New issue
Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.
By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.
Already on GitHub? Sign in to your account
Segmentation fault at Softmax #43
Comments
Typically I observe these kind of errors when there is an invalid label. ie. if you are outputting four classes then a pixel which is labelled > 3? do you only observe this in cpu mode? |
Hi Alex, Thanks for the quick response. The fourth class is void (black) and as you noted I hardly have any instances where it is marked (except for couple of images with very less pixels). Does this affect my training and cause this error somehow? |
The pixels in your label images should be between 0 and 3 inclusive. |
Hi Alex, Ok, looks like I made some silly mistake somewhere. I'm bit confused after reading your above reply. Where exactly do we label the pixels with 0-3 values? I have used the Interactive Labeler tool found here to paint the regions I'm interested in and the results look like this. Nowhere did I have to do any sort of color labeling. Did I do something wrong here? I could successfully train using camvid dataset though. I get this error only when I switch to my dataset. The error is seen when I issue caffe train command. The following are the changes I did in my train.prototxt layer { Please let me know what else I missed to start the training. |
Hey - all looks good except the labels should be grayscale, with label values (in your case 0,1,2 or 3) in each pixel. See the camvid data as an example. |
Oh! Thanks for the clarification. Appreciate your patience with all my novice queries. I had read about the single channel ground truth images but had not quite understood it. So, if I understood correctly, I will convert my images to grayscale and assign each color a fixed value (0,1,2 or 3) which are basically intensity values, right? |
correct :) |
Hi Alex, |
Hi Alex,
Hope someone can help me with this issue.
I'm just reusing the network architecture with only 4 output classes. Image dimensions remain same. Made the necessary changes in the prototext files. I get the following error when I start the training.
I0531 09:45:15.546435 2031686400 net.cpp:248] Memory required for data: 1068595224
I0531 09:45:15.546778 2031686400 solver.cpp:42] Solver scaffolding done.
I0531 09:45:15.546954 2031686400 solver.cpp:250] Solving VGG_ILSVRC_16_layer
I0531 09:45:15.546959 2031686400 solver.cpp:251] Learning Rate Policy: step
*** Aborted at 1464668121 (unix time) try "date -d @1464668121" if you are using GNU date ***
PC: @ 0x10b25b442 caffe::SoftmaxWithLossLayer<>::Forward_cpu()
*** SIGSEGV (@0x216caa000) received by PID 55425 (TID 0x7fff79191300) stack trace: ***
@ 0x7fff918dff1a _sigtramp
@ 0x100000000000000 (unknown)
@ 0x10b2209d9 caffe::Layer<>::Forward()
@ 0x10b2760d9 caffe::Net<>::ForwardFromTo()
@ 0x10b276718 caffe::Net<>::Forward()
@ 0x10b285dcf caffe::Solver<>::Step()
@ 0x10b28577c caffe::Solver<>::Solve()
@ 0x10b1b1594 train()
@ 0x10b1b392f main
@ 0x7fff8c7e25c9 start
@ 0x4 (unknown)
Segmentation fault: 11
Any help in this regard. Appreciate your time being spent on this.
The text was updated successfully, but these errors were encountered: