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)
def from_variable(self, leaf, output_name="output"):
def parse_variable(v, var_num):
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 get_unique_variable_name(v_name_base):
v_num = 0
v_name = v_name_base + str(v_num)
while v_name in unique_var_names:
v_num += 1
v_name = v_name_base + str(v_num)
unique_var_names.add(v_name)
return v_name
def get_variable_name(v, v_idx):
v_name = variables.get(v, None)
if v_name:
return v_name, True
else:
if v.parent is None:
v_name_base = "Input"
v_name = get_unique_variable_name(v_name_base)
elif not nodes:
v_name = output_name
else:
f_name_sections = get_func_name(v.parent).split("/")
f_name = f_name_sections[-1]
f_scope = f_name_sections[:-1]
base_name = "variable<-{}".format(f_name)
v_name_base = "/".join(f_scope + [base_name])
v_name = get_unique_variable_name(v_name_base)
variables[v] = v_name
return v_name, False
def get_func_name(func):
func_name = func_names.get(func, None)
if func_name:
return func_name
name_scope = loc_var['name_scope']
for v in func.inputs:
v_name = self.parameters.get(v.data, None)
if v_name:
name_scope = '/'.join(v_name.split('/')[:-1])
break
if name_scope:
func_name_base = '/'.join([name_scope, func.name])
else:
func_name_base = func.name
func_num = 0
func_name = func_name_base + str(func_num)
while func_name in unique_func_names:
func_num += 1
func_name = func_name_base + str(func_num)
unique_func_names.add(func_name)
func_names[func] = func_name
return func_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
name_scope = loc_var['name_scope']
if not nodes:
add_variable(v, var_num)
if v.parent is None:
add_variable(v, var_num)
else:
if not add_func(v):
return
for idx, in_var in enumerate(v.parent.inputs):
name_scope_stack.append(name_scope)
parse_variable(in_var, idx)
name_scope = name_scope_stack.pop()
nodes = []
variables = {}
loc_var = {}
loc_var['name_scope'] = ''
name_scope_stack = []
func_names = {}
func_set = set()
unique_func_names = set()
unique_var_names = set()
parameters = {v.data: k for k,
v in get_parameters(grad_only=False).items()}
parse_variable(leaf, 0)
nodes = nodes[::-1]
current_graph = GraphDef(node=nodes, versions=VersionDef(producer=22))
event = event_pb2.Event(
graph_def=current_graph.SerializeToString())
self.file_writer.add_event(event)
def graph(node_list):
graph_def = GraphDef(node=node_list, versions=VersionDef(producer=22))
stepstats = RunMetadata(step_stats=StepStats(
dev_stats=[DeviceStepStats(device="/device:CPU:0")]))
return graph_def, stepstats