cmd.txt

conda create --name tcc python=3.8
conda install pip; pip install --upgrade pip
conda install cudnn=8

pip install -r requirements.txt

dataset_preparation/download_pouring_data.sh

wget -P /tmp/ https://github.com/keras-team/keras-applications/releases/download/resnet/resnet50v2_weights_tf_dim_ordering_tf_kernels_notop.h5

python $root_dir/train --alsologtostderr
CUDA_VISIBLE_DEVICES=1,2 python -m tcc.train --alsologtostderr --force_train
CUDA_VISIBLE_DEVICES=1,2 python -m tcc.evaluate --alsologtostderr
CUDA_VISIBLE_DEVICES=1,2 python -m tcc.extract_embeddings --alsologtostderr --dataset pouring  --split val --logdir /tmp/alignment_logs
CUDA_VISIBLE_DEVICES=1,2 python -m tcc.visualize_alignment --alsologtostderr --video_path /tmp/aligned.mp4 --embs_path /tmp/embeddings.npy

conda create --name tcc2 --clone test2
export LD_LIBRARY_PATH=/home/srinitca/anaconda3/envs/tcc2/lib:$LD_LIBRARY_PATH
python extract_embeddings --alsologtostderr --dataset <DATASET> \ --split <SPLIT> --logdir <LOGDIR>


## Create TF records for the dark dataset
VAL=test
python -m tcc.dataset_preparation.videos_to_tfrecords --input_dir /home/srinitca/vlr/project/ARID_Dataset_v1/Version_1/tcc/${VAL}/Drink \
                                    --name arid_drink_${VAL} \
                                    --file_pattern *.avi \
                                    --output_dir /home/srinitca/vlr/project/ARID_Dataset_v1/Version_1/tcc_tfrecords/arid_drink/ \
                                    --action_label 0 \
                                    --files_per_shard 100

cp ./tcc/configs/arid_drink.yml ./tmp/arid_drink/config.yml

VAL=train
python -m tcc.dataset_preparation.videos_to_tfrecords --input_dir /home/srinitca/vlr/project/ARID_Dataset_v1/Version_1/tcc/${VAL}/Sit \
                                    --name arid_sit_${VAL} \
                                    --file_pattern *.avi \
                                    --output_dir /home/srinitca/vlr/project/ARID_Dataset_v1/Version_1/tcc_tfrecords/arid_sit/ \
                                    --action_label 6 \
                                    --files_per_shard 100

CUDA_VISIBLE_DEVICES=0,1 python -m tcc.train --alsologtostderr --force_train --logdir ./tmp/arid_sit


# Extracts embeddings of 4 videos in the val set.
DATA=sit
CUDA_VISIBLE_DEVICES=0,1 python -m tcc.extract_embeddings --alsologtostderr --logdir ./tmp/arid_${DATA}  --dataset arid_${DATA} --split valid --max_embs 4 --path_to_tfrecords /home/srinitca/vlr/project/ARID_Dataset_v1/Version_1/tcc_tfrecords/%s_tfrecords --frames_per_batch 1 --keep_data --save_path /home/srinitca/vlr/project/ARID_Dataset_v1/Version_1/tcc_tfrecords/arid_${DATA}_tfrecords/embeds.npy

# Aligns videos using extracted embeddings.
DATA=sit
CUDA_VISIBLE_DEVICES=0,1 python -m tcc.visualize_alignment --alsologtostderr --video_path ./tmp/arid_${DATA}/aligned.gif  --embs_path /home/srinitca/vlr/project/ARID_Dataset_v1/Version_1/tcc_tfrecords/arid_${DATA}_tfrecords/embeds.npy --reference_video 0




# Pouring Dataset examples
# Extracts embeddings of 4 videos in the val set.
CUDA_VISIBLE_DEVICES=2,3 python -m tcc.extract_embeddings --alsologtostderr --logdir /tmp/alignment_logs --dataset pouring --split val --max_embs 1 --keep_data
# Aligns videos using extracted embeddings.
python -m tcc.visualize_alignment --alsologtostderr --video_path /tmp/aligned.gif  --embs_path /tmp/embeddings.npy