def replace_op(self, graph: Graph, node: Node):
attrs = {'name': node.id + "/ScaleShift_"}
param = graph.node[node.id]['pb'].bn_param
pb_model = graph.node[node.id]['model_pb']
blobs = pb_model.blobs
if len(blobs) != 4:
raise Error("Incorrect number of blobs in BN layer {}".format(
node.id))
mean = np.array(blobs[0].data)
var = np.array(blobs[1].data)
betta = np.array(blobs[2].data)
gamma = np.array(blobs[3].data)
gamma = gamma + np.repeat(param.eps, gamma.shape)
scale = 1.0 / np.sqrt(gamma) * mean
shift = var - betta * scale
ss = ScaleShiftOp(graph, attrs)
scale_shift = ss.create_node([node.in_node(0)])
input_as_const(scale_shift, attrs, 1, 'weights', scale)
input_as_const(scale_shift, attrs, 2, 'biases', shift)
return [scale_shift.id]
def replace_op(self, graph: Graph, node: Node):
in_node_0 = node.in_node(0)
in_node_1 = node.in_node(1)
in_node_2 = node.in_node(2)
ss = ScaleShiftOp(graph, {'name': node.id + "/ScaleShift_", 'axis': 0})
scale_shift = ss.create_node(inputs=[in_node_1, in_node_0])
el = Add(graph, {'name': node.id + "/Add_"})
el_node = el.create_node(inputs=[scale_shift, in_node_2])
return [el_node.id]