def forward(self, bottom, top):
"""Computes the forward pass."""
features = bottom[0].data()
output = top[0].init_data(features.shape, features.dtype)
scale = self._scale.init_data(features.shape, features.dtype)
if self._size > features.shape[-1]:
raise base.DecafError('Incorrect size: should be smaller than '
'the number of input channels.')
wrapper.lrn_forward(features, output, scale,
self._size, self._k, self._alpha, self._beta)
def testLocalResponseNormalizationForward(self):
np.random.seed(1701)
dtypes = [np.float32, np.float64]
for dtype in dtypes:
features = np.random.rand(5, 10).astype(dtype)
output = np.random.rand(5, 10).astype(dtype)
scale = np.random.rand(5, 10).astype(dtype)
# odd size, k = 1
wrapper.lrn_forward(features, output, scale, 5, 1., 1.5, 0.75)
output_ref, scale_ref = self.reference_forward_implementation(
features, 5, 1., 1.5, 0.75)
np.testing.assert_array_almost_equal(output, output_ref)
np.testing.assert_array_almost_equal(scale, scale_ref)
# odd size, k = 2
wrapper.lrn_forward(features, output, scale, 5, 2., 1.5, 0.75)
output_ref, scale_ref = self.reference_forward_implementation(
features, 5, 2., 1.5, 0.75)
np.testing.assert_array_almost_equal(output, output_ref)
np.testing.assert_array_almost_equal(scale, scale_ref)
# even size
wrapper.lrn_forward(features, output, scale, 6, 1., 1.5, 0.75)
output_ref, scale_ref = self.reference_forward_implementation(
features, 6, 1., 1.5, 0.75)
np.testing.assert_array_almost_equal(output, output_ref)
np.testing.assert_array_almost_equal(scale, scale_ref)
def testLocalResponseNormalizationForward(self):
np.random.seed(1701)
dtypes = [np.float32, np.float64]
for dtype in dtypes:
features = np.random.rand(5, 10).astype(dtype)
output = np.random.rand(5, 10).astype(dtype)
scale = np.random.rand(5, 10).astype(dtype)
# odd size, k = 1
wrapper.lrn_forward(features, output, scale, 5, 1., 1.5, 0.75)
output_ref, scale_ref = self.reference_forward_implementation(
features, 5, 1., 1.5, 0.75)
np.testing.assert_array_almost_equal(output, output_ref)
np.testing.assert_array_almost_equal(scale, scale_ref)
# odd size, k = 2
wrapper.lrn_forward(features, output, scale, 5, 2., 1.5, 0.75)
output_ref, scale_ref = self.reference_forward_implementation(
features, 5, 2., 1.5, 0.75)
np.testing.assert_array_almost_equal(output, output_ref)
np.testing.assert_array_almost_equal(scale, scale_ref)
# even size
wrapper.lrn_forward(features, output, scale, 6, 1., 1.5, 0.75)
output_ref, scale_ref = self.reference_forward_implementation(
features, 6, 1., 1.5, 0.75)
np.testing.assert_array_almost_equal(output, output_ref)
np.testing.assert_array_almost_equal(scale, scale_ref)
