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retrain_all.sh
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retrain_all.sh
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#!/bin/bash
gpu_list="${CUDA_VISIBLE_DEVICES:-0}"
IFS=',' read -ra GPULIST <<< "$gpu_list"
CHUNKS=${#GPULIST[@]}
for IDX in $(seq 0 $((CHUNKS-1))); do
CUDA_VISIBLE_DEVICES=${GPULIST[$IDX]} python -m run_model \
--model_name LLaVA-7B \
--model_path ../LLaVA/checkpoints/llava-v1.5-7b-lora \
--split val \
--dataset VizWiz \
--prompt mq \
--answers_file ./output/LLaVA-7B-FT/tmp/${CHUNKS}_${IDX}.jsonl \
--num_chunks $CHUNKS \
--chunk_idx $IDX \
--temperature 0.0 \
--top_p 0.9 \
--num_beams 1 &
done
wait
output_file=./output/LLaVA-7B-FT/VizWiz_val_mq.jsonl
# Clear out the output file if it exists.
> "$output_file"
# Loop through the indices and concatenate each file.
for IDX in $(seq 0 $((CHUNKS-1))); do
cat ./output/LLaVA-7B-FT/tmp/${CHUNKS}_${IDX}.jsonl >> "$output_file"
rm ./output/LLaVA-7B-FT/tmp/${CHUNKS}_${IDX}.jsonl
done
# VizWiz train
for IDX in $(seq 0 $((CHUNKS-1))); do
CUDA_VISIBLE_DEVICES=${GPULIST[$IDX]} python -m run_model \
--model_name LLaVA-7B \
--model_path ../LLaVA/checkpoints/llava-v1.5-7b-lora \
--split train \
--dataset VizWiz \
--prompt mq \
--answers_file ./output/LLaVA-7B-FT/tmp/${CHUNKS}_${IDX}.jsonl \
--num_chunks $CHUNKS \
--chunk_idx $IDX \
--temperature 0.0 \
--top_p 0.9 \
--num_beams 1 &
done
wait
output_file=./output/LLaVA-7B-FT/VizWiz_train_mq.jsonl
# Clear out the output file if it exists.
> "$output_file"
# Loop through the indices and concatenate each file.
for IDX in $(seq 0 $((CHUNKS-1))); do
cat ./output/LLaVA-7B-FT/tmp/${CHUNKS}_${IDX}.jsonl >> "$output_file"
rm ./output/LLaVA-7B-FT/tmp/${CHUNKS}_${IDX}.jsonl
done
for IDX in $(seq 0 $((CHUNKS-1))); do
CUDA_VISIBLE_DEVICES=${GPULIST[$IDX]} python -m run_model \
--model_name LLaVA-7B \
--model_path ../LLaVA/checkpoints/llava-v1.5-7b-lora \
--split SD_TYPO \
--dataset MMSafety \
--prompt mq \
--theme safety \
--answers_file ./output/LLaVA-7B-FT/tmp/${CHUNKS}_${IDX}.jsonl \
--num_chunks $CHUNKS \
--chunk_idx $IDX \
--temperature 0.0 \
--top_p 0.9 \
--num_beams 1 &
done
wait
output_file=./output/LLaVA-7B-FT/Safety_mq.jsonl
# Clear out the output file if it exists.
> "$output_file"
# Loop through the indices and concatenate each file.
for IDX in $(seq 0 $((CHUNKS-1))); do
cat ./output/LLaVA-7B-FT/tmp/${CHUNKS}_${IDX}.jsonl >> "$output_file"
rm ./output/LLaVA-7B-FT/tmp/${CHUNKS}_${IDX}.jsonl
done
for IDX in $(seq 0 $((CHUNKS-1))); do
CUDA_VISIBLE_DEVICES=${GPULIST[$IDX]} python -m run_model \
--model_name LLaVA-7B \
--model_path ../LLaVA/checkpoints/llava-v1.5-7b-lora \
--split val \
--dataset MAD \
--prompt mq \
--theme mad \
--answers_file ./output/LLaVA-7B-FT/tmp/${CHUNKS}_${IDX}.jsonl \
--num_chunks $CHUNKS \
--chunk_idx $IDX \
--temperature 0.0 \
--top_p 0.9 \
--num_beams 1 &
done
wait
output_file=./output/LLaVA-7B-FT/MAD_val_mq.jsonl
# Clear out the output file if it exists.
> "$output_file"
# Loop through the indices and concatenate each file.
for IDX in $(seq 0 $((CHUNKS-1))); do
cat ./output/LLaVA-7B-FT/tmp/${CHUNKS}_${IDX}.jsonl >> "$output_file"
rm ./output/LLaVA-7B-FT/tmp/${CHUNKS}_${IDX}.jsonl
done
for IDX in $(seq 0 $((CHUNKS-1))); do
CUDA_VISIBLE_DEVICES=${GPULIST[$IDX]} python -m run_model \
--model_name LLaVA-7B \
--model_path ../LLaVA/checkpoints/llava-v1.5-7b-lora \
--split train \
--dataset MAD \
--prompt mq \
--theme mad \
--answers_file ./output/LLaVA-7B-FT/tmp/${CHUNKS}_${IDX}.jsonl \
--num_chunks $CHUNKS \
--chunk_idx $IDX \
--temperature 0.0 \
--top_p 0.9 \
--num_beams 1 &
done
wait
output_file=./output/LLaVA-7B-FT/MAD_train_mq.jsonl
# Clear out the output file if it exists.
> "$output_file"
# Loop through the indices and concatenate each file.
for IDX in $(seq 0 $((CHUNKS-1))); do
cat ./output/LLaVA-7B-FT/tmp/${CHUNKS}_${IDX}.jsonl >> "$output_file"
rm ./output/LLaVA-7B-FT/tmp/${CHUNKS}_${IDX}.jsonl
done
for IDX in $(seq 0 $((CHUNKS-1))); do
CUDA_VISIBLE_DEVICES=${GPULIST[$IDX]} python -m run_model \
--model_name LLaVA-7B \
--model_path ../LLaVA/checkpoints/llava-v1.5-7b-lora \
--split val \
--dataset POPE \
--prompt oe \
--theme general \
--answers_file ./output/LLaVA-7B-FT/tmp/${CHUNKS}_${IDX}.jsonl \
--num_chunks $CHUNKS \
--chunk_idx $IDX \
--temperature 0.0 \
--top_p 0.9 \
--num_beams 1 &
done
wait
output_file=./output/LLaVA-7B-FT/POPE_val_oe.jsonl
# Clear out the output file if it exists.
> "$output_file"
# Loop through the indices and concatenate each file.
for IDX in $(seq 0 $((CHUNKS-1))); do
cat ./output/LLaVA-7B-FT/tmp/${CHUNKS}_${IDX}.jsonl >> "$output_file"
rm ./output/LLaVA-7B-FT/tmp/${CHUNKS}_${IDX}.jsonl
done
for IDX in $(seq 0 $((CHUNKS-1))); do
CUDA_VISIBLE_DEVICES=${GPULIST[$IDX]} python -m run_model \
--model_name LLaVA-7B \
--model_path ../LLaVA/checkpoints/llava-v1.5-7b-lora \
--split train \
--dataset POPE \
--prompt oe \
--theme general \
--answers_file ./output/LLaVA-7B-FT/tmp/${CHUNKS}_${IDX}.jsonl \
--num_chunks $CHUNKS \
--chunk_idx $IDX \
--temperature 0.0 \
--top_p 0.9 \
--num_beams 1 &
done
wait
output_file=./output/LLaVA-7B-FT/POPE_train_oe.jsonl
# Clear out the output file if it exists.
> "$output_file"
# Loop through the indices and concatenate each file.
for IDX in $(seq 0 $((CHUNKS-1))); do
cat ./output/LLaVA-7B-FT/tmp/${CHUNKS}_${IDX}.jsonl >> "$output_file"
rm ./output/LLaVA-7B-FT/tmp/${CHUNKS}_${IDX}.jsonl
done
for IDX in $(seq 0 $((CHUNKS-1))); do
CUDA_VISIBLE_DEVICES=${GPULIST[$IDX]} python -m run_model \
--model_name LLaVA-7B \
--model_path ../LLaVA/checkpoints/llava-v1.5-7b-lora \
--split LLaVA-7B \
--dataset MathVista \
--answers_file ./output/LLaVA-7B-FT/tmp/${CHUNKS}_${IDX}.jsonl \
--num_chunks $CHUNKS \
--chunk_idx $IDX \
--temperature 0.0 \
--top_p 0.9 \
--num_beams 1 &
done
wait
output_file=./output/LLaVA-7B-FT/MathV_self_eval.jsonl
# Clear out the output file if it exists.
> "$output_file"
# Loop through the indices and concatenate each file.
for IDX in $(seq 0 $((CHUNKS-1))); do
cat ./output/LLaVA-7B-FT/tmp/${CHUNKS}_${IDX}.jsonl >> "$output_file"
rm ./output/LLaVA-7B-FT/tmp/${CHUNKS}_${IDX}.jsonl
done
for IDX in $(seq 0 $((CHUNKS-1))); do
CUDA_VISIBLE_DEVICES=${GPULIST[$IDX]} python -m run_model \
--model_name LLaVA-7B \
--model_path ../LLaVA/checkpoints/llava-v1.5-7b-lora \
--split val \
--dataset ImageNet \
--prompt oe \
--answers_file ./output/LLaVA-7B-FT/tmp/${CHUNKS}_${IDX}.jsonl \
--num_chunks $CHUNKS \
--chunk_idx $IDX \
--temperature 0.0 \
--top_p 0.9 \
--num_beams 1 &
done
wait
output_file=./output/LLaVA-7B-FT/ImageNet_val.jsonl
# Clear out the output file if it exists.
> "$output_file"
# Loop through the indices and concatenate each file.
for IDX in $(seq 0 $((CHUNKS-1))); do
cat ./output/LLaVA-7B-FT/tmp/${CHUNKS}_${IDX}.jsonl >> "$output_file"
rm ./output/LLaVA-7B-FT/tmp/${CHUNKS}_${IDX}.jsonl
done
for IDX in $(seq 0 $((CHUNKS-1))); do
CUDA_VISIBLE_DEVICES=$IDX python -m run_model \
--num_samples 16 \
--sampling class \
--model_name LLaVA-7B \
--model_path ../LLaVA/checkpoints/llava-v1.5-7b-lora \
--split train \
--dataset ImageNet \
--prompt oe \
--answers_file ./output/LLaVA-7B-FT/ImageNet_Train/${CHUNKS}_${IDX}.jsonl \
--num_chunks $CHUNKS \
--chunk_idx $IDX \
--temperature 0.0 \
--top_p 0.9 \
--num_beams 1 &
done
wait