def attr_value_proto(shape, dtype, attr):
"""Creates a dict of objects matching
https://github.com/tensorflow/tensorboard/blob/master/tensorboard/compat/proto/attr_value.proto
specifically designed for a NodeDef. The values have been
reverse engineered from standard TensorBoard logged data.
"""
attr_proto = {}
if shape is not None:
shapeproto = tensor_shape_proto(shape)
attr_proto["_output_shapes"] = AttrValue(list=AttrValue.ListValue(
shape=[shapeproto]))
if dtype is not None:
attr_proto["dtype"] = AttrValue(s=dtype.encode(encoding="utf-8"))
if attr is not None:
for key in attr.keys():
attr_proto[key] = AttrValue(s=attr[key].encode(encoding="utf-8"))
return attr_proto
def add_variable(v, v_idx):
v_name = parameters.get(v.data, None)
exist = False
if not v_name:
v_name, exist = get_variable_name(v, v_idx)
if not exist:
shape_proto = TensorShapeProto(
dim=[TensorShapeProto.Dim(size=d) for d in v.shape])
if v.parent is None:
inputs = []
else:
inputs = [get_func_name(v.parent)]
# print("Variable: {}:{}".format(v_name, inputs))
nodes.append(
NodeDef(name=v_name.encode(encoding='utf-8'),
op='Variable',
input=inputs,
attr={
'shape': AttrValue(shape=shape_proto),
'dtype': AttrValue(type=DT_FLOAT)
}))
return v_name
def add_func(v):
input_names = []
for index, v_input in enumerate(v.parent.inputs):
v_name = add_variable(v_input, index)
input_names.append(v_name)
# print("Function: {}:{}".format(get_func_name(v.parent), input_names))
f_name = get_func_name(v.parent)
if f_name in func_set:
return False
attrs = []
for k, a in v.parent.info.args.items():
attr = "{}={}".format(k, a)
attrs.append(attr)
attr_str = ','.join(attrs).encode(encoding='utf-8')
nodes.append(
NodeDef(name=f_name,
op=v.parent.info.type_name,
input=input_names,
attr={"parameters": AttrValue(s=attr_str)}))
func_set.add(f_name)
return True
def from_graph_def(self, graph_def):
variables = graph_def.variables
parameters = graph_def.parameters
functions = graph_def.functions
inputs = graph_def.inputs
nodes = []
scope = {}
for n, v in parameters.items():
shape_proto = TensorShapeProto(
dim=[TensorShapeProto.Dim(size=d) for d in v.shape])
node = NodeDef(
name=n.encode(encoding='utf-8'),
op='Parameter',
input=[],
attr={
'shape': AttrValue(shape=shape_proto),
'dtype': AttrValue(type=DT_FLOAT)
}
)
nodes.append(node)
scope[n] = node
for n, v in inputs.items():
shape_proto = TensorShapeProto(
dim=[TensorShapeProto.Dim(size=d) for d in v.shape])
nodes.append(NodeDef(
name=n.encode(encoding='utf-8'),
op='Variable',
input=[],
attr={
'shape': AttrValue(shape=shape_proto),
'dtype': AttrValue(type=DT_FLOAT)
}
))
for func_name, func in functions.items():
for o in func['outputs']:
if o in scope:
node = scope[o]
node.input.extend([func_name])
else:
if o in variables:
v = variables[o]
shape_proto = TensorShapeProto(
dim=[TensorShapeProto.Dim(size=d) for d in v.shape])
node = NodeDef(
name=o.encode(encoding='utf-8'),
op='Variable',
input=[func_name],
attr={
'shape': AttrValue(shape=shape_proto),
'dtype': AttrValue(type=DT_FLOAT)
}
)
nodes.append(node)
for i in func['inputs']:
if i in variables:
v = variables[i]
shape_proto = TensorShapeProto(
dim=[TensorShapeProto.Dim(size=d) for d in v.shape])
node = NodeDef(
name=o.encode(encoding='utf-8'),
op='Variable',
input=[],
attr={
'shape': AttrValue(shape=shape_proto),
'dtype': AttrValue(type=DT_FLOAT)
}
)
nodes.append(node)
scope[o] = node
nodes.append(NodeDef(
name=func_name,
op=func['type'],
input=func['inputs'],
attr={"arguments": AttrValue(s='a=1'.encode(encoding='utf-8'))}
))
current_graph = GraphDef(node=nodes, versions=VersionDef(producer=22))
event = event_pb2.Event(
graph_def=current_graph.SerializeToString())
self.file_writer.add_event(event)