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)