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#!/bin/bash
gpu_list="${CUDA_VISIBLE_DEVICES:-0}"
IFS=',' read -ra GPULIST <<< "$gpu_list"
CHUNKS=${#GPULIST[@]}
SPLIT="llava_gqa_testdev_balanced"
GQADIR="/home/ai/data/llava/dataset/eval/gqa"
MODEL_PATH="/mnt/data/sata/yinghu/checkpoints/llava_factory/tiny-llava-phi-2-siglip-so400m-patch14-384-base-finetune/"
MODEL_NAME="tiny-llava-phi-2-siglip-so400m-patch14-384-base-finetune"
EVAL_DIR="/home/ai/data/llava/dataset/eval"
for IDX in $(seq 0 $((CHUNKS-1))); do
CUDA_VISIBLE_DEVICES=${GPULIST[$IDX]} python -m tinyllava.eval.model_vqa_loader \
--model-path $MODEL_PATH \
--question-file $EVAL_DIR/gqa/$SPLIT.jsonl \
--image-folder $EVAL_DIR/gqa/images \
--answers-file $EVAL_DIR/gqa/answers/$SPLIT/$MODEL_NAME/${CHUNKS}_${IDX}.jsonl \
--num-chunks $CHUNKS \
--chunk-idx $IDX \
--temperature 0 \
--conv-mode phi &
done
wait
output_file=$EVAL_DIR/gqa/answers/$SPLIT/$MODEL_NAME/merge.jsonl
# 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 $EVAL_DIR/gqa/answers/$SPLIT/$MODEL_NAME/${CHUNKS}_${IDX}.jsonl >> "$output_file"
done
python scripts/convert_gqa_for_eval.py --src $output_file --dst $GQADIR/testdev_balanced_predictions.json
cd $GQADIR
python eval/eval.py --tier testdev_balanced
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