""" 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.middle.pattern_match import apply_pattern def move_scaleshift_to_preprocess_action(graph, match): mean_values = {} input_op = match['input_op'] scale_shift = match['scale_shift'] weights = np.squeeze(match['weights'].value) biases = np.squeeze(match['biases'].value) if any([x != 1 for x in weights]): return # Keep biases (mean values) for current input as graph attr and remove ScaleShift layer # Input->data->ScaleShift->scsh_data => Input->scsh_data graph.remove_edge(input_op.id, input_op.out_node().id) graph.add_edge(input_op.id, scale_shift.out_node().id, out=0) graph.remove_edge(scale_shift.id, scale_shift.out_node().id) # If bias contains zeros we just remove it if all([x == 0 for x in biases]): return # In pre-process section, mean_values are subtracted biases *= -1 mean_values.update({input_op.name: np.array(biases)}) # Add graph attribute 'mean_values' that stores mean_values per input if exists if graph.graph.get('mean_values', None): graph.graph['mean_values'].update(mean_values) else: graph.graph['mean_values'] = mean_values def move_scaleshift_to_preprocess(graph: nx.MultiDiGraph): """ This function finds scaleshift layer after input layer and if it has weights with ones, it deletes scaleshift layer and creates graph dict attribute : {'input':np.array(...), 'input2': ... } """ apply_pattern( graph, nodes=[ ('weights', dict(kind='data')), ('biases', dict(kind='data')), ('input_output', dict(kind='data')), ('scsh_output', dict(kind='data')), ('input_op', dict(kind='op', type='Input')), ('scale_shift', dict(kind='op', type='ScaleShift')), ], edges=[ ('input_op', 'input_output'), ('scale_shift', 'scsh_output'), ('input_output', 'scale_shift', {'in': 0}), ('weights', 'scale_shift', {'in': 1}), ('biases', 'scale_shift', {'in': 2}), ], action=move_scaleshift_to_preprocess_action )