# first, make sure that your conda is setup properly with the right environment
# for that, check that `which conda`, `which pip` and `which python` points to the
# right path. From a clean conda env, this is what you need to do

conda create --name seam -y python=3
conda activate seam

pip install cython tqdm opencv-python

# follow PyTorch installation in https://pytorch.org/get-started/locally/
conda install pytorch torchvision cudatoolkit=10.2 -c pytorch

conda install tensorboard

export INSTALL_DIR=$PWD

# install pycocotools
cd $INSTALL_DIR
git clone https://github.com/cocodataset/cocoapi.git
cd cocoapi/PythonAPI
python setup.py build_ext install

# download SEAM
cd $INSTALL_DIR
git clone https://github.com/VIPS4/SEAM-Match-RCNN.git
cd SEAM-Match-RCNN
mkdir data
mkdir ckpt

unset INSTALL_DIR

python DeepFtoCoco.py --path <dataset_root>

#training of Match-RCNN
python train_matchrcnn.py --root_train <path_of_images_folder> --train_annots <json_path> --save_path <save_path> 

#training on movingfashion
python train_movingfashion.py --root <path_of_dataset_root> --train_annots <json_path> --test_annots <json_path> --pretrained_path <path_of_matchrcnn_model>


#training on multi-deepfashion2
python train_multiDF2.py --root <path_of_dataset_root> --train_annots <json_path> --test_annots <json_path> --pretrained_path <path_of_matchrcnn_model>

#training of Match-RCNN
python -m torch.distributed.launch --nproc_per_node=<NUM_GPUS> train_matchrcnn.py --root_train <path_of_images_folder> --train_annots <json_path> --save_path <save_path>

#training on movingfashion
python -m torch.distributed.launch --nproc_per_node=<NUM_GPUS> train_movingfashion.py --root <path_of_dataset_root> --train_annots <json_path> --test_annots <json_path> --pretrained_path <path_of_matchrcnn_model> 

#training on multi-deepfashion2
python -m torch.distributed.launch --nproc_per_node=<NUM_GPUS> train_multiDF2.py --root <path_of_dataset_root> --train_annots <json_path> --test_annots <json_path> --pretrained_path <path_of_matchrcnn_model> 

#evaluation on movingfashion
python evaluate_movingfashion.py --root_test <path_of_dataset_root> --test_annots <json_path> --ckpt_path <checkpoint_path>


#evaluation on multi-deepfashion2
python evaluate_multiDF2.py --root_test <path_of_dataset_root> --test_annots <json_path> --ckpt_path <checkpoint_path>

@misc{godi2021movingfashion,
      title={MovingFashion: a Benchmark for the Video-to-Shop Challenge}, 
      author={Marco Godi and Christian Joppi and Geri Skenderi and Marco Cristani},
      year={2021},
      eprint={2110.02627},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}