Merge branch 'main'

Author: Kin-Zhang

README.md

@@ -41,21 +41,22 @@ International Conference on Robotics and Automation (**ICRA**) 2025
 European Conference on Computer Vision (**ECCV**) 2024  
 [ Strategy ] [ Self-Supervised ] - [ [arXiv](https://arxiv.org/abs/2407.01702) ] [ [Project](https://github.com/KTH-RPL/SeFlow) ] → [here](#seflow)
 
+
 - **DeFlow: Decoder of Scene Flow Network in Autonomous Driving**  
 *Qingwen Zhang, Yi Yang, Heng Fang, Ruoyu Geng, Patric Jensfelt*  
 International Conference on Robotics and Automation (**ICRA**) 2024  
 [ Backbone ] [ Supervised ] - [ [arXiv](https://arxiv.org/abs/2401.16122) ] [ [Project](https://github.com/KTH-RPL/DeFlow) ] → [here](#deflow)
 
 🎁 <b>One repository, All methods!</b> 
-Additionally, *OpenSceneFlow* integrates following excellent works: [ICLR'24 ZeroFlow](https://arxiv.org/abs/2305.10424), [ICCV'23 FastNSF](https://arxiv.org/abs/2304.09121), [RA-L'21 FastFlow3D](https://arxiv.org/abs/2103.01306), [NeurIPS'21 NSFP](https://arxiv.org/abs/2111.01253). (More on the way...)
+Additionally, *OpenSceneFlow* integrates following excellent works: [ICLR'24 ZeroFlow](https://arxiv.org/abs/2305.10424), [CVPR'24 ICP-Flow](https://arxiv.org/abs/2402.17351), [ICCV'23 FastNSF](https://arxiv.org/abs/2304.09121), [RA-L'21 FastFlow3D](https://arxiv.org/abs/2103.01306), [NeurIPS'21 NSFP](https://arxiv.org/abs/2111.01253). (More on the way...)
 
 <details> <summary> Summary of them:</summary>
 
 - [x] [FastFlow3D](https://arxiv.org/abs/2103.01306): RA-L 2021, a basic backbone model.
 - [x] [ZeroFlow](https://arxiv.org/abs/2305.10424): ICLR 2024, their pre-trained weight can covert into our format easily through [the script](tools/zerof2ours.py).
 - [x] [NSFP](https://arxiv.org/abs/2111.01253): NeurIPS 2021, faster 3x than original version because of [our CUDA speed up](assets/cuda/README.md), same (slightly better) performance.
-- [x] [FastNSF](https://arxiv.org/abs/2304.09121): ICCV 2023. SSL optimization-based.
-- [ ] [ICP-Flow](https://arxiv.org/abs/2402.17351): CVPR 2024. SSL optimization-based. Done coding, public after review.
+- [x] [FastNSF](https://arxiv.org/abs/2304.09121): ICCV 2023. SSL Optimization-based.
+- [x] [ICP-Flow](https://arxiv.org/abs/2402.17351): CVPR 2024. SSL Optimization-based.
 - [ ] [EulerFlow](https://arxiv.org/abs/2410.02031): ICLR 2025. SSL optimization-based. In my plan, haven't coding yet.
 
 </details>
@@ -157,7 +158,7 @@ wget https://huggingface.co/kin-zhang/OpenSceneFlow/resolve/main/flow4d_best.ckp
 
 #### SSF
 
-Extra pakcges needed for SSF model:
+Extra packages needed for SSF model:
 ```bash
 pip install mmengine-lite
 pip install torch-scatter -f https://data.pyg.org/whl/torch-2.0.0+cu117.html
@@ -235,6 +236,20 @@ python train.py model=deflowpp save_top_model=3 val_every=3 voxel_size="[0.2, 0.
 wget https://huggingface.co/kin-zhang/OpenSceneFlow/resolve/main/seflowpp_best.ckpt
 ```
 
+
+### Optimization-based Unsupervised Methods
+
+For all optimization-based methods, you can directly run `eval.py`/`save.py` to get the result without training, while the running might take really long time, maybe tmux for run it. For multi-program running, the master port can be set through `+master_port=12346`.
+
+```bash
+# you can change another model by passing model name.
+python eval.py model=fastnsf
+
+# or save the result directly
+python save.py model=fastnsf
+```
+
+
 ## 3. Evaluation
 
 You can view Wandb dashboard for the training and evaluation results or upload result to online leaderboard.

src/models/__init__.py

@@ -49,6 +49,6 @@
 # * pip install pytorch3d assets/cuda/histlib
 try:
     from .icpflow import ICPFlow
-except ImportError:
+except ImportError as e:
     print("--- WARNING [model]: ICPFlow is not imported, as it requires pytorch3d lib which is not installed.")
     print(f"Detail error message\033[0m: {e}. Just ignore this warning if code runs without these models.")

src/runner.py

@@ -101,11 +101,12 @@ def __init__(self, cfg, rank, world_size, mode):
         self.mode = mode
 
         self.model.to(self.device)
-        self.metrics = OfficialMetrics() if self.mode in ['val', 'eval'] else None
+        self.metrics = OfficialMetrics() if self.mode in ['val', 'eval', 'valid'] else None
+        self.res_name = cfg.get('res_name', cfg.model.name)
         self.save_res_path = cfg.get('save_res_path', None)
 
     def _setup_dataloader(self):
-        if self.mode in ['val', 'test', 'eval']:
+        if self.mode in ['val', 'test', 'eval', 'valid']:
             dataset_path = self.cfg.dataset_path + f"/{self.cfg.data_mode}"
             is_eval_mode = True
         else: # 'save'
@@ -153,7 +154,7 @@ def _process_step(self, batch):
         final_flow = pose_flow.clone()
         final_flow[~batch['gm0']] = res_dict['flow'] + pose_flow[~batch['gm0']]
 
-        if self.mode in ['val', 'eval']:
+        if self.mode in ['val', 'eval', 'valid']:
             eval_mask = batch['eval_mask'].squeeze()
             gt_flow = batch["flow"]
             v1_dict = evaluate_leaderboard(final_flow[eval_mask], pose_flow[eval_mask], pc0[eval_mask], \
@@ -257,7 +258,7 @@ def _run_process(cfg, mode):
         gathered_metrics_objects = [runner.metrics]
 
     if rank == 0:
-        if mode in ['val', 'eval']:
+        if mode in ['val', 'eval', 'valid']:
             final_metrics = OfficialMetrics()
             print(f"\n--- [LOG] Finished processing. Aggregating results from {world_size} GPUs with {len(gathered_metrics_objects)} metrics objects...")
             for metrics_obj in gathered_metrics_objects:
@@ -301,8 +302,6 @@ def _run_process(cfg, mode):
 
 def _spawn_wrapper(rank, world_size, cfg, mode):
     torch.cuda.set_device(rank)
-
-    # FIXME(Qingwen): better to set these through command, since we might have more nodes to connected.
     os.environ['RANK'] = str(rank)
     os.environ['WORLD_SIZE'] = str(world_size)
     os.environ['MASTER_ADDR'] = 'localhost'