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CUDA_DEVICE=${1} # Current GPU number
NUM_INDEX=${2} # Current eval set index
TOTAL_GPU=8 # Total number of GPUs (We used 8 GPUs)
# PATHS
OUTPUT_PATH=./outputs/
LOG_FOLDER_NAME=log
STAGE2=./checkpoints/vtimellm/vtimellm-vicuna-v1-5-7b-stage2
STAGE3=./checkpoints/vtimellm/vtimellm-vicuna-v1-5-7b-stage3
ACT_FEAT_FOLDER=./data/activitynet/clipvitl14-vtimellm.pth
YCOOK_FEAT_FOLDER=./data/YouCook2/clipvitl14-vtimellm.pth
BASE_MODEL=./checkpoints/vtimellm/vicuna-7b-v1.5
#================= CoTasks ================#
# STAGE4=$OUTPUT_PATH/vtimellm-vicuna-v1-5-7b-activitynet-stage4
# CUDA_VISIBLE_DEVICES=$CUDA_DEVICE python vtimellm/eval/eval_combined.py \
# --data_path ./data/activitynet/val_2.json \
# --feat_folder $ACT_FEAT_FOLDER \
# --model_base $BASE_MODEL \
# --stage2 $STAGE2 \
# --stage3 $STAGE3 \
# --stage4 $STAGE4 \
# --total_gpu $TOTAL_GPU \
# --num_gpu $NUM_INDEX \
# --log_path $STAGE4/$LOG_FOLDER_NAME
#================= CoTasks + MDPO ================#
STAGE4=$OUTPUT_PATH/vtimellm-vicuna-v1-5-7b-activitynet-stage4
STAGE5=$OUTPUT_PATH/vtimellm-vicuna-v1-5-7b-activitynet-stage5
CUDA_VISIBLE_DEVICES=$CUDA_DEVICE python vtimellm/eval/eval_combined.py \
--data_path ./data/activitynet/val_2.json \
--feat_folder $ACT_FEAT_FOLDER \
--model_base $BASE_MODEL \
--stage2 $STAGE2 \
--stage3 $STAGE3 \
--stage4 $STAGE4 \
--stage5 $STAGE5 \
--total_gpu $TOTAL_GPU \
--num_gpu $NUM_INDEX \
--log_path $STAGE5/$LOG_FOLDER_NAME
# Automatic metric evaluation if CUDA_DEVICE is 0
if [ "$CUDA_DEVICE" -eq "0" ]; then
echo "Metric Evaluation starts. After 3 minutes (just in case)"
sleep 3m
bash scripts/eval/metric-act.sh
else
echo "Finished Generating Results."
fi
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