Error while using the yolo2_onnx model

When I try this :
from Inference import Inference

b = Inference(modelName="yolo2",backend='tensorflow', device='CPU')
str_, time_cost = b.predict()

I get the following error:

Onnx file export [./onnx/yolo2.onnx]...start
Traceback (most recent call last):

File "", line 1, in
runfile('C:/Users/hi/Desktop/bp/onnx/yolo2_onnx/test4.py', wdir='C:/Users/hi/Desktop/bp/onnx/yolo2_onnx')

File "C:\Users\hi\Anaconda3\lib\site-packages\spyder\utils\site\sitecustomize.py", line 705, in runfile
execfile(filename, namespace)

File "C:\Users\hi\Anaconda3\lib\site-packages\spyder\utils\site\sitecustomize.py", line 102, in execfile
exec(compile(f.read(), filename, 'exec'), namespace)

File "C:/Users/hi/Desktop/bp/onnx/yolo2_onnx/test4.py", line 3, in
b = Inference(modelName="yolo2",backend='tensorflow', device='CPU')

File "C:\Users\hi\Desktop\bp\onnx\yolo2_onnx\Inference.py", line 27, in init
self.prepare_model_and_backend()

File "C:\Users\hi\Desktop\bp\onnx\yolo2_onnx\Inference.py", line 35, in prepare_model_and_backend
self.is_obj_det, self.w_img, self.h_img = ONNX.save_pretrained_model_to_ONNX(self.modelName)

File "C:\Users\hi\Desktop\bp\onnx\yolo2_onnx\Onnx.py", line 96, in save_pretrained_model_to_ONNX
self.onnx_file_export(m, onnxfilepath)

File "C:\Users\hi\Desktop\bp\onnx\yolo2_onnx\Onnx.py", line 54, in onnx_file_export
torch.onnx.export(model, dummy_input, onnxfilepath )

File "C:\Users\hi\Anaconda3\lib\site-packages\torch\onnx_init_.py", line 26, in export
return utils.export(*args, **kwargs)

File "C:\Users\hi\Anaconda3\lib\site-packages\torch\onnx\utils.py", line 94, in export
operator_export_type=operator_export_type)

File "C:\Users\hi\Anaconda3\lib\site-packages\torch\onnx\utils.py", line 226, in _export
example_outputs, propagate)

File "C:\Users\hi\Anaconda3\lib\site-packages\torch\onnx\utils.py", line 177, in _model_to_graph
graph, torch_out = _trace_and_get_graph_from_model(model, args, training)

File "C:\Users\hi\Anaconda3\lib\site-packages\torch\onnx\utils.py", line 144, in _trace_and_get_graph_from_model
trace, torch_out = torch.jit.get_trace_graph(model, args)

File "C:\Users\hi\Anaconda3\lib\site-packages\torch\jit_init_.py", line 77, in get_trace_graph
return LegacyTracedModule(f)(*args, **kwargs)

File "C:\Users\hi\Anaconda3\lib\site-packages\torch\nn\modules\module.py", line 475, in call
result = self._slow_forward(*input, **kwargs)

File "C:\Users\hi\Anaconda3\lib\site-packages\torch\nn\modules\module.py", line 455, in _slow_forward
return self.forward(*input, **kwargs)

File "C:\Users\hi\Anaconda3\lib\site-packages\torch\jit_init_.py", line 109, in forward
out = self.inner(*trace_inputs)

File "C:\Users\hi\Anaconda3\lib\site-packages\torch\nn\modules\module.py", line 475, in call
result = self._slow_forward(*input, **kwargs)

File "C:\Users\hi\Anaconda3\lib\site-packages\torch\nn\modules\module.py", line 465, in _slow_forward
result = self.forward(*input, **kwargs)

File "C:\Users\hi\Desktop\bp\onnx\yolo2_onnx\darknet.py", line 91, in forward
x = self.modelsind

File "C:\Users\hi\Anaconda3\lib\site-packages\torch\nn\modules\module.py", line 475, in call
result = self._slow_forward(*input, **kwargs)

File "C:\Users\hi\Anaconda3\lib\site-packages\torch\nn\modules\module.py", line 465, in _slow_forward
result = self.forward(*input, **kwargs)

File "C:\Users\hi\Anaconda3\lib\site-packages\torch\nn\modules\container.py", line 91, in forward
input = module(input)

File "C:\Users\hi\Anaconda3\lib\site-packages\torch\nn\modules\module.py", line 475, in call
result = self._slow_forward(*input, **kwargs)

File "C:\Users\hi\Anaconda3\lib\site-packages\torch\nn\modules\module.py", line 465, in _slow_forward
result = self.forward(*input, **kwargs)

File "C:\Users\hi\Anaconda3\lib\site-packages\torch\nn\modules\conv.py", line 301, in forward
self.padding, self.dilation, self.groups)

TypeError: 'float' object cannot be interpreted as an integer

Reproduce the yolo2 path from Inference.py:27-35 and follow the export from Onnx.py:54 and :96 into darknet.py:91. Start with the torch.onnx.export call and the convolution traceback, then verify that model export completes without the reported TypeError and that the shown predict call proceeds.