def test_scale_ext(self):
mean_blob = np.array([1., 2.])
variance_blob = np.array([3., 4.])
blobs = [mean_blob, variance_blob]
params = {'type': 'Scale', 'axis': 0, 'bias_term': True}
res = scale_ext(FakeProtoLayer(FakeMultiParam(params)),
FakeModelLayer(blobs))
exp_res = {
'op':
'ScaleShift',
'type':
'ScaleShift',
'axis':
0,
'infer':
copy_shape_infer,
'weights':
mean_blob,
'biases':
variance_blob,
'embedded_inputs': [(1, 'weights', {
'bin': 'weights'
}), (2, 'biases', {
'bin': 'biases'
})]
}
for i in exp_res:
if i in ('weights', 'biases'):
np.testing.assert_array_equal(res[i], exp_res[i])
else:
self.assertEqual(res[i], exp_res[i])
def test_create_default_weights(self):
"""
There are situations when scale layer doesn't have weights and biases. This test checks that if they are not
available in the caffemodel file then default values [1] and [0] are generated.
"""
scale_blob = np.array([1])
bias_blob = np.array([0])
params = {'type': 'Scale', 'axis': 0, 'bias_term': True}
res = scale_ext(FakeProtoLayer(FakeMultiParam(params)), None)
exp_res = {
'op':
'ScaleShift',
'type':
'ScaleShift',
'axis':
0,
'infer':
copy_shape_infer,
'weights':
scale_blob,
'biases':
bias_blob,
'embedded_inputs': [(1, 'weights', {
'bin': 'weights'
}), (2, 'biases', {
'bin': 'biases'
})]
}
self.assertDictEqual(exp_res, res)
def test_scale_2inputs_ext(self):
params = {'type': 'Scale', 'axis': 0, 'bias_term': False}
res = scale_ext(FakeProtoLayer(FakeMultiParam(params), True), None)
exp_res = {
'op': 'ScaleShift',
'type': 'ScaleShift',
'axis': 0,
'infer': copy_shape_infer,
}
for i in exp_res:
self.assertEqual(res[i], exp_res[i])
