def _get_inout(self):
input_features = []
output_features = []
for input_node in self.IR_graph.input_layers:
shape = shape_to_list(self.IR_graph.get_node(input_node).get_attr('shape'))
shape = _infer_coreml_input_shape(shape)
input_features.append((str(input_node), shape))
print("CoreML Model Input Layer: [{}] {}".format(input_node, shape))
for output_node in self.IR_graph.output_layers:
node = self.IR_graph.get_node(output_node)
node.out_edges.append(node.name)
shape = node.get_attr('_output_shapes')
if shape:
shape = shape_to_list(shape[0])
else:
shape = [1]
if shape == []:
pre_output_node = self.IR_graph.get_node(node.in_edges[0])
pre_output_node.out_edges.append(pre_output_node.name)
shape = pre_output_node.get_attr('_output_shapes')
shape = shape_to_list(shape[0])
# else:
shape = _infer_coreml_input_shape(shape)
output_features.append((str(node.in_edges[0]), shape))
print("CoreML Model Output Layer: [{}] {}".format(output_node, shape))
return list(input_features), list(output_features)
def _get_inout(self):
input_features = []
output_features = []
for input_node in self.IR_graph.input_layers:
shape = shape_to_list(self.IR_graph.get_node(input_node).get_attr('shape'))
shape = _infer_coreml_input_shape(shape)
input_features.append((input_node.encode(), shape))
print("CoreML Model Input Layer: [{}] {}".format(input_node, shape))
for output_node in self.IR_graph.output_layers:
node = self.IR_graph.get_node(output_node)
node.out_edges.append(node.name)
shape = node.get_attr('_output_shapes')
if shape:
shape = shape_to_list(shape[0])
else:
shape = [1]
if shape == []:
pre_output_node = self.IR_graph.get_node(node.in_edges[0])
pre_output_node.out_edges.append(pre_output_node.name)
shape = pre_output_node.get_attr('_output_shapes')
shape = shape_to_list(shape[0])
# else:
shape = _infer_coreml_input_shape(shape)
output_features.append((node.in_edges[0].encode(), shape))
print("CoreML Model Output Layer: [{}] {}".format(output_node, shape))
return list(input_features), list(output_features)
def _get_inout(self):
input_features = []
output_features = []
for input_node in self.IR_graph.input_layers:
if self.IR_graph.get_node(input_node).type == 'Const':
continue
shape = shape_to_list(
self.IR_graph.get_node(input_node).get_attr('shape'))
shape = _infer_coreml_input_shape(shape)
input_features.append((str(input_node), shape))
print("CoreML Model Input Layer: [{}] {}".format(
input_node, shape))
for output_node in self.IR_graph.output_layers:
node = self.IR_graph.get_node(output_node)
if node.type == 'Pack':
continue
node.out_edges.append(node.name)
shape = node.get_attr('_output_shapes')
if shape:
shape = shape_to_list(shape[0])
else:
shape = [1]
shape = _infer_coreml_input_shape(shape)
output_features.append((str(output_node), shape))
print("CoreML Model Output Layer: [{}] {}".format(
output_node, shape))
return list(input_features), list(output_features)