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_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_inner_product_ext(self):
params = {'num_output': 10, 'bias_term': True}
mean_blob = np.array([1., 2.])
variance_blob = np.array([3., 4.])
blobs = [mean_blob, variance_blob]
res = inner_product_ext(FakeProtoLayer(FakeMultiParam(params)),
FakeModelLayer(blobs))
exp_res = {
'type':
'FullyConnected',
'out-size':
10,
'infer':
caffe_inner_product,
'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_bias(self, embed_input_mock):
embed_input_mock.return_value = {}
params = {'axis': 1}
add_node = FakeNode(FakeBiasProtoLayer(FakeMultiParam(params)),
FakeModelLayer([1, 2, 3, 4, 5]))
BiasToAdd.extract(add_node)
exp_res = {'type': "Add", 'axis': 1}
for key in exp_res.keys():
self.assertEqual(add_node[key], exp_res[key])
def test_weights_biases_layer_bias(self, embed_input_mock):
weights_biases(True, FakeModelLayer([[1, 2], [3, 4]]))
calls = [call({}, 1, 'weights', [1, 2]), call({}, 2, 'biases', [3, 4])]
embed_input_mock.assert_has_calls(calls)
def test_weights_biases_layer_no_bias(self, embed_input_mock):
weights_biases(False, FakeModelLayer([
[1, 2],
]))
calls = [call({}, 1, 'weights', [1, 2])]
embed_input_mock.assert_has_calls(calls)
def test_bn_ext_ml_one_blob(self):
self.assertRaises(AssertionError, batch_norm_ext, FakeBNProtoLayer(10),
FakeModelLayer([np.array([1, 2])]))