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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
1.1 KiB
Python
39 lines
1.1 KiB
Python
# @package regularizer_context
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# Module caffe2.python.normalizer_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 NormalizerContext(ModifierContext):
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"""
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provide context to allow param_info to have different normalizers
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"""
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def has_normalizer(self, name):
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return self._has_modifier(name)
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def get_normalizer(self, name):
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assert self.has_normalizer(name), (
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"{} normalizer is not provided!".format(name))
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return self._get_modifier(name)
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class UseNormalizer(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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normalizers = {'norm1': norm1, 'norm2': norm2}
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with UseNormalizer(normalizers):
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norm = NormalizerContext.current().get_normalizer('norm1')
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layer(norm=norm)
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'''
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def _context_class(self):
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return NormalizerContext
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