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"""
Copyright (c) 2018 Intel Corporation
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
"""
import networkx as nx
import numpy as np
from mo.front.common.replacement import FrontReplacementOp
from mo.ops.const import Const
from mo.ops.lin_op import Mul, Add
class ImageScaler(FrontReplacementOp):
op = "ImageScaler"
enabled = True
def replace_sub_graph(self, graph: nx.MultiDiGraph, match: dict):
# This replacer replace ImageScalar operation to Mul->Add sequence
# Also it check that weights and biases are good
op = match['op']
# Check that weights and biases are not useless
has_bias, has_weights = True, True
if all([x == 1 for x in np.nditer(op.scale)]):
has_weights = False
if all([x == 0 for x in np.nditer(op.bias)]):
has_bias = False
# Get all outputs for op node
out_nodes = [node for node in op.out_nodes().values()]
assert len(op.in_nodes()) == 1
last_node = op.in_node()
# Create Mul & Add nodes
if has_weights:
mul_weights = Const(graph, dict(value=op.scale, shape=op.scale.shape))
mul_op = Mul(graph, dict(name=op.id + '/mul_'))
last_node = mul_op.create_node(inputs=[last_node, mul_weights.create_node()])
if has_bias:
add_bias = Const(graph, dict(value=op.bias, shape=op.bias.shape))
add_op = Add(graph, dict(name=op.id + '/add_'))
last_node = add_op.create_node(inputs=[last_node, add_bias.create_node()])
# Move edges from ImageScaler to last_node (Mul or Add)
for out_node in out_nodes:
edge_attrs = graph.get_edge_data(op.id, out_node.id)[0]
graph.remove_edge(op.id, out_node.id)
graph.add_edges_from([(last_node.id, out_node.id, edge_attrs)])
# Disconnect ImageScalar node
graph.remove_edge(op.in_node().id, op.id)
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