Use layouts in legacy pre-processing (#9355)
* Use layouts in legacy pre-processing * Use layouts for batch in MO * Unify code for channel and batch dimension * Fix issue when idx is None
This commit is contained in:
Maxim Vafin
GitHub
parent
50a33436d4
commit
738a571742
@ -5,16 +5,17 @@ import logging as log
|
||||
|
||||
import numpy as np
|
||||
|
||||
from openvino.tools.mo.ops.gather import Gather
|
||||
from openvino.tools.mo.ops.split import Split
|
||||
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 mo_array
|
||||
from openvino.tools.mo.front.tf.graph_utils import create_op_with_const_inputs
|
||||
from openvino.tools.mo.graph.graph import Graph
|
||||
from openvino.tools.mo.graph.graph import Node
|
||||
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
|
||||
|
||||
|
||||
class ReverseChannels(Op):
|
||||
@ -52,50 +53,47 @@ class InsertReverseChannels(BackReplacementPattern):
|
||||
enabled = False
|
||||
|
||||
@staticmethod
|
||||
def get_fw_index(node: Node, idx: int) -> int:
|
||||
if not node.has_valid('rt_info'):
|
||||
return idx
|
||||
|
||||
def get_channel_index(node: Node) -> int:
|
||||
guessed_layout = 'NCHW'
|
||||
if node.has_valid('rt_info'):
|
||||
rt_info = node.rt_info
|
||||
if not rt_info.contains('old_api_map_order'):
|
||||
return idx
|
||||
|
||||
if rt_info.contains('old_api_map_order'):
|
||||
old_api_map_version = rt_info.get_attribute_version('old_api_map_order')
|
||||
old_api_map = rt_info.info['old_api_map_order', old_api_map_version]
|
||||
if 'inverse_order' not in old_api_map.info:
|
||||
return idx
|
||||
|
||||
if 'inverse_order' in old_api_map.info:
|
||||
order = old_api_map.info['inverse_order']
|
||||
node_name = node.soft_get('name', node.id)
|
||||
|
||||
if idx < 0:
|
||||
assert not node.out_port(0).disconnected(), 'Cannot normalize negative axis {} in node {} ' \
|
||||
'as out port is disconnected.'.format(idx, node_name)
|
||||
data_rank = len(list(node.out_port(0).data.get_shape()))
|
||||
idx = data_rank + idx
|
||||
|
||||
assert len(order) > idx >= 0, \
|
||||
'Channel index {} is incompatible with old_api_map in node {}.'.format(idx, node_name)
|
||||
return list(order).index(idx)
|
||||
assert len(order) == len(guessed_layout)
|
||||
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:
|
||||
return idx
|
||||
else:
|
||||
return get_features_dim(guessed_layout, len(node.shape))
|
||||
|
||||
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 = [(name, p, shape) for name, p, shape in all_params if
|
||||
len(shape) == 4 and shape[self.get_fw_index(p, 1)] == 3]
|
||||
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))
|
||||
|
||||
|
||||
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}))
|
||||
log.debug('Will reverse input channels for: {}'.format({name: shape for name, _, shape, _ in suitable_params}))
|
||||
if 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),
|
||||
{name: shape for name, _, shape in all_params},
|
||||
{name: shape for name, _, shape in suitable_params}),
|
||||
{name: shape for name, _, shape, _ in suitable_params}),
|
||||
extra={'is_warning': True})
|
||||
|
||||
for name, parameter, _ in suitable_params:
|
||||
reverse_index = int64_array(self.get_fw_index(parameter, 1))
|
||||
for name, parameter, _, idx in suitable_params:
|
||||
reverse_index = int64_array(idx)
|
||||
|
||||
if parameter.out_port(0).disconnected():
|
||||
continue
|
||||
|
||||
@ -5,6 +5,7 @@ import numpy as np
|
||||
|
||||
from openvino.tools.mo.front.common.partial_infer.utils import dynamic_dimension_value
|
||||
from openvino.tools.mo.front.common.partial_infer.utils import mo_array, int64_array
|
||||
from openvino.tools.mo.graph.graph import Node
|
||||
from openvino.tools.mo.utils.error import Error
|
||||
|
||||
nchw_to_nhwc_permute = int64_array([0, 2, 3, 1])
|
||||
@ -112,3 +113,41 @@ def shape_for_layout(layout: str, **kwargs):
|
||||
if depth is not None:
|
||||
output_shape[get_depth_dim(layout, shape_len)] = depth
|
||||
return output_shape
|
||||
|
||||
|
||||
def get_dim_from_layout(node: Node, dim: str):
|
||||
"""
|
||||
Gets index of dimension from layout specified for node.
|
||||
:param node: node to get dim for.
|
||||
:param dim: name of dimension to get index for.
|
||||
:return: tuple with index of the dimension and bool flag if the node has layout specified or no.
|
||||
"""
|
||||
layout = None
|
||||
graph = node.graph
|
||||
if 'layout_values' in graph.graph['cmd_params'] and graph.graph['cmd_params'].layout_values:
|
||||
layout_values = graph.graph['cmd_params'].layout_values.copy()
|
||||
if '' in layout_values:
|
||||
in_nodes = graph.get_op_nodes(op='Parameter')
|
||||
if len(in_nodes) == 1:
|
||||
in_node = in_nodes[0]
|
||||
layout_values[in_node.soft_get('name', in_node.id)] = layout_values['']
|
||||
del layout_values['']
|
||||
name = node.soft_get('name', node.id)
|
||||
if name in layout_values:
|
||||
if layout_values[name]['source_layout']:
|
||||
layout = layout_values[name]['source_layout']
|
||||
|
||||
if layout:
|
||||
from openvino.runtime import Layout # pylint: disable=no-name-in-module,import-error
|
||||
|
||||
layout_parsed = Layout(layout)
|
||||
has_dim = layout_parsed.has_name(dim)
|
||||
if has_dim:
|
||||
idx = layout_parsed.get_index_by_name(dim)
|
||||
if idx < 0:
|
||||
idx = len(node.shape) + idx
|
||||
return idx, True
|
||||
else:
|
||||
return None, True
|
||||
else:
|
||||
return None, False
|
||||
|
||||
@ -5,13 +5,13 @@ import logging as log
|
||||
|
||||
import numpy as np
|
||||
|
||||
from openvino.tools.mo.ops.elementwise import Add, Mul
|
||||
from openvino.tools.mo.front.common.layout import get_features_dim
|
||||
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 compatible_dims
|
||||
from openvino.tools.mo.front.extractor import get_node_id_with_ports
|
||||
from openvino.tools.mo.front.tf.graph_utils import create_op_with_const_inputs
|
||||
from openvino.tools.mo.graph.graph import Graph, Node
|
||||
from openvino.tools.mo.middle.replacement import MiddleReplacementPattern
|
||||
from openvino.tools.mo.ops.elementwise import Add, Mul
|
||||
from openvino.tools.mo.utils.cli_parser import get_node_name_with_port_from_input_value
|
||||
from openvino.tools.mo.utils.error import Error
|
||||
from openvino.tools.mo.utils.utils import refer_to_faq_msg
|
||||
@ -42,14 +42,21 @@ class AddMeanScaleValues(MiddleReplacementPattern):
|
||||
if all([x == optimize_value for x in value]):
|
||||
return
|
||||
assert input_node.has_valid('shape')
|
||||
in_name = input_node.soft_get('name', input_node.id)
|
||||
features_dim_idx, has_layout = get_dim_from_layout(input_node, 'C')
|
||||
if features_dim_idx is None:
|
||||
if has_layout:
|
||||
log.warning('Layout for input {} doesn\'t have channel ("C") dimension to apply {} preprocessing. '
|
||||
'Skipping this input.'.format(in_name, preprocessing_name))
|
||||
features_dim_idx = get_features_dim(graph.graph['layout'], len(input_node.shape))
|
||||
assert compatible_dims(value.size, input_node.shape[features_dim_idx]) or value.size == 1
|
||||
assert compatible_dims(value.size, input_node.shape[features_dim_idx]) or value.size == 1, \
|
||||
"Incompatible layout, please specify correct layout for the node"
|
||||
|
||||
shape = np.ones(len(input_node.shape), dtype=np.int64)
|
||||
shape[features_dim_idx] = value.size
|
||||
value = value.reshape(shape)
|
||||
|
||||
name = input_node.soft_get('name', input_node.id) + '/' + preprocessing_name
|
||||
name = in_name + '/' + preprocessing_name
|
||||
preprocessing = create_op_with_const_inputs(graph, op=op, port_value_dict={1: value}, op_attrs={'name': name})
|
||||
|
||||
if input_node.op == 'Parameter' and input_node.has_and_set('data_type'):
|
||||
|
||||
@ -6,6 +6,7 @@ from typing import List
|
||||
|
||||
import networkx as nx
|
||||
|
||||
from openvino.tools.mo.front.common.layout import get_dim_from_layout
|
||||
from openvino.tools.mo.front.common.partial_infer.utils import dynamic_dimension
|
||||
from openvino.tools.mo.graph.graph import Node, Graph, dict_includes
|
||||
from openvino.tools.mo.utils.error import Error
|
||||
@ -220,10 +221,20 @@ def override_batch(graph: Graph, batch: int):
|
||||
batch: user defined integer value to override batch
|
||||
"""
|
||||
if batch is not None:
|
||||
for node_id, data in graph.nodes(data=True):
|
||||
if 'op' in data and data['op'] == 'Parameter' and not data.get('fixed_batch', False):
|
||||
validate_batch_in_shape(data['shape'], data['name'])
|
||||
data['shape'][0] = batch
|
||||
in_nodes = graph.get_op_nodes(op='Parameter')
|
||||
for node in in_nodes:
|
||||
if not node.soft_get('fixed_batch', False):
|
||||
name = node.soft_get('name', node.id)
|
||||
idx, has_layout = get_dim_from_layout(node, 'N')
|
||||
if has_layout:
|
||||
if idx is not None:
|
||||
node['shape'][idx] = batch
|
||||
else:
|
||||
log.warning(
|
||||
'Layout for input {} doesn\'t have batch dimension. Skipping this input.'.format(name))
|
||||
else:
|
||||
validate_batch_in_shape(node['shape'], name)
|
||||
node['shape'][0] = batch
|
||||
|
||||
|
||||
def validate_batch_in_shape(shape, layer_name: str):
|
||||
@ -242,6 +253,7 @@ def validate_batch_in_shape(shape, layer_name: str):
|
||||
'dimension or not.\n\n For example, you want to set batch dimension equals 100 ' +
|
||||
'for the input layer "data" with shape (10,34). Although you can not use --batch, ' +
|
||||
'you should pass --input_shape (100,34) instead of --batch 100. \n\n' +
|
||||
'You can also tell Model Optimizer where batch dimension is located by specifying --layout. \n\n' +
|
||||
refer_to_faq_msg(39))
|
||||
.format(layer_name, shape))
|
||||
|
||||
@ -328,4 +340,3 @@ def reverse_infer(graph: Graph, nodes: list):
|
||||
if node.has_valid('reverse_infer'):
|
||||
log.debug("Executed reverse infer for node '{}'".format(node.soft_get('name', node.id)))
|
||||
node.reverse_infer(node)
|
||||
|
||||
|
||||
@ -2,6 +2,7 @@
|
||||
# SPDX-License-Identifier: Apache-2.0
|
||||
|
||||
import unittest
|
||||
from argparse import Namespace
|
||||
|
||||
import numpy as np
|
||||
|
||||
@ -53,6 +54,22 @@ nodes3 = {
|
||||
}
|
||||
|
||||
|
||||
def get_nodes(shape, axis=1):
|
||||
return {
|
||||
**regular_op_with_shaped_data('placeholder1', shape,
|
||||
{'type': 'Parameter', 'shape': shape, 'rt_info': RTInfo()}),
|
||||
**regular_op_with_shaped_data('placeholder2', [1, 1, 1, 1], {'type': 'Parameter', 'shape': [1, 1, 1, 1]}),
|
||||
|
||||
**regular_op_with_shaped_data('mul', shape, {'type': 'Multiply'}),
|
||||
**regular_op_with_shaped_data('reverse_channels', shape,
|
||||
{'op': 'ReverseChannels', 'type': None, 'axis': int64_array(axis)}),
|
||||
|
||||
**regular_op_with_shaped_data('pad', shape, {'type': 'Pad'}),
|
||||
|
||||
**result('result'),
|
||||
}
|
||||
|
||||
|
||||
class ReverseInputChannelsTest(unittest.TestCase):
|
||||
def check_graph_attrs(self, graph: Graph, parameter_node_names: list):
|
||||
for node in graph.get_op_nodes():
|
||||
@ -229,15 +246,53 @@ class ReverseInputChannelsTest(unittest.TestCase):
|
||||
self.assertTrue(reverse_channels.axis == 1)
|
||||
self.assertTrue(type(reverse_channels.axis) == np.ndarray)
|
||||
|
||||
def test_get_fw_index(self):
|
||||
graph = build_graph(nodes, [*connect('placeholder1', 'result')])
|
||||
def test_insert(self):
|
||||
graph = build_graph(get_nodes([1, 3, 10, 10]),
|
||||
[*connect('placeholder1', '0:mul'), *connect('placeholder2', '1:mul'),
|
||||
*connect('mul', 'result')], nodes_with_edges_only=True,
|
||||
cli=Namespace(reverse_input_channels=True))
|
||||
|
||||
InsertReverseChannels().find_and_replace_pattern(graph)
|
||||
graph_ref = build_graph(get_nodes([1, 3, 10, 10]),
|
||||
[*connect('placeholder1', 'reverse_channels'), *connect('reverse_channels', '0:mul'),
|
||||
*connect('placeholder2', '1:mul'), *connect('mul', 'result')])
|
||||
(flag, resp) = compare_graphs(graph, graph_ref, 'result', check_op_attrs=True)
|
||||
self.assertTrue(flag, resp)
|
||||
|
||||
def test_insert_old_api_map(self):
|
||||
graph = build_graph(get_nodes([1, 10, 10, 3]),
|
||||
[*connect('placeholder1', '0:mul'), *connect('placeholder2', '1:mul'),
|
||||
*connect('mul', 'result')], nodes_with_edges_only=True,
|
||||
cli=Namespace(reverse_input_channels=True))
|
||||
|
||||
node = Node(graph, 'placeholder1')
|
||||
old_api_map = OldAPIMapOrder(version=0)
|
||||
node.rt_info.info[('old_api_map_order', old_api_map.get_version())] = old_api_map
|
||||
node.rt_info.info[('old_api_map_order', old_api_map.get_version())].old_api_transpose_parameter([0, 2, 3, 1])
|
||||
self.assertTrue(InsertReverseChannels.get_fw_index(node, 0) == 0)
|
||||
self.assertTrue(InsertReverseChannels.get_fw_index(node, 1) == 3)
|
||||
self.assertTrue(InsertReverseChannels.get_fw_index(node, 2) == 1)
|
||||
self.assertTrue(InsertReverseChannels.get_fw_index(node, 3) == 2)
|
||||
self.assertTrue(InsertReverseChannels.get_fw_index(node, -2) == 1)
|
||||
self.assertTrue(type(InsertReverseChannels.get_fw_index(node, 0)) == int)
|
||||
|
||||
InsertReverseChannels().find_and_replace_pattern(graph)
|
||||
graph_ref = build_graph(get_nodes([1, 10, 10, 3], 3),
|
||||
[*connect('placeholder1', 'reverse_channels'), *connect('reverse_channels', '0:mul'),
|
||||
*connect('placeholder2', '1:mul'), *connect('mul', 'result')])
|
||||
|
||||
node2 = Node(graph_ref, 'placeholder1')
|
||||
node2.rt_info = node.rt_info
|
||||
|
||||
(flag, resp) = compare_graphs(graph, graph_ref, 'result', check_op_attrs=True)
|
||||
self.assertTrue(flag, resp)
|
||||
|
||||
def test_insert_layout(self):
|
||||
graph = build_graph(get_nodes([1, 10, 10, 3]),
|
||||
[*connect('placeholder1', '0:mul'), *connect('placeholder2', '1:mul'),
|
||||
*connect('mul', 'result')], nodes_with_edges_only=True,
|
||||
cli=Namespace(reverse_input_channels=True,
|
||||
layout_values={
|
||||
'placeholder1': {'source_layout': 'nhwc', 'target_layout': None}}))
|
||||
|
||||
InsertReverseChannels().find_and_replace_pattern(graph)
|
||||
graph_ref = build_graph(get_nodes([1, 10, 10, 3], 3),
|
||||
[*connect('placeholder1', 'reverse_channels'), *connect('reverse_channels', '0:mul'),
|
||||
*connect('placeholder2', '1:mul'), *connect('mul', 'result')])
|
||||
|
||||
(flag, resp) = compare_graphs(graph, graph_ref, 'result', check_op_attrs=True)
|
||||
self.assertTrue(flag, resp)
|
||||
|
||||
@ -375,3 +375,93 @@ class AddMeanScaleValuesTest(unittest.TestCase):
|
||||
self.check_graph_attrs(graph, graph_ref, [])
|
||||
add_node = graph.get_op_nodes(type="Add")[0]
|
||||
self.assertTrue(add_node.in_port(1).get_connection().get_source().node['value'].dtype == np.float32)
|
||||
|
||||
def test_mean_values_explicit_and_optimized_layout(self):
|
||||
graph_ref = build_graph(nodes, [
|
||||
*connect('parameter', '0:add_mean'),
|
||||
*connect('mean', '1:add_mean'),
|
||||
*connect('add_mean', 'result'),
|
||||
*connect('parameter_2', 'result_2'),
|
||||
])
|
||||
|
||||
argv = Namespace(mean_scale_values={'parameter': {'mean': np.array([1., 2., 3.])},
|
||||
'parameter_2': {'mean': np.array([0., 0., 0.])}},
|
||||
layout_values={'parameter': {'source_layout': 'nchw', 'target_layout': None},
|
||||
'parameter_2': {'source_layout': 'nchw', 'target_layout': None}}
|
||||
)
|
||||
graph = build_graph(nodes, [*connect('parameter', 'result'), *connect('parameter_2', 'result_2')],
|
||||
nodes_with_edges_only=True, cli=argv)
|
||||
self.set_graph_attrs(graph, ['parameter', 'parameter_2'])
|
||||
self.set_graph_attrs(graph_ref, ['parameter', 'parameter_2'])
|
||||
graph.graph['layout'] = 'NHWC'
|
||||
|
||||
AddMeanScaleValues().find_and_replace_pattern(graph)
|
||||
(flag, resp) = compare_graphs(graph, graph_ref, 'result', check_op_attrs=True)
|
||||
self.assertTrue(flag, resp)
|
||||
(flag, resp) = compare_graphs(graph, graph_ref, 'result_2', check_op_attrs=True)
|
||||
self.assertTrue(flag, resp)
|
||||
self.check_graph_attrs(graph, graph_ref, ['parameter', 'parameter_2'])
|
||||
|
||||
def test_mean_values_explicit_and_scale_values_optimized_layout(self):
|
||||
graph_ref = build_graph(nodes, [
|
||||
*connect('parameter', '0:add_mean'),
|
||||
*connect('mean', '1:add_mean'),
|
||||
*connect('add_mean', 'result'),
|
||||
])
|
||||
|
||||
argv = Namespace(mean_scale_values={'parameter': {'scale': np.array([1.]), 'mean': np.array([1., 2., 3.])}},
|
||||
layout_values={'': {'source_layout': 'nchw', 'target_layout': None}}
|
||||
)
|
||||
graph = build_graph(nodes, [*connect('parameter', 'result')], nodes_with_edges_only=True, cli=argv)
|
||||
self.set_graph_attrs(graph, ['parameter'])
|
||||
self.set_graph_attrs(graph_ref, ['parameter'])
|
||||
graph.graph['layout'] = 'NHWC'
|
||||
|
||||
AddMeanScaleValues().find_and_replace_pattern(graph)
|
||||
(flag, resp) = compare_graphs(graph, graph_ref, 'result', check_op_attrs=True)
|
||||
self.assertTrue(flag, resp)
|
||||
self.check_graph_attrs(graph, graph_ref, ['parameter'])
|
||||
|
||||
def test_mean_values_optimized_and_scale_values_explicit_layout(self):
|
||||
graph_ref = build_graph(nodes, [
|
||||
*connect('parameter', '0:mul_scale'),
|
||||
*connect('scale', '1:mul_scale'),
|
||||
*connect('mul_scale', 'result'),
|
||||
])
|
||||
|
||||
argv = Namespace(
|
||||
mean_scale_values={'parameter': {'scale': np.array([1., 2., 3.]), 'mean': np.array([0., 0., 0.])}},
|
||||
layout_values={'': {'source_layout': 'nchw', 'target_layout': None}}
|
||||
)
|
||||
graph = build_graph(nodes, [*connect('parameter', 'result')], nodes_with_edges_only=True, cli=argv)
|
||||
self.set_graph_attrs(graph, ['parameter'])
|
||||
self.set_graph_attrs(graph_ref, ['parameter'])
|
||||
graph.graph['layout'] = 'NHWC'
|
||||
|
||||
AddMeanScaleValues().find_and_replace_pattern(graph)
|
||||
(flag, resp) = compare_graphs(graph, graph_ref, 'result', check_op_attrs=True)
|
||||
self.assertTrue(flag, resp)
|
||||
self.check_graph_attrs(graph, graph_ref, ['parameter'])
|
||||
|
||||
def test_mean_values_explicit_and_scale_values_explicit_layout(self):
|
||||
graph_ref = build_graph(nodes, [
|
||||
*connect('parameter', '0:add_mean'),
|
||||
*connect('mean', '1:add_mean'),
|
||||
*connect('add_mean', '0:mul_scale'),
|
||||
*connect('scale', '1:mul_scale'),
|
||||
*connect('mul_scale', 'result'),
|
||||
])
|
||||
|
||||
argv = Namespace(mean_scale_values=[[np.array([1., 2., 3.]), np.array([1., 2., 3.])]],
|
||||
layout_values={'': {'source_layout': 'nchw', 'target_layout': None}}
|
||||
)
|
||||
graph = build_graph(nodes, [*connect('parameter', 'result')],
|
||||
nodes_with_edges_only=True, cli=argv)
|
||||
self.set_graph_attrs(graph, ['parameter'])
|
||||
self.set_graph_attrs(graph_ref, ['parameter'])
|
||||
graph.graph['layout'] = 'NHWC'
|
||||
|
||||
AddMeanScaleValues().find_and_replace_pattern(graph)
|
||||
(flag, resp) = compare_graphs(graph, graph_ref, 'result', check_op_attrs=True)
|
||||
self.assertTrue(flag, resp)
|
||||
self.check_graph_attrs(graph, graph_ref, ['parameter'])
|
||||
|
||||
Loading…
Reference in New Issue
Block a user