def replace_pattern(graph: Graph, match: dict):
node = match['op']
if not node.has_port('in', 2) or node.in_port(2).disconnected() or not node.has_and_set('shape_input'):
return
if node.has_valid('layout') and not node.layout.startswith('NC') and graph.graph['layout'] == 'NCHW':
input_shape_rank = len(node.in_port(0).data.get_shape())
permutation = PermuteAttrs.get_nhwc_to_nchw_permutation(input_shape_rank)
data_node = node.in_node(2)
name = node.soft_get('name', node.id) + '/ShapeGather'
const = Const(graph, {'value': permutation.perm, 'name': name + '/Const',
'need_shape_inference': True}).create_node_with_data()
axis_const = Const(graph, {'value': int64_array(0), 'name': name + '/Axis'}).create_node_with_data()
gather = Gather(graph, {'name': name,
'need_shape_inference': True}).create_node_with_data([data_node, const, axis_const])
attrs = graph.get_edge_data(data_node.id, node.id, key=0).copy()
graph.add_edge(gather.id, node.id, **attrs)
graph.remove_edge(data_node.id, node.id)
def strided_slice(op_node: Node, port_info: str, input_port: int):
"""
StridedSLice must be permuted even if input or output tensors have rank lesser than 4
e.g. input_shape = (1, 10, 10), out = input[:, 0:10, :, new_axis], input_rank < 4
input_shape = (1, 10, 10, 3), out = input[:, 0:5, 0:4, 0], output_rank < 4
in both examples slice_rank is >= 4
slice_rank is defined by length of begin, end, strides (they all are of the same length)
"""
permutation_data_node = get_node_with_permutation(op_node, port_info)
assert permutation_data_node.has_and_set('permutation'), 'Data node "{}" does not have permutation for node {}, ' \
'port_info "{}".'.format(permutation_data_node.id,
op_node.id, port_info)
permute_indices_for_gather = permutation_data_node.permutation.perm
if len(permute_indices_for_gather) == 0:
return
from openvino.tools.mo.ops.op import PermuteAttrs
slice_rank = op_node.in_port(input_port).data.get_shape()[
0] # length of begin, end or strides
permute_indices_for_gather = PermuteAttrs.get_nhwc_to_nchw_permutation(
slice_rank).perm
reorder_inputs_for_shape_or_slice(op_node, input_port,
permute_indices_for_gather)
def create_topK_net(shape, k, ir_version, use_new_frontend):
"""
Tensorflow net:
|-> Values
Input -> TopK |
|-> Indices
IR net:
|-> Values
Input -> TopK |
|-> Indices
"""
#
# Create Tensorflow model
#
import tensorflow as tf
tf.compat.v1.reset_default_graph()
# Create the graph and model
with tf.compat.v1.Session() as sess:
shape_net = permute_nchw_to_nhwc(shape)
input_tensor = tf.compat.v1.placeholder(tf.int32,
shape=shape_net,
name='Input')
values, indices = tf.nn.top_k(input_tensor,
k=k,
sorted=True,
name='Operation')
tf.compat.v1.global_variables_initializer()
tf_net = sess.graph_def
#
# Create reference IR net
#
topk_output_shape = shape.copy()
inverse_nhwc_nchw = PermuteAttrs.get_nhwc_to_nchw_permutation(
len(topk_output_shape)).inv
topk_axis = permute_axis(
len(topk_output_shape) - 1,
inverse_nhwc_nchw) # we need to permute axis attribute
topk_output_shape[topk_axis] = k
ref_net = None
if check_ir_version(10, None, ir_version) and not use_new_frontend:
nodes_attributes = {
'input': {
'kind': 'op',
'type': 'Parameter'
},
'input_data': {
'shape': shape,
'kind': 'data'
},
'Const_k_input_data': {
'shape': [],
'kind': 'data'
},
'Const_k': {
'kind': 'op',
'type': 'Const'
},
'Const_k_data': {
'shape': [],
'kind': 'data'
},
'TopK': {
'kind': 'op',
'type': 'TopK',
'axis': topk_axis,
'mode': 'max',
'sort': 'value'
},
'TopK_data_1': {
'shape': topk_output_shape,
'kind': 'data'
},
'TopK_data_2': {
'shape': topk_output_shape,
'kind': 'data'
},
'result_1': {
'kind': 'op',
'type': 'Result'
},
'result_2': {
'kind': 'op',
'type': 'Result'
},
}
ref_net = build_graph(nodes_attributes, [
('input', 'input_data'),
('input_data', 'TopK', {
'in': 0
}),
('Const_k_input_data', 'Const_k'),
('Const_k', 'Const_k_data'),
('Const_k_data', 'TopK', {
'in': 1
}),
('TopK', 'TopK_data_1', {
'out': 0
}),
('TopK', 'TopK_data_2', {
'out': 1
}),
('TopK_data_1', 'result_1'),
('TopK_data_2', 'result_2'),
])
return tf_net, ref_net
def permute_nhwc_to_nchw(shape, use_new_frontend=False):
if use_new_frontend:
return shape
perm = PermuteAttrs.get_nhwc_to_nchw_permutation(len(shape)).perm
new_shape = np.array(shape)[perm]
return new_shape
def find_and_replace_pattern(self, graph: Graph):
for node in list(graph.nodes()):
node = Node(graph, node)
node_name = node.soft_get('name', node.id)
# Check that node layout mismatch with graph layout
# For example: NHWC and NCHW or NCDHW and NDHWC
if node.kind == 'op' and node.has_valid(
'layout') and node.layout != indices_mapping[len(
node.layout)][graph.graph['layout']]:
input = node.in_node()
output = node.out_node()
# Calculate permutation for further Transpose operations
if graph.graph['layout'] == 'NCHW':
# if Node has NCHW and graph has NHWC layout
permutation = PermuteAttrs.get_nhwc_to_nchw_permutation(
len(node.layout))
else:
# if Node has NHWC and graph has NCHW layout
permutation = PermuteAttrs.get_nchw_to_nhwc_permutation(
len(node.layout))
# Schematic representation of transformation below
#
# \ NCHW NCHW
# NHWC -- \ | permutation permutation |
# data-->Convolution(example)-->data -- / | | NCHW | |
# / data->Transpose->data->Convolution->data->Transpose->data
# 1. Insert input Transpose
# This Transpose will permute input from original input layout to operation layout
edge_attrs = graph.get_edge_data(input.id, node.id)[0]
graph.remove_edge(input.id, node.id)
input_permute_name = node_name + '/input_transpose'
input_order_const = Const(
graph, {
'name': input_permute_name + '/order',
'value': permutation.perm
}).create_node_with_data()
input_permute_op = Transpose(graph,
{'name': input_permute_name})
input_permute_data_node = input_permute_op.create_node_with_data(
[input, input_order_const])
graph.add_edge(input_permute_data_node.id, node.id,
**edge_attrs)
# 2. Insert output Transpose
# This Transpose will permute output from operation layout to original input layout
edge_attrs = graph.get_edge_data(node.id, output.id)[0]
graph.remove_edge(node.id, output.id)
input_data_node = Op.create_data_node(
graph, node, {'shape': output.shape[permutation.perm]},
edge_attrs)
output_permute_name = node_name + '/output_transpose'
output_order_const = Const(
graph, {
'name': output_permute_name + '/order',
'value': permutation.inv
}).create_node_with_data()
output_permute_op = Transpose(graph, {
'name': output_permute_name
}).create_node_with_data([input_data_node, output_order_const],
data_nodes=output)
# 3. Add permutations for Node
# Here we use permutation mechanism where data nodes takes permutation attribute.
# And then we call permute_attrs method that permutes node attributes according to permutations on
# data nodes.
node.in_node()['permutation'] = permutation
node.out_node()['permutation'] = permutation
node.permute_attrs.permute_attrs(node)
node.in_node()['permutation'] = None
node.out_node()['permutation'] = None
