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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
45 lines
1.4 KiB
Python
45 lines
1.4 KiB
Python
# @package optimizer
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# Module caffe2.python.normalizer
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class Normalizer(object):
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def __init__(self):
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pass
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"""
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Adds normalization to train_net for given parameter. Its factor ahead of
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regularization is given when initialization.
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The param should be a BlobReference.
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"""
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def __call__(self, net, param):
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return self._run(net, param)
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def _run(self, net, param):
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raise Exception("Not Impelemented")
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class BatchNormalizer(Normalizer):
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def __init__(self, momentum, scale_init_value=1.0):
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super(BatchNormalizer, self).__init__()
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self._momentum = float(momentum)
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self._scale_init_value = float(scale_init_value)
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def _run(self, layer_model, param):
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return layer_model.BatchNormalization(
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param, momentum=self._momentum, scale_init_value=self._scale_init_value
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)
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class LayerNormalizer(Normalizer):
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def __init__(self, epsilon, use_layer_norm_op=True, scale_init_value=1.0):
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super(LayerNormalizer, self).__init__()
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self._epsilon = float(epsilon)
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self._use_layer_norm_op = use_layer_norm_op
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self._scale_init_value = float(scale_init_value)
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def _run(self, layer_model, param):
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return layer_model.LayerNormalization(
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param, epsilon=self._epsilon, use_layer_norm_op=self._use_layer_norm_op, scale_init_value=self._scale_init_value
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)
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