Try to convert pth to onnx. But error

[caizhaoxin]

I wanna convert pth to onnx format. This is my code:

import torch
from model.faster_rcnn.vgg16 import vgg16
from model.faster_rcnn.resnet import resnet
import numpy as np
from torch.autograd import Variable

def load_model(model, pretrained_path):
print('Loading pretrained model from {}'.format(pretrained_path))
pretrained_dict = torch.load(pretrained_path, map_location=lambda storage, loc: storage)
model.load_state_dict(pretrained_dict, strict=False)
return model

output_onnx = './output.onnx'
raw_weights = './faster_rcnn_1_10_2504.pth'
pascal_classes = np.asarray(['background',
'aeroplane', 'bicycle', 'bird', 'boat',
'bottle', 'bus', 'car', 'cat', 'chair',
'cow', 'diningtable', 'dog', 'horse',
'motorbike', 'person', 'pottedplant',
'sheep', 'sofa', 'train', 'tvmonitor'])

load weight

net = resnet(pascal_classes, 101, pretrained=False, class_agnostic=False)
net.create_architecture()
checkpoint = torch.load(raw_weights)
for k in checkpoint.keys():
print(k)
net.load_state_dict(checkpoint['model'])

initilize the tensor holder here.

im_data = torch.FloatTensor(1)
im_info = torch.FloatTensor(1)
num_boxes = torch.LongTensor(1)
gt_boxes = torch.FloatTensor(1)

ship to cuda

im_data = im_data.cuda()
im_info = im_info.cuda()
num_boxes = num_boxes.cuda()
gt_boxes = gt_boxes.cuda()

make variable

im_data = Variable(im_data, volatile=True)
im_info = Variable(im_info, volatile=True)
num_boxes = Variable(num_boxes, volatile=True)
gt_boxes = Variable(gt_boxes, volatile=True)

net.eval()
print('Finished loading model!')
device = torch.device("cuda")
net = net.to(device)

input_names = ["input0"]
output_names = ["output0"]
inputs = torch.randn(1, 3, 300, 300).to(device)

output model

torch_out = torch.onnx.export(net, inputs, output_onnx, export_params=True, verbose=False,keep_initializers_as_inputs=True, input_names=input_names, output_names=output_names)

but when I run it, I got this error. How can I fix it?Thanks!
Traceback (most recent call last):
File "pth2onnx.py", line 67, in
torch_out = torch.onnx.export(net, inputs, output_onnx, export_params=True, verbose=False,keep_initializers_as_inputs=True, input_names=input_names, output_names=output_names)
File "/usr/local/lib/python3.6/dist-packages/torch/onnx/init.py", line 276, in export
custom_opsets, enable_onnx_checker, use_external_data_format)
File "/usr/local/lib/python3.6/dist-packages/torch/onnx/utils.py", line 94, in export
use_external_data_format=use_external_data_format)
File "/usr/local/lib/python3.6/dist-packages/torch/onnx/utils.py", line 701, in _export
dynamic_axes=dynamic_axes)
File "/usr/local/lib/python3.6/dist-packages/torch/onnx/utils.py", line 459, in _model_to_graph
use_new_jit_passes)
File "/usr/local/lib/python3.6/dist-packages/torch/onnx/utils.py", line 420, in _create_jit_graph
graph, torch_out = _trace_and_get_graph_from_model(model, args)
File "/usr/local/lib/python3.6/dist-packages/torch/onnx/utils.py", line 380, in _trace_and_get_graph_from_model
torch.jit._get_trace_graph(model, args, strict=False, _force_outplace=False, _return_inputs_states=True)
File "/usr/local/lib/python3.6/dist-packages/torch/jit/_trace.py", line 1139, in _get_trace_graph
outs = ONNXTracedModule(f, strict, _force_outplace, return_inputs, _return_inputs_states)(*args, **kwargs)
File "/usr/local/lib/python3.6/dist-packages/torch/nn/modules/module.py", line 889, in _call_impl
result = self.forward(*input, **kwargs)
File "/usr/local/lib/python3.6/dist-packages/torch/jit/_trace.py", line 130, in forward
self._force_outplace,
File "/usr/local/lib/python3.6/dist-packages/torch/jit/_trace.py", line 116, in wrapper
outs.append(self.inner(*trace_inputs))
File "/usr/local/lib/python3.6/dist-packages/torch/nn/modules/module.py", line 887, in _call_impl
result = self._slow_forward(*input, **kwargs)
File "/usr/local/lib/python3.6/dist-packages/torch/nn/modules/module.py", line 860, in _slow_forward
result = self.forward(*input, **kwargs)
TypeError: forward() missing 3 required positional arguments: 'im_info', 'gt_boxes', and 'num_boxes'

[sisinote]

Did you finish the onnx conversion ?