textvqa.sh

#!/bin/bash

CUDA_VISIBLE_DEVICES='0,1,2,3,4,5,6,7'
gpu_list="${CUDA_VISIBLE_DEVICES:-0}"
IFS=',' read -ra GPULIST <<< "$gpu_list"

CHUNKS=${#GPULIST[@]}
 
CKPT="MGM/MGM-34B"

for IDX in $(seq 0 $((CHUNKS-1))); do
    CUDA_VISIBLE_DEVICES=${GPULIST[$IDX]} python -m mgm.eval.model_vqa_loader \
        --model-path ./work_dirs/$CKPT \
        --question-file ./data/MGM-Eval/textvqa/llava_textvqa_val_v051_ocr.jsonl \
        --image-folder ./data/MGM-Eval/textvqa/train_images \
        --answers-file ./work_dirs/textvqa/answers/$CKPT/${CHUNKS}_${IDX}.jsonl \
        --num-chunks $CHUNKS \
        --chunk-idx $IDX \
        --temperature 0 \
        --load_8bit True \
        --conv-mode chatml_direct &
done

wait

output_file=./work_dirs/textvqa/answers/$CKPT/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 $((CHUNKS-1))); do
    cat ./work_dirs/textvqa/answers/$CKPT/${CHUNKS}_${IDX}.jsonl >> "$output_file"
done

python -m mgm.eval.eval_textvqa \
    --annotation-file ./data/MGM-Eval/textvqa/TextVQA_0.5.1_val.json \
    --result-file $output_file