cyj5030 / plcd Goto Github PK

git clone [email protected]:cyj5030/PLCD.git

The code has been tested with PyTorch 1.11 and Cuda 11.3.

conda create --name plcd python=3.10
conda activate plcd
conda install pytorch==1.11.0 torchvision==0.12.0 torchaudio==0.11.0 cudatoolkit=11.3 -c pytorch
conda install pandas=1.5.2 matplotlib=3.5.1 scipy=1.7.3 scikit-image=0.19.3 numba=0.56.4 astropy=5.1 pyyaml=6.0 seaborn=0.12.1
pip install opencv-python==4.5.5.64 shap==0.41.0 einops==0.4.1

0. Download the pre-compute results of BoTW in KITTI, BoW in KITTI, visual label of KITTI and trajectories.

1. unzip the package to the folder of "data"

unzip BoTW_in_KITTI.zip -d data/
unzip BoW_in_KITTI.zip -d data/
unzip trajectory.zip -d data/
unzip visual_label_of_kitti.zip -d data/

2. Download the pre-trained model.

3. unzip the package to the root

unzip checkpoint.zip

Note: only include the results in kitti dataset, because the pre-compute results on kaist is too large. All of the visualization results are stored in the folder: "train_log/attention_12_16_hidden_cell_mix_fbatt"

For compute all of the quantitative results, run the script in:

./evaluation_scripts/eval.sh

If you want to run separate, run the following commands:

• To compute the results of Table 1 in folder: "train_log/attention_12_16_hidden_cell_mix_fbatt/VO_evals", run

python evaluation_scripts/eval_vo.py --gpu_id 0

• To compute the results of Table 2 in folder: "train_log/attention_12_16_hidden_cell_mix_fbatt/VLCD_evals", run

python evaluation_scripts/eval_visual.py --gpu_id 0

• To compute the results of Table A1 in folder: "train_log/attention_12_16_hidden_cell_mix_fbatt/Merge_evals_BoW", run

python evaluation_scripts/eval_merge_bow.py --gpu_id 0

• To compute the results of Table A1 in folder: "train_log/attention_12_16_hidden_cell_mix_fbatt/Merge_evals_BoTW", run

python evaluation_scripts/eval_merge_botw.py --gpu_id 0

For compute all of the quantitative results, run the script in:

./evaluation_scripts/visualization.sh

If you want to run separate, run the following commands:

• To compute the visualization of pose ground-truth, run

python evaluation_scripts/vis_traj_gt.py --gpu_id 0

• To compute the visualization of plcd results, run

python evaluation_scripts/vis_traj_plcd.py --gpu_id 0

• To compute the visualization of bow results, run

python evaluation_scripts/vis_traj_bow.py --gpu_id 0

• To compute the visualization of botw results, run

python evaluation_scripts/vis_traj_botw.py --gpu_id 0

• To compute the visualization of bow+PLCD-GVO, run

python evaluation_scripts/vis_traj_merge_bow.py --gpu_id 0

• To compute the visualization of botw+PLCD-GVO, run

python evaluation_scripts/vis_traj_merge_botw.py --gpu_id 0

For compute all of the results on features, run the script in:

./feature_analyse/feature.sh

If you want to run separate, run the following commands step by step:

• First, collect feature active

python feature_analyse/static_feature.py --gpu_id 0

• Then, compute shap value (very slow if run on all datasets, so the example is only on kitti using ORB-SLAM_mono)

python feature_analyse/shap_explainer.py --gpu_id 0

• General the grid map (Fig. 2(a)) and effect of feature (Fig. 2(b))

python feature_analyse/grid_score.py --plot --shap

• General the head-direction map (Fig. 2(a)) and effect of feature (Fig. 2(b))

python feature_analyse/hd_score.py --plot --shap

Grid_cell_analysis