def test_merge_mul(self):
z1 = ZLayer.InputLayer(input_shape=(3, 5))
z2 = ZLayer.InputLayer(input_shape=(3, 5))
zlayer = ZLayer.Merge(layers=[z1, z2], mode="mul")
k1 = KLayer.InputLayer(input_shape=(3, 5))
k2 = KLayer.InputLayer(input_shape=(3, 5))
klayer = KLayer.Merge(layers=[k1, k2], mode="mul")
input_data = [np.random.random([2, 3, 5]), np.random.random([2, 3, 5])]
self.compare_layer(klayer, zlayer, input_data)
def test_merge_concat(self):
z1 = ZLayer.InputLayer(input_shape=(2, 5, 11))
z2 = ZLayer.InputLayer(input_shape=(2, 5, 8))
zlayer = ZLayer.Merge(layers=[z1, z2], mode="concat")
k1 = KLayer.InputLayer(input_shape=(2, 5, 11))
k2 = KLayer.InputLayer(input_shape=(2, 5, 8))
klayer = KLayer.Merge(layers=[k1, k2], mode="concat")
input_data = [np.random.random([3, 2, 5, 11]), np.random.random([3, 2, 5, 8])]
self.compare_layer(klayer, zlayer, input_data)
def _to_tensor(self):
axis = 0
if "axis" in self.onnx_attr.keys():
axis = int(self.onnx_attr['axis'])
assert axis != 0, "Currently axis=0 is not supported"
data = [i.zvalue for i in self.model_inputs]
return zlayers.Merge(mode="concat", concat_axis=axis)(data)
def _to_tensor(self):
assert 'axis' in self.onnx_attr, "axis is a required attribute"
axis = int(self.onnx_attr['axis'])
data = [i.zvalue for i in self.model_inputs]
return zlayers.Merge(mode="concat", concat_axis=axis)(data)
