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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 testSpatialBNTestingSpeed(self):
input_channel = 10
X = np.random.rand(1, input_channel, 100, 100).astype(np.float32) - 0.5
scale = np.random.rand(input_channel).astype(np.float32) + 0.5
bias = np.random.rand(input_channel).astype(np.float32) - 0.5
mean = np.random.randn(input_channel).astype(np.float32)
var = np.random.rand(input_channel).astype(np.float32) + 0.5
mkl_do = core.DeviceOption(caffe2_pb2.MKLDNN)
# Makes sure that feed works.
workspace.FeedBlob("X", X)
workspace.FeedBlob("scale", scale)
workspace.FeedBlob("bias", bias)
workspace.FeedBlob("mean", mean)
workspace.FeedBlob("var", var)
workspace.FeedBlob("X_mkl", X, device_option=mkl_do)
workspace.FeedBlob("scale_mkl", scale, device_option=mkl_do)
workspace.FeedBlob("bias_mkl", bias, device_option=mkl_do)
workspace.FeedBlob("mean_mkl", mean, device_option=mkl_do)
workspace.FeedBlob("var_mkl", var, device_option=mkl_do)
net = core.Net("test")
# Makes sure that we can run relu.
net.SpatialBN(["X", "scale", "bias","mean","var"], "Y", order="NCHW",
is_test=True,
epsilon=1e-5)
net.SpatialBN(["X_mkl", "scale_mkl", "bias_mkl","mean_mkl","var_mkl"], "Y_mkl", order="NCHW",
is_test=True,
epsilon=1e-5, 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("FC CPU runtime {}, MKL runtime {}.".format(runtime[1], runtime[2]))
def testSpatialBNTrainingSpeed(self):
input_channel = 10
X = np.random.rand(1, input_channel, 100, 100).astype(np.float32) - 0.5
scale = np.random.rand(input_channel).astype(np.float32) + 0.5
bias = np.random.rand(input_channel).astype(np.float32) - 0.5
mean = np.random.randn(input_channel).astype(np.float32)
var = np.random.rand(input_channel).astype(np.float32) + 0.5
#mean = np.zeros(input_channel)
#var = np.zeros(input_channel)
mkl_do = core.DeviceOption(caffe2_pb2.MKLDNN)
# Makes sure that feed works.
workspace.FeedBlob("X", X)
workspace.FeedBlob("scale", scale)
workspace.FeedBlob("bias", bias)
workspace.FeedBlob("mean", mean)
workspace.FeedBlob("var", var)
workspace.FeedBlob("X_mkl", X, device_option=mkl_do)
workspace.FeedBlob("scale_mkl", scale, device_option=mkl_do)
workspace.FeedBlob("bias_mkl", bias, device_option=mkl_do)
workspace.FeedBlob("mean_mkl", mean, device_option=mkl_do)
workspace.FeedBlob("var_mkl", var, device_option=mkl_do)
net = core.Net("test")
# Makes sure that we can run relu.
net.SpatialBN(["X", "scale", "bias","mean", "var"],
["Y", "mean", "var", "saved_mean", "saved_var"],
order="NCHW",
is_test=False,
epsilon=1e-5)
net.SpatialBN(["X_mkl", "scale_mkl", "bias_mkl","mean_mkl","var_mkl"],
["Y_mkl", "mean_mkl", "var_mkl", "saved_mean_mkl", "saved_var_mkl"],
order="NCHW",
is_test=False,
epsilon=1e-5,
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)
np.testing.assert_allclose(
workspace.FetchBlob("mean"),
workspace.FetchBlob("mean_mkl"),
atol=1e-2,
rtol=1e-2)
np.testing.assert_allclose(
workspace.FetchBlob("var"),
workspace.FetchBlob("var_mkl"),
atol=1e-2,
rtol=1e-2)
runtime = workspace.BenchmarkNet(net.Proto().name, 1, 100, True)
print("FC CPU runtime {}, MKL runtime {}.".format(runtime[1], runtime[2]))
if __name__ == '__main__':
unittest.main()
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