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Refactored legacy code for mean_scale_values transformations (#1936)

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* Remove move_to_preproc. Not actual.

* Updated documentation
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iliya mironov 2020-09-07 10:41:47 +03:00 committed by GitHub
parent b683b5501d
commit 9939253fed
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6 changed files with 2 additions and 276 deletions
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1
docs/MO_DG/prepare_model/convert_model/Converting_Model_General.md
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@ -109,7 +109,6 @@ Framework-agnostic parameters:
--disable_gfusing Turn off fusing of grouped convolutions
--enable_concat_optimization
Turn on Concat optimization.
--move_to_preprocess Move mean values to IR preprocess section
--extensions EXTENSIONS
Directory or a comma separated list of directories
with extensions. To disable all extensions including

1
model-optimizer/automation/package_BOM.txt
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@ -872,7 +872,6 @@ mo/middle/passes/fusing/mark_unfused_nodes.py
mo/middle/passes/fusing/resnet_optimization.py
mo/middle/passes/infer.py
mo/middle/passes/leaky_relu.py
mo/middle/passes/mean_scale_values.py
mo/middle/passes/tensor_names.py
mo/middle/pattern_match.py
mo/middle/replacement.py

19
model-optimizer/extensions/middle/preprocessing.py
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@ -16,36 +16,19 @@
from extensions.middle.LeakyReluPattern import LeakyReLU
from extensions.middle.pass_separator import PostMiddleStart
from mo.graph.graph import Graph
from mo.middle.passes.mean_scale_values import move_scaleshift_to_preprocess
from mo.middle.replacement import MiddleReplacementPattern
from mo.utils.error import Error
from mo.utils.find_inputs import find_inputs
from mo.utils.utils import refer_to_faq_msg
class Preprocessing(MiddleReplacementPattern):
enabled = True
force_clean_up = True
def run_after(self):
return [LeakyReLU]
def run_before(self):
return [PostMiddleStart]
def find_and_replace_pattern(self, graph: Graph):
argv = graph.graph['cmd_params']
if argv.move_to_preprocess:
move_scaleshift_to_preprocess(graph)
class CaffeMeanFileProcessing(MiddleReplacementPattern):
enabled = True
force_clean_up = True
graph_condition = [lambda graph: graph.graph['fw'] == 'caffe']
def run_after(self):
return [Preprocessing]
return [LeakyReLU]
def run_before(self):
return [PostMiddleStart]

81
model-optimizer/mo/middle/passes/mean_scale_values.py
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@ -1,81 +0,0 @@
"""
Copyright (C) 2018-2020 Intel Corporation
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
"""
import numpy as np
from mo.graph.graph import Graph
from mo.middle.pattern_match import apply_pattern
def move_scaleshift_to_preprocess_action(graph, match):
mean_values = {}
input_op = match['input_op']
scale_shift = match['scale_shift']
weights = np.squeeze(match['weights'].value)
biases = np.squeeze(match['biases'].value)
if graph.graph['cmd_params'].reverse_input_channels:
biases = np.flip(biases)
if any([x != 1 for x in weights]):
return
# Keep biases (mean values) for current input as graph attr and remove ScaleShift layer
# Input->data->ScaleShift->scsh_data => Input->scsh_data
graph.remove_edge(input_op.id, input_op.out_node().id)
graph.add_edge(input_op.id, scale_shift.out_node().id, out=0)
graph.remove_edge(scale_shift.id, scale_shift.out_node().id)
# If bias contains zeros we just remove it
if all([x == 0 for x in biases]):
return
# In pre-process section, mean_values are subtracted
biases *= -1
mean_values.update({input_op.name: np.array(biases)})
# Add graph attribute 'mean_values' that stores mean_values per input if exists
if graph.graph.get('mean_values', None):
graph.graph['mean_values'].update(mean_values)
else:
graph.graph['mean_values'] = mean_values
def move_scaleshift_to_preprocess(graph: Graph):
"""
This function finds scaleshift layer after input layer and if it has weights with ones, it deletes scaleshift layer
and creates graph dict attribute : {'input':np.array(...), 'input2': ... }
"""
apply_pattern(
graph,
nodes=[
('weights', dict(kind='data')),
('biases', dict(kind='data')),
('input_output', dict(kind='data')),
('scsh_output', dict(kind='data')),
('input_op', dict(kind='op', type='Parameter')),
('scale_shift', dict(kind='op', type='ScaleShift')),
],
edges=[
('input_op', 'input_output'),
('scale_shift', 'scsh_output'),
('input_output', 'scale_shift', {'in': 0}),
('weights', 'scale_shift', {'in': 1}),
('biases', 'scale_shift', {'in': 2}),
],
action=move_scaleshift_to_preprocess_action
)

174
model-optimizer/mo/middle/passes/mean_scale_values_test.py
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@ -1,174 +0,0 @@
"""
Copyright (C) 2018-2020 Intel Corporation
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
"""
import unittest
from argparse import Namespace
import numpy as np
from mo.middle.passes.mean_scale_values import move_scaleshift_to_preprocess
from mo.utils.ir_engine.compare_graphs import compare_graphs
from mo.utils.unittest.graph import build_graph
nodes_attributes = {'node_1': {'type': 'Identity', 'value': None, 'kind': 'op'},
'node_2': {'type': 'Identity', 'value': None, 'kind': 'op'},
'concat': {'type': 'Concat', 'value': None, 'kind': 'op'},
'node_3': {'type': 'Identity', 'value': None, 'kind': 'op'},
# Placeholders
'placeholder_1': {'value': None, 'shape': None, 'type': 'Parameter', 'kind': 'op', 'op': 'Parameter'},
'placeholder_1_data': {'value': None, 'shape': None, 'kind': 'data', 'data_type': None},
'placeholder_2': {'value': None, 'shape': None, 'type': 'Parameter', 'kind': 'op', 'op': 'Parameter'},
'placeholder_2_data': {'value': None, 'shape': None, 'kind': 'data', 'data_type': None},
# ScaleShift layer
'scaleshift_1': {'type': 'ScaleShift', 'value': None, 'kind': 'op', 'op': 'ScaleShift'},
'scaleshift_1_w': {'value': None, 'shape': None, 'kind': 'data'},
'scaleshift_1_b': {'value': None, 'shape': None, 'kind': 'data'},
'scaleshift_1_data': {'value': None, 'shape': None, 'kind': 'data'},
'op_output': { 'kind': 'op', 'op': 'Result'},
'op_output_1': { 'kind': 'op', 'op': 'Result'}
}
class TestScaleShift_To_Preprocess(unittest.TestCase):
def test_move_scaleshift_to_preprocess_1(self):
graph = build_graph(nodes_attributes,
[('placeholder_1', 'placeholder_1_data'),
('placeholder_1_data', 'scaleshift_1'),
('scaleshift_1', 'scaleshift_1_data'),
('scaleshift_1_w', 'scaleshift_1'),
('scaleshift_1_b', 'scaleshift_1'),
('scaleshift_1_data', 'op_output')
],
{'placeholder_1_data': {'shape': np.array([1, 227, 227, 3])},
'scaleshift_1_w': {'shape': np.array([3]), 'value': np.ones(3)},
'scaleshift_1_b': {'shape': np.array([3]), 'value': np.array([-1, -2, -3])},
})
graph.graph['cmd_params'] = Namespace(reverse_input_channels=False)
del graph['placeholder_1']['placeholder_1_data'][0]['in']
del graph['scaleshift_1']['scaleshift_1_data'][0]['in']
graph_ref = build_graph(nodes_attributes,
[('placeholder_1', 'scaleshift_1_data'),
('scaleshift_1_data', 'op_output')
])
move_scaleshift_to_preprocess(graph)
self.assertTrue(graph.graph['mean_values'] is not None)
self.assertTrue(np.array_equal(graph.graph['mean_values']['placeholder_1'], np.array([1, 2, 3])))
(flag, resp) = compare_graphs(graph, graph_ref, 'scaleshift_1_data')
self.assertTrue(flag, resp)
def test_move_scaleshift_to_preprocess_2(self):
graph = build_graph(nodes_attributes,
[('placeholder_1', 'placeholder_1_data'),
('placeholder_1_data', 'scaleshift_1'),
('scaleshift_1', 'scaleshift_1_data'),
('scaleshift_1_w', 'scaleshift_1'),
('scaleshift_1_b', 'scaleshift_1'),
('scaleshift_1_data', 'op_output'),
('placeholder_1_data', 'op_output_1')
],
{'placeholder_1_data': {'shape': np.array([1, 227, 227, 3])},
'scaleshift_1_w': {'shape': np.array([3]), 'value': np.array((1, 2, 3))},
'scaleshift_1_b': {'shape': np.array([3]), 'value': np.array([-1, -2, -3])},
})
graph.graph['cmd_params'] = Namespace(reverse_input_channels=False)
del graph['placeholder_1']['placeholder_1_data'][0]['in']
del graph['scaleshift_1']['scaleshift_1_data'][0]['in']
graph_ref = build_graph(nodes_attributes,
[('placeholder_1', 'placeholder_1_data'),
('placeholder_1_data', 'scaleshift_1'),
('scaleshift_1', 'scaleshift_1_data'),
('scaleshift_1_w', 'scaleshift_1'),
('scaleshift_1_b', 'scaleshift_1'),
('placeholder_1_data', 'op_output_1'),
('scaleshift_1_data', 'op_output')
],
{'placeholder_1_data': {'shape': np.array([1, 227, 227, 3])},
'scaleshift_1_w': {'shape': np.array([3]), 'value': np.array((1, 2, 3))},
'scaleshift_1_b': {'shape': np.array([3]), 'value': np.array([-1, -2, -3])},
})
move_scaleshift_to_preprocess(graph)
self.assertTrue(graph.graph.get('mean_values', None) is None)
(flag, resp) = compare_graphs(graph, graph_ref, 'scaleshift_1_data')
self.assertTrue(flag, resp)
def test_move_scaleshift_to_preprocess_3(self):
graph = build_graph(nodes_attributes,
[('placeholder_1', 'placeholder_1_data'),
('placeholder_1_data', 'scaleshift_1'),
('scaleshift_1', 'scaleshift_1_data'),
('scaleshift_1_w', 'scaleshift_1'),
('scaleshift_1_data', 'op_output'),
('placeholder_1_data', 'op_output_1')
],
{'placeholder_1_data': {'shape': np.array([1, 227, 227, 3])},
'scaleshift_1_w': {'shape': np.array([3]), 'value': np.array((1, 2, 3))},
})
graph.graph['cmd_params'] = Namespace(reverse_input_channels=False)
del graph['placeholder_1']['placeholder_1_data'][0]['in']
del graph['scaleshift_1']['scaleshift_1_data'][0]['in']
graph_ref = build_graph(nodes_attributes,
[('placeholder_1', 'placeholder_1_data'),
('placeholder_1_data', 'scaleshift_1'),
('scaleshift_1', 'scaleshift_1_data'),
('scaleshift_1_w', 'scaleshift_1'),
('scaleshift_1_data', 'op_output'),
('placeholder_1_data', 'op_output_1')
],
{'placeholder_1_data': {'shape': np.array([1, 227, 227, 3])},
'scaleshift_1_w': {'shape': np.array([3]), 'value': np.array((1, 2, 3))},
})
move_scaleshift_to_preprocess(graph)
self.assertTrue(graph.graph.get('mean_values', None) == None)
(flag, resp) = compare_graphs(graph, graph_ref, 'scaleshift_1_data')
self.assertTrue(flag, resp)
def test_move_scaleshift_to_preprocess_4(self):
graph = build_graph(nodes_attributes,
[('placeholder_1', 'placeholder_1_data'),
('placeholder_1_data', 'scaleshift_1'),
('scaleshift_1', 'scaleshift_1_data'),
('scaleshift_1_w', 'scaleshift_1'),
('scaleshift_1_b', 'scaleshift_1'),
('scaleshift_1_data', 'op_output')
],
{'placeholder_1_data': {'shape': np.array([1, 227, 227, 3])},
'scaleshift_1_w': {'shape': np.array([3]), 'value': np.ones(3)},
'scaleshift_1_b': {'shape': np.array([3]), 'value': np.zeros(3)},
})
graph.graph['cmd_params'] = Namespace(reverse_input_channels=False)
del graph['placeholder_1']['placeholder_1_data'][0]['in']
del graph['scaleshift_1']['scaleshift_1_data'][0]['in']
graph_ref = build_graph(nodes_attributes,
[('placeholder_1', 'scaleshift_1_data'),
('scaleshift_1_data', 'op_output')
])
move_scaleshift_to_preprocess(graph)
self.assertTrue(graph.graph.get('mean_values', None) is None)
(flag, resp) = compare_graphs(graph, graph_ref, 'scaleshift_1_data')
self.assertTrue(flag, resp)

2
model-optimizer/mo/utils/cli_parser.py
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@ -283,7 +283,7 @@ def get_common_cli_parser(parser: argparse.ArgumentParser = None):
action='store_true')
common_group.add_argument('--move_to_preprocess',
help='Move mean values to IR preprocess section',
action='store_true')
action=DeprecatedStoreTrue)
# we use CanonicalizeDirCheckExistenceAction instead of readable_dirs to handle empty strings
common_group.add_argument("--extensions",
help="Directory or a comma separated list of directories with extensions. To disable all "