|
|
|
|
|
|
|
|
|
|
| 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() |
|
|
