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command.sh
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command.sh
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kitti_odometry_dir='/home/tianwei/Data/kitti/odometry/dataset/odometry/'
kitti_dir='/home/tianwei/Data/kitti/'
kitti_raw_dir=$kitti_dir'/raw/'
make3d_dir='/home/tianwei/Data/make3d/'
kitti_raw_dump_dir=$kitti_dir'/raw_dump/'
kitti_eigen_test_dir=$kitti_dir'/eigen_test/'
kitti_odom=$kitti_dir'/kitti_odom/'
kitti_odom5=$kitti_dir'/kitti_odom5/'
kitti_odom_match3=$kitti_dir'/kitti_odom_match3/'
kitti_odom_match5=$kitti_dir'/kitti_odom_match5/'
kitti_raw_odom=$kitti_dir'/odometry/dataset/odometry/'
cityscapes_dir='/home/tianwei/Data/cityscapes'
cityscapes_dump='/home/tianwei/Data/cityscapes/dump/'
output_folder=./output/
model_idx=258000
save_freq_step=4000
checkpoint_dir=./ckpt/
# kitti eval depth
depth_pred_file='output/model-'$model_idx'.npy'
# Generate training and testing data
## for odometry dataset
python data/prepare_train_data.py --dataset_dir=$kitti_raw_odom --dataset_name=kitti_odom \
--dump_root=$kitti_odom_match3 --seq_length=3 --img_width=416 --img_height=128 \
--num_threads=8 --generate_test True
## for raw dataset (Eigen split)
python data/prepare_train_data.py --dataset_dir=$kitti_raw_dir --dataset_name=kitti_raw_eigen \
--dump_root=$kitti_raw_dump_dir --seq_length=3 --img_width=416 --img_height=128 \
--num_threads=8 --match_num $match_num
# Train on KITTI odometry dataset
match_num=100
python train.py --dataset_dir=$kitti_odom_match3 --checkpoint_dir=$checkpoint_dir --img_width=416 --img_height=128 --batch_size=4 --seq_length 3 \
--max_steps 300000 --save_freq 2000 --learning_rate 0.001 --num_scales 1 --init_ckpt_file $checkpoint_dir'model-'$model_idx --continue_train=True --match_num $match_num
# Train on KITTI Eigen split
python train.py --dataset_dir=$kitti_raw_dump_dir --checkpoint_dir=$checkpoint_dir --img_width=416 --img_height=128 --batch_size=4 --seq_length 3 \
--max_steps 300000 --save_freq $save_freq_step --learning_rate 0.001 --num_scales 1 --match_num $match_num --init_ckpt_file $checkpoint_dir'model-'$model_idx --continue_train=True
# Testing depth model
r=250000
depth_ckpt_file=$checkpoint_dir'model-'$r
depth_pred_file='output/model-'$r'.npy'
python test_kitti_depth.py --dataset_dir $kitti_raw_dir --output_dir $output_folder --ckpt_file $depth_ckpt_file #--show
python kitti_eval/eval_depth.py --kitti_dir=$kitti_raw_dir --pred_file $depth_pred_file #--show True --use_interp_depth True
# Testing pose model
sl=3
r=258000
pose_ckpt_file=$checkpoint_dir'model-'$r
for seq_num in 09 10
do
rm -rf $output_folder/$seq_num/
echo 'seq '$seq_num
python test_kitti_pose.py --test_seq $seq_num --dataset_dir $kitti_raw_odom --output_dir $output_folder'/'$seq_num'/' --ckpt_file $pose_ckpt_file --seq_length $sl --concat_img_dir $kitti_odom_match3
python kitti_eval/eval_pose.py --gtruth_dir='kitti_eval/pose_data/ground_truth/seq'$sl'/'$seq_num/ --pred_dir=$output_folder'/'$seq_num'/'
done