docs(readme): update visualization readme.

Author: Kin-Zhang

save.py

@@ -8,7 +8,7 @@
 # listed on the above website.
 
 # Description: produce flow based on model predict and write into the dataset, 
-#              then use tools/scene_flow.py flow_mode="flow_est" to visualize the flow.
+#              then use `tools/visualization.py` to visualize the flow.
 """
 
 import torch

tools/README.md

@@ -8,15 +8,15 @@ Here we introduce some tools to help you:
 
 ## Visualization
 
-run `tools/scene_flow.py` to view the scene flow dataset with ground truth flow. Note the color wheel in under world coordinate.
+run `tools/visualization.py` to view the scene flow dataset with ground truth flow. Note the color wheel in under world coordinate.
 
 ```bash
 # view gt flow
-python3 tools/scene_flow.py --data_dir /home/kin/data/av2/preprocess/sensor/mini --res_name flow
+python3 tools/visualization.py --data_dir /home/kin/data/av2/preprocess/sensor/mini --res_name flow
 
 # view est flow
-python3 tools/scene_flow.py --data_dir /home/kin/data/av2/preprocess/sensor/mini --res_name deflow_best
-python3 tools/scene_flow.py --data_dir /home/kin/data/av2/preprocess/sensor/mini --res_name seflow_best
+python3 tools/visualization.py --data_dir /home/kin/data/av2/preprocess/sensor/mini --res_name deflow_best
+python3 tools/visualization.py --data_dir /home/kin/data/av2/preprocess/sensor/mini --res_name seflow_best
 ```
 
 Demo Effect (press `SPACE` to stop and start in the visualization window):

tools/visualization.py

@@ -27,7 +27,7 @@
 def check_flow(
     data_dir: str ="/home/kin/data/av2/preprocess/sensor/mini",
     res_name: str = "flow", # "flow", "flow_est"
-    start_id: int = -1,
+    start_id: int = 0,
     point_size: float = 3.0,
 ):
     dataset = HDF5Data(data_dir, vis_name=res_name, flow_view=True)
@@ -37,10 +37,7 @@ def check_flow(
     opt.background_color = np.asarray([80/255, 90/255, 110/255])
     opt.point_size = point_size
 
-    for data_id in (pbar := tqdm(range(0, len(dataset)))):
-        # for easy stop and jump to any id, and save same id always from 0.
-        if data_id < start_id and start_id != -1:
-            continue
+    for data_id in (pbar := tqdm(range(start_id, len(dataset)))):
         data = dataset[data_id]
         now_scene_id = data['scene_id']
         pbar.set_description(f"id: {data_id}, scene_id: {now_scene_id}, timestamp: {data['timestamp']}")
@@ -77,10 +74,7 @@ def vis(
     # opt.background_color = np.asarray([1, 1, 1])
     opt.point_size = point_size
 
-    for data_id in (pbar := tqdm(range(0, len(dataset)))):
-        # for easy stop and jump to any id, and save same id always from 0.
-        if data_id < start_id and start_id != -1:
-            continue
+    for data_id in (pbar := tqdm(range(start_id, len(dataset)))):
         data = dataset[data_id]
         now_scene_id = data['scene_id']
         pbar.set_description(f"id: {data_id}, scene_id: {now_scene_id}, timestamp: {data['timestamp']}")

tools/write4conf.py

@@ -1,3 +1,6 @@
+'''
+# Usage for old model file to new version code
+'''
 import torch
 import fire, time