text: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from __future__ import unicode_literals
  import unittest
  
  import numpy as np
  from caffe2.proto import caffe2_pb2
  from caffe2.python import cnn, core, workspace, test_util
  
  
  @unittest.skipIf(not workspace.C.has_mkldnn, "Skipping as we do not have mkldnn.")
  class TestMKLBasic(test_util.TestCase):
      def testReLUSpeed(self):
          X = np.random.randn(128, 4096).astype(np.float32)
          mkl_do = core.DeviceOption(caffe2_pb2.MKLDNN)
          # Makes sure that feed works.
          workspace.FeedBlob("X", X)
          workspace.FeedBlob("X_mkl", X, device_option=mkl_do)
          net = core.Net("test")
          # Makes sure that we can run relu.
          net.Relu("X", "Y")
          net.Relu("X_mkl", "Y_mkl", device_option=mkl_do)
          workspace.CreateNet(net)
          workspace.RunNet(net)
          # makes sure that the results are good.
          np.testing.assert_allclose(
              workspace.FetchBlob("Y"),
              workspace.FetchBlob("Y_mkl"),
              atol=1e-10,
              rtol=1e-10)
          runtime = workspace.BenchmarkNet(net.Proto().name, 1, 100, True)
  
          # The returned runtime is the time of
          # [whole_net, cpu_op, mkl_op]
          # so we will assume that the MKL one runs faster than the CPU one.
  
          # Note(Yangqing): in fact, it seems that in optimized mode, this is
          # not always guaranteed - MKL runs slower than the Eigen vectorized
          # version, so I am turning this assertion off.
          #self.assertTrue(runtime[1] >= runtime[2])
  
          print("Relu CPU runtime {}, MKL runtime {}.".format(runtime[1], runtime[2]))
  
  
      def testConvSpeed(self):
          # We randomly select a shape to test the speed. Intentionally we
          # test a batch size of 1 since this may be the most frequent use
          # case for MKL during deployment time.
          X = np.random.rand(1, 256, 27, 27).astype(np.float32) - 0.5
          W = np.random.rand(192, 256, 3, 3).astype(np.float32) - 0.5
          b = np.random.rand(192).astype(np.float32) - 0.5
          mkl_do = core.DeviceOption(caffe2_pb2.MKLDNN)
          # Makes sure that feed works.
          workspace.FeedBlob("X", X)
          workspace.FeedBlob("W", W)
          workspace.FeedBlob("b", b)
          workspace.FeedBlob("X_mkl", X, device_option=mkl_do)
          workspace.FeedBlob("W_mkl", W, device_option=mkl_do)
          workspace.FeedBlob("b_mkl", b, device_option=mkl_do)
          net = core.Net("test")
          # Makes sure that we can run relu.
          net.Conv(["X", "W", "b"], "Y", pad=1, stride=1, kernel=3)
          net.Conv(["X_mkl", "W_mkl", "b_mkl"], "Y_mkl",
                   pad=1, stride=1, kernel=3, device_option=mkl_do)
          workspace.CreateNet(net)
          workspace.RunNet(net)
          # makes sure that the results are good.
          np.testing.assert_allclose(
              workspace.FetchBlob("Y"),
              workspace.FetchBlob("Y_mkl"),
              atol=1e-2,
              rtol=1e-2)
          runtime = workspace.BenchmarkNet(net.Proto().name, 1, 100, True)
  
          print("Conv CPU runtime {}, MKL runtime {}.".format(runtime[1], runtime[2]))
  
  
  if __name__ == '__main__':
      unittest.main()
  