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Running
on
Zero
Running
on
Zero
File size: 2,866 Bytes
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import os
import importlib
from typing import Dict, Optional, Sequence, List
import transformers
from tinychart.constants import DEFAULT_IMAGE_TOKEN, DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN
from tinychart import conversation as conversation_lib
from tinychart.arguments import *
PREPROCESS_REGISTRY = {}
def register_preprocess(name):
def register_preprocess_cls(cls):
if name in PREPROCESS_REGISTRY:
return PREPROCESS_REGISTRY[name]
PREPROCESS_REGISTRY[name] = cls
return cls
return register_preprocess_cls
def import_modules(modules_dir, namespace):
for file in os.listdir(modules_dir):
path = os.path.join(modules_dir, file)
if (
not file.startswith("_")
and not file.startswith(".")
and (file.endswith(".py") or os.path.isdir(path))
):
module_name = file[: file.find(".py")] if file.endswith(".py") else file
importlib.import_module(namespace + "." + module_name)
models_dir = os.path.join(os.path.dirname(__file__), 'preprocess')
import_modules(models_dir, "tinychart.data.preprocess")
def PreprocessSelect(version):
result = PREPROCESS_REGISTRY.get(version, None)
if result is None:
for name in PREPROCESS_REGISTRY.keys():
if version in name:
result = PREPROCESS_REGISTRY[name]
break
if result is None:
result = PREPROCESS_REGISTRY['default']
return result
def preprocess_multimodal(
sources: Sequence[str],
data_args: DataArguments
) -> Dict:
is_multimodal = data_args.is_multimodal
if not is_multimodal:
return sources
for source in sources:
for sentence in source:
if DEFAULT_IMAGE_TOKEN in sentence['value']:
sentence['value'] = sentence['value'].replace(DEFAULT_IMAGE_TOKEN, '').strip()
sentence['value'] = DEFAULT_IMAGE_TOKEN + '\n' + sentence['value']
sentence['value'] = sentence['value'].strip()
if "mmtag" in conversation_lib.default_conversation.version:
sentence['value'] = sentence['value'].replace(DEFAULT_IMAGE_TOKEN,
'<Image>' + DEFAULT_IMAGE_TOKEN + '</Image>')
replace_token = DEFAULT_IMAGE_TOKEN
if data_args.mm_use_im_start_end:
replace_token = DEFAULT_IM_START_TOKEN + replace_token + DEFAULT_IM_END_TOKEN
sentence["value"] = sentence["value"].replace(DEFAULT_IMAGE_TOKEN, replace_token)
return sources
def preprocess(
sources: Sequence[str],
tokenizer: transformers.PreTrainedTokenizer,
has_image: bool = False
) -> Dict:
return PreprocessSelect(conversation_lib.default_conversation.version)(sources, tokenizer, has_image)
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