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from tensorboard.compat.proto.graph_pb2 import GraphDef
from tensorboard.compat.proto.node_def_pb2 import NodeDef
from tensorboard.compat.proto.versions_pb2 import VersionDef
from tensorboard.compat.proto.attr_value_pb2 import AttrValue
from tensorboard.compat.proto.tensor_shape_pb2 import TensorShapeProto
def load_onnx_graph(fname):
import onnx
m = onnx.load(fname)
g = m.graph
return parse(g)
def parse(graph):
nodes_proto = []
nodes = []
import itertools
for node in itertools.chain(graph.input, graph.output):
nodes_proto.append(node)
for node in nodes_proto:
print(node.name)
shapeproto = TensorShapeProto(
dim=[
TensorShapeProto.Dim(size=d.dim_value)
for d in node.type.tensor_type.shape.dim
]
)
nodes.append(
NodeDef(
name=node.name.encode(encoding="utf_8"),
op="Variable",
input=[],
attr={
"dtype": AttrValue(type=node.type.tensor_type.elem_type),
"shape": AttrValue(shape=shapeproto),
},
)
)
for node in graph.node:
_attr = []
for s in node.attribute:
_attr.append(" = ".join([str(f[1]) for f in s.ListFields()]))
attr = ", ".join(_attr).encode(encoding="utf_8")
print(node.output[0])
nodes.append(
NodeDef(
name=node.output[0].encode(encoding="utf_8"),
op=node.op_type,
input=node.input,
attr={"parameters": AttrValue(s=attr)},
)
)
# two pass token replacement, appends opname to object id
mapping = {}
for node in nodes:
mapping[node.name] = node.op + "_" + node.name
return GraphDef(node=nodes, versions=VersionDef(producer=22))
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