| import sys |
| import torch |
| from PIL import Image |
| import os.path as osp |
| import warnings |
| from .base import BaseModel |
| from ..smp import * |
| from huggingface_hub import snapshot_download |
|
|
|
|
| class OpenFlamingo(BaseModel): |
|
|
| INSTALL_REQ = True |
| INTERLEAVE = True |
|
|
| def __init__(self, |
| name, |
| mpt_pth=None, |
| ckpt_pth=None, |
| **kwargs): |
|
|
| if mpt_pth is None: |
| raise ValueError( |
| 'Please set `mpt_pth` to the directory of MPT-7B, which is cloned from here: ' |
| 'https://huggingface.co/mosaicml/mpt-7b. ' |
| ) |
| raise ValueError |
| if ckpt_pth is None: |
| raise ValueError( |
| 'Please set `ckpt_pth` to the openflamingo ckpt, which is the `checkpoint.pt` file downloaded ' |
| 'from: https://huggingface.co/openflamingo/OpenFlamingo-9B-vitl-mpt7b/tree/main. ' |
| ) |
| else: |
| if osp.exists(ckpt_pth): |
| if ckpt_pth.endswith('checkpoint.pt'): |
| pass |
| elif osp.isdir(ckpt_pth): |
| ckpt_pth = osp.join(ckpt_pth, 'checkpoint.pt') |
| if not osp.exists(ckpt_pth): |
| raise ValueError(f'File {ckpt_pth} does not exist. ') |
| elif splitlen(ckpt_pth, '/') == 2: |
| cache_path = get_cache_path(ckpt_pth) |
| if cache_path is None: |
| snapshot_download(ckpt_pth) |
| cache_path = get_cache_path(ckpt_pth) |
| if cache_path is None: |
| raise ValueError(f'Directory {cache_path} does not exist. ') |
| else: |
| ckpt_pth = osp.join(cache_path, 'checkpoint.pt') |
|
|
| self.name = name |
| assert name in ['v2'] |
| self.mpt_pth = mpt_pth |
| try: |
| from open_flamingo import create_model_and_transforms |
| except Exception as e: |
| logging.critical('Please first install open_flamingo to use OpenFlamingo') |
| raise e |
|
|
| model, image_processor, tokenizer = create_model_and_transforms( |
| clip_vision_encoder_path='ViT-L-14', |
| clip_vision_encoder_pretrained='openai', |
| lang_encoder_path=mpt_pth, |
| tokenizer_path=mpt_pth, |
| cross_attn_every_n_layers=4) |
| ckpt = torch.load(ckpt_pth) |
| model.load_state_dict(ckpt, strict=False) |
| torch.cuda.empty_cache() |
| self.model = model.eval().cuda() |
| self.tokenizer = tokenizer |
| self.tokenizer.padding_side = 'left' |
| self.image_proc = image_processor |
|
|
| kwargs_default = dict(max_new_tokens=512, num_beams=3) |
| 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): |
| vision_x = [] |
| prompt = '' |
| for msg in message: |
| if msg['type'] == 'image': |
| img = Image.open(msg['value']) |
| vision_x.append(self.image_proc(img).unsqueeze(0)) |
| prompt += '<image>' |
| elif msg['type'] == 'text': |
| prompt += msg['value'] |
| prompt += 'Answer: ' |
| vision_x = torch.cat(vision_x, dim=0) if len(vision_x) > 1 else vision_x[0] |
| vision_x = vision_x.unsqueeze(1).unsqueeze(0) |
| lang_x = self.tokenizer([prompt], return_tensors='pt') |
| generated_text = self.model.generate( |
| vision_x=vision_x.cuda(), |
| lang_x=lang_x['input_ids'].cuda(), |
| attention_mask=lang_x['attention_mask'].cuda(), |
| **self.kwargs) |
| generated_text = self.tokenizer.decode(generated_text[0]) |
| text = generated_text[len(prompt):].split('<|endofchunk|>')[0] |
| return text |
|
|
