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3.4 KiB
3.4 KiB
Evaluation Instruction for MiniGPT-v2
Data preparation
Images download
| Image source | Download path |
|---|---|
| OKVQA | annotations images |
| gqa | annotations images |
| hateful meme | images and annotations |
| iconqa | images and annotation |
| vizwiz | images and annotation |
| RefCOCO | annotations |
| RefCOCO+ | annotations |
| RefCOCOg | annotations |
Evaluation dataset structure
${MINIGPTv2_EVALUATION_DATASET}
├── gqa
│ └── test_balanced_questions.json
│ ├── testdev_balanced_questions.json
│ ├── gqa_images
├── hateful_meme
│ └── hm_images
│ ├── dev.jsonl
├── iconvqa
│ └── iconvqa_images
│ ├── choose_text_val.json
├── vizwiz
│ └── vizwiz_images
│ ├── val.json
├── vsr
│ └── vsr_images
├── okvqa
│ ├── okvqa_test_split.json
│ ├── mscoco_val2014_annotations_clean.json
│ ├── OpenEnded_mscoco_val2014_questions_clean.json
├── refcoco
│ └── instances.json
│ ├── refs(google).p
│ ├── refs(unc).p
├── refcoco+
│ └── instances.json
│ ├── refs(unc).p
├── refercocog
│ └── instances.json
│ ├── refs(google).p
│ ├── refs(und).p
...
environment setup
export PYTHONPATH=$PYTHONPATH:/path/to/directory/of/MiniGPT-4
start evalauting RefCOCO, RefCOCO+, RefCOCOg
port=port_number cfg_path=/path/to/eval_configs/minigptv2_eval.yaml eval_file_path=/path/to/eval/image/path save_path=/path/to/save/path ckpt=/path/to/evaluation/checkpoint
split=/evaluation/data/split/type # e.g. val, testA, testB, test dataset=/data/type #refcoco, refcoco+, refcocog
torchrun --master-port ${port} --nproc_per_node 1 eval_ref.py \
--cfg-path ${cfg_path} --img_path ${IMG_PATH} --eval_file_path ${eval_file_path} --save_path ${save_path} \
--ckpt ${ckpt} --split ${split} --dataset ${dataset} --lora_r 64 --lora_alpha 16 \
--batch_size 10 --max_new_tokens 20 --resample
start evaluating visual question answering
port=port_number cfg_path=/path/to/eval_configs/minigptv2_eval.yaml eval_file_path=/path/to/eval/image/path save_path=/path/to/save/path ckpt=/path/to/evaluation/checkpoint
split=/evaluation/data/split/type # e.g. val,test dataset=/data/type # vqa data types: okvqa, vizwiz, iconvqa, gqa, vsr, hm
torchrun --master-port ${port} --nproc_per_node 1 eval_ref.py \
--cfg-path ${cfg_path} --img_path ${IMG_PATH} --eval_file_path ${eval_file_path} --save_path ${save_path} \
--ckpt ${ckpt} --split ${split} --dataset ${dataset} --lora_r 64 --lora_alpha 16 \
--batch_size 10 --max_new_tokens 20 --resample