text: 
  
  
  
  
  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()
  