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62 lines
1.8 KiB
Python
62 lines
1.8 KiB
Python
## @package optimizers
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# Module caffe2.python.layers.optimizers
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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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@context.define_context(allow_default=True)
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class OptimizerContext(object):
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"""
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Scope driven way to provide optimizers to layers.
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Optimizer can be fetched through the 'get_optimizer' method.
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"""
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def __init__(self):
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self._optimizers = {}
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self._optimizers_list = []
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def _rebuild_optimizers(self):
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self._optimizers = {}
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for m in self._optimizers_list:
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self._optimizers.update(m)
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def get_optimizer(self, name):
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assert name in self._optimizers, (
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"{} optimizer is not provided!".format(name))
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return self._optimizers.get(name)
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def push_optimizers(self, optimizers):
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# optimizer override is allowed
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self._optimizers_list.append(optimizers)
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self._optimizers.update(optimizers)
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def pop_optimizers(self):
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assert len(self._optimizers_list) > 0
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self._optimizers_list.pop()
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self._rebuild_optimizers()
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class Optimizers(object):
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"""
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Optimizers context to provide optimizers to layers
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within the context.
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Example usage:
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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 __init__(self, optimizers):
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self._optimizers = optimizers
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def __enter__(self):
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OptimizerContext.current().push_optimizers(self._optimizers)
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return self
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def __exit__(self, type, value, traceback):
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OptimizerContext.current().pop_optimizers()
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