| import os.path as osp |
| import warnings |
| from .base import BaseModel |
| from ..smp import * |
| from PIL import Image |
| import torch |
|
|
|
|
| class Chameleon(BaseModel): |
|
|
| INSTALL_REQ = False |
| INTERLEAVE = True |
|
|
| def __init__(self, model_path='facebook/chameleon-7b', **kwargs): |
| try: |
| from transformers import ChameleonProcessor, ChameleonForConditionalGeneration |
| except Exception as e: |
| logging.critical('Please install the latest transformers.') |
| raise e |
|
|
| processor = ChameleonProcessor.from_pretrained(model_path) |
| model = ChameleonForConditionalGeneration.from_pretrained(model_path, torch_dtype=torch.bfloat16) |
|
|
| self.model = model.cuda().eval() |
| self.processor = processor |
|
|
| def generate_inner(self, message, dataset=None): |
| content, images = '', [] |
| for x in message: |
| if x['type'] == 'text': |
| content += x['value'] |
| elif x['type'] == 'image': |
| content += '<image>\n' |
| images.append(Image.open(x['value'])) |
|
|
| inputs = self.processor( |
| text=[content], |
| images=images, |
| padding=True, |
| return_tensors='pt' |
| ).to(device='cuda', dtype=torch.bfloat16) |
| generate_ids = self.model.generate(**inputs, max_new_tokens=2048) |
| input_token_len = inputs.input_ids.shape[1] |
| text = self.processor.batch_decode( |
| generate_ids[:, input_token_len:], |
| skip_special_tokens=True, |
| clean_up_tokenization_spaces=False |
| )[0] |
| return text |
|
|
