Development

Developing new layers

• Add a class declaration for your layer to the appropriate one of common_layers.hpp, data_layers.hpp, loss_layers.hpp, neuron_layers.hpp, or vision_layers.hpp. Include an inline implementation of type and the *Blobs() methods to specify blob number requirements. Omit the *_gpu declarations if you'll only be implementing CPU code.
• Implement your layer in layers/your_layer.cpp.
  • (optional) LayerSetUp for one-time initialization: reading parameters, fixed-size allocations, etc.
  • Reshape for computing the sizes of top blobs, allocating buffers, and any other work that depends on the shapes of bottom blobs
  • Forward_cpu for the function your layer computes
  • Backward_cpu for its gradient
• (Optional) Implement the GPU versions Forward_gpu and Backward_gpu in layers/your_layer.cu.
• Add your layer to proto/caffe.proto, updating the next available ID. Also declare parameters, if needed, in this file.
• Make your layer createable by adding it to layer_factory.cpp.
• Write tests in test/test_your_layer.cpp. Use test/test_gradient_check_util.hpp to check that your Forward and Backward implementations are in numerical agreement.

Differences when writing Forward-only layers

If you want to write a layer that you will only ever include in a test net, you do not have to code the backward pass. For example, you might want a layer that measures performance metrics at test time that haven't already been implemented. Doing this is very simple. You can write an inline implementation of Backward_cpu (/Backward_gpu) together with the definition of your layer in include/_layers.hpp that looks like:

virtual void Backward_cpu(const vector<Blob<Dtype>*>& top, const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) {
  NOT_IMPLEMENTED;
}