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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 sampling_trainable_mixin
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# Module caffe2.python.layers.sampling_trainable_mixin
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import abc
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import six
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class SamplingTrainableMixin(six.with_metaclass(abc.ABCMeta, object)):
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def __init__(self, *args, **kwargs):
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super(SamplingTrainableMixin, self).__init__(*args, **kwargs)
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self._train_param_blobs = None
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self._train_param_blobs_frozen = False
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@property
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@abc.abstractmethod
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def param_blobs(self):
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"""
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List of parameter blobs for prediction net
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"""
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pass
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@property
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def train_param_blobs(self):
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"""
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If train_param_blobs is not set before used, default to param_blobs
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"""
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if self._train_param_blobs is None:
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self.train_param_blobs = self.param_blobs
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return self._train_param_blobs
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@train_param_blobs.setter
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def train_param_blobs(self, blobs):
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assert not self._train_param_blobs_frozen
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assert blobs is not None
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self._train_param_blobs_frozen = True
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self._train_param_blobs = blobs
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@abc.abstractmethod
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def _add_ops(self, net, param_blobs):
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"""
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Add ops to the given net, using the given param_blobs
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"""
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pass
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def add_ops(self, net):
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self._add_ops(net, self.param_blobs)
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def add_train_ops(self, net):
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self._add_ops(net, self.train_param_blobs)
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