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Apply RIC for dynamic dimension in legacy MO (#10130)

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* Apply RIC for dynamic dimension in legacy MO and fail if RIC wasn't applied to any input

* Fix moc tests
This commit is contained in:
Maxim Vafin 2022-02-08 22:17:19 +03:00 committed by GitHub
parent d951433b12
commit 1970baeb1c
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GPG Key ID: 4AEE18F83AFDEB23
3 changed files with 45 additions and 49 deletions
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29
tools/mo/openvino/tools/mo/back/ReverseInputChannels.py
View File

@ -7,7 +7,7 @@ import numpy as np
from openvino.tools.mo.back.replacement import BackReplacementPattern
from openvino.tools.mo.front.common.layout import get_dim_from_layout, get_features_dim
from openvino.tools.mo.front.common.partial_infer.utils import int64_array
from openvino.tools.mo.front.common.partial_infer.utils import int64_array, compatible_dims
from openvino.tools.mo.front.common.partial_infer.utils import mo_array
from openvino.tools.mo.front.tf.graph_utils import create_op_with_const_inputs
from openvino.tools.mo.graph.graph import Graph
@ -16,6 +16,7 @@ from openvino.tools.mo.ops.concat import Concat
from openvino.tools.mo.ops.gather import Gather
from openvino.tools.mo.ops.op import Op, PermuteAttrs
from openvino.tools.mo.ops.split import Split
from openvino.tools.mo.utils.error import Error
class ReverseChannels(Op):
@ -53,7 +54,10 @@ class InsertReverseChannels(BackReplacementPattern):
enabled = False
@staticmethod
def get_channel_index(node: Node) -> int:
def get_suitable_channel_index(node: Node, shape):
if len(shape) != 4:
return None
guessed_layout = 'NCHW'
if node.has_valid('rt_info'):
rt_info = node.rt_info
@ -66,25 +70,30 @@ class InsertReverseChannels(BackReplacementPattern):
guessed_layout = np.array(list(guessed_layout))[order]
guessed_layout = ''.join(guessed_layout)
idx, has_layout = get_dim_from_layout(node, 'C')
if has_layout:
if not has_layout:
idx = get_features_dim(guessed_layout, len(node.shape))
if compatible_dims(shape[idx], 3):
return idx
else:
return get_features_dim(guessed_layout, len(node.shape))
return None
def find_and_replace_pattern(self, graph: Graph):
all_params = [(p.soft_get('name', p.id), p, list(p.out_port(0).data.get_shape()))
for p in graph.get_op_nodes(type='Parameter')]
suitable_params = []
for name, p, shape in all_params:
if len(shape) == 4:
idx = self.get_channel_index(p)
if idx is not None and shape[idx] == 3:
suitable_params.append((name, p, shape, idx))
idx = self.get_suitable_channel_index(p, shape)
if idx is not None:
suitable_params.append((name, p, shape, idx))
log.debug('All network inputs: {}'.format({name: shape for name, _, shape in all_params}))
log.debug('Will reverse input channels for: {}'.format({name: shape for name, _, shape, _ in suitable_params}))
if len(suitable_params) < len(all_params):
if not len(suitable_params):
raise Error('Network has {} inputs overall, but none of them are suitable for input channels reversing.\n'
'Suitable for input channel reversing inputs are 4-dimensional with 3 channels (in case of '
'dynamic dimensions C channel must be provided in a layout for this input)\n'
'All inputs: {}'.format(len(all_params), all_params))
elif len(suitable_params) < len(all_params):
log.error('Network has {} inputs overall, but only {} of them are suitable for input channels reversing.\n'
'Suitable for input channel reversing inputs are 4-dimensional with 3 channels\nAll inputs: {}\n'
'Suitable inputs {}'.format(len(all_params), len(suitable_params),

10
tools/mo/openvino/tools/mo/back/preprocessing.py
View File

@ -310,9 +310,15 @@ def guess_source_layouts_for_reverse_channels(ov_function: Model, layout_values)
if layout and check_suitable_for_reverse(Layout(layout), ov_input):
suitable_params.append(param_info)
if len(suitable_params) < len(all_params):
if not len(suitable_params):
raise Error('Network has {} inputs overall, but none of them are suitable for input channels reversing.\n'
'Suitable for input channel reversing inputs are 4-dimensional with 3 channels (in case of dynamic '
'dimensions C channel must be provided in a layout for this input)\nAll inputs: {}'.format(
len(all_params), all_params))
elif len(suitable_params) < len(all_params):
log.error('Network has {} inputs overall, but only {} of them are suitable for input channels reversing.\n'
'Suitable for input channel reversing inputs are 4-dimensional with 3 channels\nAll inputs: {}\n'
'Suitable for input channel reversing inputs are 4-dimensional with 3 channels (in case of dynamic '
'dimensions C channel must be provided in a layout for this input)\nAll inputs: {}\n'
'Suitable inputs {}'.format(len(all_params), len(suitable_params), all_params, suitable_params),
extra={'is_warning': True})
return suitable_params

55
tools/mo/unit_tests/mo/back/moc_preprocessing_test_actual.py
View File

@ -434,13 +434,9 @@ class TestPreprocessingMOC(UnitTestWithMockedTelemetry):
'input2a': { 'source_layout': 'nchw' }
})
function = create_function2(shape1=[1, 224, 224, 4], shape2=[1, 4, 224, 224])
process_function(ov_function=function, argv=argv)
# In future, consider using mock PrePostProcessor to verify that 'reverse_channels' was not called
# Verify that reverse_channels are not applied.
op_node0 = list(function.get_parameters()[0].output(0).get_target_inputs())[0].get_node()
self.assertTrue(op_node0.get_type_name() == 'Relu')
op_node1 = list(function.get_parameters()[1].output(0).get_target_inputs())[0].get_node()
self.assertTrue(op_node1.get_type_name() == 'Relu')
# no suitable inputs
with self.assertRaises(Exception):
process_function(ov_function=function, argv=argv)
def test_reverse_input_channels_3d(self):
argv = Namespace(reverse_input_channels=True, mean_scale_values=None, scale=None,
@ -457,23 +453,17 @@ class TestPreprocessingMOC(UnitTestWithMockedTelemetry):
argv = Namespace(reverse_input_channels=True, mean_scale_values=None, scale=None,
layout_values=None)
function = create_function2(shape1=[4, 4, 4, 4, 4, 3], shape2=[4, 3, 4, 4, 4, 4])
process_function(ov_function=function, argv=argv)
# Verify that reverse_channels are NOT applied.
op_node0 = list(function.get_parameters()[0].output(0).get_target_inputs())[0].get_node()
self.assertTrue(op_node0.get_type_name() == 'Relu')
op_node1 = list(function.get_parameters()[1].output(0).get_target_inputs())[0].get_node()
self.assertTrue(op_node1.get_type_name() == 'Relu')
# no suitable inputs
with self.assertRaises(Exception):
process_function(ov_function=function, argv=argv)
def test_reverse_input_channels_dynamic(self):
argv = Namespace(reverse_input_channels=True, mean_scale_values=None, scale=None,
layout_values=None)
function = create_function2(shape1=[1, -1, 5, 5], shape2=[-1, -1, -1, -1])
process_function(ov_function=function, argv=argv)
# Verify that reverse_channels are NOT applied.
op_node0 = list(function.get_parameters()[0].output(0).get_target_inputs())[0].get_node()
self.assertTrue(op_node0.get_type_name() == 'Relu')
op_node1 = list(function.get_parameters()[1].output(0).get_target_inputs())[0].get_node()
self.assertTrue(op_node1.get_type_name() == 'Relu')
# no suitable inputs
with self.assertRaises(Exception):
process_function(ov_function=function, argv=argv)
def test_reverse_input_channels_dynamic_layout(self):
argv = Namespace(reverse_input_channels=True, mean_scale_values=None, scale=None,
@ -582,34 +572,25 @@ class TestPreprocessingMOC(UnitTestWithMockedTelemetry):
function = create_function2(shape1=[1, 224, 224, 3], shape2=[1, 3, 224, 224])
function.get_parameters()[0].layout = Layout("NHW?")
function.get_parameters()[1].layout = Layout("N?HW")
process_function(ov_function=function, argv=argv)
# Nothing has applied
op_node0 = list(function.get_parameters()[0].output(0).get_target_inputs())[0].get_node()
self.assertTrue(op_node0.get_type_name() == 'Relu')
op_node1 = list(function.get_parameters()[1].output(0).get_target_inputs())[0].get_node()
self.assertTrue(op_node1.get_type_name() == 'Relu')
# no suitable inputs
with self.assertRaises(Exception):
process_function(ov_function=function, argv=argv)
def test_guess_layout_reverse_channels_incorrect_pos(self):
argv = Namespace(reverse_input_channels=True, mean_scale_values=None, scale=None)
function = create_function2(shape1=[1, 4, 224, 224], shape2=[1, 224, 224, 2])
function.get_parameters()[0].layout = Layout("NCHW")
function.get_parameters()[1].layout = Layout("NHWC")
process_function(ov_function=function, argv=argv)
# Nothing has applied
op_node0 = list(function.get_parameters()[0].output(0).get_target_inputs())[0].get_node()
self.assertTrue(op_node0.get_type_name() == 'Relu')
op_node1 = list(function.get_parameters()[1].output(0).get_target_inputs())[0].get_node()
self.assertTrue(op_node1.get_type_name() == 'Relu')
# no suitable inputs
with self.assertRaises(Exception):
process_function(ov_function=function, argv=argv)
def test_no_reverse_channels_even_with_layout(self):
argv = Namespace(reverse_input_channels=True, mean_scale_values=None, scale=None)
function = create_function2(shape1=[3, 4, 224, 224], shape2=[1, 224, 3, 224])
process_function(ov_function=function, argv=argv)
# Nothing has applied
op_node0 = list(function.get_parameters()[0].output(0).get_target_inputs())[0].get_node()
self.assertTrue(op_node0.get_type_name() == 'Relu')
op_node1 = list(function.get_parameters()[1].output(0).get_target_inputs())[0].get_node()
self.assertTrue(op_node1.get_type_name() == 'Relu')
# no suitable inputs
with self.assertRaises(Exception):
process_function(ov_function=function, argv=argv)
def test_reverse_channels_and_mean_scale(self):
argv = Namespace(reverse_input_channels=True, mean_scale_values={