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set -x
EVAL_DATA_DIR=eval
OUTPUT_DIR=eval_output
CKPT=DAMO-NLP-SG/VideoLLaMA2-7B
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=${OUTPUT_DIR}/MSVD_Zero_Shot_QA/answers/${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}/MSVD_Zero_Shot_QA/videos \
--question-file ${EVAL_DATA_DIR}/MSVD_Zero_Shot_QA/test_q.json \
--answer-file ${EVAL_DATA_DIR}/MSVD_Zero_Shot_QA/test_a.json \
--output-file ${OUTPUT_DIR}/MSVD_Zero_Shot_QA/answers/${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 ${OUTPUT_DIR}/MSVD_Zero_Shot_QA/answers/${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 ${OUTPUT_DIR}/MSVD_Zero_Shot_QA/answers/${CKPT_NAME}/gpt \
--output-json ${OUTPUT_DIR}/MSVD_Zero_Shot_QA/answers/${CKPT_NAME}/results.json \
--api-key $AZURE_API_KEY \
--api-endpoint $AZURE_API_ENDPOINT \
--api-deployname $AZURE_API_DEPLOYNAME \
--num-tasks 4
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