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
95 lines
3.9 KiB
Python
95 lines
3.9 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 unittest
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import numpy as np
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from caffe2.proto import caffe2_pb2
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from caffe2.python import cnn, core, workspace, test_util
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@unittest.skipIf(not workspace.C.has_mkldnn, "Skipping as we do not have mkldnn.")
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class TestMKLBasic(test_util.TestCase):
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def testLRNSpeed(self):
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# We randomly select a shape to test the speed. Intentionally we
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# test a batch size of 1 since this may be the most frequent use
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# case for MKL during deployment time.
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X = np.random.rand(1, 2, 224, 224).astype(np.float32)
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mkl_do = core.DeviceOption(caffe2_pb2.MKLDNN)
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# Makes sure that feed works.
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workspace.FeedBlob("X", X)
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workspace.FeedBlob("X_mkl", X, device_option=mkl_do)
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net = core.Net("test")
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# Makes sure that we can run relu.
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net.LRN("X", ["Y", "Y_Scale"], size=5, alpha=0.001, beta=0.75, bias=2.0, order="NCHW")
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net.LRN("X_mkl", ["Y_mkl", "Y_Scale_mkl"], size=5, alpha=0.001, beta=0.75, bias=2.0, order="NCHW", device_option=mkl_do)
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workspace.CreateNet(net)
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workspace.RunNet(net)
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# makes sure that the results are good.
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np.testing.assert_allclose(
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workspace.FetchBlob("Y"),
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workspace.FetchBlob("Y_mkl"),
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atol=1e-2,
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rtol=1e-2)
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runtime = workspace.BenchmarkNet(net.Proto().name, 1, 100, True)
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print("LRN CPU runtime {}, MKL runtime {}.".format(runtime[1], runtime[2]))
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def testConvReluLRNSpeed(self):
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# We randomly select a shape to test the speed. Intentionally we
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# test a batch size of 1 since this may be the most frequent use
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# case for MKL during deployment time.
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X = np.random.rand(1, 3, 224, 224).astype(np.float32) - 0.5
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W = np.random.rand(64, 3, 11, 11).astype(np.float32) - 0.5
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b = np.random.rand(64).astype(np.float32) - 0.5
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mkl_do = core.DeviceOption(caffe2_pb2.MKLDNN)
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# Makes sure that feed works.
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workspace.FeedBlob("X", X)
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workspace.FeedBlob("W", W)
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workspace.FeedBlob("b", b)
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workspace.FeedBlob("X_mkl", X, device_option=mkl_do)
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workspace.FeedBlob("W_mkl", W, device_option=mkl_do)
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workspace.FeedBlob("b_mkl", b, device_option=mkl_do)
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net = core.Net("test")
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net.Conv(["X", "W", "b"], "C", pad=1, stride=1, kernel=11)
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net.Conv(["X_mkl", "W_mkl", "b_mkl"], "C_mkl",
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pad=1, stride=1, kernel=11, device_option=mkl_do)
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net.Relu("C", "R")
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net.Relu("C_mkl", "R_mkl", device_option=mkl_do)
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net.LRN("R", ["Y", "Y_Scale"], size=5, alpha=0.001, beta=0.75, bias=2.0, order="NCHW")
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net.LRN("R_mkl", ["Y_mkl", "Y_Scale_mkl"],size=5, alpha=0.001, beta=0.75, bias=2.0, order="NCHW", device_option=mkl_do)
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workspace.CreateNet(net)
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workspace.RunNet(net)
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# makes sure that the results are good.
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np.testing.assert_allclose(
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workspace.FetchBlob("Y"),
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workspace.FetchBlob("Y_mkl"),
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atol=1e-2,
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rtol=1e-2)
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runtime = workspace.BenchmarkNet(net.Proto().name, 1, 100, True)
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if __name__ == '__main__':
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unittest.main()
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