TensorRT: [TensorRT] ERROR: Network must have at least one output

Hi, I try to convert my .onnx model to .trt model using tensorrt, my script is:

import tensorrt as trt

def ONNX_build_engine(onnx_file_path, engine_file_path):
    G_LOGGER = trt.Logger(trt.Logger.WARNING)
    with trt.Builder(G_LOGGER) as builder, builder.create_network() as network, trt.OnnxParser(network, G_LOGGER) as parser:
        builder.max_batch_size = 16
        builder.max_workspace_size = 1 << 20

        print('Loading ONNX file from path {}...'.format(onnx_file_path))
        with open(onnx_file_path, 'rb') as model:
            print('Beginning ONNX file parsing')
            parser.parse(model.read())
        print('Completed parsing of ONNX file')

        print('Building an engine from file {}; this may take a while...'.format(onnx_file_path))
        engine = builder.build_cuda_engine(network)
        print("Completed creating Engine")

        with open(engine_file_path, "wb") as f:
            f.write(engine.serialize())
        return engine

ONNX_build_engine('./BiSeNet.onnx', './BiSeNet.trt')

I have tested in my two desktops, one configue:

cuda 10.0 pytorch1.3 torch 5.0.6.6

the other configure:

cuda 10.1 pytorch1.3 torch6.0.1.5

Both have the same error. there is an error:

Loading ONNX file from path ./BiSeNet_simplifier.onnx… Beginning ONNX file parsing Completed parsing of ONNX file Building an engine from file ./BiSeNet_simplifier.onnx; this may take a while… [TensorRT] ERROR: Network must have at least one output Completed creating Engine Traceback (most recent call last): File “onnx2trt.py”, line 31, in <module> ONNX_build_engine(‘./BiSeNet_simplifier.onnx’, ‘./BiSeNet.trt’) File “onnx2trt.py”, line 28, in ONNX_build_engine f.write(engine.serialize()) AttributeError: ‘NoneType’ object has no attribute ‘serialize’

The .onnx file is converted by torch.onnx.export fcn, it seems right.

import torch
from model.build_BiSeNet import BiSeNet

model = BiSeNet(13, 'resnet18').eval()  # .cuda().half().eval()
model.load_state_dict(torch.load('./checkpoints_18_sgd/best_dice_loss_13_class.pth'))
x = torch.ones((1, 3, 720, 960))  # .cuda().half()
output_names = ["output"]
onnx_model = torch.onnx._export(model, x, "BiSeNet.onnx", verbose=False, output_names=output_names, export_params=True) #,  keep_initializers_as_inputs=True)