wehub-resource-sync
70 lines
2.4 KiB
Python
70 lines
2.4 KiB
Python
# -*- coding:utf-8 -*-
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# Author: hankcs
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# Date: 2019-08-26 14:58
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from typing import Dict
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from hanlp_common.configurable import Configurable
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from hanlp_common.reflection import classpath_of
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from hanlp_common.structure import SerializableDict
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class ConfigTracker(Configurable):
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def __init__(self, locals_: Dict, exclude=('kwargs', 'self', '__class__', 'locals_')) -> None:
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"""This base class helps sub-classes to capture their arguments passed to ``__init__``, and also their types so
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that they can be deserialized from a config in dict form.
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Args:
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locals_: Obtained by :meth:`locals`.
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exclude: Arguments to be excluded.
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Examples:
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>>> class MyClass(ConfigTracker):
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>>> def __init__(self, i_need_this='yes') -> None:
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>>> super().__init__(locals())
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>>> obj = MyClass()
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>>> print(obj.config)
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{'i_need_this': 'yes', 'classpath': 'test_config_tracker.MyClass'}
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"""
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if 'kwargs' in locals_:
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locals_.update(locals_['kwargs'])
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self.config = SerializableDict(
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(k, v.config if hasattr(v, 'config') else v) for k, v in locals_.items() if k not in exclude)
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self.config['classpath'] = classpath_of(self)
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class History(object):
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def __init__(self):
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""" A history of training context. It records how many steps have passed and provides methods to decide whether
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an update should be performed, and to caculate number of training steps given dataloader size and
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``gradient_accumulation``.
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"""
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self.num_mini_batches = 0
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def step(self, gradient_accumulation):
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""" Whether the training procedure should perform an update.
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Args:
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gradient_accumulation: Number of batches per update.
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Returns:
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bool: ``True`` to update.
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"""
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self.num_mini_batches += 1
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return self.num_mini_batches % gradient_accumulation == 0
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def num_training_steps(self, num_batches, gradient_accumulation):
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""" Caculate number of training steps.
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Args:
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num_batches: Size of dataloader.
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gradient_accumulation: Number of batches per update.
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Returns:
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"""
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return len(
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[i for i in range(self.num_mini_batches + 1, self.num_mini_batches + num_batches + 1) if
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i % gradient_accumulation == 0])
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