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Summary: Should be non-semantic. Uses https://en.wikipedia.org/wiki/Wikipedia:Lists_of_common_misspellings/For_machines to find likely typos. Pull Request resolved: https://github.com/pytorch/pytorch/pull/30606 Differential Revision: D18763028 Pulled By: mrshenli fbshipit-source-id: 896515a2156d062653408852e6c04b429fc5955c
39 lines
1.2 KiB
Python
39 lines
1.2 KiB
Python
# @package regularizer_context
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# Module caffe2.python.regularizer_context
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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from __future__ import unicode_literals
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from caffe2.python import context
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from caffe2.python.modifier_context import (
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ModifierContext, UseModifierBase)
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@context.define_context(allow_default=True)
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class RegularizerContext(ModifierContext):
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"""
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provide context to allow param_info to have different regularizers
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"""
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def has_regularizer(self, name):
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return self._has_modifier(name)
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def get_regularizer(self, name):
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assert self.has_regularizer(name), (
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"{} regularizer is not provided!".format(name))
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return self._get_modifier(name)
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class UseRegularizer(UseModifierBase):
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'''
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context class to allow setting the current context.
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Example usage with layer:
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regularizers = {'reg1': reg1, 'reg2': reg2}
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with UseRegularizer(regularizers):
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reg = RegularizerContext.current().get_regularizer('reg1')
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layer(reg=reg)
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'''
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def _context_class(self):
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return RegularizerContext
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