Issue with demo_faster_rcnn.py script.

[Ram-Godavarthi]

Hi, 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 "demo_faster_rcnn.py", 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/block.py", line 413, in call return self.forward(args) File "/home/ubuntu/anaconda3/lib/python3.6/site-packages/mxnet/gluon/block.py", line 629, in forward return self.hybrid_forward(ndarray, x, args, params) File "/home/ubuntu/gluon-faster-rcnn/rcnn/rcnn.py", 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/block.py", line 413, in call return self.forward(args) File "/home/ubuntu/anaconda3/lib/python3.6/site-packages/mxnet/gluon/block.py", line 629, in forward return self.hybrid_forward(ndarray, x, args, params) File "/home/ubuntu/gluon-faster-rcnn/rcnn/proposal.py", 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/ndarray.py", line 92, in _imperative_invoke ctypes.byref(out_stypes))) File "/home/ubuntu/anaconda3/lib/python3.6/site-packages/mxnet/base.py", 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)>")

[WalterMa]

Could you provide you mxnet version? Looks like it is because of mxnet MultiProposal API parameters name doesn't match.

[Ram-Godavarthi]

I am using mxnet 1.2.0 I am using aws instance p2.large.

[WalterMa]

You need to update you mxnet to newest version, at least >1.2.1

[Ram-Godavarthi]

in ubuntu, sudo pip install --upgrade mxnet?? or other command?

[Ram-Godavarthi]

I am doing training using aws instance and inference in Windows.. It is working fine... mxnet = 1.3.0

But i have to check whether it produces good output.. I will update it once i get the output.

[WalterMa]

Just like the command in windows that I posted in another issue https://github.com/WalterMa/gluon-faster-rcnn/issues/3:

pip install --upgrade --pre mxnet-cuXX

XX is your cuda version, eg. mxnet-cu80 if you use cuda 8.0

Refer to https://pypi.org/project/mxnet/ for all aviliable packages.

[Ram-Godavarthi]

I did it.. But still when i check the mxnet version. It is showing as 1.2.0 Also i am getting error like this in AWS. What should i do??

INFO:root:Start training from [Epoch 0] terminate called after throwing an instance of 'dmlc::Error' what(): [14:48:24] /home/travis/build/dmlc/mxnet-distro/mxnet-build/3rdparty/mshadow/mshadow/./stream_gpu-inl.h:139: Check failed: err == CUSOLVER_STATUS_SUCCESS (7 vs. 0) Create cusolver handle failed

Stack trace returned 10 entries: [bt] (0) /home/ubuntu/anaconda3/lib/python3.6/site-packages/mxnet/libmxnet.so(+0x31a9ea) [0x7efcc9cc29ea] [bt] (1) /home/ubuntu/anaconda3/lib/python3.6/site-packages/mxnet/libmxnet.so(+0x31b011) [0x7efcc9cc3011] [bt] (2) /home/ubuntu/anaconda3/lib/python3.6/site-packages/mxnet/libmxnet.so(+0x246ef33) [0x7efccbe16f33] [bt] (3) /home/ubuntu/anaconda3/lib/python3.6/site-packages/mxnet/libmxnet.so(+0x246fd4a) [0x7efccbe17d4a] [bt] (4) /home/ubuntu/anaconda3/lib/python3.6/site-packages/mxnet/libmxnet.so(+0x24844cf) [0x7efccbe2c4cf] [bt] (5) /home/ubuntu/anaconda3/lib/python3.6/site-packages/mxnet/libmxnet.so(+0x24846d6) [0x7efccbe2c6d6] [bt] (6) /home/ubuntu/anaconda3/lib/python3.6/site-packages/mxnet/libmxnet.so(+0x2480cf4) [0x7efccbe28cf4] [bt] (7) /home/ubuntu/anaconda3/bin/../lib/libstdc++.so.6(+0xafc5c) [0x7efcf74cdc5c] [bt] (8) /lib/x86_64-linux-gnu/libpthread.so.0(+0x76ba) [0x7efcf87176ba] [bt] (9) /lib/x86_64-linux-gnu/libc.so.6(clone+0x6d) [0x7efcf844d41d]

Aborted (core dumped)

[WalterMa]

Nerver seen such a error. Need more information, such as: cuda version, commands you input. And have you searched for this?

[Ram-Godavarthi]

@WalterMa It is not coming everytime.. Sometimes it comes..and sometimes it works fine. I do not knwo why.. May be due to memory usage.. Now it is working fine..