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Download the Virtual KITTI and SYNTHIA-AL datasets into folders called vkitti and synthia. Then, create their info files that contain their respective metadata using the following commands:
export DATADIR=/path/to/synthia/and/vkitti/datasets
# create info files for Virtual KITTI dataset
python ./data/vkitti_dataset.py create_vkitti_info_file
--datapath=$DATASET/vkitti
# create info files for the SYNTHIA dataset
python ./data/synthia_dataset.py create_synthia_info_file
--datapath=$DATASET/synthia
Training
To train a model, run the following commands:
cd second
python ./pytorch/train.py train
--config_path=./configs/{dataset}/second/{experiment}.yaml
--model_dir=/path/to/save/model
--display_step=100
where dataset is either vkitti or synthia . We will be releasing our pre-trained models shortly.
Evaluation
To evaluate a model, run the following commands:
cd second
python ./pytorch/train.py evaluate
--config_path=./configs/{dataset}/second/{experiment}.yaml
--model_dir=/path/to/saved/model
--result_path=/path/to/save/evaluation/results
--info_path=/info/path/of/dataset/split
Launch all experiments on slurm
In order to facilitate reproducibiilty, we have created a script that launches all experiments included in our paper, on a compute clustered managed by slurm. In order to launch all experiments, simply run the following
cd second
python ./launch_all_exp.py
This will automatically schedule training of all experiments using sbatch commands provided in second/sbatch.py.
