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 MKLConvTest(hu.HypothesisTestCase):
@given(stride=st.integers(1, 3),
pad=st.integers(0, 3),
kernel=st.integers(3, 5),
size=st.integers(8, 20),
input_channels=st.integers(1, 16),
output_channels=st.integers(1, 16),
batch_size=st.integers(1, 3),
use_bias=st.booleans(),
group=st.integers(1, 8),
**mu.gcs)
def test_mkl_convolution(self, stride, pad, kernel, size,
input_channels, output_channels,
batch_size, use_bias, group, gc, dc):
op = core.CreateOperator(
"Conv",
["X", "w", "b"] if use_bias else ["X", "w"],
["Y"],
stride=stride,
pad=pad,
kernel=kernel,
group=group
)
X = np.random.rand(
batch_size, input_channels * group, size, size).astype(np.float32) - 0.5
w = np.random.rand(
output_channels * group, input_channels, kernel, kernel) \
.astype(np.float32) - 0.5
b = np.random.rand(output_channels * group).astype(np.float32) - 0.5
inputs = [X, w, b] if use_bias else [X, w]
self.assertDeviceChecks(dc, op, inputs, [0])
if __name__ == "__main__":
import unittest
unittest.main()