| import torch |
| import re |
| from PIL import Image |
| from abc import abstractproperty |
| import sys |
| import os.path as osp |
| from .base import BaseModel |
| from ..smp import * |
| from ..dataset import DATASET_TYPE |
| import copy |
|
|
|
|
| class Kosmos2(BaseModel): |
| INSTALL_REQ = True |
| INTERLEAVE = True |
|
|
| def __init__(self, |
| model_path='microsoft/kosmos-2-patch14-224', |
| **kwargs): |
| try: |
| from transformers import AutoProcessor, Kosmos2ForConditionalGeneration |
| except Exception as e: |
| logging.critical("Please install Transformers version 4.45.1 by running: pip install transformers==4.45.1") |
| raise e |
|
|
| assert osp.exists(model_path) or splitlen(model_path) == 2 |
|
|
| self.model = ( |
| Kosmos2ForConditionalGeneration.from_pretrained(model_path, torch_dtype=torch.float16) |
| .to(torch.device('cuda')) |
| ) |
| self.processor = AutoProcessor.from_pretrained(model_path) |
|
|
| default_kwargs = dict( |
| max_new_tokens=512, |
| use_cache=True |
| ) |
|
|
| default_kwargs.update(kwargs) |
| self.kwargs = default_kwargs |
| warnings.warn(f'Following kwargs received: {self.kwargs}, will use as generation config. ') |
| torch.cuda.empty_cache() |
|
|
| def generate_inner(self, message, dataset=None): |
| TASK_TOKEN = '<grounding> ' |
| QEUSTION_TOKEN = 'Question: ' |
| ANSWER_TOKEN = 'Answer: ' |
| images = [] |
| prompt = '' |
|
|
| prompt += TASK_TOKEN |
| for s in message: |
| if s['type'] == 'image': |
| images.append(s['value']) |
| elif s['type'] == 'text': |
| prompt += QEUSTION_TOKEN |
| prompt += s['value'] |
| prompt += ANSWER_TOKEN |
|
|
| images = [Image.open(s) for s in images] |
| inputs = self.processor(text=prompt, images=images[0], return_tensors='pt').to(torch.device('cuda')) |
|
|
| generated_ids = self.model.generate( |
| pixel_values=inputs['pixel_values'], |
| input_ids=inputs['input_ids'], |
| attention_mask=inputs['attention_mask'], |
| image_embeds=None, |
| image_embeds_position_mask=inputs['image_embeds_position_mask'], |
| **self.kwargs |
| ) |
|
|
| generated_text = self.processor.batch_decode(generated_ids, skip_special_tokens=True)[0] |
| processed_text = self.processor.post_process_generation(generated_text, cleanup_and_extract=True)[0] |
| cleaned_answer = re.sub(r'(Question:.*?Answer:|Question:.*)', '', processed_text).strip() |
| return cleaned_answer |
|
|
| def use_custom_prompt(self, dataset): |
| assert dataset is not None |
| if listinstr(['MMMU'], dataset): |
| return False |
| if DATASET_TYPE(dataset) == 'MCQ' or dataset == 'MMVet': |
| return True |
| return False |
|
|
| def build_prompt(self, line, dataset=None): |
| assert dataset is None or isinstance(dataset, str) |
| assert self.use_custom_prompt(dataset) |
| tgt_path = self.dump_image(line, dataset) |
| question = line['question'] |
| if dataset == 'MMVet': |
| prompt = question + '\nAnswer the question directly. ' |
| elif DATASET_TYPE(dataset) == 'MCQ': |
| options = { |
| cand: line[cand] |
| for cand in string.ascii_uppercase |
| if cand in line and not pd.isna(line[cand]) |
| } |
| options_prompt = '' |
| for key, item in options.items(): |
| options_prompt += f'{key}. {item}\n' |
|
|
| hint = line['hint'] if ('hint' in line and not pd.isna(line['hint'])) else None |
| prompt = f'Hint: {hint}\n' if hint is not None else '' |
| prompt += f'{question}\n' |
| prompt += ( |
| f'{options_prompt}\nAnswer with the option’s letter from the given choices directly. ' |
| if len(options) else 'Answer the question directly. ' |
| ) |
| else: |
| raise NotImplementedError |
|
|
| message = [dict(type='text', value=prompt)] |
| message.extend([dict(type='image', value=s) for s in tgt_path]) |
| return message |
|
|
