set -x EVAL_DATA_DIR=eval OUTPUT_DIR=eval_output CKPT=DAMO-NLP-SG/VideoLLaMA2-7B-16F CKPT_NAME=$(echo $CKPT | rev | cut -d'/' -f1 | rev) gpu_list="${CUDA_VISIBLE_DEVICES:-0}" IFS=',' read -ra GPULIST <<< "$gpu_list" # divide data via the number of GPUs per task GPUS_PER_TASK=1 CHUNKS=$((${#GPULIST[@]}/$GPUS_PER_TASK)) output_file=${OUTPUT_DIR}/videomme/answers/${CKPT_NAME}/merge.json output_sub_file=${OUTPUT_DIR}/videomme/answers/${CKPT_NAME}/merge_sub.json # judge if the number of json lines is 0 if [ ! -f "$output_file" ] || [ $(cat "$output_file" | wc -l) -eq 0 ]; then rm -f ${OUTPUT_DIR}/videomme/answers/${CKPT_NAME}/*.json fi if [ ! -f "$output_file" ]; then for IDX in $(seq 0 $((CHUNKS-1))); do # select the GPUs for the task gpu_devices=$(IFS=,; echo "${GPULIST[*]:$(($IDX*$GPUS_PER_TASK)):$GPUS_PER_TASK}") TRANSFORMERS_OFFLINE=1 CUDA_VISIBLE_DEVICES=${gpu_devices} python3 videollama2/eval/inference_video_mcqa_videomme.py \ --model-path ${CKPT} \ --video-folder ${EVAL_DATA_DIR}/videomme/videos \ --subtitle-folder ${EVAL_DATA_DIR}/videomme/subtitles \ --question-file ${EVAL_DATA_DIR}/videomme/test-00000-of-00001.parquet \ --answer-file ${OUTPUT_DIR}/videomme/answers/${CKPT_NAME}/${CHUNKS}_${IDX}.json \ --num-chunks $CHUNKS \ --chunk-idx $IDX & done wait # Clear out the output file if it exists. > "$output_file" echo "[" >> "$output_file" #Loop through the indices and concatenate each file. for IDX in $(seq 0 $((CHUNKS-1))); do cat ${OUTPUT_DIR}/videomme/answers/${CKPT_NAME}/${CHUNKS}_${IDX}.json >> "$output_file" done sed -i '$s/.$//' $output_file echo "]" >> "$output_file" # Clear out the output file if it exists. > "$output_sub_file" echo "[" >> "$output_sub_file" #Loop through the indices and concatenate each file. for IDX in $(seq 0 $((CHUNKS-1))); do cat ${OUTPUT_DIR}/videomme/answers/${CKPT_NAME}/${CHUNKS}_${IDX}_sub.json >> "$output_sub_file" done sed -i '$s/.$//' $output_sub_file echo "]" >> "$output_sub_file" fi python videollama2/eval/eval_video_mcqa_videomme.py \ --results_file $output_file \ --video_duration_type "short,medium,long" \ --return_categories_accuracy \ --return_sub_categories_accuracy \ --return_task_types_accuracy \ --skip_missing \ python videollama2/eval/eval_video_mcqa_videomme.py \ --results_file $output_sub_file \ --video_duration_type "short,medium,long" \ --return_categories_accuracy \ --return_sub_categories_accuracy \ --return_task_types_accuracy \ --skip_missing \