set -x EVAL_DATA_DIR=eval OUTPUT_DIR=eval_output # CKPT=DAMO-NLP-SG/VideoLLaMA2-7B CKPT=work_dirs/videollama2gemma2/finetune_2b_vllama2 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=${OUTPUT_DIR}/Activitynet_Zero_Shot_QA/answers/${CKPT_NAME}/merge.json 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_oqa_activitynet.py \ --model-path ${CKPT} \ --video-folder ${EVAL_DATA_DIR}/Activitynet_Zero_Shot_QA/all_test \ --question-file ${EVAL_DATA_DIR}/Activitynet_Zero_Shot_QA/test_q.json \ --answer-file ${EVAL_DATA_DIR}/Activitynet_Zero_Shot_QA/test_a.json \ --output-file ${OUTPUT_DIR}/Activitynet_Zero_Shot_QA/answers/${CKPT_NAME}/${CHUNKS}_${IDX}.json \ --num-chunks $CHUNKS \ --chunk-idx $IDX & done wait # Clear out the output file if it exists. > "$output_file" #Loop through the indices and concatenate each file. for IDX in $(seq 0 $((CHUNKS-1))); do cat ${OUTPUT_DIR}/Activitynet_Zero_Shot_QA/answers/${CKPT_NAME}/${CHUNKS}_${IDX}.json >> "$output_file" done fi AZURE_API_KEY=your_key AZURE_API_ENDPOINT=your_endpoint AZURE_API_DEPLOYNAME=your_deployname python3 videollama2/eval/eval_video_oqa_activitynet.py \ --pred-path ${output_file} \ --output-dir ${OUTPUT_DIR}/Activitynet_Zero_Shot_QA/answers/${CKPT_NAME}/gpt \ --output-json ${OUTPUT_DIR}/Activitynet_Zero_Shot_QA/answers/${CKPT_NAME}/results.json \ --api-key $AZURE_API_KEY \ --api-endpoint $AZURE_API_ENDPOINT \ --api-deployname $AZURE_API_DEPLOYNAME \ --num-tasks 4