chore(base): updates demo and weights in codebase.

Kin-Zhang · Kin-Zhang · commit a3c9766c3078 · 2024-07-16T17:31:35.000+02:00
* eval and submit: new save zip file name and output the command directly
* dataloader: add num_frames into dataloader for afterward using.
* demo data &amp; weights: update all weights for preprocess_v2 data where ego pose center in sensor center.
* config: update monitor metric to new one for all mode config.
diff --git a/1_train.py b/1_train.py
@@ -32,14 +32,14 @@ def main(cfg):
         raise ValueError("Please specify the self-supervised loss items for seflowLoss.")
     pl.seed_everything(cfg.seed, workers=True)
 
-    train_dataset = HDF5Dataset(cfg.train_data, dufo=(cfg.loss_fn == 'seflowLoss'))
+    train_dataset = HDF5Dataset(cfg.train_data, n_frames=cfg.num_frames, dufo=(cfg.loss_fn == 'seflowLoss'))
     train_loader = DataLoader(train_dataset,
                               batch_size=cfg.batch_size,
                               shuffle=True,
                               num_workers=cfg.num_workers,
                               collate_fn=collate_fn_pad,
                               pin_memory=True)
-    val_loader = DataLoader(HDF5Dataset(cfg.val_data),
+    val_loader = DataLoader(HDF5Dataset(cfg.val_data, n_frames=cfg.num_frames),
                             batch_size=cfg.batch_size,
                             shuffle=False,
                             num_workers=cfg.num_workers,
@@ -52,8 +52,8 @@ def main(cfg):
 
     # only for logging on folder name.
     if cfg.loss_fn == 'seflowLoss':
+        cfg.output = cfg.output.replace(cfg.model.name, "seflow")
         method_name = "seflow"
-        cfg.output = cfg.output.replace("deflow", "seflow")
     else:
         method_name = cfg.model.name
     output_dir = HydraConfig.get().runtime.output_dir + f"/{cfg.output}"
diff --git a/2_eval.py b/2_eval.py
@@ -27,10 +27,12 @@ def main(cfg):
     if not os.path.exists(cfg.checkpoint):
         print(f"Checkpoint {cfg.checkpoint} does not exist. Need checkpoints for evaluation.")
         sys.exit(1)
-    
-    checkpoint_params = DictConfig(torch.load(cfg.checkpoint)["hyper_parameters"])
-    cfg.output = checkpoint_params.cfg.output + f"-{cfg.av2_mode}-v{cfg.leaderboard_version}"
+        
+    torch_load_ckpt = torch.load(cfg.checkpoint)
+    checkpoint_params = DictConfig(torch_load_ckpt["hyper_parameters"])
+    cfg.output = checkpoint_params.cfg.output + f"-e{torch_load_ckpt['epoch']}-{cfg.av2_mode}-v{cfg.leaderboard_version}"
     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")
 
@@ -43,7 +45,7 @@ def main(cfg):
     trainer = pl.Trainer(logger=wandb_logger, devices=1)
     # NOTE(Qingwen): search & check: def eval_only_step_(self, batch, res_dict)
     trainer.validate(model = mymodel, \
-                     dataloaders = DataLoader(HDF5Dataset(cfg.dataset_path + f"/{cfg.av2_mode}", eval=True, leaderboard_version=cfg.leaderboard_version), batch_size=1, shuffle=False))
+                     dataloaders = DataLoader(HDF5Dataset(cfg.dataset_path + f"/{cfg.av2_mode}", n_frames=checkpoint_params.cfg.num_frames, eval=True, leaderboard_version=cfg.leaderboard_version), batch_size=1, shuffle=False))
     wandb.finish()
 
 if __name__ == "__main__":
diff --git a/3_vis.py b/3_vis.py
@@ -48,7 +48,7 @@ def main(cfg):
     trainer = pl.Trainer(logger=wandb_logger, devices=1)
     # NOTE(Qingwen): search & check in pl_model.py : def test_step(self, batch, res_dict)
     trainer.test(model = mymodel, \
-                 dataloaders = DataLoader(HDF5Dataset(cfg.dataset_path), batch_size=1, shuffle=False))
+                 dataloaders = DataLoader(HDF5Dataset(cfg.dataset_path, n_frames=checkpoint_params.cfg.num_frames), batch_size=1, shuffle=False))
     wandb.finish()
 
 if __name__ == "__main__":
diff --git a/README.md b/README.md
@@ -6,9 +6,9 @@ SeFlow: A Self-Supervised Scene Flow Method in Autonomous Driving
 [poster comming soon]
 [video coming soon]
 
-2024/07/09 16:34: I'm working on updating code here now. **Not fully ready yet** until Jul'15.
+2024/07/16 17:18: Most of codes already uploaded and tested. You can to try training directly by downloading demo data. The process script will be ready when the paper published. 
 
-Pre-trained weights for models are available in [Zenodo](https://zenodo.org/records/12632962) link. Check usage in [2. Evaluation](#2-evaluation) or [3. Visualization](#3-visualization).
+Pre-trained weights for models are available in [Zenodo](https://zenodo.org/records/12751363) link. Check usage in [2. Evaluation](#2-evaluation) or [3. Visualization](#3-visualization).
 
 Task: __Self-Supervised__ Scene Flow Estimation in Autonomous Driving. No human-label needed. Real-time inference (15-20Hz in RTX3090).
 
@@ -32,9 +32,10 @@ You can try following methods in our code without any effort to make your own be
 - [x] [SeFlow](https://arxiv.org/abs/2407.01702) (Ours 🚀): ECCV 2024
 - [x] [DeFlow](https://arxiv.org/abs/2401.16122) (Ours 🚀): ICRA 2024
 - [x] [FastFlow3d](https://arxiv.org/abs/2103.01306): RA-L 2021
-- [x] [ZeroFlow](https://arxiv.org/abs/2305.10424): ICLR 2024, their pre-trained weight can covert into our format easily through [the script](TODO).
+- [x] [ZeroFlow](https://arxiv.org/abs/2305.10424): ICLR 2024, their pre-trained weight can covert into our format easily through [the script](tests/zerof2ours.py).
 - [ ] [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. Done coding, public after review.
 - [ ] [FastNSF](https://arxiv.org/abs/2304.09121): ICCV 2023. Done coding, public after review.
+<!-- - [ ] [Flow4D](https://arxiv.org/abs/2407.07995): 1st supervise network in the new leaderboard. Done coding, public after review. -->
 - [ ] ... more on the way
 
 </details>
@@ -51,7 +52,7 @@ cd SeFlow && mamba env create -f environment.yaml
 CUDA package (need install nvcc compiler), 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)
+# change it if you use different cuda version (I tested 11.3, 11.4, 11.7, 11.8 all works)
 export PATH=/usr/local/cuda-11.7/bin:$PATH
 export LD_LIBRARY_PATH=/usr/local/cuda-11.7/lib64:$LD_LIBRARY_PATH
 
@@ -71,25 +72,23 @@ docker run -it --gpus all -v /dev/shm:/dev/shm -v /home/kin/data:/home/kin/data
 
 ## 1. Run & Train
 
-Note: Prepare raw data and process train data only needed run once for the task. No need repeat the data process steps till you delete all data.
+Note: Prepare raw data and process train data only needed run once for the task. No need repeat the data process steps till you delete all data. We use [wandb](https://wandb.ai/) to log the training process, and you may want to change all `entity="kth-rpl"` to your own entity.
 
 ### Data Preparation
 
-Check [dataprocess/README.md](dataprocess/README.md#argoverse-20) for downloading tips for the raw Argoverse 2 dataset.
+Check [dataprocess/README.md](dataprocess/README.md#argoverse-20) for downloading tips for the raw Argoverse 2 dataset. Or maybe you only want to have the **mini processed dataset** to try the code quickly, We directly provide one scene inside `train` and `val`. It already converted to `.h5` format and processed with the label data. 
+You can download it from [Zenodo](https://zenodo.org/record/12751363) and extract it to the data folder.
 
-Maybe you only want to have the mini processed dataset to try the code quickly, We directly provide one scene inside `train` and `val`. It already converted to `.h5` format and processed with the label data. 
-<!-- You can download it from [Zenodo](https://zenodo.org/record/12632962) and extract it to the data folder. -->
 ```bash
-# TODO: update the link later when the data is ready
-# wget https://zenodo.org/record/12632962/files/demo_data.zip
+wget https://zenodo.org/record/12751363/files/demo_data.zip
 unzip demo_data.zip -p /home/kin/data/av2
 ```
 
 #### Prepare raw data 
 
 Extract all data to unified h5 format. [Runtime: Normally need 10 mins finished run following commands totally in my desktop, 45 mins for the cluster I used]
 ```bash
-python dataprocess/extract_av2.py --av2_type sensor --data_mode train --argo_dir /home/kin/data/av2 --output_dir /home/kin/data/av2/preprocess
+python dataprocess/extract_av2.py --av2_type sensor --data_mode train --argo_dir /home/kin/data/av2 --output_dir /home/kin/data/av2/preprocess_v2
 python dataprocess/extract_av2.py --av2_type sensor --data_mode val --mask_dir /home/kin/data/av2/3d_scene_flow
 python dataprocess/extract_av2.py --av2_type sensor --data_mode test --mask_dir /home/kin/data/av2/3d_scene_flow
 ```
@@ -99,17 +98,19 @@ python dataprocess/extract_av2.py --av2_type sensor --data_mode test --mask_dir
 Process train data for self-supervised learning. Only training data needs this step. [Runtime: Normally need 15 hours for my desktop, 3 hours for the cluster with five available nodes parallel running.]
 
 ```bash
-python 0_process.py --data_dir /home/kin/data/av2/preprocess/sensor/train --scene_range 0,701
+python 0_process.py --data_dir /home/kin/data/av2/preprocess_v2/sensor/train --scene_range 0,701
 ```
 
 ### Train the model
 
-Train SeFlow needed to specify the loss function, we set the config of our best model in the leaderboard. [Runtime: Around 18 hours in 4x A100 GPUs.]
+Train SeFlow needed to specify the loss function, we set the config of our best model in the leaderboard. [Runtime: Around 11 hours in 4x A100 GPUs.]
 
 ```bash
-python 1_train.py model=deflow lr=2e-4 epochs=20 batch_size=16 loss_fn=seflowLoss "add_seloss={chamfer_dis: 1.0, static_flow_loss: 1.0, dynamic_chamfer_dis: 1.0, cluster_based_pc0pc1: 1.0}" "model.target.num_iters=2" "model.val_monitor=val/Dynamic/Mean"
+python 1_train.py model=deflow lr=2e-4 epochs=9 batch_size=16 loss_fn=seflowLoss "add_seloss={chamfer_dis: 1.0, static_flow_loss: 1.0, dynamic_chamfer_dis: 1.0, cluster_based_pc0pc1: 1.0}" "model.target.num_iters=2" "model.val_monitor=val/Dynamic/Mean"
 ```
 
+Or you can directly download the pre-trained weight from [Zenodo](https://zenodo.org/records/12751363/files/seflow_best.ckpt) and skip the training step. 
+
 ### Other Benchmark Models
 
 You can also train the supervised baseline model in our paper with the following command. [Runtime: Around 10 hours in 4x A100 GPUs.] 
@@ -129,27 +130,36 @@ Since in training, we save all hyper-parameters and model checkpoints, the only
 
 ```bash
 # downloaded pre-trained weight, or train by yourself
-wget https://zenodo.org/records/12632962/files/seflow_official.ckpt
+wget https://zenodo.org/records/12751363/files/seflow_best.ckpt
 
 # it will directly prints all metric
-python 2_eval.py checkpoint=/home/kin/seflow_official.ckpt av2_mode=val
+python 2_eval.py checkpoint=/home/kin/seflow_best.ckpt av2_mode=val
 
 # it will output the av2_submit.zip or av2_submit_v2.zip for you to submit to leaderboard
-python 2_eval.py checkpoint=/home/kin/seflow_official.ckpt av2_mode=test leaderboard_version=1
-python 2_eval.py checkpoint=/home/kin/seflow_official.ckpt av2_mode=test leaderboard_version=2
+python 2_eval.py checkpoint=/home/kin/seflow_best.ckpt av2_mode=test leaderboard_version=1
+python 2_eval.py checkpoint=/home/kin/seflow_best.ckpt av2_mode=test leaderboard_version=2
 ```
 
 
 ## 3. Visualization
 
-We provide a script to visualize the results of the model. You can specify the checkpoint path and the data path to visualize the results. The step is quickly similar to evaluation.
+We provide a script to visualize the results of the model also. You can specify the checkpoint path and the data path to visualize the results. The step is quickly similar to evaluation.
 
 ```bash
+python 3_vis.py checkpoint=/home/kin/seflow_best.ckpt dataset_path=/home/kin/data/av2/preprocess_v2/sensor/vis
+
+# The output of above command will be like:
+Model: DeFlow, Checkpoint from: /home/kin/model_zoo/v2/seflow_best.ckpt
+We already write the flow_est into the dataset, please run following commend to visualize the flow. Copy and paste it to your terminal:
+python tests/scene_flow.py --flow_mode 'seflow_best' --data_dir /home/kin/data/av2/preprocess_v2/sensor/vis
+Enjoy! ^v^ ------ 
 
 # Then run the command in the terminal:
-python tests/scene_flow.py --flow_mode 'seflow_official' --data_dir /home/kin/data/av2/preprocess/sensor/mini
+python tests/scene_flow.py --flow_mode 'seflow_best' --data_dir /home/kin/data/av2/preprocess_v2/sensor/vis
 ```
 
+https://github.com/user-attachments/assets/f031d1a2-2d2f-4947-a01f-834ed1c146e6
+
 
 ## Cite & Acknowledgements
 
diff --git a/assets/slurm/0_process.sh b/assets/slurm/0_process.sh
@@ -7,29 +7,29 @@
 #SBATCH -t 1-00:00:00
 #SBATCH --mail-type=END,FAIL
 #SBATCH --mail-user=qingwen@kth.se
-#SBATCH --output /proj/berzelius-2023-154/users/x_qinzh/seflow/logs/slurm/%J_data.out
-#SBATCH --error  /proj/berzelius-2023-154/users/x_qinzh/seflow/logs/slurm/%J_data.err
+#SBATCH --output /proj/berzelius-2023-154/users/x_qinzh/workspace/SeFlow/logs/slurm/%J_data.out
+#SBATCH --error  /proj/berzelius-2023-154/users/x_qinzh/workspace/SeFlow/logs/slurm/%J_data.err
 
-cd /proj/berzelius-2023-154/users/x_qinzh/seflow
+cd /proj/berzelius-2023-154/users/x_qinzh/workspace/SeFlow
 export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/proj/berzelius-2023-154/users/x_qinzh/mambaforge/lib
 # export HYDRA_FULL_ERROR=1
 
-/proj/berzelius-2023-154/users/x_qinzh/mambaforge/envs/seflow/bin/python dataprocess/extract_av2.py \
+/proj/berzelius-2023-154/users/x_qinzh/mambaforge/envs/dataprocess/bin/python dataprocess/extract_av2.py --nproc 64 \
     --av2_type sensor \
     --data_mode train \
     --argo_dir /proj/berzelius-2023-154/users/x_qinzh/av2 \
-    --output_dir /proj/berzelius-2023-154/users/x_qinzh/av2/preprocess_v2
+    --output_dir /proj/berzelius-2023-364/users/x_qinzh/data/av2/preprocess_v2
 
-/proj/berzelius-2023-154/users/x_qinzh/mambaforge/envs/seflow/bin/python dataprocess/extract_av2.py \
+/proj/berzelius-2023-154/users/x_qinzh/mambaforge/envs/dataprocess/bin/python dataprocess/extract_av2.py --nproc 64 \
     --av2_type sensor \
     --data_mode val \
     --argo_dir /proj/berzelius-2023-154/users/x_qinzh/av2 \
-    --output_dir /proj/berzelius-2023-154/users/x_qinzh/av2/preprocess_v2 \
+    --output_dir /proj/berzelius-2023-364/users/x_qinzh/data/av2/preprocess_v2 \
     --mask_dir /proj/berzelius-2023-154/users/x_qinzh/av2/3d_scene_flow
 
-/proj/berzelius-2023-154/users/x_qinzh/mambaforge/envs/seflow/bin/python dataprocess/extract_av2.py \
+/proj/berzelius-2023-154/users/x_qinzh/mambaforge/envs/dataprocess/bin/python dataprocess/extract_av2.py --nproc 64 \
     --av2_type sensor \
     --data_mode test \
     --argo_dir /proj/berzelius-2023-154/users/x_qinzh/av2 \
-    --output_dir /proj/berzelius-2023-154/users/x_qinzh/av2/preprocess_v2 \
+    --output_dir /proj/berzelius-2023-364/users/x_qinzh/data/av2/preprocess_v2 \
     --mask_dir /proj/berzelius-2023-154/users/x_qinzh/av2/3d_scene_flow
diff --git a/assets/slurm/1_train.sh b/assets/slurm/1_train.sh
@@ -33,5 +33,5 @@ echo "Start training..."
 # ====> leaderboard model = seflow_best
 /proj/berzelius-2023-154/users/x_qinzh/mambaforge/envs/seflow/bin/python 1_train.py \
     slurm_id=$SLURM_JOB_ID wandb_mode=online train_data=/scratch/local/av2/sensor/train val_data=/scratch/local/av2/sensor/val \
-    num_workers=16 model=deflow lr=2e-4 epochs=20 batch_size=16 "model.target.num_iters=2" "model.val_monitor=val/Dynamic/Mean" \
+    num_workers=16 model=deflow lr=2e-4 epochs=9 batch_size=16 "model.target.num_iters=2" "model.val_monitor=val/Dynamic/Mean" \
     loss_fn=seflowLoss "add_seloss={chamfer_dis: 1.0, static_flow_loss: 1.0, dynamic_chamfer_dis: 1.0, cluster_based_pc0pc1: 1.0}"
diff --git a/conf/config.yaml b/conf/config.yaml
@@ -8,8 +8,8 @@ slurm_id: 00000
 wandb_mode: offline # [offline, disabled, online]
 wandb_project_name: seflow
 
-train_data: /home/kin/data/dataset/av2/preprocess/sensor/train
-val_data: /home/kin/data/dataset/av2/preprocess/sensor/val
+train_data: /home/kin/data/dataset/av2/demo/preprocess_v2/sensor/train
+val_data: /home/kin/data/dataset/av2/demo/preprocess_v2/sensor/val
 
 output: ${model.name}-${slurm_id}
 
@@ -33,9 +33,10 @@ add_seloss:
 # log settings
 seed: 42069
 log_every: 10 # steps epochs*dataset_size/batch_size
-val_every: 5 # epochs
+val_every: 3 # epochs
 save_top_model: 5 # top_k model will be saved.
 
 # -----> Model and Task Parameters
 voxel_size: [0.2, 0.2, 6]
-point_cloud_range: [-51.2, -51.2, -3, 51.2, 51.2, 3]
+point_cloud_range: [-51.2, -51.2, -3, 51.2, 51.2, 3]
+num_frames: 2
diff --git a/conf/eval.yaml b/conf/eval.yaml
@@ -1,10 +1,10 @@
 
-dataset_path: /home/kin/data/av2/preprocess/sensor
+dataset_path: /home/kin/data/av2/preprocess_v2/sensor
 checkpoint: /home/kin/model_zoo/deflow.ckpt
 av2_mode: val # [val, test]
 save_res: False # [True, False]
 
-leaderboard_version: 1 # [1, 2]
+leaderboard_version: 2 # [1, 2]
 supervised_flag: True # [True, False], whether you use any label from the dataset
 
 # no need to change
@@ -14,7 +14,6 @@ gpus: 1
 seed: 42069
 eval_only: True
 wandb_mode: offline # [offline, disabled, online]
-
 defaults:
   - hydra: default
   - model: deflow
diff --git a/conf/model/deflow.yaml b/conf/model/deflow.yaml
@@ -7,4 +7,4 @@ target:
   voxel_size: ${voxel_size}
   point_cloud_range: ${point_cloud_range}
 
-val_monitor: val/Three-way
+val_monitor: val/Dynamic/Mean
diff --git a/conf/model/fastflow3d.yaml b/conf/model/fastflow3d.yaml
@@ -5,5 +5,5 @@ target:
   bottleneck_head: False
   voxel_size: ${voxel_size}
   point_cloud_range: ${point_cloud_range}
-
-val_monitor: val/Three-way
+  
+val_monitor: val/Dynamic/Mean
diff --git a/conf/model/nsfp.yaml b/conf/model/nsfp.yaml
@@ -10,5 +10,5 @@ target:
   min_delta: 5e-5
   point_cloud_range: ${point_cloud_range}
   
-val_monitor: val/Three-way
+val_monitor: val/Dynamic/Mean
 is_trainable: False
diff --git a/conf/vis.yaml b/conf/vis.yaml
@@ -1,5 +1,5 @@
-dataset_path: /home/kin/data/av2/preprocess/sensor/mini
-checkpoint: /home/kin/model_zoo/deflow.ckpt
+dataset_path: /home/kin/data/av2/preprocess_v2/sensor/vis
+checkpoint: /home/kin/model_zoo/seflow_best.ckpt
 res_name: # if None will directly be the `model_name.ckpt` in checkpoint path
 
 
diff --git a/dataprocess/README.md b/dataprocess/README.md
@@ -88,7 +88,7 @@ And flowlabel data can be downloaded here with ground segmentation by HDMap foll
 You can download the processed map folder here to free yourself downloaded another type of data again:
 
 ```bash
-wget https://zenodo.org/records/12632962/files/waymo_map.tar.gz
+wget https://zenodo.org/records/12751363/files/waymo_map.tar.gz
 tar -xvf waymo_map.tar.gz -C /home/kin/data/waymo/flowlabel
 # you will see there is a `map` folder in the `flowlabel` folder now.
 ```
diff --git a/dataprocess/extract_av2.py b/dataprocess/extract_av2.py
@@ -218,6 +218,9 @@ def create_group_data(group, pc, gm, pose, flow_0to1=None, flow_valid=None, flow
         for cnt, ts0 in enumerate(timestamps):
             group = f.create_group(str(ts0))
             pc0, pose0, is_ground_0 = read_pose_pc_ground(data_dir, log_id, ts0, avm)
+            if pc0.shape[0] < 256:
+                print(f'{log_id}/{ts0} has less than 256 points, skip this scenarios. Please check the data if needed.')
+                break
             if cnt == len(timestamps) - 1:
                 create_group_data(group, pc0, is_ground_0.astype(np.bool_), pose0.transform_matrix.astype(np.float32))
             else:
diff --git a/dataprocess/extract_waymo.py b/dataprocess/extract_waymo.py
@@ -357,6 +357,9 @@ def create_group_data(group, pc, pose, gm = None, flow_0to1=None, flow_valid=Non
         pose_flow = car_frame_pc[:, :3] @ ego_motion[:3, :3].T + ego_motion[:3, 3] - car_frame_pc[:, :3]
         ground_mask = is_ground_points(raster_heightmap, transform_se2, transform_scale, global_frame_pc)
         timestamp = frame.timestamp_micros
+        if car_frame_pc.shape[0] < 256:
+            print(f'{scene_id}/{timestamp} has less than 256 points, skip this scenarios. Please check the data if needed.')
+            break
         with h5py.File(output_dir/f'{scene_id}.h5', 'a') as f:
             group = f.create_group(str(timestamp))
             create_group_data(group, car_frame_pc, pose, ego_motion=ego_motion, gm=np.array(ground_mask), flow_0to1=(flow/10.0+pose_flow), flow_category=label)
diff --git a/environment.yaml b/environment.yaml
@@ -24,12 +24,14 @@ dependencies:
   - tabulate
   - scikit-learn==1.3.2
   - hdbscan
+  - setuptools==69.5.1
   - pip:
     - open3d==0.18.0
     - dztimer
     - av2==0.2.1
 
 # Reason about the version fixed:
+# setuptools==68.5.1: https://github.com/aws-neuron/aws-neuron-sdk/issues/893
 # mkl==2024.0.0: https://github.com/pytorch/pytorch/issues/123097#issue-2218541307
 # av2==0.2.1: in case other version deleted some functions.
 # lightning==2.0.1: https://stackoverflow.com/questions/76647518/how-to-fix-error-cannot-import-name-modelmetaclass-from-pydantic-main
diff --git a/scripts/network/dataloader.py b/scripts/network/dataloader.py
diff --git a/scripts/network/loss_func.py b/scripts/network/loss_func.py
diff --git a/scripts/network/models/basic/embedder_model.py b/scripts/network/models/basic/embedder_model.py
diff --git a/scripts/network/models/basic/process_voxels.py b/scripts/network/models/basic/process_voxels.py
diff --git a/scripts/pl_model.py b/scripts/pl_model.py
diff --git a/scripts/utils/mics.py b/scripts/utils/mics.py
diff --git a/tests/zerof2ours.py b/tests/zerof2ours.py
