| import torch | |
| from PIL import Image | |
| import os.path as osp | |
| import sys | |
| from .base import BaseModel | |
| from ..smp import * | |
| class InstructBLIP(BaseModel): | |
| INSTALL_REQ = True | |
| INTERLEAVE = False | |
| def __init__(self, name): | |
| self.config_map = { | |
| 'instructblip_7b': 'misc/blip2_instruct_vicuna7b.yaml', | |
| 'instructblip_13b': 'misc/blip2_instruct_vicuna13b.yaml', | |
| } | |
| self.file_path = __file__ | |
| config_root = osp.dirname(self.file_path) | |
| try: | |
| from lavis.models import load_preprocess | |
| from omegaconf import OmegaConf | |
| from lavis.common.registry import registry | |
| except Exception as e: | |
| logging.critical('Please install lavis before using InstructBLIP. ') | |
| raise e | |
| assert name in self.config_map | |
| cfg_path = osp.join(config_root, self.config_map[name]) | |
| cfg = OmegaConf.load(cfg_path) | |
| model_cfg = cfg.model | |
| assert osp.exists(model_cfg.llm_model) or splitlen(model_cfg.llm_model) == 2 | |
| model_cls = registry.get_model_class(name='blip2_vicuna_instruct') | |
| model = model_cls.from_config(model_cfg) | |
| model.eval() | |
| self.device = torch.device('cuda') if torch.cuda.is_available() else 'cpu' | |
| device = self.device | |
| model.to(device) | |
| self.model = model | |
| self.kwargs = {'max_length': 512} | |
| preprocess_cfg = cfg.preprocess | |
| vis_processors, _ = load_preprocess(preprocess_cfg) | |
| self.vis_processors = vis_processors | |
| def generate_inner(self, message, dataset=None): | |
| prompt, image_path = self.message_to_promptimg(message, dataset=dataset) | |
| vis_processors = self.vis_processors | |
| raw_image = Image.open(image_path).convert('RGB') | |
| image_tensor = vis_processors['eval'](raw_image).unsqueeze(0).to(self.device) | |
| outputs = self.model.generate(dict(image=image_tensor, prompt=prompt)) | |
| return outputs[0] | |