VTimeLLM/scripts/build/generate-act.sh

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