pytorch

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## @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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class OptimizerContext(ModifierContext, context.DefaultManaged):
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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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