def test_bn_ext_ml_three_blobs(self):
mean_blob = np.array([1., 2.])
variance_blob = np.array([3., 4.])
scale_blob = np.array([
5.,
])
blobs = [mean_blob, variance_blob, scale_blob]
res = batch_norm_ext(FakeBNProtoLayer(10), FakeModelLayer(blobs))
exp_res = {
'type':
'BatchNormalization',
'eps':
10,
'infer':
copy_shape_infer,
'mean':
mean_blob * 0.2,
'variance':
variance_blob * 0.2,
'embedded_inputs': [(1, 'gamma', {
'bin': 'gamma'
}), (2, 'beta', {
'bin': 'beta'
}), (3, 'mean', {
'bin': 'biases'
}), (4, 'variance', {
'bin': 'weights'
})]
}
for i in exp_res:
if i in ('mean', 'variance'):
np.testing.assert_array_equal(res[i], exp_res[i])
else:
self.assertEqual(res[i], exp_res[i])
def test_bn_ext_no_ml(self):
res = batch_norm_ext(FakeBNProtoLayer(10), None)
exp_res = {
'op': 'BatchNormalization',
'type': 'BatchNormalization',
'eps': 10,
'infer': copy_shape_infer
}
self.assertEqual(res, exp_res)