mirror of
https://github.com/modelscope/modelscope.git
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5e469008fd
* add preprocessor module * add model base and builder * update task constant * add load image preprocessor and its dependency * add pipeline interface and UT covered * support default pipeline for task * add image matting pipeline * refine nlp tokenize interface * add nlp pipeline * fix UT failed * add test for Compose Link: https://code.aone.alibaba-inc.com/Ali-MaaS/MaaS-lib/codereview/8769235 * add preprocessor module * add test for Compose * fix citest error * fix abs class error * add model base and builder * update task constant * add load image preprocessor and its dependency * add pipeline interface and UT covered * support default pipeline for task * refine models and pipeline interface * add pipeline folder structure * add image matting pipeline * refine nlp tokenize interface * add nlp pipeline 1.add preprossor model pipeline for nlp text classification 2. add corresponding test Link: https://code.aone.alibaba-inc.com/Ali-MaaS/MaaS-lib/codereview/8757371 * new nlp pipeline * format pre-commit code * update easynlp pipeline * update model_name for easynlp pipeline; add test for maas_lib/utils/typeassert.py * update test_typeassert.py * refactor code 1. rename typeassert to type_assert 2. use lazy import to make easynlp dependency optional 3. refine image matting UT * fix linter test failed * update requirements.txt * fix UT failed * fix citest script to update requirements
66 lines
2.3 KiB
Python
66 lines
2.3 KiB
Python
# Copyright (c) Alibaba, Inc. and its affiliates.
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from typing import Union
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from maas_lib.models.base import Model
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from maas_lib.utils.config import ConfigDict
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from maas_lib.utils.constant import Tasks
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from maas_lib.utils.registry import Registry, build_from_cfg
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from .base import Pipeline
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PIPELINES = Registry('pipelines')
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def build_pipeline(cfg: ConfigDict,
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task_name: str = None,
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default_args: dict = None):
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""" build pipeline given model config dict
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Args:
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cfg (:obj:`ConfigDict`): config dict for model object.
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task_name (str, optional): task name, refer to
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:obj:`Tasks` for more details
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default_args (dict, optional): Default initialization arguments.
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"""
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return build_from_cfg(
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cfg, PIPELINES, group_key=task_name, default_args=default_args)
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def pipeline(task: str = None,
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model: Union[str, Model] = None,
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config_file: str = None,
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pipeline_name: str = None,
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framework: str = None,
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device: int = -1,
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**kwargs) -> Pipeline:
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""" Factory method to build a obj:`Pipeline`.
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Args:
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task (str): Task name defining which pipeline will be returned.
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model (str or obj:`Model`): model name or model object.
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config_file (str, optional): path to config file.
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pipeline_name (str, optional): pipeline class name or alias name.
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framework (str, optional): framework type.
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device (int, optional): which device is used to do inference.
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Return:
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pipeline (obj:`Pipeline`): pipeline object for certain task.
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Examples:
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```python
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>>> p = pipeline('image-classification')
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>>> p = pipeline('text-classification', model='distilbert-base-uncased')
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>>> # Using model object
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>>> resnet = Model.from_pretrained('Resnet')
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>>> p = pipeline('image-classification', model=resnet)
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"""
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if task is not None and model is None and pipeline_name is None:
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# get default pipeline for this task
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assert task in PIPELINES.modules, f'No pipeline is registerd for Task {task}'
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pipeline_name = list(PIPELINES.modules[task].keys())[0]
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if pipeline_name is not None:
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cfg = dict(type=pipeline_name, **kwargs)
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return build_pipeline(cfg, task_name=task)
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