The difference between running the code in c ++ and python

[sawakk]

Hi, I tried to run an example on the python and everything turned out fine. But when I run an example on c ++, the result does not match the declared one.

The console displays:
Processing time = 361 ms
Output_blob (n, c, h, w) = 1, 19, 512, 1024
Processing time on the python is 1800 ms.

If I use weights with Segnet, then I get a fine result, but the speed has not changed.

[TimoSaemann]

Hi, I have updated the C++ code. Please try it again.

[sawakk]

Does not work(
In the c ++ example
test_segmentation ENet/final_model_weights/bn_conv_merged_model.prototxt
ENet/final_model_weights/bn_conv_merged_weights.caffemodel
But I do not have bn_conv_merged_model.prototkst and bn_conv_merged_weights.caffemodel.
Instead of them I use the ENet/prototxts/enet_deploy_final.prototxt and
ENet/enet_weights_zoo/cityscapes_weights.caffemodel.

[TimoSaemann]

Yes, thats right.
I've checked it, it works.

Are you sure you are using the latest version?

[sawakk]

I really want to run a neural network, but I do not know what's wrong anymore
`QT += core
QT -= gui

CONFIG += c++11

TARGET = untitled
CONFIG += console
CONFIG -= app_bundle

TEMPLATE = app

CONFIG += link_pkgconfig
PKGCONFIG += opencv

INCLUDEPATH += /usr/local/cuda/targets/x86_64-linux/include
INCLUDEPATH += /usr/local/cuda/lib64

INCLUDEPATH += /home/user/lib/caffe/ENet-master/caffe-enet/include
/home/user/lib/caffe/ENet-master/caffe-enet/build/include
/usr/include/boost

LIBS += -L/home/user/lib/caffe/ENet-master/caffe-enet/build/lib
-lcaffe

LIBS += /usr/lib/x86_64-linux-gnu/libglog.so
LIBS += -L/usr/lib/x86_64-linux-gnu
-lboost_system

SOURCES += main.cpp

DEFINES += QT_DEPRECATED_WARNINGS
`
Here is my .pro file. The project is compiled on qt. If I run / home / user / lib / caffe / ENet-master / caffe-enet / build / examples / ENet_with_C ++ / test_segmentation, it works fine too

[TimoSaemann]

Do you get an error message or what do you mean by "does not work"?
If you still get the same prediction image, which you posted in the first post, then you do not use the latest version of caffe-enet.
The best way is to clone and build the ENet repository again.

[sawakk]

Yes, I still have an image as in the first post.
I deleted and re-downloaded everything. (2 hours ago)

[TimoSaemann]

The problem was the missing argmax layer. I have implemented this function in the script "test_segmentation.cpp". If you are using the old version, you can also add the following to the enet_deploy_final.prototxt.

layer {
  name: "argmax"
  type: "ArgMax"
  bottom: "deconv6_0_0"
  top: "argmax"
  argmax_param {
    axis: 1
  }
}

[sawakk]

Working! Thank you very much!
The error was in using the ENet/scripts/test_segmentation.cpp but need ENet/caffe-enet/examples/ENet_with_C++/test_segmentation.cpp.

Thank =)

[TimoSaemann]

Ah, I have missed to adapt this script in this directory. Thanks for pointing this out. I am glad to hear that it works now.

[ThomasLengeling]

@sawakk @TimoSaemann

I tried using the ENet/caffe-enet/examples/ENet_with_C++/test_segmentation.cpp.

I am using the following models

ENet/prototxts/enet_deploy_final.prototxt
ENet/enet_weights_zoo/cityscapes_weights.caffemodel

I added the "argmx" layer to enet_deploy_final.prototxt.
But still the result is not the same as the python code,

any suggestion?

Thank you for your time!