| #!/bin/bash |
|
|
| baseDir=/path/to/baseDir |
| baseModel='LLAVA' |
| |
|
|
| modelPath=${1} |
| if [ -z "${modelPath}" ] |
| then |
| echo "\$modelPath is empty Using robust model from here: " |
| modelPath=/path/to/ckpt.pt |
| modelPath1=ckpt_name |
| else |
| echo "\$modelPath is NOT empty" |
| modelPath1=${modelPath} |
| fi |
|
|
| answerFile="${baseModel}_${modelPath1}" |
| echo "Will save to the following json: " |
| echo $answerFile |
|
|
| python -m llava.eval.model_vqa_science \ |
| --model-path liuhaotian/llava-v1.5-7b \ |
| --eval-model ${baseModel} \ |
| --pretrained_rob_path ${modelPath} \ |
| --question-file "${baseDir}/llava_test_CQM-A.json" \ |
| --image-folder PATH-TO-scienceQA/test \ |
| --answers-file ${baseDir}/answers/${answerFile}.jsonl \ |
| --temperature 0 \ |
| --conv-mode vicuna_v1 |
|
|
| python llava/eval/eval_science_qa.py \ |
| --base-dir ${baseDir} \ |
| --result-file ${baseDir}/answers/${answerFile}.jsonl \ |
| --output-file ${baseDir}/answers/${answerFile}_output.jsonl \ |
| --output-result ${baseDir}/answers/${answerFile}_result.json |
|
|
