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Summary: `ModifierContext` is the base class for `OptimizerContext` and `RegularizationContext`. `UseModifierBase` is the base class for `UseRegularizer `and `UseOptimizer` Most of codes in `OptimizerContext`, `RegularizationContext` and other potential Context class in future could be shared. We thus implemented a new base class, called `ModifierContext` to support it. It happens to be the same for `UseRegularizer` and `UseOptimizer`, and we implemented a new base class called `UseModifierBase`. In this way, users only need to provide API for **get** and **has** operation. Also, they need to tell what's the **context class**. **Note** Mirrored code in fbandroid and fbobj would be added when finally check in. Reviewed By: kittipatv, xianjiec Differential Revision: D5724613 fbshipit-source-id: de19bb822dcd41ec5c459d65065603a0abe2fd20
55 lines
1.6 KiB
Python
55 lines
1.6 KiB
Python
## @package optimizer_context
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# Module caffe2.python.optimizer_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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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 useage 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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