Issue with demo script. mxnet.base.MXNetError: Cannot find argument 'cls_prob', Possible Arguments:

Hi, I am using this repo for doing Faster rcnn task on my own dataset.
I have done training on my own dataset and i got 70% accuracy after 4 epochs…
I want to visualize the output… so i tried with demo script… i gave 1 input image and tried with the trained model. i changed the class names in demo script.
but i got this error… Could you please let me know whats the problem… Thank You

Traceback (most recent call last):
File “”, line 65, in
cls, scores, bboxes = net(data.as_in_context(ctx), im_info.as_in_context(ctx))
File “/home/ubuntu/anaconda3/lib/python3.6/site-packages/mxnet/gluon/”, line 413, in call
return self.forward(*args)
File “/home/ubuntu/anaconda3/lib/python3.6/site-packages/mxnet/gluon/”, line 629, in forward
return self.hybrid_forward(ndarray, x, *args, **params)
File “/home/ubuntu/gluon-faster-rcnn/rcnn/”, line 69, in hybrid_forward
rois = self.proposal(rpn_cls_prob, rpn_bbox_pred, im_info)
File “/home/ubuntu/anaconda3/lib/python3.6/site-packages/mxnet/gluon/”, line 413, in call
return self.forward(*args)
File “/home/ubuntu/anaconda3/lib/python3.6/site-packages/mxnet/gluon/”, line 629, in forward
return self.hybrid_forward(ndarray, x, *args, **params)
File “/home/ubuntu/gluon-faster-rcnn/rcnn/”, line 32, in hybrid_forward
threshold=self.rpn_nms_threshold, rpn_min_size=self.rpn_min_size)
File “”, line 82, in MultiProposal
File “/home/ubuntu/anaconda3/lib/python3.6/site-packages/mxnet/_ctypes/”, line 92, in _imperative_invoke
File “/home/ubuntu/anaconda3/lib/python3.6/site-packages/mxnet/”, line 149, in check_call
raise MXNetError(py_str(_LIB.MXGetLastError()))
mxnet.base.MXNetError: Cannot find argument ‘cls_prob’, Possible Arguments:
rpn_pre_nms_top_n : int, optional, default=‘6000’
Number of top scoring boxes to keep after applying NMS to RPN proposals
rpn_post_nms_top_n : int, optional, default=‘300’
Overlap threshold used for non-maximumsuppresion(suppress boxes with IoU >= this threshold
threshold : float, optional, default=0.7
NMS value, below which to suppress.
rpn_min_size : int, optional, default=‘16’
Minimum height or width in proposal
scales : tuple of , optional, default=[4,8,16,32]
Used to generate anchor windows by enumerating scales
ratios : tuple of , optional, default=[0.5,1,2]
Used to generate anchor windows by enumerating ratios
feature_stride : int, optional, default=‘16’
The size of the receptive field each unit in the convolution layer of the rpn,for example the product of all stride’s prior to this layer.
output_score : boolean, optional, default=0
Add score to outputs
iou_loss : boolean, optional, default=0
Usage of IoU Loss
, in operator _contrib_MultiProposal(name="", feature_stride=“16”, ratios="(0.5, 1, 2)", rpn_min_size=“16”, scales="(8, 16, 32)", rpn_post_nms_top_n=“300”, rpn_pre_nms_top_n=“6000”, threshold=“0.7”, cls_prob="
[[[[9.2525011e-01 9.8686647e-01 9.9559492e-01 … 9.6093690e-01
9.3473071e-01 8.3388972e-01]
[9.8144472e-01 9.9909139e-01 9.9984789e-01 … 9.9409735e-01
9.8589975e-01 9.3193233e-01]
[9.8883343e-01 9.9964535e-01 9.9995410e-01 … 9.9763453e-01
9.9354243e-01 9.5571983e-01]

[9.8543328e-01 9.9948043e-01 9.9991584e-01 … 9.9969471e-01
9.9917930e-01 9.8851913e-01]
[9.7469234e-01 9.9870670e-01 9.9970120e-01 … 9.9901140e-01
9.9767345e-01 9.7834754e-01]
[9.0814865e-01 9.8365211e-01 9.9276966e-01 … 9.8466349e-01
9.7399849e-01 9.0880662e-01]]

[[9.0745032e-01 9.8171026e-01 9.9309546e-01 … 9.4973421e-01
9.1728598e-01 8.1418854e-01]
[9.7337264e-01 9.9846858e-01 9.9970394e-01 … 9.9094427e-01
9.7995251e-01 9.1768110e-01]
[9.8243284e-01 9.9936765e-01 9.9990177e-01 … 9.9625152e-01
9.9037081e-01 9.4557309e-01]

[9.7682333e-01 9.9898654e-01 9.9980742e-01 … 9.9938107e-01
9.9859077e-01 9.8415011e-01]
[9.6041822e-01 9.9727988e-01 9.9929476e-01 … 9.9794215e-01
9.9601054e-01 9.7078675e-01]
[8.7193352e-01 9.7200722e-01 9.8639816e-01 … 9.7515827e-01
9.6249181e-01 8.8967586e-01]]

[[5.2806801e-01 5.3886396e-01 5.5010569e-01 … 5.2669793e-01
5.2231640e-01 5.0962281e-01]
[5.3768706e-01 5.5899465e-01 5.7622200e-01 … 5.5065542e-01
5.4214233e-01 5.2825642e-01]
[5.4670048e-01 5.8282024e-01 6.0394657e-01 … 5.6064773e-01
5.5870861e-01 5.4004127e-01]

[5.3445053e-01 5.7814318e-01 6.0343522e-01 … 5.9942263e-01
5.9564185e-01 5.6060779e-01]
[5.3276056e-01 5.7079929e-01 5.9411222e-01 … 5.9032643e-01
5.8910215e-01 5.5843079e-01]
[5.2759832e-01 5.5251533e-01 5.7285386e-01 … 5.6627262e-01
5.6415069e-01 5.4235542e-01]]

[[1.6489255e-01 5.4860741e-02 2.7824294e-02 … 1.1167015e-01
1.5454119e-01 2.7348977e-01]
[7.4070774e-02 1.0540956e-02 3.4242510e-03 … 3.8886167e-02
6.8945184e-02 1.8131968e-01]
[6.2780201e-02 6.9735665e-03 1.9518270e-03 … 2.3429820e-02
4.5364555e-02 1.4465846e-01]

[8.8465296e-02 1.4774417e-02 5.4020169e-03 … 1.1072693e-02
1.9699827e-02 8.5111000e-02]
[1.4203803e-01 3.7630506e-02 2.2168955e-02 … 3.7781410e-02
5.5475168e-02 1.4689194e-01]
[2.8729475e-01 1.7887905e-01 1.5341425e-01 … 1.8521468e-01
2.1700267e-01 3.0219343e-01]]

[[7.6535888e-02 1.3174757e-02 4.5662634e-03 … 3.9024629e-02
6.6420421e-02 1.6296616e-01]
[1.8801216e-02 8.8067626e-04 1.5799509e-04 … 5.9979130e-03
1.4321312e-02 6.7583486e-02]
[1.2135372e-02 3.7127602e-04 5.5430377e-05 … 2.5837927e-03
6.8379878e-03 4.4826828e-02]

[1.6011752e-02 5.7889975e-04 1.1127151e-04 … 3.9832355e-04
9.8138128e-04 1.2958074e-02]
[2.6029671e-02 1.4883390e-03 3.9916934e-04 … 1.2645581e-03
2.7250603e-03 2.4580965e-02]
[1.0069502e-01 1.9385004e-02 9.5264316e-03 … 1.9384181e-02
3.1448375e-02 1.0509791e-01]]

[[4.3997696e-01 3.9228746e-01 3.6535779e-01 … 4.2972672e-01
4.3877992e-01 4.6890491e-01]
[4.0322891e-01 3.3196816e-01 3.0024055e-01 … 3.9013031e-01
4.0616569e-01 4.5436901e-01]
[4.0472379e-01 3.2188171e-01 2.8730047e-01 … 3.8169590e-01
3.9514536e-01 4.5027012e-01]

[4.1938949e-01 3.4382537e-01 3.1303972e-01 … 3.3924583e-01
3.4913608e-01 4.1282722e-01]
[4.3390730e-01 3.7113073e-01 3.4658235e-01 … 3.7191394e-01
3.8552991e-01 4.3190357e-01]
[4.6732298e-01 4.3559861e-01 4.2490643e-01 … 4.4331262e-01
4.5451128e-01 4.7383672e-01]]]]
<NDArray 1x18x37x37 @gpu(0)>")

Hi @Ram124,

It looks like you’re having an issue with the MultiProposal operator, and you’re getting an error because the argument cls_prob doesn’t exist in the code you have. I’ve tracked down the change to this commit which renamed the argument from cls_score to cls_prob that happened 3 months ago. So I think you’re running with a slightly older version of MXNet that expects this to be using cls_score. I recommend that you update your version of MXNet and see if this fixes the issue.

Hi , Thank you for your reply…
I am using mxnet 1.2.0 version.

I am using aws instance p2.large.

How to check those files where you have made changes… I am not able to figure out where exactly the files are located… please help me out in this… Thank You again