mirror of
https://github.com/zebrajr/pytorch.git
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8286ce1e3a
Summary: Closes https://github.com/caffe2/caffe2/pull/1260 Differential Revision: D5906739 Pulled By: Yangqing fbshipit-source-id: e482ba9ba60b5337d9165f28f7ec68d4518a0902
70 lines
2.2 KiB
Python
70 lines
2.2 KiB
Python
# Copyright (c) 2016-present, Facebook, Inc.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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##############################################################################
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## @package sampling_trainable_mixin
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# Module caffe2.python.layers.sampling_trainable_mixin
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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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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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