Evgeny Lazarev
970b1301b5
* Removed back phase transformations related to IRv7 * Fixed setting value for the input port using the 'set_value' method * Removed front and middle phase transformations related to IRv7 * Cleanup the rest of the Model Optimizer transformations from IRv7 specific transformations * Final cleanup of the deprecated IR v7 related code * Removed 'blobs_as_input' usage in the Model Optimizer. * Removed function '_fuse_add' from the Model Optimizer since it is not used anymore. * Removed 'keep_in_IR' node attribute for FakeQuantize ops in the MO * Disabled failing gpu_engine.user_context test
78 lines
3.0 KiB
Python
78 lines
3.0 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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from extensions.front.rank_decomposer import RankDecomposer
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from extensions.ops.ReduceOps import ReduceMax, ReduceMean
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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.partial_infer.utils import int64_array
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from mo.front.common.replacement import FrontReplacementPattern
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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 GlobalPoolingToReduce(FrontReplacementPattern):
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op = "Pooling"
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enabled = True
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pool_method_to_reduce_type = {
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'max': ReduceMax,
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'avg': ReduceMean,
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}
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def run_before(self):
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return [RankDecomposer]
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def find_and_replace_pattern(self, graph: Graph):
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global_poolings = graph.get_op_nodes(type='Pooling', global_pool=True)
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if len(global_poolings) == 0:
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return
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layout = graph.graph['layout']
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assert layout != 'NHWC', 'Global pooling transformation depends on layout (NHWC not enabled)'
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for pooling in global_poolings:
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name = pooling.soft_get('name', pooling.id)
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assert pooling.has_valid('pool_method'), 'Global Pooling {} has no `pool_method` attribute'.format(name)
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method = pooling['pool_method']
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assert method in self.pool_method_to_reduce_type, \
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'Unexpected Global Pooling method `{}` for node `{}`'.format(method, name)
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reduce_op_class = self.pool_method_to_reduce_type[method]
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reduce = reduce_op_class(graph, {'name': name + '/reduce', 'keep_dims': True}).create_node()
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pooling.out_port(0).get_connection().set_source(reduce.out_port(0))
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src = pooling.in_port(0).get_connection().get_source()
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pooling.in_port(0).disconnect()
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src.connect(reduce.in_port(0))
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start = Const(graph, {'value': int64_array(2)}).create_node()
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end = Rank(graph, {'name': name + '/input_rank'}).create_node()
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delta = Const(graph, {'value': int64_array(1)}).create_node()
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axis = Range(graph, {'name': name + '/global_pooling_reduce_axis'}).create_node()
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axis.in_port(0).connect(start.out_port(0))
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src.connect(end.in_port(0))
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axis.in_port(1).connect(end.out_port(0))
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axis.in_port(2).connect(delta.out_port(0))
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axis.out_port(0).connect(reduce.in_port(1))
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log.debug('Global {} pooling was converted to reduce: `{}`'.format(method, name))
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