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evaluation readme
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@ -60,13 +60,16 @@ export PYTHONPATH=$PYTHONPATH:/path/to/directory/of/MiniGPT-4
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### start evalauting RefCOCO, RefCOCO+, RefCOCOg
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port=port_number
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cfg_path=/path/to/eval_configs/minigptv2_eval.yaml
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eval_file_path=/path/to/eval/image/path
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save_path=/path/to/save/path
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ckpt=/path/to/evaluation/checkpoint
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split=/evaluation/data/split/type
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dataset=/data/type
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split=/evaluation/data/split/type # e.g. val, testA, testB, test
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dataset=/data/type #refcoco, refcoco+, refcocog
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dataset | split
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--- | :---:
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refcoco | val, testA, testB
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refcoco+ | val, testA, testB
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refcocog | val, test
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```
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torchrun --master-port ${port} --nproc_per_node 1 eval_ref.py \
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@ -80,17 +83,26 @@ torchrun --master-port ${port} --nproc_per_node 1 eval_ref.py \
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port=port_number
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cfg_path=/path/to/eval_configs/minigptv2_eval.yaml
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eval_file_path=/path/to/eval/image/path
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eval_file_path=/path/to/eval/annotation/path
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image_path=/path/to/eval/image/path
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save_path=/path/to/save/path
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ckpt=/path/to/evaluation/checkpoint
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split=/evaluation/data/split/type
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dataset=/data/type
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dataset | image_path | eval_file_path
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--- | :---:
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okvqa | coco_2017 | /path/to/okvqa/folder
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vizwiz | vizwiz_images | /path/to/vizwiz/folder
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iconvqa | iconvqa_images | /path/to/iconvqa/folder
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gqa | gqa_images | /path/to/gqa/folder
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vsr | vsr_images | None
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hateful meme | hm_images | /path/to/hateful_mem/folder
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split=/evaluation/data/split/type # e.g. val,test
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dataset=/data/type # vqa data types: okvqa, vizwiz, iconvqa, gqa, vsr, hm
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```
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torchrun --master-port ${port} --nproc_per_node 1 eval_ref.py \
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--cfg-path ${cfg_path} --img_path ${IMG_PATH} --eval_file_path ${eval_file_path} --save_path ${save_path} \
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torchrun --master-port ${port} --nproc_per_node 1 eval_vqa.py \
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--cfg-path ${cfg_path} --img_path ${image_path} --eval_file_path ${eval_file_path} --save_path ${save_path} \
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--ckpt ${ckpt} --split ${split} --dataset ${dataset} --lora_r 64 --lora_alpha 16 \
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--batch_size 10 --max_new_tokens 20 --resample
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```
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@ -162,7 +162,7 @@ if 'gqa' in args.dataset:
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json.dump(minigpt4_predict, f)
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if 'vsr' in args.dataset:
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annotation = load_dataset(args.eval_file_path, split='test')
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annotation = load_dataset("cambridgeltl/vsr_zeroshot", split='test')
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img_path = args.img_path
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data = VSREvalData(annotation, vis_processor, img_path)
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eval_dataloader = DataLoader(data, batch_size=args.batch_size, shuffle=False)
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