| import sys |
| import torch |
| import os.path as osp |
| import warnings |
| from .base import BaseModel |
| from ..smp import * |
|
|
|
|
| class PandaGPT(BaseModel): |
|
|
| INSTALL_REQ = True |
| INTERLEAVE = False |
|
|
| def __init__(self, name, root=None, **kwargs): |
| if root is None: |
| raise ValueError('Please set `root` to PandaGPT code directory, which is cloned from here: ') |
|
|
| assert name == 'PandaGPT_13B' |
| self.name = name |
| sys.path.append(osp.join(root, 'code')) |
| try: |
| from model.openllama import OpenLLAMAPEFTModel |
| except Exception as e: |
| logging.critical( |
| 'Please first install PandaGPT and set the root path to use PandaGPT, ' |
| 'which is cloned from here: https://github.com/yxuansu/PandaGPT. ' |
| ) |
| raise e |
|
|
| self.args = { |
| 'model': 'openllama_peft', |
| 'imagebind_ckpt_path': osp.join(root, 'pretrained_ckpt/imagebind_ckpt'), |
| 'vicuna_ckpt_path': osp.join(root, 'pretrained_ckpt/vicuna_ckpt/13b_v0'), |
| 'delta_ckpt_path': osp.join(root, 'pretrained_ckpt/pandagpt_ckpt/13b/pytorch_model.pt'), |
| 'stage': 2, |
| 'max_tgt_len': 512, |
| 'lora_r': 32, |
| 'lora_alpha': 32, |
| 'lora_dropout': 0.1, |
| } |
| model = OpenLLAMAPEFTModel(**self.args) |
| delta_ckpt = torch.load(self.args['delta_ckpt_path'], map_location=torch.device('cpu')) |
| model.load_state_dict(delta_ckpt, strict=False) |
| torch.cuda.empty_cache() |
| self.model = model.eval().half().cuda() |
| kwargs_default = {'top_p': 0.9, 'do_sample': False, 'max_tgt_len': 128, 'temperature': 0.001} |
| kwargs_default.update(kwargs) |
| self.kwargs = kwargs_default |
| warnings.warn(f'Following kwargs received: {self.kwargs}, will use as generation config. ') |
|
|
| def generate_inner(self, message, dataset=None): |
| prompt, image_path = self.message_to_promptimg(message, dataset=dataset) |
| struct = { |
| 'prompt': prompt, |
| 'image_paths': [image_path], |
| 'audio_paths': [], |
| 'video_paths': [], |
| 'thermal_paths': [], |
| 'modality_embeds': [] |
| } |
| struct.update(self.kwargs) |
| resp = self.model.generate(struct) |
| return resp |
|
|
