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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/
GENERATE_FOLDER_NAME=mdpo-dataset/vtimellm/generated-samples-act/
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/train.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 $OUTPUT_PATH/$GENERATE_FOLDER_NAME --generate_samples
CUDA_VISIBLE_DEVICES=$CUDA_DEVICE python vtimellm/eval/eval_combined.py \
--data_path ./data/activitynet/train.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 $OUTPUT_PATH/$GENERATE_FOLDER_NAME --generate_samples --task2 |