132 lines
5.8 KiB
Python
132 lines
5.8 KiB
Python
"""
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Copyright (C) 2018-2020 Intel Corporation
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License.
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"""
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import logging as log
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import numpy as np
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from extensions.ops.elementwise import Add, Mul
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from mo.front.common.layout import get_features_dim
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from mo.front.extractor import split_node_in_port
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from mo.front.tf.graph_utils import create_op_with_const_inputs
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from mo.graph.graph import Graph, Node
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from mo.middle.replacement import MiddleReplacementPattern
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from mo.utils.error import Error
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from mo.utils.utils import refer_to_faq_msg
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class AddMeanScaleValues(MiddleReplacementPattern):
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enabled = True
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run_not_recursively = True
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def run_after(self):
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return []
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def run_before(self):
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from extensions.middle.pass_separator import MiddleStart
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return [MiddleStart]
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@staticmethod
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def insert_pre_processing(graph: Graph, input_node: Node, node_mean_scale_values: np.array,
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preprocessing_name: str):
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assert preprocessing_name in ['scale', 'mean']
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if node_mean_scale_values.get(preprocessing_name) is None:
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return
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user_value = node_mean_scale_values[preprocessing_name]
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value = 1 / user_value if preprocessing_name == 'scale' else user_value * (-1)
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optimize_value = int(preprocessing_name == 'scale')
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op = Mul if preprocessing_name == 'scale' else Add
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if all([x == optimize_value for x in value]):
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return
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assert input_node.has_valid('shape')
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features_dim_idx = get_features_dim(graph.graph['layout'], len(input_node.shape))
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assert value.size == input_node.shape[features_dim_idx] or value.size == 1
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shape = np.ones(len(input_node.shape), dtype=np.int64)
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shape[features_dim_idx] = value.size
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value = value.reshape(shape)
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name = input_node.soft_get('name', input_node.id) + '/' + preprocessing_name
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preprocessing = create_op_with_const_inputs(graph, op=op, port_value_dict={1: value}, op_attrs={'name': name})
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for dst in input_node.out_port(0).get_destinations():
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if dst.node.soft_get('type') != 'ShapeOf':
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dst.get_connection().set_source(preprocessing.out_port(0))
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input_node.out_port(0).connect(preprocessing.in_port(0))
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@staticmethod
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def apply_scale(graph: Graph, input_node: Node, node_mean_scale_values: dict):
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AddMeanScaleValues.insert_pre_processing(graph, input_node, node_mean_scale_values, preprocessing_name='scale')
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@staticmethod
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def apply_mean_value(graph: Graph, input_node: Node, node_mean_scale_values: dict):
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AddMeanScaleValues.insert_pre_processing(graph, input_node, node_mean_scale_values, preprocessing_name='mean')
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def find_and_replace_pattern(self, graph: Graph):
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values = graph.graph['cmd_params'].mean_scale_values
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input_nodes = graph.get_op_nodes(op='Parameter')
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if not isinstance(values, dict):
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if len(values) != len(input_nodes):
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raise Error('Numbers of inputs and mean/scale values do not match. ' + refer_to_faq_msg(61))
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data = np.copy(values)
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values = {}
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for idx, node in enumerate(input_nodes):
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assert node.has_valid('name')
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values.update(
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{
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node['name']: {
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'mean': data[idx][0],
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'scale': data[idx][1]
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}
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}
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)
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for node_name in values:
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node_mean_scale_values = values[node_name]
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node_name, port = split_node_in_port(node_name)
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node_id = None
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try:
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node_id = graph.get_node_id_by_name(node_name)
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except Error as e:
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log.warning('node_name {} is not found in graph'.format(node_name))
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if Node(graph, node_id) not in input_nodes:
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# if the user cutted-off input of the network then input node name specified in the --scale_values
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# or --mean_values doesn't correspond to a real input node generated by Model Optimizer. But
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# the information about initial input node name is stored in Placeholder's attribute 'initial_node_name'
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new_node_id = None
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for placeholder in input_nodes:
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try:
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placeholder_port = int(placeholder.id.split("_")[-1])
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except Exception as ex:
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log.debug('Can not get the port number from the node {}'.format(placeholder.id))
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log.debug('Port will be defined as None')
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port = None
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if placeholder.has('initial_node_name') and placeholder.initial_node_name == node_name and (
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port is None or placeholder_port == port):
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new_node_id = placeholder.id
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break
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if new_node_id is None:
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raise Error('Input with name {} wasn\'t found!'.format(node_name) +
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refer_to_faq_msg(83))
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node_id = new_node_id
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input_node = Node(graph, node_id)
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AddMeanScaleValues.apply_scale(graph, input_node, node_mean_scale_values)
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AddMeanScaleValues.apply_mean_value(graph, input_node, node_mean_scale_values)
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