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b5beb60 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 | from ..smp import *
from ..dataset import img_root_map, DATASET_TYPE
from abc import abstractmethod
class BaseModel:
INTERLEAVE = False
allowed_types = ['text', 'image', 'video']
def __init__(self):
self.dump_image_func = None
def use_custom_prompt(self, dataset):
"""Whether to use custom prompt for the given dataset.
Args:
dataset (str): The name of the dataset.
Returns:
bool: Whether to use custom prompt. If True, will call `build_prompt` of the VLM to build the prompt.
Default to False.
"""
return False
@abstractmethod
def build_prompt(self, line, dataset):
"""Build custom prompts for a specific dataset. Called only if `use_custom_prompt` returns True.
Args:
line (line of pd.DataFrame): The raw input line.
dataset (str): The name of the dataset.
Returns:
str: The built message.
"""
raise NotImplementedError
def set_dump_image(self, dump_image_func):
self.dump_image_func = dump_image_func
def dump_image(self, line, dataset):
return self.dump_image_func(line)
@abstractmethod
def generate_inner(self, message, dataset=None):
raise NotImplementedError
def check_content(self, msgs):
"""Check the content type of the input. Four types are allowed: str, dict, liststr, listdict.
"""
if isinstance(msgs, str):
return 'str'
if isinstance(msgs, dict):
return 'dict'
if isinstance(msgs, list):
types = [self.check_content(m) for m in msgs]
if all(t == 'str' for t in types):
return 'liststr'
if all(t == 'dict' for t in types):
return 'listdict'
return 'unknown'
def preproc_content(self, inputs):
"""Convert the raw input messages to a list of dicts.
Args:
inputs: raw input messages.
Returns:
list(dict): The preprocessed input messages. Will return None if failed to preprocess the input.
"""
if self.check_content(inputs) == 'str':
return [dict(type='text', value=inputs)]
elif self.check_content(inputs) == 'dict':
assert 'type' in inputs and 'value' in inputs
return [inputs]
elif self.check_content(inputs) == 'liststr':
res = []
for s in inputs:
mime, pth = parse_file(s)
if mime is None or mime == 'unknown':
res.append(dict(type='text', value=s))
else:
res.append(dict(type=mime.split('/')[0], value=pth))
return res
elif self.check_content(inputs) == 'listdict':
for item in inputs:
assert 'type' in item and 'value' in item
mime, s = parse_file(item['value'])
if mime is None:
assert item['type'] == 'text'
else:
assert mime.split('/')[0] == item['type']
item['value'] = s
return inputs
else:
return None
def generate(self, message, dataset=None):
"""Generate the output message.
Args:
message (list[dict]): The input message.
dataset (str, optional): The name of the dataset. Defaults to None.
Returns:
str: The generated message.
"""
assert self.check_content(message) in ['str', 'dict', 'liststr', 'listdict'], f'Invalid input type: {message}'
message = self.preproc_content(message)
assert message is not None and self.check_content(message) == 'listdict'
for item in message:
assert item['type'] in self.allowed_types, f'Invalid input type: {item["type"]}'
return self.generate_inner(message, dataset)
def chat(self, messages, dataset=None):
"""The main function for multi-turn chatting. Will call `chat_inner` with the preprocessed input messages."""
assert hasattr(self, 'chat_inner'), 'The API model should has the `chat_inner` method. '
for msg in messages:
assert isinstance(msg, dict) and 'role' in msg and 'content' in msg, msg
assert self.check_content(msg['content']) in ['str', 'dict', 'liststr', 'listdict'], msg
msg['content'] = self.preproc_content(msg['content'])
while len(messages):
try:
return self.chat_inner(messages, dataset=dataset)
except Exception as e:
logging.info(f'{type(e)}: {e}')
messages = messages[1:]
while len(messages) and messages[0]['role'] != 'user':
messages = messages[1:]
continue
return 'Chat Mode: Failed with all possible conversation turns.'
def message_to_promptimg(self, message, dataset=None):
assert not self.INTERLEAVE
model_name = self.__class__.__name__
warnings.warn(
f'Model {model_name} does not support interleaved input. '
'Will use the first image and aggregated texts as prompt. ')
num_images = len([x for x in message if x['type'] == 'image'])
if num_images == 0:
prompt = '\n'.join([x['value'] for x in message if x['type'] == 'text'])
image = None
else:
prompt = '\n'.join([x['value'] for x in message if x['type'] == 'text'])
images = [x['value'] for x in message if x['type'] == 'image']
if 'BLINK' == dataset:
image = concat_images_vlmeval(images, target_size=512)
else:
image = images[0]
return prompt, image
def message_to_promptvideo(self, message):
if self.VIDEO_LLM:
num_videos = len([x for x in message if x['type'] == 'video'])
if num_videos == 0:
prompt = '\n'.join([x['value'] for x in message if x['type'] == 'text'])
video = None
else:
prompt = '\n'.join([x['value'] for x in message if x['type'] == 'text'])
video = [x['value'] for x in message if x['type'] == 'video'][0]
return prompt, video
else:
logging.critical('Model does not support video input.')
raise NotImplementedError
def message_to_promptvideo_withrole(self, message, dataset=None):
if self.VIDEO_LLM:
system, user, assistant, video_list = '', '', '', []
for msg in message:
if msg['type'] == 'text':
if 'role' in msg and msg['role'] == 'system':
system += msg['value']
elif 'role' in msg and msg['role'] == 'assistant':
assistant += msg['value']
else:
user += msg['value']
elif msg['type'] == 'video':
video_list.append(msg['value'])
question = {
'system': system,
'user': user,
'assistant': assistant
}
if assistant == '':
if listinstr(['MCQ'], DATASET_TYPE(dataset)):
question['assistant'] = 'Best Option: ('
else:
del question['assistant']
if len(video_list) > 1:
print('VLMEvalKit only support single video as input, take first video as input')
video = video_list[0]
return question, video
else:
logging.critical('Model does not support video input.')
raise NotImplementedError
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