ -98,6 +122,7 @@ python -c "from av2.utils.io import read_feather; print('av2 package ok')"
 2. In cluster have error: `pandas ImportError: /lib64/libstdc++.so.6: version 'GLIBCXX_3.4.29' not found`
     Solved by `export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/proj/berzelius-2023-154/users/x_qinzh/mambaforge/lib`
 
+4. nvidia channel cannot put into env.yaml file otherwise, the cuda-toolkit will always be the latest one, for me (2025-04-30) I struggling on an hour and get nvcc -V also 12.8 at that time. py=3.10 for cuda >=12.1. (seems it's nvidia cannot be in the channel list???); py<3.10 for cuda <=11.8.0: otherwise 10x, 20x series GPU won't work on cuda compiler. (half precision)
 
 3. torch_scatter problem: `OSError: /home/kin/mambaforge/envs/opensf-v2/lib/python3.10/site-packages/torch_scatter/_version_cpu.so: undefined symbol: _ZN5torch3jit17parseSchemaOrNameERKNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEE`
    Solved by install the torch-cuda version: `pip install https://data.pyg.org/whl/torch-2.0.0%2Bcu118/torch_scatter-2.1.2%2Bpt20cu118-cp310-cp310-linux_x86_64.whl`