based on EndlessSora/TSIT
Following [original code], or just
git clone https://github.com/trngpg/TSIT.git
cd TSIT
conda env create -f conda_env.yml
The code requires batch-norm (consult original code)
cd models/networks/
git clone https://github.com/vacancy/Synchronized-BatchNorm-PyTorch
cp -rf Synchronized-BatchNorm-PyTorch/sync_batchnorm .
rm -rf Synchronized-BatchNorm-PyTorch
cd ../../
We are going to train TSIT on day2night unpaired data. The two datasets in used are BDD100K and VinAI's Scale.
- Get BDD100K data to your machine
After unzipping, there is a folder
MYHOME=lustre/scratch/client/vinai/users/trungpq3/datasets cp /vinai-public-dataset/BDD100K/bdd100k_images_100k.zip $MYHOME cd $MYHOME && unzip bdd100k_images_100k.zip
bdd100k
with the following structure:./bdd100k/images/100k/{train-val-test}/{image_files}
- Get Scale data
After unzipping, the
cp /vinai-autopilot/data/lane/scale_combined $MYHOME
scale_combined
folder has the following structure:./scale_combined/train_672w/images/{Downtown-Highway-Rural-Sub-urban}/{Clear-CLoudy-Night-etc}/{image_files}
These data will be read by data loader in ./data/scale_day2night_dataset.py
Training file is run_train_superpod.sh
.
- First, provide bdd and scale database paths
- Run with
bash run_train_superpod.sh
To train with sbatch
on superpod (submit queued job), see run_sbatch_tsit.sh