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GETTING_STARTED.md

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Getting Started

This document provides tutorials to train and evaluate Quasi-Dense 3D Detection and Tracking. Before getting started, make sure you have finished installation and dataset setup.

Benchmark evaluation

First, download the models you want to evaluate from our model zoo and put them in the corresponding directory following the description.

nuScenes

  • Inference on Validation set with full frames information. It will show your 3D detection and 3D Tracking score (both AMOTA@1 and AMOTA@0.2) on validation set.
$ ./scripts/run_eval_nusc.sh ${WORK_DIR} ${CONFIG} ${gpu_id} 1 --data_split_prefix ${EXP_NAME} --full_frames

# ${WORK_DIR} is the path to place the model output.
# ${CONFIG} is the corresponding config file you use.
# ${EXP_NAME} is the experiment name you want to specify.
  • To reproduce our result, please run the following code:
$ ./scripts/run_eval_nusc.sh work_dirs/Nusc/quasi_r101_dcn_3dmatch_multibranch_conv_dep_dim_cen_clsrot_sep_aug_confidence_scale_no_filter/output_val_box3d_deep_depth_motion_lstm_3dcen configs/Nusc/quasi_r101_dcn_3dmatch_multibranch_conv_dep_dim_cen_clsrot_sep_aug_confidence_scale_no_filter.py 0 1 --data_split_prefix val --full_frames
  • Inference on Test set with full frames information. It will generate the result json files (detection_result.json & tracking_result.json) under ${WORK_DIR} to submit to the benchmark server.
$ ./scripts/run_test_nusc.sh ${WORK_DIR} ${CONFIG} ${gpu_id} 1 --data_split_prefix ${EXP_NAME}
  • To reproduce our result, please run the following code:
$ ./scripts/run_test_nusc.sh work_dirs/Nusc/quasi_r101_dcn_3dmatch_multibranch_conv_dep_dim_cen_clsrot_sep_aug_confidence_scale_no_filter/output_test_box3d_deep_depth_motion_lstm_3dcen configs/Nusc/quasi_r101_dcn_3dmatch_multibranch_conv_dep_dim_cen_clsrot_sep_aug_confidence_scale_no_filter.py 0 1 --data_split_prefix test --full_frames

Waymo

  • Inference on Validation set. It will show your 3D detection and 3D Tracking score on validation set and generate the result tar files (result_3D_DET.tar.gz and result_3D_MOT.tar.gz) under ${WORK_DIR} to submit to the validation server.
$ ./scripts/run_eval_waymo.sh ${WORK_DIR} ${CONFIG} ${gpu_id} 1 --data_split_prefix ${EXP_NAME}

# ${WORK_DIR} is the path to place the model output.
# ${CONFIG} is the corresponding config file you use.
# ${EXP_NAME} is the experiment name you want to specify.
  • To reproduce our result, please run the following code:
$ ./scripts/run_eval_waymo.sh work_dirs/Waymo/quasi_r101_dcn_3dmatch_multibranch_conv_dep_dim_cen_clsrot_sep_aug_confidence_scale_no_filter_scaled_res/output_val_box3d_deep_depth_motion_lstm_3dcen configs/Waymo/quasi_r101_dcn_3dmatch_multibranch_conv_dep_dim_cen_clsrot_sep_aug_confidence_scale_no_filter_scaled_res.py 0 1 --data_split_prefix val
  • Inference on Test set. It will generate the result tar files (result_3D_DET.tar.gz and result_3D_MOT.tar.gz) under ${WORK_DIR} to submit to the benchmark server.
$ ./scripts/run_test_waymo.sh ${WORK_DIR} ${CONFIG} ${gpu_id} 1 --data_split_prefix ${EXP_NAME}

# ${WORK_DIR} is the path to place the model output.
# ${CONFIG} is the corresponding config file you use.
# ${EXP_NAME} is the experiment name you want to specify.
  • To reproduce our result, please run the following code:
$ ./scripts/run_test_waymo.sh work_dirs/Waymo/quasi_r101_dcn_3dmatch_multibranch_conv_dep_dim_cen_clsrot_sep_aug_confidence_scale_no_filter_scaled_res/output_test_box3d_deep_depth_motion_lstm_3dcen configs/Waymo/quasi_r101_dcn_3dmatch_multibranch_conv_dep_dim_cen_clsrot_sep_aug_confidence_scale_no_filter_scaled_res.py 0 1 --data_split_prefix test

KITTI

  • Inference on sub-val set. It will show your 3D detection and 3D Tracking score on validation set and generate the result txt files (txts/{:04}.txt) under ${WORK_DIR} for further offline evaluation.
$ ./scripts/test_eval_exp.sh ${dataset} ${CONFIG} ${gpu_id} 1 --data_split_prefix ${EXP_NAME} --add_ablation_exp ${FLAG}

# ${WORK_DIR} is the path to place the model output.
# ${CONFIG} is the corresponding config file you use.
# ${EXP_NAME} is the experiment name you want to specify.
# ${FLAG} is the experiment flag from tools/test_eval_video_exp.py
  • To reproduce our result, please run the following code:
$ ./scripts/test_eval_exp.sh kitti configs/KITTI/quasi_dla34_dcn_3dmatch_multibranch_conv_dep_dim_cen_clsrot_sep_aug_confidence_subtrain_mod_anchor_ratio_small_strides_GTA.py 0 1 --data_split_prefix subval_dla34_regress_GTA_VeloLSTM --add_ablation_exp all
$ ./scripts/test_eval_exp.sh ${dataset} ${CONFIG} ${gpu_id} 1 --data_split_prefix ${EXP_NAME} --add_ablation_exp ${FLAG}

# ${WORK_DIR} is the path to place the model output.
# ${CONFIG} is the corresponding config file you use.
# ${EXP_NAME} is the experiment name you want to specify.
# ${FLAG} is the experiment flag from tools/test_eval_video_exp.py
  • To reproduce our result, please run the following code:
$ ./scripts/test_eval_exp.sh kitti configs/KITTI/quasi_dla34_dcn_3dmatch_multibranch_conv_dep_dim_cen_clsrot_sep_aug_confidence_mod_anchor_ratio_small_strides_GTA.py 0 1 --data_split_prefix test_dla34_regress_GTA_VeloLSTM --add_test_set

Training

Tracking model

  • Train on the dataset you want by specifying the corresponding config file.
./scripts/train.sh ${CONFIG} ${gpu_id} ${gpu_nums}
  • If the training is terminated before finish, you can add --resume to the command above to resume the training.
./scripts/train.sh ${CONFIG} ${gpu_id} ${gpu_nums} --resume

LSTM motion model training

  • Get the pure detection result from the required dataset for both training and validation set after tracking model finish training. It will generate results as work_dirs/${DATASET}/{CONFIG}/output_train_pure_det_3dcen/output.json or work_dirs/${DATASET}/{CONFIG}/output_val_pure_det_3dcen/output.json.
./scripts/test_eval_exp.sh ${dataset} ${CONFIG} ${gpu_id} 1 --data_split_prefix ${EXP_NAME} --pure_det

# ${EXP_NAME} stands for which set is to be generated for LSTM motion model and only accept train / val.

Take nuScenes as example

  • To reproduce our result, please run the following code:
# get the pure detection result for training set
./scripts/test_eval_exp.sh nuscenes configs/Nusc/quasi_r101_dcn_3dmatch_multibranch_conv_dep_dim_cen_clsrot_sep_aug_confidence_scale_no_filter.py 0 1 --data_split_prefix train --pure_det

# get the pure detection result for validation set
./scripts/test_eval_exp.sh nuscenes configs/Nusc/quasi_r101_dcn_3dmatch_multibranch_conv_dep_dim_cen_clsrot_sep_aug_confidence_scale_no_filter.py 0 1 --data_split_prefix val --pure_det
  • Soft-link pure detection results under data/${DATASET}/anns as tracking_output_train.json and tracking_output_val.json.
# nuScenes example
${QD-3DT_ROOT}
|-- data
`-- |-- nuscenes
    `-- |-- anns
        `-- |-- tracking_output_train.json
            |-- tracking_output_val.json
            |-- tracking_train.json
            |-- tracking_val.json
  • Train LSTM motion model for required dataset.
CUDA_VISIBLE_DEVICES=${gpu_id} python qd3dt/models/detectrackers/tracker/motion_lstm.py ${DATASET} train \
--session batch128_min10_seq10_dim7_VeloLSTM \
--min_seq_len 10 --seq_len 10 \
--lstm_model_name VeloLSTM --tracker_model_name KalmanBox3DTracker \
--input_gt_path ${GT_training} \
--input_pd_path ${pure_det_training} \
--cache_name work_dirs/LSTM/nuscenes_train_pure_det_min10.pkl \
--loc_dim 7 -b 128 --is_plot --show_freq 500

# ${GT_training} is the corresponding training annotation file.
# ${pure_det_training} is the corresponding pure detection result for training set.
  • To reproduce our result, please run the following code:
CUDA_VISIBLE_DEVICES=1 python qd3dt/models/detectrackers/tracker/motion_lstm.py nuscenes train \
--session batch128_min10_seq10_dim7_VeloLSTM \
--min_seq_len 10 --seq_len 10 \
--lstm_model_name VeloLSTM --tracker_model_name KalmanBox3DTracker \
--input_gt_path data/nuscenes/anns/tracking_train.json \
--input_pd_path data/nuscenes/anns/tracking_output_train.json \
--cache_name work_dirs/LSTM/nuscenes_train_pure_det_min10.pkl \
--loc_dim 7 -b 128 --is_plot --show_freq 500