docs(README): update readme based on our refactor.

chore: move voxel to encoder script.

fix(dataset): index eval in the script.

style(trainer): log print, eval cfg check.

Author: Kin-Zhang

README.md

@@ -3,8 +3,8 @@ SeFlow: A Self-Supervised Scene Flow Method in Autonomous Driving
 
 [![arXiv](https://img.shields.io/badge/arXiv-2407.01702-b31b1b?logo=arxiv&logoColor=white)](https://arxiv.org/abs/2407.01702)
 [![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/seflow-a-self-supervised-scene-flow-method-in/self-supervised-scene-flow-estimation-on-1)](https://paperswithcode.com/sota/self-supervised-scene-flow-estimation-on-1?p=seflow-a-self-supervised-scene-flow-method-in)
-[poster comming soon]
-[video coming soon]
+[![poster](https://img.shields.io/badge/ICRA24|Poster-6495ed?style=flat&logo=Shotcut&logoColor=wihte)](https://hkustconnect-my.sharepoint.com/:b:/g/personal/qzhangcb_connect_ust_hk/EWyWD-tAX4xIma5U7ZQVk9cBVjsFv0Y_jAC2G7xAB-w4cg?e=c3FbMg) 
+[![video](https://img.shields.io/badge/video-YouTube-FF0000?logo=youtube&logoColor=white)](https://youtu.be/fQqx2IES-VI)
 
 ![](assets/docs/seflow_arch.png)
 
@@ -24,9 +24,9 @@ We directly follow our previous work [code structure](https://github.com/KTH-RPL
 
 - `train.py`: Train the model and get model checkpoints. Pls remember to check the config.
 
-- `eval.py` : Evaluate the model on the validation/test set. And also upload to online leaderboard.
+- `eval.py` : Evaluate the model on the validation/test set. And also output the zip file to upload to online leaderboard.
 
-- `save.py` : For visualization of the results with a video.
+- `save.py` : Will save result into h5py file, using [tool/visualization.py] to show results with interactive window.
 
 <details> <summary>🎁 <b>One repository, All methods!</b> </summary>
 <!-- <br> -->
@@ -118,7 +118,7 @@ Or you can directly download the pre-trained weight from [Zenodo](https://zenodo
 
 You can also train the supervised baseline model in our paper with the following command. [Runtime: Around 10 hours in 4x A100 GPUs.] 
 ```bash
-python train.py model=fastflow3d lr=2e-4 epochs=20 batch_size=16 loss_fn=ff3dLoss
+python train.py model=fastflow3d lr=4e-5 epochs=20 batch_size=16 loss_fn=ff3dLoss
 python train.py model=deflow lr=2e-4 epochs=20 batch_size=16 loss_fn=deflowLoss
 ```
 

assets/README.md

@@ -37,21 +37,17 @@ git clone https://github.com/KTH-RPL/SeFlow.git
 mamba env create -f assets/environment.yml
 ```
 
-CUDA package (need install nvcc compiler), the compile time is around 1-5 minutes:
+CUDA package (nvcc compiler already installed through conda), the compile time is around 1-5 minutes:
 ```bash
 mamba activate seflow
-# change it if you use different cuda version (I tested 11.3, 11.4, 11.7 all works)
-export PATH=/usr/local/cuda-11.7/bin:$PATH
-export LD_LIBRARY_PATH=/usr/local/cuda-11.7/lib64:$LD_LIBRARY_PATH
-
 cd assets/cuda/mmcv && python ./setup.py install && cd ../../..
 cd assets/cuda/chamfer3D && python ./setup.py install && cd ../../..
 ```
 
 
 Checking important packages in our environment now:
 ```bash
-mamba activate deflow
+mamba activate seflow
 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 "from assets.cuda.mmcv import Voxelization, DynamicScatter;print('successfully import on our lite mmcv package')"
@@ -78,8 +74,8 @@ python -c "from assets.cuda.chamfer3D import nnChamferDis;print('successfully im
 
 If you want to contribute to new model, here are tips you can follow:
 1. Dataloader: we believe all data could be process to `.h5`, we named as different scene and inside a scene, the key of each data is timestamp. Check [dataprocess/README.md](../dataprocess/README.md#process) for more details.
-2. Model: All model files can be found [here: scripts/network/models](../scripts/network/models). You can view deflow and fastflow3d to know how to implement a new model.
-3. Loss: All loss files can be found [here: scripts/network/loss_func.py](../scripts/network/loss_func.py). There are three loss functions already inside the file, you can add a new one following the same pattern.
+2. Model: All model files can be found [here: src/models](../src/models). You can view deflow and fastflow3d to know how to implement a new model. Don't forget to add to the `__init__.py` [file to import class](../src/models/__init__.py).
+3. Loss: All loss files can be found [here: src/lossfuncs.py](../src/lossfuncs.py). There are three loss functions already inside the file, you can add a new one following the same pattern.
 4. Training: Once you have implemented the model, you can add the model to the config file [here: conf/model](../conf/model) and train the model using the command `python train.py model=your_model_name`. One more note here may: if your res_dict from model output is different, you may need add one pattern in `def training_step` and `def validation_step`.
 
 All others like eval and vis will be changed according to the model you implemented as you follow the above steps.

eval.py

@@ -14,12 +14,21 @@
 from torch.utils.data import DataLoader
 import lightning.pytorch as pl
 from lightning.pytorch.loggers import WandbLogger
-from omegaconf import DictConfig, OmegaConf
+from omegaconf import DictConfig
 import hydra, wandb, os, sys
 from hydra.core.hydra_config import HydraConfig
 from src.dataset import HDF5Dataset
 from src.trainer import ModelWrapper
 
+def precheck_cfg_valid(cfg):
+    if os.path.exists(cfg.dataset_path + f"/{cfg.av2_mode}") is False:
+        raise ValueError(f"Dataset {cfg.dataset_path}/{cfg.av2_mode} does not exist. Please check the path.")
+    if cfg.supervised_flag not in [True, False]:
+        raise ValueError(f"Supervised flag {cfg.supervised_flag} is not valid. Please set it to True or False.")
+    if cfg.leaderboard_version not in [1, 2]:
+        raise ValueError(f"Leaderboard version {cfg.leaderboard_version} is not valid. Please set it to 1 or 2.")
+    return cfg
+
 @hydra.main(version_base=None, config_path="conf", config_name="eval")
 def main(cfg):
     pl.seed_everything(cfg.seed, workers=True)
@@ -35,7 +44,7 @@ def main(cfg):
     cfg.model.update(checkpoint_params.cfg.model)
 
     mymodel = ModelWrapper.load_from_checkpoint(cfg.checkpoint, cfg=cfg, eval=True)
-    print(f"\n---LOG[eval]: Loaded model from {cfg.checkpoint}. The model is {checkpoint_params.cfg.model.name}.\n")
+    print(f"\n---LOG[eval]: Loaded model from {cfg.checkpoint}. The backbone network is {checkpoint_params.cfg.model.name}.\n")
 
     wandb_logger = WandbLogger(save_dir=output_dir,
                                entity="kth-rpl",

logs/.gitkeep

@@ -16,7 +16,7 @@
 from torch.utils.data import Dataset, DataLoader
 import h5py, os, pickle, argparse, sys
 from tqdm import tqdm
-BASE_DIR = os.path.abspath(os.path.join( os.path.dirname( __file__ ), '../..' ))
+BASE_DIR = os.path.abspath(os.path.join( os.path.dirname( __file__ ), '..' ))
 sys.path.append(BASE_DIR)
 
 def collate_fn_pad(batch):