def load_graph(fname):
with open(fname, "rb") as f:
data = f.read()
model_proto = onnx.ModelProto()
model_proto.ParseFromString(data)
onnx_nodes = model_proto.graph.node
output_names = []
# some pytorch model had empty names - make one up
for node in onnx_nodes:
if not node.name:
node.name = tf2onnx.utils.make_name("was_empty")
g = Graph(onnx_nodes, output_shapes={}, dtypes={}, output_names=output_names)
for i in model_proto.graph.initializer:
v = numpy_helper.to_array(i)
name = i.name
g.initializers[name] = i
dtype = i.data_type
g.set_dtype(name, dtype)
g.set_shape(name, v.shape)
for i in model_proto.graph.input:
name = i.name
if name in g.initializers:
# ignore if it is not a model input
continue
shape = [j.dim_value if hasattr(i.type.tensor_type, "dim_value") else -1
for j in i.type.tensor_type.shape.dim]
dtype = i.type.tensor_type.elem_type
g.set_dtype(name, dtype)
g.set_shape(name, shape)
g.add_graph_input(name, dtype, shape)
for i in model_proto.graph.output:
name = i.name
shape = [j.dim_value if hasattr(i.type.tensor_type, "dim_value") else -1
for j in i.type.tensor_type.shape.dim]
dtype = i.type.tensor_type.elem_type
g.set_dtype(name, dtype)
g.set_shape(name, shape)
output_names.append(name)
# TODO: this is a hack in case a output name does not follow tensorflow convention
for node in g.get_nodes():
for name in node.output:
g._nodes_by_name[name] = node # pylint: disable=protected-access
return g, model_proto.producer_name
def _create_empty_graph(self, inputs, shapes, dtypes):
graph = Graph([], target=self.config.target, opset=self.config.opset)
for inp, shape, dtype in zip(inputs, shapes, dtypes):
graph.add_graph_input(inp, dtype, shape)
return graph
def rewrite(self):
log.debug("enter custom rnn late rewriter")
nodes = self.g.get_nodes()
nodes_to_remove = []
for scan_node in nodes:
if scan_node.type != "Scan":
continue
log.debug("late write for scan node %s", scan_node.name)
num_scan_inputs = scan_node.get_attr("num_scan_inputs").i
if not BodyGraphDict.has_body_graph_info(scan_node.name):
continue
body_graph_meta = BodyGraphDict.pop_body_graph_info(scan_node.name)
onnx_nodes, _ = LoopRewriterBase.find_subgraph(
body_graph_meta, self.g)
log.debug("start creating body graph for scan node %s ",
scan_node.name)
const_nodes = [
n for n in onnx_nodes if n.type in ("Const", "ConstV2")
]
for n in const_nodes:
# when set nodes, Const should be removed, they need be replaced as initializers.
onnx_nodes.remove(n)
onnx_nodes = set(onnx_nodes)
nodes_to_remove.extend(onnx_nodes)
ops = []
for op in onnx_nodes:
onnx_op = op.op
ops.append(onnx_op)
body_g = Graph(ops,
output_shapes=self.g._output_shapes,
dtypes=self.g._dtypes)
log.debug("start preparing body graph inputs nodes")
temp_nodes = body_g.get_nodes()
i = 0
input_count = len(body_graph_meta.input_ids)
for input_name, init_input_id in zip(
body_graph_meta.input_ids,
body_graph_meta.initial_input_ids):
shape = body_g.get_shape(input_name)
dtype = body_g.get_dtype(input_name)
if shape is None:
shape = self.g.get_shape(init_input_id)
if i >= input_count - num_scan_inputs:
loop_input_shape = list(shape)[2:] # delete [1, time,]
else:
loop_input_shape = list(shape)
else:
loop_input_shape = list(shape)
body_g.add_graph_input(input_name, dtype, loop_input_shape)
i += 1
log.debug("start preparing body graph outputs nodes")
new_output_names = []
for o in body_graph_meta.output_ids:
# insert identity node, since sometimes we need output same output_id as state_output
# and scan_out, but ONNX don't allow the same output_id appeared more than once as
# output node.
node = body_g.make_node("Identity",
inputs=[o],
shapes=[body_g.get_shape(o)],
dtypes=[body_g.get_dtype(o)])
new_output_names.append(node.output[0])
temp_nodes.append(node)
body_g.set_nodes(temp_nodes)
body_g.topological_sort(body_g.get_nodes())
log.debug("start make graph based on body graph nodes")
body_g.outputs = new_output_names
graph = body_g.make_graph("scan body graph",
graph_name=scan_node.name +
"_body_graph")
scan_node.set_attr("body", graph)
# remove nodes in body graph from g
for n in set(nodes_to_remove):
if n in nodes:
nodes.remove(n)
return nodes