def add_input_data_to_prior_boxes(graph: Graph, input_names: str = ''):
"""
PriorBox layer has data input unlike mxnet.
Need to add data input to _contrib_MultiBoxPrior for
for correct conversion to PriorBox layer.
Parameters
----------
graph : Graph
Graph with loaded model.
"""
if not input_names:
input_names = ('data',)
else:
input_names = input_names.split(',')
input_nodes = {}
for node in graph.nodes():
node = Node(graph, node)
if node.has_valid('op') and node.name in input_names:
input_nodes.update({node.id: node})
if len(input_nodes) > 0:
for node in graph.nodes():
node = Node(graph, node)
if node.has_valid('op') and node.op == '_contrib_MultiBoxPrior':
node.add_input_port(idx=1)
graph.create_edge(list(input_nodes.values())[0], node, out_port=0, in_port=1)
def test_run_with_const_input(self):
inp_shape = (1, 3, 1000, 1000)
nodes = {
**shaped_const_with_data('input', int64_array(inp_shape)),
**regular_op('sizes_const', {'op': 'Const'}),
**{'sizes_const_d': {'kind': 'data', 'value': float32_array([1., 1., 1., 100.])}},
**regular_op_with_empty_data('interpolate', {'type': 'Interpolate', 'shape_calculation_model': 'scales'}),
**result('res'),
}
nodes_ref = {
**shaped_const_with_data('input', int64_array(inp_shape)),
**regular_op('sizes_const', {'op': 'Const', 'returns_shape_value': True}),
**{'sizes_const_d': {'kind': 'data', 'value': float32_array([1., 1., 1., 100.])}},
**regular_op_with_empty_data('interpolate', {'type': 'Interpolate', 'shape_calculation_model': 'scales'}),
**result('res'),
}
edges = [
*connect('input', '0:interpolate'),
*connect('sizes_const', '1:interpolate'),
*connect('interpolate', 'res'),
]
graph = build_graph(nodes, edges)
interp_node = Node(graph, 'interpolate')
interp_node.add_input_port(2)
MarkNodesWithShapeValues().find_and_replace_pattern(graph)
graph_ref = build_graph(nodes_ref, edges)
(flag, resp) = compare_graphs(graph, graph_ref, 'res', check_op_attrs=True)
self.assertTrue(flag, resp)
def test_case1_source(self):
graph = build_graph(nodes,
[('input', 'Op1', {
'in': 0,
'out': 0,
'fw_tensor_debug_info': [('input', 0, 'input')]
})])
graph_ref = build_graph(
nodes, [('input', 'NewOp', {
'in': 0,
'out': 0,
'fw_tensor_debug_info': [('input', 0, 'input')]
})])
input_node = Node(graph, 'input')
new_node = Node(graph, 'NewOp')
new_node.add_input_port(0)
graph.stage = 'front'
new_node.in_port(0).get_connection().set_source(
input_node.out_port(0), "source")
(flag, resp) = compare_graphs(graph,
graph_ref,
'NewOp',
check_op_attrs=True)
self.assertTrue(flag, resp)
self.check_graph_attrs_front(graph, graph_ref)
def normalize_body_graph(loop_node: Node):
loop_name = loop_node.soft_get('name', loop_node.id)
# connect "trip count" input if it is not connected with default value "Infinity" (-1)
if not loop_node.is_in_port_connected(0):
loop_node.add_input_port(0, skip_if_exist=True)
Const(loop_node.graph, {'name': loop_name + '/trip_count', 'value': int64_array(-1)}).\
create_node().out_port(0).connect(loop_node.in_port(0))
# connect "execution condition" input if it is not connected with default value True
if not loop_node.is_in_port_connected(1):
loop_node.add_input_port(1, skip_if_exist=True)
Const(loop_node.graph, {'name': loop_name + '/execution_cond', 'value': np.array(True, dtype=np.bool)}).\
create_node().out_port(0).connect(loop_node.in_port(1))
# scan output need Unsqueeze over axis 0
for record in loop_node.output_port_map:
body_node = Loop.get_body_node_by_internal_id(
loop_node, record['internal_layer_id'])
assert body_node is not None
assert body_node.soft_get('type') == 'Result'
if record['axis'] is not None:
unsqueeze = create_op_with_const_inputs(
loop_node.body, Unsqueeze, {1: int64_array([0])})
body_node.in_port(0).get_connection().insert_node(unsqueeze)
Loop.normalize_input_output_ports(loop_node)
def replace_output_edges(graph: Graph, output_edges_match: dict):
"""
Replacing existing input/output edges with a new ones to a new sub-graph.
:param graph: networkX graph to operate on.
:param output_edges_match: match of output edges between old and new sub-graph.
:return: None
"""
for old_name_port, new_name_port in output_edges_match.items():
old_node_name, old_out_port = __class__.extract_port(old_name_port)
new_node_name, new_out_port = __class__.extract_port(new_name_port)
for src, dst, edge_attrs in graph.out_edges(old_node_name, data=True):
if edge_attrs['out'] == old_out_port:
new_edge_attrs = edge_attrs.copy()
new_edge_attrs['out'] = new_out_port
# Add control_flow ports, as we do not copy control flow ports to new node
if 'control_flow_edge' in new_edge_attrs and new_edge_attrs['control_flow_edge'] is True:
in_port_id = 'control_flow_{}'.format(new_edge_attrs['in'])
out_port_id = 'control_flow_{}'.format(new_edge_attrs['out'])
in_node, out_node = Node(graph, dst), Node(graph, new_node_name)
# if not out_node.has_port('out', out_port_id, control_flow=True):
out_node.add_output_port(out_port_id, control_flow=True, skip_if_exist=True)
# if not in_node.has_port('in', in_port_id, control_flow=True):
in_node.add_input_port(in_port_id, control_flow=True, skip_if_exist=True)
graph.add_edge(new_node_name, dst, **new_edge_attrs)
log.debug("Created edge from {} to {} with attrs: {}".format(new_node_name, dst, new_edge_attrs))
def re_number_input_port(loop_node: Node, old_port_id: int,
new_port_id: int):
loop_node.add_input_port(new_port_id, skip_if_exist=True)
loop_node.in_port(old_port_id).get_connection().set_destination(
loop_node.in_port(new_port_id))
Loop.update_port_map_value(loop_node.input_port_map,
'external_port_id', old_port_id,
new_port_id)
def input_as_const(node: Node, attrs: dict, port: int, bin: str,
value: np.ndarray):
"""
Inserts constant node on input `port` of `node` with `values` and `attrs`. Marks input edge with bin `attribute`
"""
graph = node.graph
const = Const(graph, {'value': value, **attrs}).create_node()
node.add_input_port(port, skip_if_exist=True)
const.out_port(0).connect(node.in_port(port))
node.in_port(port).bin = bin
node.in_port(port).in_attrs.append('bin')
def find_and_replace_pattern(self, graph: Graph):
graph.stage = 'front'
for node_id in graph.nodes(data=False):
node = Node(graph, node_id)
inputs = node.get_sorted_inputs()
outputs = node.get_sorted_outputs()
in_ports_count = node.in_ports_count if node.has_valid(
'in_ports_count') else len(inputs)
out_ports_count = node.out_ports_count if node.has_valid(
'out_ports_count') else len(outputs)
if len(outputs) > out_ports_count > 1:
raise Error("Node {} has more children than it should: " +
"should be {} but there is {}".format(
node_id, out_ports_count, len(outputs)))
node['_in_ports'] = {}
node['_out_ports'] = {}
if in_ports_count is not None:
for idx in range(in_ports_count):
node.add_input_port(idx=idx)
if out_ports_count is not None:
for idx in range(out_ports_count):
node.add_output_port(idx=idx)
idx = 0
for in_node_id, edge_attrs in inputs:
graph.remove_edge(in_node_id, node_id)
if len(Node(graph, in_node_id).out_ports()) == 0:
Node(graph, in_node_id).add_output_port(0)
in_node = Node(graph, in_node_id)
in_node.out_port(edge_attrs['out']).connect(node.in_port(idx))
# need to keep this attribute in edge for correct .mapping file generation and
# for generation of "names" field in IR
in_node.out_edge(
edge_attrs['out']
)['fw_tensor_debug_info'] = edge_attrs['fw_tensor_debug_info']
if idx < in_ports_count - 1:
idx = idx + 1
idx = 0
for out_node_id, edge_attrs in outputs:
graph.remove_edge(node_id, out_node_id)
if len(Node(graph, out_node_id).in_ports()) == 0:
Node(graph, out_node_id).add_input_port(0)
node.out_port(idx).connect(
Node(graph, out_node_id).in_port(edge_attrs['in']))
# need to keep this attribute in edge for correct .mapping file generation and
# for generation of "names" field in IR
node.out_edge(idx)['fw_tensor_debug_info'] = edge_attrs[
'fw_tensor_debug_info']
if idx < out_ports_count - 1:
idx = idx + 1
def create_and_connect_input_data_node(graph: Graph, op_node: Node, attrs: dict = None, edge_attrs: dict = None):
assert op_node is not None and op_node.kind == 'op'
if attrs is None:
attrs = {}
if edge_attrs is None:
edge_attrs = {}
data_node = graph.unique_id(op_node.id)
default_attrs = dict(kind='data', name=data_node, value=None, shape=None, data_type=None, infer=None)
default_attrs.update(attrs)
graph.add_node(data_node, **add_attrs_props(default_attrs))
data_node = Node(graph, data_node)
op_node.add_input_port(edge_attrs['in'], skip_if_exist=True)
graph.add_edges_from([(data_node.id, op_node.id, edge_attrs)])
return data_node
def test_case2_source(self):
graph = build_graph(nodes, [('input', 'input_data'),
('input_data', 'Op1')])
graph_ref = build_graph(nodes, [('input', 'input_data'),
('input_data', 'NewOp')])
op1_node = Node(graph, 'Op1')
new_node = Node(graph, 'NewOp')
new_node.add_input_port(0)
op1_node.in_port(0).get_connection().set_destination(
new_node.in_port(0), "source")
(flag, resp) = compare_graphs(graph,
graph_ref,
'NewOp',
check_op_attrs=True)
self.assertTrue(flag, resp)
self.check_graph_attrs_middle(graph, graph_ref)
def find_and_replace_pattern(self, graph: Graph):
graph.stage = 'front'
# nodes = graph.nodes(data=False).keys()
nodes = graph.nodes(data=False)
for node_id in nodes:
node = Node(graph, node_id)
inputs = node.get_sorted_inputs()
outputs = node.get_sorted_outputs()
in_ports_count = node.in_ports_count if node.has_valid(
'in_ports_count') else len(inputs)
out_ports_count = node.out_ports_count if node.has_valid(
'out_ports_count') else len(outputs)
node['_in_ports'] = {}
node['_out_ports'] = {}
if in_ports_count is not None:
for idx in range(in_ports_count):
node.add_input_port(idx=idx)
if out_ports_count is not None:
for idx in range(out_ports_count):
node.add_output_port(idx=idx)
idx = 0
for in_node_id, edge_attrs in inputs:
graph.remove_edge(in_node_id, node_id)
if len(Node(graph, in_node_id).out_ports()) == 0:
Node(graph, in_node_id).add_output_port(0)
Node(graph, in_node_id).out_port(edge_attrs['out']).connect(
node.in_port(idx))
if idx < in_ports_count - 1:
idx = idx + 1
idx = 0
for out_node_id, edge_attrs in outputs:
graph.remove_edge(node_id, out_node_id)
if len(Node(graph, out_node_id).in_ports()) == 0:
Node(graph, out_node_id).add_input_port(0)
node.out_port(idx).connect(
Node(graph, out_node_id).in_port(edge_attrs['in']))
if idx < out_ports_count - 1:
idx = idx + 1
def test_case1_dest(self):
graph = build_graph(nodes, [('input', 'input_data'),
('input_data', 'Op1')])
graph_ref = build_graph(nodes, [('input', 'input_data'),
('input_data', 'Op1'),
('input_data', 'NewOp')])
input_node_data = Node(graph_ref, 'input_data')
del input_node_data['fw_tensor_debug_info']
input_node = Node(graph, 'input')
new_node = Node(graph, 'NewOp')
new_node.add_input_port(0)
new_node.in_port(0).get_connection().set_source(
input_node.out_port(0), "dest")
(flag, resp) = compare_graphs(graph,
graph_ref,
'NewOp',
check_op_attrs=True)
self.assertTrue(flag, resp)
self.check_graph_attrs_middle(graph, graph_ref)
def read_node(file_descr, graph, component_layer_map, layer_node_map):
s = file_descr.readline()
if s == b'\n':
return False
tokens = s.split(b' ')
if tokens[0] == b'input-node':
in_name = s[s.find(b'name=') + len(b'name='):].split(b' ')[0]
in_name = str(in_name).strip('b').replace('\'', "")
in_shape = np.array(
[1, s[s.find(b'dim=') + len(b'dim='):].split(b' ')[0]],
dtype=np.int)
if in_name not in layer_node_map:
graph.add_node(in_name,
name=in_name,
kind='op',
op='Parameter',
parameters=None,
shape=in_shape)
layer_node_map[in_name] = in_name
else:
Node(graph, in_name)['op'] = 'Parameter'
Node(graph, in_name)['shape'] = in_shape
elif tokens[0] == b'component-node':
layer_name = s[s.find(b'name=') + len(b'name='):].split(b' ')[0]
layer_name = str(layer_name).strip('b').replace('\'', "")
component_name = s[s.find(b'component=') +
len(b'component='):].split(b' ')[0]
if layer_name not in layer_node_map:
node_name = graph.unique_id(prefix=layer_name)
graph.add_node(node_name, parameters=None, op=None, kind='op')
layer_node_map[layer_name] = node_name
else:
node_name = layer_node_map[layer_name]
if component_name in component_layer_map:
component_layer_map[component_name].append(node_name)
else:
component_layer_map[component_name] = [node_name]
# parse input
in_node_id = parse_input_for_node(s[s.find(b'input=') + 6:], graph,
layer_node_map)
# don't create cyclic edges node to itself to avoid removing later
if in_node_id != node_name:
out_port = len(Node(graph, in_node_id).out_nodes())
in_port = len(Node(graph, node_name).in_nodes())
Node(graph, node_name).add_input_port(in_port)
Node(graph, in_node_id).add_output_port(out_port,
skip_if_exist=True)
graph.add_edge(
in_node_id, node_name,
**create_edge_attrs(in_node_id, node_name, in_node_id, in_port,
out_port))
elif tokens[0] == b'output-node':
layer_name = s[s.find(b'name=') + len(b'name='):].split(b' ')[0]
layer_name = str(layer_name).strip('b').replace('\'', "")
node_name = graph.unique_id(prefix=layer_name)
graph.add_node(node_name, parameters=None, op='Identity', kind='op')
out_name = graph.unique_id(prefix=node_name + "_out")
graph.add_node(out_name, parameters=None, op='Result', kind='op')
Node(graph, node_name).add_input_port(0)
Node(graph, node_name).add_output_port(0)
Node(graph, out_name).add_input_port(0)
graph.add_edge(node_name, out_name,
**create_edge_attrs(node_name, out_name, node_name))
# parse input
in_node_id = parse_input_for_node(
s[s.find(b'input=') + len(b'input='):], graph, layer_node_map)
out_port = len(Node(graph, in_node_id).out_nodes())
Node(graph, in_node_id).add_output_port(out_port)
graph.create_edge(
Node(graph, in_node_id), Node(graph, node_name), out_port, 0,
create_edge_attrs(in_node_id, node_name, in_node_id, 0, out_port))
objective_type = s[s.find(b'objective=') +
10:].split(b' ')[0].split(b'\n')[0]
if objective_type != b'linear':
raise Error(
"Unsupported objective-type for output {}".format(node_name))
elif tokens[0] == b'dim-range-node':
layer_name = s[s.find(b'name=') + len(b'name='):].split(b' ')[0]
layer_name = str(layer_name).strip('b').replace('\'', "")
offset = int(s[s.find(b'dim-offset=') +
len(b'dim-offset='):].split(b' ')[0])
dim = int(s[s.find(b'dim=') + len(b'dim='):].split(b' ')[0])
if layer_name in layer_node_map:
node_name = layer_node_map[layer_name]
node = Node(graph, node_name)
node['parameters'] = {
'offset': np.array([offset]),
'dim': np.array([dim]),
'axis': np.array([1])
}
node['op'] = 'Crop'
else:
node_name = graph.unique_id(prefix=layer_name)
graph.add_node(node_name,
parameters={
'offset': np.array([offset]),
'dim': np.array([dim]),
'axis': np.array([1])
},
op='Crop',
kind='op')
layer_node_map[layer_name] = node_name
node = Node(graph, node_name)
in_node_id = parse_input_for_node(
s[s.find(b'input-node=') + len(b'input-node='):], graph,
layer_node_map)
out_port = len(Node(graph, in_node_id).out_nodes())
in_port = len(Node(graph, node_name).in_nodes())
node.add_input_port(in_port)
Node(graph, in_node_id).add_output_port(out_port)
graph.create_edge(
Node(graph, in_node_id), node, out_port, in_port,
create_edge_attrs(in_node_id, node_name, in_node_id, in_port,
out_port))
# read dim info where possible to simplify shape calculation for MemoryOffset
# shape calculation for MemoryOffset can't be done through shape of previous layer because
# it is separated in 2 parts to remove cycle from graph
for o_n_name, params in node.get_outputs():
o_n = Node(graph, o_n_name)
if o_n['op'] == 'MemoryOffset':
o_n['parameters']['element_size'] = int64_array([1, dim])
else:
raise Error("Unsupported node specifier {}".format(tokens[0]))
return True
def load_parallel_component(file_descr, graph: Graph, prev_layer_id):
"""
Load ParallelComponent of the Kaldi model.
ParallelComponent contains parallel nested networks.
VariadicSplit is inserted before nested networks.
Outputs of nested networks concatenate with layer Concat.
:param file_descr: descriptor of the model file
:param graph: graph with the topology.
:param prev_layer_id: id of the input layers for parallel component layer
:return: id of the concat layer - last layer of the parallel component layers
"""
nnet_count = read_token_value(file_descr, b'<NestedNnetCount>')
log.debug(
'Model contains parallel component with {} nested networks'.format(
nnet_count))
split_points = []
outputs = []
inputs = []
for i in range(nnet_count):
read_token_value(file_descr, b'<NestedNnet>')
collect_until_token(file_descr, b'<Nnet>')
g = Graph()
load_kalid_nnet1_model(g, file_descr, 'Nested_net_{}'.format(i))
# input to nnet1 models is of a rank 1 but we also insert batch_size to 0th axis
# 1st axis contains input_size of the nested subnetwork
# we split input from the main network to subnetworks
input_node = Node(g, 'Parameter')
split_points.append(input_node['shape'][1])
g.remove_node(input_node.id)
mapping = {
node: graph.unique_id(node)
for node in g.nodes(data=False) if node in graph
}
g = nx.relabel_nodes(g, mapping)
for val in mapping.values():
g.node[val]['name'] = val
graph.add_nodes_from(g.nodes(data=True))
graph.add_edges_from(g.edges(data=True))
sorted_nodes = tuple(nx.topological_sort(g))
outputs.append(Node(graph, sorted_nodes[-1]))
inputs.append(Node(graph, sorted_nodes[0]))
split_id = graph.unique_id(prefix='NestedNets/VariadicSplit')
attrs = {
'out_ports_count': nnet_count,
'size_splits': split_points,
'axis': 1,
'name': split_id
}
variadic_split_node = AttributedVariadicSplit(graph, attrs).create_node()
prev_layer_node = Node(graph, prev_layer_id)
prev_layer_node.add_output_port(0)
graph.create_edge(
prev_layer_node, variadic_split_node, 0, 0,
create_edge_attrs(prev_layer_id, variadic_split_node.id,
prev_layer_id))
concat_id = graph.unique_id(prefix='Concat')
graph.add_node(concat_id, parameters=None, op='concat', kind='op')
concat_node = Node(graph, concat_id)
# Connect each output of variadic_split_node to each subnetwork's inputs in ParallelComponent
# and each subnetwork's output to concat_node
for i, (input_node, output_node) in enumerate(zip(inputs, outputs)):
output_node.add_output_port(0)
concat_node.add_input_port(i)
graph.create_edge(
output_node, concat_node, 0, i,
create_edge_attrs(output_node.id, concat_id, output_node.id, i, 0))
graph.create_edge(
variadic_split_node, input_node, i, 0,
create_edge_attrs(variadic_split_node.id, input_node.id,
variadic_split_node.id, 0, i))
return concat_id
def build_graph_with_attrs(nodes_with_attrs: list,
edges_with_attrs: list,
new_nodes_with_attrs: list = [],
new_edges_with_attrs: list = [],
update_edge_attrs: dict = None,
update_nodes_attributes: dict = None,
nodes_with_edges_only: bool = False,
add_nodes_from_edges: bool = False):
"""
Build the Graph with specific nodes and edges. Also update of edge and node parameters is supported.
:param nodes_with_attrs: list of tuples ('node_name', {node_attrs})
:param edges_with_attrs: list of tuples like (start node, end node, (optional) {attrs of the edge}).
:param new_nodes_with_attrs: analogically nodes_with_attrs
:param new_edges_with_attrs: analogically new_edges
:param update_edge_attrs: optional dictionary like {('from_node', 'to_node', key): {edge_attrs}}.
:param update_nodes_attributes: optional dictionary which specifies nodes names and their attributes to be updated. The
key is a node name to update attribute and the value is a dictionary with attribute name and its value.
:param nodes_with_edges_only: add nodes which has at least one incoming or outcoming edge.
:param add_nodes_from_edges: whether nodes that is not listed in all_nodes but are in all_edges is allowed.
:return: generated graph.
"""
if not_all_new([node[0] for node in nodes_with_attrs],
[node[0] for node in new_nodes_with_attrs]):
raise Error(
'Some nodes from new_nodes_with_attrs are already in nodes.'
' Please, add to new_nodes_with_attrs only NEW nodes.')
if not_all_new([(edge[0], edge[1]) for edge in edges_with_attrs],
[(edge[0], edge[1]) for edge in new_edges_with_attrs]):
raise Error(
'Some edges from new_edges_with_attrs are already in edges.'
' Please, add to new_edges_with_attrs only NEW edges.')
# Check that all nodes from list of edges are in nodes
all_nodes = nodes_with_attrs + new_nodes_with_attrs
all_edges = edges_with_attrs + new_edges_with_attrs
all_nodes_names = [node[0] for node in all_nodes]
if not add_nodes_from_edges and not all_edges_in_nodes(
nodes=all_nodes_names, edges=all_edges):
raise Error(
"Some nodes from list of edges is not in nodes. Please, add all necessary nodes."
)
graph = Graph()
# Create dict for nodes with attrs
nodes_attrs = {}
for node_name, attrs in all_nodes:
nodes_attrs[node_name] = attrs
if 'name' not in attrs:
attrs['name'] = node_name
if nodes_with_edges_only:
# filter nodes to keep only ones with edges connected
filtered_nodes = {}
for edge in all_edges:
node_1, node_2 = edge[0], edge[1]
filtered_nodes[node_1] = nodes_attrs[node_1]
filtered_nodes[node_2] = nodes_attrs[node_2]
nodes_attrs = filtered_nodes
# Create all nodes
for node, attrs in nodes_attrs.items():
graph.add_node(node, **deepcopy(attrs))
# Connect nodes with edges (also unpack edge params)
for edge in all_edges:
node_1, node_2 = edge[0], edge[1]
edge_attrs = edge[2] if len(edge) == 3 else {}
graph.add_edge(node_1, node_2, **edge_attrs)
# Update attributes of edges
if update_edge_attrs:
# it will work in 2.x networkx only
for edge, attr in update_edge_attrs.items():
for k, v in attr.items():
nx.set_edge_attributes(G=graph, name=k, values={edge: v})
# Update attributes of nodes
if update_nodes_attributes is not None:
for node_name, new_attrs in update_nodes_attributes:
assert (node_name in graph.nodes())
for attr, value in new_attrs.items():
graph.node[node_name][attr] = value
for node_id in graph.nodes():
node = Node(graph, node_id)
check_and_update_ports(node, [
graph.get_edge_data(edge[0], node_id)[0]
for edge in graph.in_edges(node_id)
], True)
check_and_update_ports(node, [
graph.get_edge_data(node_id, edge[1])[0]
for edge in graph.out_edges(node_id)
], False)
for node in graph.get_op_nodes():
# Add in_ports attribute
in_edges = node.in_edges()
for i in range(len(in_edges)):
node.add_input_port(idx=i)
# Add out_ports attribute
out_edges = node.out_edges()
for i in range(len(out_edges)):
node.add_output_port(idx=i)
return graph
def muladd_to_scaleshift_action(graph: Graph, match: dict):
mul = match['mul']
add = match['add']
output = match['output']
# Pass works correctly only in case when node have only 1 output
if len(mul.out_port(0).get_destinations()) > 1:
return
if mul.soft_get('can_be_scaleshift') is False or add.soft_get(
'can_be_scaleshift') is False:
return
mul_weights_id = get_value_id(mul)
mul_input_id = get_tensor_id(mul)
add_weights_id = get_value_id(add)
if mul_weights_id is None:
log.debug("Mul->Add to ScaleShift: Mul {} has no weights".format(
mul.name))
return
if mul_input_id is None:
log.debug("Mul->Add to ScaleShift: Mul {} has no input".format(
mul.name))
return
if add_weights_id is None:
log.debug("Mul->Add to ScaleShift: Add {} has no weights".format(
add.name))
return
input = mul.in_node(mul_input_id)
weights = mul.in_node(mul_weights_id)
bias = add.in_node(add_weights_id)
# Transform values
weights.value = np.squeeze(weights.value)
weights.shape = np.array(weights.value.shape, dtype=np.int64)
bias.value = np.squeeze(bias.value)
bias.shape = np.array(bias.value.shape, dtype=np.int64)
# Broadcast weights if they are scalar
if weights.value.ndim == 0 and bias.value.ndim == 1:
weights.value = np.full(bias.shape, weights.value.item())
weights.shape = np.array(weights.value.shape, dtype=np.int64)
if bias.shape != weights.shape:
log.warning('Mul->Add to ScaleShift conversion stoped {} != {}'.format(
weights.shape, bias.shape))
return
if bias.value.ndim != weights.value.ndim or bias.value.size != weights.value.size:
log.debug(
"Skipping Mul->Add to ScaleShift conversion for nodes {}, {} because of different weights "
"and biases".format(mul.name, add.name))
return
if bias.value.size == 1 and weights.value.size == 1:
log.debug(
"Skipping Mul->Add to ScaleShift conversion for nodes {}, {}. Will be converted to Power"
"".format(mul.name, add.name))
return
op_name = "ScaleShift"
log.debug(
"Fusing Mul->Add to {}. Input nodes: {} and {}, bias.shape = {}, weights.shape = {}"
"".format(op_name, mul.id, add.id, bias.shape, weights.shape))
graph.remove_edge(input.node, mul.id)
graph.remove_edge(weights.node, mul.id)
graph.remove_edge(bias.node, add.id)
graph.remove_edge(add.node, output.id)
op_node = graph.unique_id(mul.name + '/Fused{}_'.format(op_name))
graph.add_node(
op_node,
**add_attrs_props(
dict(kind='op',
type=op_name,
name=op_node,
op=op_name,
data_type=input.data_type)))
scsh = Node(graph, op_node)
scsh.add_input_port(0)
scsh.add_input_port(1)
scsh.add_input_port(2)
scsh.add_output_port(0)
update_ie_fields(graph.node[op_node])
graph.add_edges_from([(input.node, op_node, {
'in': 0
}), (weights.node, op_node, {
'in': 1,
'bin': 'weights'
}), (bias.node, op_node, {
'in': 2,
'bin': 'biases'
}), (op_node, output.node, {
'out': 0
})])
return
def merge_nodes(graph: Graph,
nodes_to_merge_names: list,
inputs_desc: list = None,
outputs_desc: list = None):
"""
Merges nodes specified in the set 'nodes_to_merge_names' into one mega-node, creating new edges between mega-node
and inputs/outputs nodes of the mega-node. The added edges contain name of input/output nodes which will be used for
generation of placeholders and will be saved to the IR xml so IE plug-in know how to map input/output data for the
layer. Also the function adds protobufs of the nodes of the sub-graph and 'Const' ops consumed by nodes in the
sub-graph to the node's attribute 'pbs'.
:param graph: the graph object to operate on.
:param nodes_to_merge_names: list of nodes names that should be merged into a single node.
:param inputs_desc: optional list describing input nodes order.
:param outputs_desc: optional list describing output nodes order.
"""
if not is_connected_component(graph, nodes_to_merge_names):
log.warning(
"The following nodes do not form connected sub-graph: {}".format(
nodes_to_merge_names))
# graph.dump_graph_for_graphviz(nodes_to_dump=nodes_to_merge_names)
new_node_name = graph.unique_id("TFSubgraphCall_")
log.info("Create new node with name '{}' for nodes '{}'".format(
new_node_name, ', '.join(nodes_to_merge_names)))
graph.add_node(new_node_name)
new_node_attrs = graph.node[new_node_name]
new_node_attrs['name'] = new_node_name
set_tf_custom_call_node_attrs(new_node_attrs)
new_node = Node(graph, new_node_name)
added_input_tensors_names = set(
) # set of tensors that are were added as input to the sub-graph
added_new_node_output_tensors = dict(
) # key - tensor name, value - out port
for node_name in nodes_to_merge_names:
node = Node(graph, node_name)
add_node_pb_if_not_yet_added(node, new_node)
# TODO: any improvements?
for in_node_name, edge_attrs in Node(graph, node_name).get_inputs():
in_node = Node(graph, in_node_name)
# internal edges between nodes of the sub-graph
if in_node_name in nodes_to_merge_names:
add_node_pb_if_not_yet_added(in_node, new_node)
continue
# edge outside of sub-graph into sub-graph
if in_node_name not in nodes_to_merge_names:
# we cannot use the 'in_node_name' as a protobuf operation name here
# because the 'in_node_name' could be a sub-graph matched before.
input_tensor_name = node.pb.input[edge_attrs['in']]
if input_tensor_name not in added_input_tensors_names:
if not new_node.has_port('in', edge_attrs['in']):
new_node.add_input_port(edge_attrs['in'])
graph.add_edge(
in_node_name, new_node_name,
**merge_edge_props(
{
'in':
find_input_port(new_node, inputs_desc,
node_name, edge_attrs['in']),
'out':
edge_attrs['out'],
'internal_input_node_name':
input_tensor_name,
'original_dst_node_name':
node_name,
'original_dst_port':
edge_attrs['in'],
'in_attrs': [
'in', 'internal_input_node_name',
'original_dst_node_name',
'original_dst_port', 'placeholder_name'
],
'out_attrs': ['out']
}, edge_attrs))
log.debug(
"Creating edge from outside of sub-graph to inside sub-graph: {} -> {}"
.format(in_node_name, new_node_name))
added_input_tensors_names.add(input_tensor_name)
# edge from inside sub-graph to outside sub-graph
for out_node_name, edge_attrs in Node(graph, node_name).get_outputs():
if out_node_name not in nodes_to_merge_names:
log.debug(
"Creating edge from inside of sub-graph to outside sub-graph: {} -> {}"
.format(new_node_name, out_node_name))
out_name = internal_output_name_for_node(
node_name, edge_attrs['out'])
if out_name not in added_new_node_output_tensors.keys():
added_new_node_output_tensors[out_name] = find_output_port(
new_node, outputs_desc, node_name, edge_attrs['out'])
if not new_node.has_port(
'out', added_new_node_output_tensors[out_name]):
new_node.add_output_port(
added_new_node_output_tensors[out_name])
graph.add_edge(
new_node_name, out_node_name,
**merge_edge_props(
{
'in': edge_attrs['in'],
'out': added_new_node_output_tensors[out_name],
'internal_output_node_name': out_name,
'in_attrs': ['in', 'internal_input_node_name'],
'out_attrs': ['out', 'internal_output_node_name']
}, edge_attrs))
new_node['output_tensors_names'] = [
val for val in
{v: k
for k, v in added_new_node_output_tensors.items()}.values()
]
# add nodes using the same order as in initial GraphDef so we can dump them to IR in "correct" order
new_node['nodes_order'] = [
node for node in graph.graph['initial_nodes_order']
if node in new_node['pbs'].keys()
]
for n in nodes_to_merge_names:
if graph.has_node(
n): # check if not deleted by another (similar) pattern
graph.remove_node(n)
return Node(graph, new_node_name)
def load_parallel_component(file_descr, graph: Graph, prev_layer_id):
"""
Load ParallelComponent of the Kaldi model.
ParallelComponent contains parallel nested networks.
Slice is inserted before nested networks.
Outputs of nested networks concatenate with layer Concat.
:param file_descr: descriptor of the model file
:param graph: graph with the topology.
:param prev_layer_id: id of the input layers for parallel component layer
:return: id of the concat layer - last layer of the parallel component layers
"""
nnet_count = read_token_value(file_descr, b'<NestedNnetCount>')
log.debug(
'Model contains parallel component with {} nested networks'.format(
nnet_count))
slice_id = graph.unique_id(prefix='Slice')
graph.add_node(slice_id, parameters=None, op='slice', kind='op')
slice_node = Node(graph, slice_id)
Node(graph, prev_layer_id).add_output_port(0)
slice_node.add_input_port(0)
graph.create_edge(Node(graph, prev_layer_id), slice_node, 0, 0)
slices_points = []
outputs = []
for i in range(nnet_count):
read_token_value(file_descr, b'<NestedNnet>')
collect_until_token(file_descr, b'<Nnet>')
g = Graph()
load_kalid_nnet1_model(g, file_descr, 'Nested_net_{}'.format(i))
input_nodes = [
n for n in graph.nodes(data=True) if n[1]['op'] == 'Parameter'
]
shape = input_nodes[0][1]['shape']
if i != nnet_count - 1:
slices_points.append(shape[1])
g.remove_node(input_nodes[0][0])
mapping = {
node: graph.unique_id(node)
for node in g.nodes(data=False) if node in graph
}
g = nx.relabel_nodes(g, mapping)
for val in mapping.values():
g.node[val]['name'] = val
graph.add_nodes_from(g.nodes(data=True))
graph.add_edges_from(g.edges(data=True))
sorted_nodes = tuple(nx.topological_sort(g))
edge_attrs = create_edge_attrs(slice_id, sorted_nodes[0])
edge_attrs['out'] = i
Node(graph, slice_id).add_output_port(i)
Node(graph, sorted_nodes[0]).add_input_port(
len(Node(graph, sorted_nodes[0]).in_ports()))
graph.create_edge(Node(graph, slice_id), Node(graph, sorted_nodes[0]),
i, 0)
outputs.append(sorted_nodes[-1])
packed_sp = struct.pack("B", 4) + struct.pack("I", len(slices_points))
for i in slices_points:
packed_sp += struct.pack("I", i)
slice_node.parameters = io.BytesIO(packed_sp)
concat_id = graph.unique_id(prefix='Concat')
graph.add_node(concat_id, parameters=None, op='concat', kind='op')
for i, output in enumerate(outputs):
edge_attrs = create_edge_attrs(output, concat_id)
edge_attrs['in'] = i
Node(graph, output).add_output_port(0)
Node(graph, concat_id).add_input_port(i)
graph.create_edge(Node(graph, output), Node(graph, concat_id), 0, i)
return concat_id
