CNN_forward

A Convolution Neural Network forward code for caffe implemented in C++.

• Features

  • Load model converted from *.caffemodel, model encrypt is supported.
  • Define layers' topology simply.
  • Using Intel's TBB which makes convolution faster on multicore CPU.
  • Supported layers currently: INPUT, CONVOLUTION, POOLING, DENSE(or INNER_PRODUCT), RELU.
  • Easy to be compiled into a single .exe, .dll, or .so, it can be executed without any additional library.
  • For example, you can use this code to break some captcha code like what was used at xk.fudan.edu.cn.

• How to use

  • In main.cpp there is a example:
    1. First initialize a CnnNet object net, and then call net.init('model', 'key'), which will load the model named model and the blowfish key is key.
    2. Then call net.forward('test.jpg', GRAY), which will read the file test.jpg in GRAY mode and do the net forward.
    3. Finally you can get the result and process it by yourself, or use net.argmax(). The function argmax is not really a argmax, and its result is not between 0 to 1, in fact, it will fetch all layers' max values whose output is defined as true and return them in vector.
    4. Since this example does a captcha recognition job, I call a simple function in utils.cpp to convert the numbers in the vector above to letters.
  • Define net's topology.
    1. In CnnNet.cpp, we can define net in CnnNet::init. Just new a LayerConfig and push_back it's address.
    2. If the INPUT layer's size(w, h) is set, all images will be resized when doing forward. Leave blank or set to 0 means pass the resize process.
    3. Some information is read from model, and here we don't need to define them, for example, the kernel size of convolution.
    4. When you don't set a layer's parent, it will be set to its previously pushed layer. If you want to set it, you can use string or vector to set it.
  • The model can be converted using model_convertor.py.

• Todo jobs

  • Make it faster and faster, maybe support GPU.
  • Separate the net's weights and the images calculated to make it threadsafe.
  • Add more layer support, such as LRN.
  • Zip the model file.
  • More secure model encryption.

• Copyright

  • Open-source now. Please make sure you keep the copyright acknowledgement in source code.
  • Not for commercial use(If you did this or you want to do so please contact me).