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)