126 lines
5.1 KiB
Python
126 lines
5.1 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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import logging as log
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from extensions.back.ElementwiseOpsToEltwiseOps import SimpleEltwiseToEltwiseOp
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from extensions.back.ReshapeMutation import ReshapeMutation
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from mo.back.replacement import BackReplacementPattern
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from mo.front.common.partial_infer.utils import int64_array
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from mo.front.tf.graph_utils import create_op_node_with_second_input
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from mo.graph.graph import Graph
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from mo.ops.reshape import Reshape
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# Temporary nGraph workaround. TODO: REMOVE
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class ScalarNormalize(BackReplacementPattern):
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enabled = False
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graph_condition = [lambda graph: graph.graph['cmd_params'].generate_experimental_IR_V10]
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force_clean_up = True
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def run_before(self):
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return [ReshapeMutation]
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@staticmethod
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def pattern():
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return dict(
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nodes=[
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('op', dict(kind='op', type='Const'))],
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edges=[]
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)
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@staticmethod
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def replace_pattern(graph: Graph, match: dict):
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node = match['op']
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if node.value.ndim == 0:
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reshape = create_op_node_with_second_input(graph, Reshape, int64_array([1]),
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{'name': node.id + '/Dims'})
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node.out_port(0).get_connection().set_source(reshape.out_port(0))
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node.out_port(0).connect(reshape.in_port(0))
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reshape.infer(reshape)
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class ScalarNormalizeForSpecificOps(BackReplacementPattern):
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"""
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Transformation performs safe replacement of the 0D constants with 1D for a specific operations. This transformation
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allows to avoid problems with the fact that not all layers correctly handle 0D tensors during the constant folding.
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"""
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enabled = True
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graph_condition = [lambda graph: not graph.graph['cmd_params'].generate_experimental_IR_V10]
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force_clean_up = True
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def run_before(self):
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return [ReshapeMutation]
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def run_after(self):
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return [SimpleEltwiseToEltwiseOp]
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def find_and_replace_pattern(self, graph: Graph):
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graph.strict_mode = False
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# key is the type of the operation. The value is list of ports to convert from 0D to 1D
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rules = {'Broadcast': [0],
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'Unsqueeze': [1],
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'Squeeze': [1],
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'Eltwise': [1],
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'Range': [0, 1, 2],
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'FakeQuantize': [1, 2, 3, 4]
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}
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for node in graph.get_op_nodes():
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if node.has_and_set('type') and node.type in rules:
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for port in rules[node.type]:
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if port in node.in_ports() and not node.in_port(port).disconnected():
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src_node = node.in_port(port).get_connection().get_source().node
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if src_node is not None and src_node.has_and_set('type') and src_node.type == 'Const' and \
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src_node.value.ndim == 0:
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log.info('Converting constant node "{}" from 0D to 1D'.format(src_node.soft_get('name')))
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reshape = create_op_node_with_second_input(graph, Reshape, int64_array([1]),
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{'name': src_node.id + '/Dims'})
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src_node.out_port(0).get_connection().set_source(reshape.out_port(0))
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src_node.out_port(0).connect(reshape.in_port(0))
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reshape.infer(reshape)
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graph.strict_mode = True
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class RangeInputNormalize(BackReplacementPattern):
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enabled = True
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graph_condition = [lambda graph: not graph.graph['cmd_params'].generate_experimental_IR_V10]
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force_clean_up = True
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def run_after(self):
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return [ScalarNormalizeForSpecificOps]
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def find_and_replace_pattern(self, graph: Graph):
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graph.strict_mode = False
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rules = {
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'Range': [0, 1, 2],
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}
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for node in graph.get_op_nodes():
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if node.soft_get('type') in rules:
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for port in rules[node.type]:
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if port in node.in_ports() and not node.in_port(port).disconnected():
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src_node = node.in_port(port).get_connection().get_source().node
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reshape = create_op_node_with_second_input(
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graph, Reshape, int64_array([1]),
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{'name': src_node.id + '/1D', 'override_output_shape': True})
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src_node.out_port(0).get_connection().set_source(reshape.out_port(0))
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src_node.out_port(0).connect(reshape.in_port(0))
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graph.strict_mode = True
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