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import unittest
import hypothesis.strategies as st
from hypothesis import given, settings
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 MKLConvTest(hu.HypothesisTestCase):
@given(stride=st.integers(1, 3),
pad=st.integers(0, 3),
kernel=st.integers(3, 5),
size=st.integers(8, 8),
input_channels=st.integers(1, 3),
output_channels=st.integers(1, 3),
batch_size=st.integers(1, 3),
**mu.gcs)
@settings(max_examples=2, deadline=100)
def test_mkl_convolution(self, stride, pad, kernel, size,
input_channels, output_channels,
batch_size, gc, dc):
op = core.CreateOperator(
"Conv",
["X", "w", "b"],
["Y"],
stride=stride,
pad=pad,
kernel=kernel,
)
X = np.random.rand(
batch_size, input_channels, size, size).astype(np.float32) - 0.5
w = np.random.rand(
output_channels, input_channels, kernel, kernel) \
.astype(np.float32) - 0.5
b = np.random.rand(output_channels).astype(np.float32) - 0.5
inputs = [X, w, b]
self.assertDeviceChecks(dc, op, inputs, [0])
if __name__ == "__main__":
import unittest
unittest.main()
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