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TRAIN_EVAL.MD

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Train and Evaluate

1. Generate the Inflated Occupancy Annotations as described here:

  1. Set only_generate_dataset=True in both projects/configs/inflated/action_condition_GMO.py and projects/configs/inflated/action_condition_MMO.py
  2. Run the training and evaluation scripts using these configurations. Since the model's forward process is not executed, this will not consume GPU memory:
CONFIG=projects/configs/inflated/action_condition_GMO.py
GPU_NUM=8   # 8 GPUs(Processes) consume ~20 hours

# Generate training data
./tools/dist_train.sh ${CONFIG} ${GPU_NUM}

# Generate validation data
CKPT=work_dirs/action_condition_GMO/epoch_1.pth
./tools/dist_test.sh ${CONFIG} ${CKPT} ${GPU_NUM}

2. Train

CONFIG=path/to/config.py
GPU_NUM=8

./tools/dist_train.sh ${CONFIG} ${GPU_NUM}

Configuration Files:

  1. inflated/action_condition_GMO.py: Predicts General Moveable Objects (GMO) using the inflated occupancy annotations as defined in Cam4DOcc.
  2. inflated/action_condition_MMO.py: Predicts Multi-class Moveable Objects (MMO) using the inflated occupancy annotations defined in Cam4DOcc.
  3. fine_grained/action_condition_GMO.py: Predicts General Moveable Objects (GMO) using the fine-grained occupancy annotations defined in nuScenes-Occupancy.
  4. fine_grained/action_condition_GMO_GSO.py: Predicts General Moveable Objects (GMO) and General Static Objects (GSO) using the fine-grained occupancy annotations defined in nuScenes-Occupancy.
  5. fine_grained/action_condition_MMO_MSO.py: Predicts Multi-class Moveable Objects (MMO) and Multi-class Static Objects (MSO) using the fine-grained occupancy annotations defined in nuScenes-Occupancy.

3. Evaluate

CONFIG=path/to/config.py
CKPT=work_dirs/config_file_name/epoch_24.pth
GPU_NUM=8

./tools/dist_test.sh ${CONFIG} ${CKPT} ${GPU_NUM}