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import unittest
import hypothesis.strategies as st
from hypothesis import given
import numpy as np
from caffe2.python import core, workspace
import caffe2.python.hypothesis_test_util as hu
import caffe2.python.mkl_test_util as mu
@unittest.skipIf(not workspace.C.has_mkldnn,
"Skipping as we do not have mkldnn.")
class MKLSpatialBNTest(hu.HypothesisTestCase):
@given(size=st.integers(7, 10),
input_channels=st.integers(1, 10),
batch_size=st.integers(1, 3),
seed=st.integers(0, 65535),
#order=st.sampled_from(["NCHW", "NHWC"]),
order=st.sampled_from(["NCHW"]),
epsilon=st.floats(1e-5, 1e-2),
**mu.gcs)
def test_spatialbn_test_mode(self, size, input_channels,
batch_size, seed, order, epsilon, gc, dc):
np.random.seed(seed)
scale = np.random.rand(input_channels).astype(np.float32) + 0.5
bias = np.random.rand(input_channels).astype(np.float32) - 0.5
mean = np.random.randn(input_channels).astype(np.float32)
var = np.random.rand(input_channels).astype(np.float32) + 0.5
X = np.random.rand(
batch_size, input_channels, size, size).astype(np.float32) - 0.5
op = core.CreateOperator(
"SpatialBN",
["X", "scale", "bias", "mean", "var"],
["Y"],
order=order,
is_test=True,
epsilon=epsilon,
)
self.assertDeviceChecks(dc, op, [X, scale, bias, mean, var], [0])
@given(size=st.integers(7, 10),
input_channels=st.integers(1, 10),
batch_size=st.integers(1, 3),
seed=st.integers(0, 65535),
#order=st.sampled_from(["NCHW", "NHWC"]),
order=st.sampled_from(["NCHW"]),
epsilon=st.floats(1e-5, 1e-2),
**mu.gcs)
def test_spatialbn_train_mode(
self, size, input_channels, batch_size, seed, order, epsilon,
gc, dc):
op = core.CreateOperator(
"SpatialBN",
["X", "scale", "bias", "running_mean", "running_var"],
["Y", "running_mean", "running_var", "saved_mean", "saved_var"],
order=order,
is_test=False,
epsilon=epsilon,
)
np.random.seed(seed)
scale = np.random.rand(input_channels).astype(np.float32) + 0.5
bias = np.random.rand(input_channels).astype(np.float32) - 0.5
mean = np.random.randn(input_channels).astype(np.float32)
var = np.random.rand(input_channels).astype(np.float32) + 0.5
X = np.random.rand(
batch_size, input_channels, size, size).astype(np.float32) - 0.5
# Note: it seems that the running mean and var do not pass the device
# test, suggesting that the semantics are a bit different. Only
# checking the output and saved mean and var at this stage.
self.assertDeviceChecks(dc, op, [X, scale, bias, mean, var],
[0, 3, 4])
if __name__ == "__main__":
import unittest
unittest.main()
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