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
64 lines
2.8 KiB
Python
64 lines
2.8 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 numpy as np
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from extensions.ops.elementwise import Add
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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, rename_nodes
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from mo.middle.replacement import MiddleReplacementPattern
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from mo.ops.reshape import Reshape
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class DecomposeBias(MiddleReplacementPattern):
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enabled = True
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force_clean_up = True
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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', op=lambda op: op in ['Conv', 'ConvTranspose', 'Conv2D',
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'Conv3D', 'Conv2DBackpropInput', 'MatMul',
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'Conv3DBackpropInputV2', 'Convolution',
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'Deconvolution', 'ConvND', 'Conv2D', 'Deconv2D']))],
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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.has_port('in', 2) and not node.in_port(2).disconnected() and not node.has_and_set('shape_input'):
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bias_name = node.name
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new_node_name = node.name + '/WithoutBiases'
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add = Add(graph, dict(name=bias_name)).create_node()
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rename_nodes([(node, new_node_name), (add, bias_name)])
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node.out_port(0).get_connection().set_source(add.out_port(0))
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node.out_port(0).connect(add.in_port(0))
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node.in_port(2).get_connection().set_destination(add.in_port(1))
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bias = add.in_port(1).get_source().node
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if bias.has_valid("type") and bias.type == "Const":
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input_shape = add.in_port(0).data.get_shape()
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if len(input_shape) > 2:
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dims_to_add = len(input_shape) - 2 if graph.graph['layout'] == 'NCHW' else 0
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if dims_to_add > 0:
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reshape = create_op_node_with_second_input(
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graph, Reshape, np.array([input_shape[1]] + [1] * dims_to_add, dtype=np.int64),
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{'name': node.id + '/Dims'})
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add.in_port(1).get_connection().set_destination(reshape.in_port(0))
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reshape.out_port(0).connect(add.in_port(1))
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