mirror of
https://github.com/zebrajr/pytorch.git
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e816c777eb
Reviewed By: xianjiec Differential Revision: D5767997 fbshipit-source-id: b9b7c47d11417fbe67d861a2a6b4daa38adbe57b
115 lines
3.7 KiB
Python
115 lines
3.7 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 optimizer
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# Module caffe2.python.optimizer
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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 core
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class Regularizer(object):
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def __init__(self):
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self.apply_after_optimizer = False
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'''
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Adds regularization 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_init_net, param, grad=None):
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assert isinstance(param, core.BlobReference)
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return self._run(net, param_init_net, param, grad)
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def _run(self, net, param_init_net, param, grad):
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raise Exception("Not Impelemented")
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class L1Norm(Regularizer):
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def __init__(self, reg_lambda):
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super(L1Norm, self).__init__()
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assert reg_lambda >= 0,\
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'factor ahead of regularization should be 0 or positive'
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self.reg_lambda = reg_lambda
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def _run(self, net, param_init_net, param, grad=None):
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output_blob = net.NextScopedBlob(param + '_l1_regularization')
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net.LpNorm([param], [output_blob], p=1)
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net.Scale([output_blob], [output_blob], scale=self.reg_lambda)
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return output_blob
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class L2Norm(Regularizer):
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def __init__(self, reg_lambda):
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super(L2Norm, self).__init__()
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assert reg_lambda >= 0,\
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'factor ahead of regularization should be 0 or positive'
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self.reg_lambda = reg_lambda
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def _run(self, net, param_init_net, param, grad=None):
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output_blob = net.NextScopedBlob(param + '_l2_regularization')
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net.LpNorm([param], [output_blob], p=2)
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net.Scale([output_blob], [output_blob], scale=self.reg_lambda)
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return output_blob
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class MaxNorm(Regularizer):
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def __init__(self, norm=1.0):
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super(MaxNorm, self).__init__()
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self.norm = norm
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self.apply_after_optimizer = True
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def _run(self, net, param_init_net, param, grad):
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assert self.norm > 0, 'norm should be bigger than 0.'
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if isinstance(grad, core.GradientSlice):
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net.SparseNormalize(
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[param, grad.indices, grad.values],
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[param],
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use_max_norm=True,
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norm=self.norm,
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)
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else:
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raise NotImplementedError(
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"MaxNorm is not supported for dense parameters"
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)
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class ConstantNorm(Regularizer):
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def __init__(self, norm=1.0):
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super(ConstantNorm, self).__init__()
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self.norm = norm
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self.apply_after_optimizer = True
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def _run(self, net, param_init_net, param, grad):
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assert self.norm > 0, 'norm should be bigger than 0.'
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if isinstance(grad, core.GradientSlice):
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net.SparseNormalize(
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[param, grad.indices, grad.values],
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[param],
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use_max_norm=False,
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norm=self.norm,
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)
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else:
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raise NotImplementedError(
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"ConstantNorm is not supported for dense parameters"
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)
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