import unittest
import numpy as np
from caffe2.proto import caffe2_pb2
from caffe2.python import core, workspace, test_util
@unittest.skipIf(not workspace.C.has_mkldnn, "Skipping as we do not have mkldnn.")
class TestMKLBasic(test_util.TestCase):
def testMaxPoolingSpeed(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, 64, 224, 224).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.MaxPool("X", "Y", stride=2, kernel=3)
net.MaxPool("X_mkl", "Y_mkl",
stride=2, 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("Maxpooling CPU runtime {}, MKL runtime {}.".format(runtime[1], runtime[2]))
def testAveragePoolingSpeed(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, 64, 224, 224).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.AveragePool("X", "Y", stride=2, kernel=3)
net.AveragePool("X_mkl", "Y_mkl",
stride=2, 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("Averagepooling CPU runtime {}, MKL runtime {}.".format(runtime[1], runtime[2]))
def testConvReluMaxPoolSpeed(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, 3, 224, 224).astype(np.float32) - 0.5
W = np.random.rand(64, 3, 11, 11).astype(np.float32) - 0.5
b = np.random.rand(64).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")
net.Conv(["X", "W", "b"], "C", pad=1, stride=1, kernel=11)
net.Conv(["X_mkl", "W_mkl", "b_mkl"], "C_mkl",
pad=1, stride=1, kernel=11, device_option=mkl_do)
net.Relu("C", "R")
net.Relu("C_mkl", "R_mkl", device_option=mkl_do)
net.AveragePool("R", "Y", stride=2, kernel=3)
net.AveragePool("R_mkl", "Y_mkl",
stride=2, 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)
if __name__ == '__main__':
unittest.main()