caffe2/python/ideep/LRN_op_test.py

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.ideep_test_util as mu


@unittest.skipIf(not workspace.C.use_mkldnn, "No MKLDNN support.")
class LRNTest(hu.HypothesisTestCase):
    @given(input_channels=st.integers(1, 3),
           batch_size=st.integers(1, 3),
           im_size=st.integers(1, 10),
           order=st.sampled_from(["NCHW"]),
           **mu.gcs)
    @settings(deadline=10000)
    def test_LRN(self, input_channels,
                            batch_size, im_size, order,
                             gc, dc):
        op = core.CreateOperator(
            "LRN",
            ["X"],
            ["Y", "Y_scale"],
            size=5,
            alpha=0.001,
            beta=0.75,
            bias=2.0,
            order=order,
        )
        X = np.random.rand(
            batch_size, input_channels, im_size, im_size).astype(np.float32)

        self.assertDeviceChecks(dc, op, [X], [0])

        self.assertGradientChecks(gc, op, [X], 0, [0])


if __name__ == "__main__":
    unittest.main()