eval_yiprompt.sh

# export CUDA_VISIBLE_DEVICES=0,3,4,5,6,7
export OPENAI_API_KEY=...
num_frames=16
test_ratio=200

model_dir=MODELS/pllava-34b
weight_dir=MODELS/pllava-34b
SAVE_DIR=test_results/test_pllava_34b
lora_alpha=4
conv_mode=eval_vcg_llavanext
python -m tasks.eval.vcgbench.pllava_eval_vcgbench \
    --pretrained_model_name_or_path ${model_dir} \
    --save_path ${SAVE_DIR}/vcgbench \
    --num_frames ${num_frames} \
    --use_lora \
    --lora_alpha ${lora_alpha} \
    --weight_dir ${weight_dir} \
    --pooling_shape 16-12-12 \
    --test_ratio ${test_ratio} \
    --conv_mode $conv_mode

conv_mode=eval_mvbench_llavanext
python -m tasks.eval.mvbench.pllava_eval_mvbench \
    --pretrained_model_name_or_path ${model_dir} \
    --save_path ${SAVE_DIR}/mvbench \
    --use_lora \
    --lora_alpha ${lora_alpha} \
    --num_frames ${num_frames} \
    --weight_dir ${weight_dir} \
    --pooling_shape 16-12-12 \
    --conv_mode $conv_mode

conv_mode=eval_videoqa_llavanext
python -m tasks.eval.videoqabench.pllava_eval_videoqabench \
    --pretrained_model_name_or_path ${model_dir} \
    --save_path ${SAVE_DIR}/videoqabench \
    --num_frames ${num_frames} \
    --use_lora \
    --lora_alpha ${lora_alpha} \
    --weight_dir ${weight_dir} \
    --test_ratio ${test_ratio} \
    --conv_mode ${conv_mode}

conv_mode=eval_recaption_llavanext
python -m tasks.eval.recaption.pllava_recaption \
    --pretrained_model_name_or_path ${model_dir} \
    --save_path ${SAVE_DIR}/recaption \
    --num_frames ${num_frames} \
    --use_lora \
    --weight_dir ${weight_dir} \
    --lora_alpha ${lora_alpha} \
    --test_ratio ${test_ratio} \
    --conv_mode $conv_mode