125a462400
* Initial version of ReduceL1, ReduceL2 and ReduceLp enabling in the MO * Added operations ReduceL1 and ReduceL2 to nGraph * Removed ReduceLp. Added ReduceL1 and ReduceL2 * Separated specification of ReduceLp into ReduceL1 and ReduceL2 * Updated ReduceL1 and ReduceL2 specification * Fixed ReduceL1 and ReduceL2 type prop tests * Implemented nGraph transformation to decompose ReduceL1 and ReduceL2. Disabled them for CPU and GPU plugins * Updated supported framework layers * Added unit tests for ReduceL1 and ReduceL2 reference implementation * Fixed ReduceXXX operations reference implementation by adding support for a new parameter 'keep_dims' * Fixed constant folding for v0::Any * Added ReduceL1 and ReduceL2 to Python API * Implemented ReduceL1 and ReduceL2 decomposition tests and fixed ReduceL2 decomposition * Added specific creator for ReduceXXX operations instead of NodeBuilders * Fixed conversion ReduceXXX to CNNLayer * Fixed parser for ReduceLogicalXXX operations
73 lines
3.1 KiB
Python
73 lines
3.1 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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from extensions.ops.ReduceOps import reduce_map
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from extensions.ops.range import Range
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from extensions.ops.rank import Rank
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from mo.front.common.replacement import FrontReplacementSubgraph
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from mo.front.subgraph_matcher import SubgraphMatch
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from mo.graph.graph import Graph
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from mo.ops.const import Const
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class ReduceAxisNormalizer(FrontReplacementSubgraph):
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"""
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Reduce operation requires information about axis, that is represented in original frameworks differently:
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- by layer parameter
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- by 1-port input value
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ReduceAxisNormalizer reforms Reduce operations to store axis info in 1-port input.
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"""
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enabled = True
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force_shape_inference = True
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def pattern(self):
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return dict(
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nodes=[
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('reduce', dict(kind='op', op=lambda op: op in reduce_map))
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],
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edges=[]
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)
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def replace_sub_graph(self, graph: Graph, match: [dict, SubgraphMatch]):
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node = match['reduce']
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connected_in_ports = [port for port in node.in_ports().values() if not port.disconnected()]
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if len(connected_in_ports) == 1:
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node_name = node.soft_get('name', node.id)
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# if the 'axis' is None then we still add a second input to the layer with a 1D array with 1 element equal
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# to None. The infer function handles this case because the input shape is known at this stage only
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if node.has('axis'):
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const = Const(graph, {'name': node_name + '/axis', 'value': node.axis}).create_node()
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node.add_input_port(1, skip_if_exist=True)
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const.out_port(0).connect(node.in_port(1))
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del graph.node[node.id]['axis']
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else:
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# The default (if there is no 'axis') is to reduce over all the dimensions of the input tensor.
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begin_of_range = Const(graph, dict(name=node_name + '/range_begin_', value=0)).create_node()
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step = Const(graph, dict(name=node_name + '/range_step_', value=1)).create_node()
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end_of_range = Rank(graph, dict(name=node_name + '/range_end_')).create_node()
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axes = Range(graph, dict(name=node_name + '/axes_')).create_node()
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begin_of_range.out_port(0).connect(axes.in_port(0))
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end_of_range.out_port(0).connect(axes.in_port(1))
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step.out_port(0).connect(axes.in_port(2))
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node.add_input_port(1, skip_if_exist=True)
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axes.out_port(0).connect(node.in_port(1))
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node.in_port(0).get_connection().get_source().connect(end_of_range.in_port(0))
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