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27c7158166
Summary: There is a module called `2to3` which you can target for future specifically to remove these, the directory of `caffe2` has the most redundant imports: ```2to3 -f future -w caffe2``` Pull Request resolved: https://github.com/pytorch/pytorch/pull/45033 Reviewed By: seemethere Differential Revision: D23808648 Pulled By: bugra fbshipit-source-id: 38971900f0fe43ab44a9168e57f2307580d36a38
55 lines
1.4 KiB
Python
55 lines
1.4 KiB
Python
## @package optimizer_context
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# Module caffe2.python.optimizer_context
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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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DEFAULT_OPTIM = 'DEFAULT'
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@context.define_context(allow_default=True)
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class OptimizerContext(ModifierContext):
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"""
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provide context to allow param_info to have different optimizers
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"""
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def has_optimizer(self, name):
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return self._has_modifier(name)
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def get_optimizer(self, name):
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assert self.has_optimizer(name), (
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"{} optimizer is not provided!".format(name))
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return self._get_modifier(name)
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class UseOptimizer(UseModifierBase):
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'''
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context class to allow setting the current context.
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Example usage with brew:
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- with UseOptimizer(optim):
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brew.func
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- with UseOptimizer({'WEIGHT': weight_optim}):
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brew.func
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- with UseOptimizer({'DEFAULT': optim, 'BIAS': bias_optim,
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'WEIGHT': weight_optim}):
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brew.func
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- with UseOptimizer(optim1):
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brew.func
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with UseOptimizer(optim2):
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brew.func
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Example usage with layer:
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optimizers = {'optim1': optim1, 'optim2': optim2}
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with Optimizers(optimizers):
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optim = OptimizerContext.current().get_optimizer('optim1')
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layer(optim=optim)
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'''
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def _context_class(self):
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return OptimizerContext
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