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import json
import os.path
from mmengine.model import BaseModel
from lmdeploy import pipeline, TurbomindEngineConfig
from lmdeploy.vl import load_image
import torch
import torch.nn as nn
class LLM_Annotor(BaseModel):
def __init__(self,
model=None,
save_folder='./work_dirs/qwen2_72b_obj_referring/'
):
super().__init__()
print(torch.cuda.device_count())
print(f"\n\n Using {model} !!! \n\n")
pipe = pipeline(model,
backend_config=TurbomindEngineConfig(
# session_len=8192,
session_len=4096,
tp=torch.cuda.device_count()),
)
self.pipe = [pipe]
self._zero = nn.Linear(10, 10)
self.results_list = []
self.item_idx = 0
if not os.path.exists(save_folder):
os.mkdir(save_folder)
self.save_folder = save_folder
def forward(self, **kwargs):
return None
def predict_forward_text_vertify(self, data_dicts):
prompts = []
print('vertify forward !!!')
# remove ignore items
if 'ignore' in data_dicts[0].keys():
data_dicts_ = []
for _item in data_dicts:
if _item['ignore']:
continue
data_dicts_.append(_item)
data_dicts = data_dicts_
for data_dict in data_dicts:
texts = data_dict['text_prompt']
prompts.append(texts)
response_list = self.pipe[0](prompts)
text_lsit = [item.text for item in response_list]
results_list = []
for i, text in enumerate(text_lsit):
print('\n\n', text, '\n\n')
if 'No conflict' in text:
results_list.append({
'video_id': data_dicts[i]['video_id'],
'obj_id': data_dicts[i]['obj_id'],
'ori_captions': data_dicts[i]['ori_captions'],
})
print('\n\n', data_dicts[i]['ori_captions'], '\n\n')
self.results_list += results_list
if len(self.results_list) > 100:
self.save_step()
return {}
def predict_forward_text_summarize(self, data_dicts):
prompts = []
print('summarize forward !!!')
for data_dict in data_dicts:
texts = data_dict['text_prompt']
prompts.append(texts)
response_list = self.pipe[0](prompts)
text_lsit = [item.text for item in response_list]
results_list = []
for i, text in enumerate(text_lsit):
results_list.append({
'video_id': data_dicts[i]['video_id'],
'obj_id': data_dicts[i]['obj_id'],
'ori_captions': data_dicts[i]['ori_captions'],
'summarized': text
})
print('\n\n', data_dicts[i]['ori_captions'], '\n', text, '\n\n')
self.results_list += results_list
if len(self.results_list) > 100:
self.save_step()
return {}
def predict_forward_text_formatting(self, data_dicts):
prompts = []
print('formatting forward !!!')
for data_dict in data_dicts:
texts = data_dict['text_prompt']
prompts.append(texts)
response_list = self.pipe[0](prompts)
text_lsit = [item.text for item in response_list]
results_list = []
for i, text in enumerate(text_lsit):
text = text.split(':')[-1]
text = text.strip()
text = text.replace('\"', '')
results_list.append({
'video_id': data_dicts[i]['video_id'],
'obj_id': data_dicts[i]['obj_id'],
'ori_captions': data_dicts[i]['ori_captions'],
'summarized': data_dicts[i]['summarized'],
'formated': text
})
print('\n\n', text, '\n\n')
self.results_list += results_list
if len(self.results_list) > 100:
self.save_step()
return {}
def predict_forward_short_cap(self, data_dicts):
prompts = []
print('short caption forward !!!')
for data_dict in data_dicts:
texts = data_dict['text_prompt']
prompts.append(texts)
response_list = self.pipe[0](prompts)
text_lsit = [item.text for item in response_list]
results_list = []
for i, text in enumerate(text_lsit):
print('\n\n', data_dicts[i]['formated'], '\n', text, '\n\n')
results_list.append({
'video_id': data_dicts[i]['video_id'],
'obj_id': data_dicts[i]['obj_id'],
'formated': data_dicts[i]['formated'],
'short_cap': text
})
self.results_list += results_list
if len(self.results_list) > 100:
self.save_step()
return {}
def predict_forward_filter_unindentified(self, data_dicts):
prompts = []
print('filter_unindentified forward !!!')
for data_dict in data_dicts:
texts = data_dict['text_prompt']
prompts.append(texts)
response_list = self.pipe[0](prompts)
text_lsit = [item.text for item in response_list]
results_list = []
for i, text in enumerate(text_lsit):
# print('\n\n', data_dicts[i]['caption'], '\n', text, '\n\n')
if "Unidentified" in text or "unidentified" in text:
pass
else:
results_list.append({
'video_id': data_dicts[i]['video_id'],
'obj_id': data_dicts[i]['obj_id'],
'caption': data_dicts[i]['caption'],
'type': data_dicts[i]['type'],
'category': text,
})
self.results_list += results_list
if len(self.results_list) > 100:
self.save_step()
return {}
def predict_forward_consistency(self, data_dicts):
prompts = []
print('Consistency forward !!!')
for data_dict in data_dicts:
texts = data_dict['text_prompt']
prompts.append(texts)
response_list = self.pipe[0](prompts)
text_lsit = [item.text for item in response_list]
results_list = []
out_num = 0
for i, text in enumerate(text_lsit):
if "Yes" in text or "yes" in text:
print('\n\n', data_dicts[i]['text_prompt'], '\n', text, '\n\n')
out_num += 1
results_list.append({
'video_id': data_dicts[i]['video_id'],
'obj_id': data_dicts[i]['obj_id'],
'caption': data_dicts[i]['caption'],
'category': data_dicts[i]['category'],
})
print(f"***************Input {len(text_lsit)} items and keep {out_num} items !!!\n")
self.results_list += results_list
if len(self.results_list) > 100:
self.save_step()
return {}
def predict_forward_re_consistency(self, data_dicts):
prompts = []
print('Re consistency forward !!!')
for data_dict in data_dicts:
texts = data_dict['text_prompt']
prompts.append(texts)
response_list = self.pipe[0](prompts)
text_lsit = [item.text for item in response_list]
results_list = []
out_num = 0
for i, text in enumerate(text_lsit):
if "Yes" in text or "yes" in text:
# print('\n\n', data_dicts[i]['text_prompt'], '\n', text, '\n\n')
out_num += 1
results_list.append({
'video_id': data_dicts[i]['video_id'],
'obj_id': data_dicts[i]['obj_id'],
'crop_caption': data_dicts[i]['crop_caption'],
'crop_category': data_dicts[i]['crop_category'],
'image_caption': data_dicts[i]['image_caption'],
'video_caption': data_dicts[i]['video_caption'],
})
# else:
# print('\n\n', data_dicts[i]['text_prompt'], '\n', text, '\n\n')
print(f"***************Input {len(text_lsit)} items and keep {out_num} items !!!\n")
self.results_list += results_list
if len(self.results_list) > 100:
self.save_step()
return {}
def predict_forward_change_style(self, data_dicts):
prompts = []
print('Change Style forward !!!')
for data_dict in data_dicts:
texts = data_dict['text_prompt']
prompts.append(texts)
response_list = self.pipe[0](prompts)
text_lsit = [item.text for item in response_list]
results_list = []
for i, text in enumerate(text_lsit):
results_list.append({
'video_id': data_dicts[i]['video_id'],
'obj_id': data_dicts[i]['obj_id'],
'crop_caption': data_dicts[i]['crop_caption'],
'crop_category': data_dicts[i]['crop_category'],
'image_caption': data_dicts[i]['image_caption'],
'video_caption': data_dicts[i]['video_caption'],
'final_caption': text,
})
# print('\n\n', data_dicts[i]['text_prompt'], '\n', text, '\n\n')
self.results_list += results_list
if len(self.results_list) > 100:
self.save_step()
return {}
def predict_forward_translation(self, data_dicts):
prompts = []
print('translation forward !!!')
for data_dict in data_dicts:
texts = data_dict['text_prompt']
prompts.append(texts)
response_list = self.pipe[0](prompts)
text_lsit = [item.text for item in response_list]
results_list = []
for i, text in enumerate(text_lsit):
results_list.append({
'video_id': data_dicts[i]['video_id'],
'obj_id': data_dicts[i]['obj_id'],
'crop_caption': data_dicts[i]['crop_caption'],
'crop_category': data_dicts[i]['crop_category'],
'image_caption': data_dicts[i]['image_caption'],
'video_caption': data_dicts[i]['video_caption'],
'final_caption': data_dicts[i]['final_caption'],
'translation': text
})
print(text, '\n')
# print('\n\n', data_dicts[i]['text_prompt'], '\n', text, '\n\n')
self.results_list += results_list
if len(self.results_list) > 100:
self.save_step()
return {}
def predict_forward_text(self, data_dicts):
if 'task' in data_dicts[0].keys() and data_dicts[0]['task'] == 'vertify':
return self.predict_forward_text_vertify(data_dicts)
prompts = []
# remove ignore items
if 'ignore' in data_dicts[0].keys():
data_dicts_ = []
for _item in data_dicts:
if _item['ignore']:
continue
data_dicts_.append(_item)
data_dicts = data_dicts_
for data_dict in data_dicts:
texts = data_dict['text_prompt']
prompts.append(texts)
response_list = self.pipe[0](prompts)
text_lsit = [item.text for item in response_list]
results_list = []
for i, text in enumerate(text_lsit):
results_list.append({
'video_id': data_dicts[i]['video_id'],
'obj_id': data_dicts[i]['obj_id'],
'caption': text,
})
print('\n\n', text, '\n\n')
self.results_list += results_list
if len(self.results_list) > 100:
self.save_step()
return {}
def save_step(self, last=False):
if last:
save_list = self.results_list
else:
save_list = self.results_list[:100]
self.results_list = self.results_list[100:]
json_path = os.path.join(self.save_folder, f'{self.item_idx}.json')
self.item_idx += 1
with open(json_path, 'w') as f:
json.dump(save_list, fp=f)
return
def predict_forward(self, image_paths, **kwargs):
if 'type' in kwargs.keys() and kwargs['type'] == 'text':
if 'task' in kwargs.keys() and kwargs['task'] == 'vertify':
return self.predict_forward_text_vertify(kwargs['data_dicts'])
elif 'task' in kwargs.keys() and kwargs['task'] == 'summarize':
return self.predict_forward_text_summarize(kwargs['data_dicts'])
elif 'task' in kwargs.keys() and kwargs['task'] == 'formatting':
return self.predict_forward_text_formatting(kwargs['data_dicts'])
elif 'task' in kwargs.keys() and kwargs['task'] == 'short_cap':
return self.predict_forward_short_cap(kwargs['data_dicts'])
elif 'task' in kwargs.keys() and kwargs['task'] == 'filter_unindentified':
return self.predict_forward_filter_unindentified(kwargs['data_dicts'])
elif 'task' in kwargs.keys() and kwargs['task'] == 'consistency':
return self.predict_forward_consistency(kwargs['data_dicts'])
elif 'task' in kwargs.keys() and kwargs['task'] == 're_consistency':
return self.predict_forward_re_consistency(kwargs['data_dicts'])
elif 'task' in kwargs.keys() and kwargs['task'] == 'change_style':
return self.predict_forward_change_style(kwargs['data_dicts'])
elif 'task' in kwargs.keys() and kwargs['task'] == 'translation':
return self.predict_forward_translation(kwargs['data_dicts'])
return self.predict_forward_text(kwargs['data_dicts'])
images = [load_image(image_path) for image_path in image_paths]
prompts = [('Please briefly describe this image in a sentence.', image) for image in images]
response_list = self.pipe[0](prompts)
text_lsit = [item.text for item in response_list]
print(text_lsit)
return {}
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