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
290 lines
10 KiB
Python
290 lines
10 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.back.ReshapeMutation import ReshapeMutation
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from extensions.back.ReverseInputChannels import ApplyReverseChannels
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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.const import Const
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from mo.ops.reshape import Reshape
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from mo.ops.strided_slice import StridedSlice
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class ConvolutionNormalizer(BackReplacementPattern):
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enabled = True
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def pattern(self):
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return dict(
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nodes=[
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('node', dict(kind='op', type='Convolution'))
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],
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edges=[]
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)
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def replace_pattern(self, graph: Graph, match: dict):
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node = match['node']
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if node.has_valid('kernel_spatial'):
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del node['kernel_spatial']
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class V7ConvolutionWithGroupsResolver(BackReplacementPattern):
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"""
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Normalizes grouped convolution weights shape to fit special weights format [G*O I X Y]
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"""
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enabled = False
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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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('node', dict(type='Convolution', group=lambda g: g is not None and g != 1))
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],
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edges=[]
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)
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def replace_pattern(self, graph: Graph, match: dict):
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node = match['node']
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group = node.group
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assert group > 1
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weights_shape = node.in_port(1).data.get_shape()
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assert weights_shape is not None
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assert weights_shape[0] % group == 0
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if weights_shape[0] == node.output:
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# weights are already is in [G*O I X Y] format
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return
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new_shape = int64_array([node.output, -1, *weights_shape[2:]])
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reshape = create_op_node_with_second_input(graph, Reshape, int64_array(new_shape),
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{'override_output_shape': True})
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node.in_port(1).get_connection().insert_node(reshape)
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class V10ConvolutionWithGroupsResolver(BackReplacementPattern):
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"""
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Normalizes grouped convolution weights shape to fit special weights format
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V10 IR: [G O I X Y]
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"""
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enabled = False
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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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('node', dict(type='Convolution', group=lambda g: g is not None and g != 1))
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],
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edges=[]
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)
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def replace_pattern(self, graph: Graph, match: dict):
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node = match['node']
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group = node.group
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assert group > 1
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weights_shape = node.in_port(1).data.get_shape()
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assert weights_shape is not None
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assert weights_shape[0] % group == 0
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I = node.in_port(0).data.get_shape()[1]
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new_shape = int64_array([group, node.output / group, I / group, *weights_shape[2:]])
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assert np.prod(weights_shape) == np.prod(new_shape), \
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'Initial weights shape {}, grouped weights shape {}'.format(weights_shape, new_shape)
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del node['group']
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node['type'] = 'GroupConvolution'
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reshape = create_op_node_with_second_input(graph, Reshape, int64_array(new_shape),
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{'override_output_shape': True})
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node.in_port(1).get_connection().insert_node(reshape)
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class ConvolutionWithGroupsResolver(BackReplacementPattern):
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"""
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Normalizes grouped convolution weights shape to fit special weights format
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V10 IR: [G O I X Y]
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lower IR versions: [G*O I X Y]
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"""
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enabled = True
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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 [ApplyReverseChannels]
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def find_and_replace_pattern(self, graph: Graph):
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V7ConvolutionWithGroupsResolver().find_and_replace_pattern(graph)
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PullReshapeThroughFQ().find_and_replace_pattern(graph)
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V10ConvolutionWithGroupsResolver().find_and_replace_pattern(graph)
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class PullReshapeThroughFQ(BackReplacementPattern):
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"""
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Before:
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... -> FQ -> Reshape -> Convolution -> ...
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After:
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... -> Reshape -> FQ (with aligned limits) -> Convolution -> ...
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"""
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enabled = False
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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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('FQ', dict(type='FakeQuantize')),
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('FQed', dict()),
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('reshape', dict(type='Reshape')),
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('reshaped', dict()),
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('node', dict(type=lambda t: t in ['Convolution', 'GroupConvolution'])),
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],
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edges=[
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('FQ', 'FQed'),
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('FQed', 'reshape', {'in': 0}),
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('reshape', 'reshaped'),
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('reshaped', 'node', {'in': 1}),
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]
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)
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def replace_pattern(self, graph: Graph, match: dict):
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FQ = match['FQ']
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reshape = match['reshape']
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conv = match['node']
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rank_reshape = reshape.in_port(0).data.get_shape().size != reshape.out_port(0).data.get_shape().size
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if not all([np.prod(FQ.in_port(i).data.get_shape()) == 1 for i in range(1, 5)]):
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# FakeQuantize has limits with multiple values, that should be reshaped too
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# Pulling Reshape through such FQ is a complex procedure because of broadcasting rules
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return
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new_rank = reshape.out_port(0).data.get_shape().size
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reshape.in_port(0).disconnect()
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reshape.out_port(0).disconnect()
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FQ.out_port(0).connect(conv.in_port(1))
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FQ.in_port(0).get_connection().insert_node(reshape)
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reshape['need_shape_inference'] = True
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reshape['override_output_shape'] = True
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FQ['need_shape_inference'] = True
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FQ['override_output_shape'] = True
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if rank_reshape:
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# force rank of limit inputs to match 0-input rank
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# reshaping to lower range needs it the most due to FQ inner broadcast semantics
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for i in range(1, 5):
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reshape = create_op_node_with_second_input(graph, Reshape, int64_array([1] * new_rank),
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{'override_output_shape': True})
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FQ.in_port(i).get_connection().insert_node(reshape)
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class DeconvolutionNormalizer(BackReplacementPattern):
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enabled = True
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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 [ApplyReverseChannels]
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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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('node', dict(type='Deconvolution'))
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],
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edges=[]
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)
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def replace_pattern(self, graph: Graph, match: dict):
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node = match['node']
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if 2 in node.in_ports() and not node.in_port(2).disconnected():
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# Third input represents output shape. Cutting its value according to scheme:
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# [N, C, spatial_dim_0, ..., spatial_dim_n] -> [spatial_dim_0, ..., spatial_dim_n]
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in_rank = node.in_port(0).data.get_shape().size
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shape_src = node.in_port(2).get_source()
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node.in_port(2).disconnect()
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begin = Const(graph, {'value': np.array([2], dtype=np.int32)}).create_node()
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end = Const(graph, {'value': np.array([in_rank], dtype=np.int32)}).create_node()
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stride = Const(graph, {'value': np.array([1], dtype=np.int32)}).create_node()
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ss_0 = StridedSlice(graph, {'name': node.name + '/ss_0_port',
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'begin_mask': np.array([1], dtype=np.int32),
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'end_mask': np.array([0], dtype=np.int32),
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'new_axis_mask': np.array([0], dtype=np.int32),
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'shrink_axis_mask': np.array([0], dtype=np.int32),
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'ellipsis_mask': np.array([0], dtype=np.int32)}).create_node()
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shape_src.connect(ss_0.in_port(0))
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begin.out_port(0).connect(ss_0.in_port(1))
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end.out_port(0).connect(ss_0.in_port(2))
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stride.out_port(0).connect(ss_0.in_port(3))
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ss_0.out_port(0).connect(node.in_port(2))
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# Specification: *padding amount* is deduced from relation of input and output spatial shapes
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del node['pad']
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elif node.has_valid('original_output_spatial_shape'):
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# node had fixed output spatial shape set in original framework, so we restore it here
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const = Const(graph, {'value': int64_array(node.original_output_spatial_shape)}).create_node()
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node.add_input_port(2, skip_if_exist=True)
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const.out_port(0).connect(node.in_port(2))
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# Specification: *padding amount* is deduced from relation of input and output spatial shapes
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del node['pad']
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group = node.soft_get('group', 1)
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if group != 1:
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assert group > 1
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weights_shape = node.in_port(1).data.get_shape()
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assert weights_shape is not None
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I = node.in_port(0).data.get_shape()[1]
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assert I % group == 0
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assert node.output % group == 0
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new_shape = int64_array([group, I / group, node.output / group, *weights_shape[2:]])
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assert np.prod(weights_shape) == np.prod(new_shape), \
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'Initial weights shape {}, grouped weights shape {}'.format(weights_shape, new_shape)
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reshape = create_op_node_with_second_input(graph, Reshape, int64_array(new_shape),
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{'override_output_shape': True},
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node.in_port(1).get_source().node)
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node.in_port(1).get_connection().set_source(reshape.out_port(0))
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node['type'] = 'GroupConvolutionBackpropData'
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else:
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node['type'] = 'ConvolutionBackpropData'
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