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e13101e069
Summary: For MKL-DNN,the filter data will be reorderd to primitive format, it takes a lot of time. So the patch provide a method to convert filter format before training. And "OptimizeForIdeep" will be changed to "OptimizeForMkldnn" in this patch. This patch depends on https://github.com/pytorch/pytorch/pull/12866 Pull Request resolved: https://github.com/pytorch/pytorch/pull/15171 Differential Revision: D14590741 Pulled By: yinghai fbshipit-source-id: 07971c9977edac3c8eec08ca2c39cda639683492
59 lines
1.9 KiB
Python
59 lines
1.9 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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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 caffe2.python._import_c_extension as C
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class Transformer(object):
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def __init__(self):
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pass
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@classmethod
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def runTransform(cls, transform_name, net):
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pb = net.Proto().SerializeToString()
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if C.transform_exists(transform_name):
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output = C.run_transform(transform_name, pb)
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elif C.workspace_transform_exists(transform_name):
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output = C.run_workspace_transform(transform_name, pb)
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else:
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raise AttributeError('Transformation {} not found.'.format(transform_name))
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net.Proto().ParseFromString(output)
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def __getattr__(self, transform_name):
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return lambda net : self.runTransform(transform_name, net)
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def fuseNNPACKConvRelu(net):
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net.Proto().ParseFromString(
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C.transform_fuseNNPACKConvRelu(net.Proto().SerializeToString())
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)
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def optimizeForMKLDNN(net, training_mode = False):
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net.Proto().ParseFromString(
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C.transform_optimizeForMKLDNN(net.Proto().SerializeToString(), training_mode)
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)
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def fuseConvBN(net):
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net.Proto().ParseFromString(
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C.transform_fuseConvBN(net.Proto().SerializeToString())
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)
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