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100 lines
3.1 KiB
Python
100 lines
3.1 KiB
Python
# @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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