File size: 1,792 Bytes
256a159 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 |
from opencompass.multimodal.models.instructblip import (
InstructBlipScienceQAPromptConstructor,
InstructBlipScienceQAPostProcessor,
)
# dataloader settings
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)
instruct_blip_scienceqa_dataloader = dict(
batch_size=1,
num_workers=4,
dataset=dataset,
collate_fn=dict(type='pseudo_collate'),
sampler=dict(type='DefaultSampler', shuffle=False))
# model settings
instruct_blip_scienceqa_model = dict(
type='blip2-vicuna-instruct',
prompt_constructor=dict(type=InstructBlipScienceQAPromptConstructor),
post_processor=dict(type=InstructBlipScienceQAPostProcessor),
freeze_vit=True,
low_resource=False,
llm_model='/path/to/vicuna-7b/',
max_output_txt_len=10,
)
# evaluation settings
instruct_blip_scienceqa_evaluator = [dict(type='mmpretrain.ScienceQAMetric')]
instruct_blip_load_from = '/path/to/instruct_blip_vicuna7b_trimmed.pth'
|