|
from opencompass.multimodal.models import (MiniGPT4ScienceQAPromptConstructor, |
|
MiniGPT4ScienceQAPostProcessor) |
|
|
|
|
|
val_pipeline = [ |
|
dict(type='mmpretrain.LoadImageFromFile'), |
|
dict(type='mmpretrain.ToPIL', to_rgb=True), |
|
dict(type='mmpretrain.torchvision/Resize', |
|
size=(224, 224), |
|
interpolation=3), |
|
dict(type='mmpretrain.torchvision/ToTensor'), |
|
dict(type='mmpretrain.torchvision/Normalize', |
|
mean=(0.48145466, 0.4578275, 0.40821073), |
|
std=(0.26862954, 0.26130258, 0.27577711)), |
|
dict(type='mmpretrain.PackInputs', |
|
algorithm_keys=[ |
|
'question', 'gt_answer', 'choices', 'hint', 'lecture', 'solution', 'has_image' |
|
]) |
|
] |
|
|
|
dataset = dict(type='mmpretrain.ScienceQA', |
|
data_root='./data/scienceqa', |
|
split='val', |
|
split_file='pid_splits.json', |
|
ann_file='problems.json', |
|
image_only=True, |
|
data_prefix=dict(img_path='val'), |
|
pipeline=val_pipeline) |
|
|
|
minigpt_4_scienceqa_dataloader = dict(batch_size=1, |
|
num_workers=4, |
|
dataset=dataset, |
|
collate_fn=dict(type='pseudo_collate'), |
|
sampler=dict(type='DefaultSampler', |
|
shuffle=False)) |
|
|
|
|
|
minigpt_4_scienceqa_model = dict( |
|
type='minigpt-4', |
|
low_resource=False, |
|
img_size=224, |
|
max_length=10, |
|
llama_model='/path/to/vicuna_weights_7b/', |
|
prompt_constructor=dict(type=MiniGPT4ScienceQAPromptConstructor, |
|
image_prompt='###Human: <Img><ImageHere></Img>', |
|
reply_prompt='###Assistant:'), |
|
post_processor=dict(type=MiniGPT4ScienceQAPostProcessor)) |
|
|
|
|
|
minigpt_4_scienceqa_evaluator = [dict(type='mmpretrain.ScienceQAMetric')] |
|
|
|
minigpt_4_scienceqa_load_from = '/path/to/prerained_minigpt4_7b.pth' |
|
|
