File size: 1,732 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 (
InstructBlipCOCOCaotionPromptConstructor,
InstructBlipCOCOCaptionPostProcessor,
)
# dataloader settings
val_pipeline = [
dict(type='mmpretrain.LoadImageFromFile'),
dict(type='mmpretrain.ToPIL', to_rgb=True),
dict(type='mmpretrain.torchvision/Resize',
size=(384, 384),
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=['image_id'])
]
dataset = dict(type='mmpretrain.COCOCaption',
data_root='data/coco',
data_prefix=dict(img_path='images'),
ann_file='annotations/coco_karpathy_val.json',
pipeline=val_pipeline)
instruct_blip_coco_caption_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_coco_caption_model = dict(
type='blip2-vicuna-instruct',
prompt_constructor=dict(type=InstructBlipCOCOCaotionPromptConstructor),
post_processor=dict(type=InstructBlipCOCOCaptionPostProcessor),
freeze_vit=True,
low_resource=False,
llm_model='/path/to/vicuna-7b/',
img_size=384,
is_caption_task=True,
)
# evaluation settings
instruct_blip_coco_caption_evaluator = [
dict(
type='mmpretrain.COCOCaption',
ann_file='data/coco/annotations/coco_karpathy_val_gt.json',
) # noqa
]
instruct_blip_load_from = '/path/to/instruct_blip_vicuna7b_trimmed.pth'
|