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Domenic Curro edited this page Feb 9, 2016
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- Add a class declaration for your layer to
include/caffe/layers/your_layer.hpp
.- Include an inline implementation of
type
overriding the methodvirtual inline const char* type() const { return "YourLayerName"; }
replacingYourLayerName
with your layer's name. - Implement the
{*}Blobs()
methods to specify blob number requirements; see /caffe/include/caffe/layers.hpp to enforce strict top and bottom Blob counts using the inline{*}Blobs()
methods. - Omit the
*_gpu
declarations if you'll only be implementing CPU code.
- Include an inline implementation of
- Implement your layer in
src/caffe/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 -- a layer can be forward-only)
- (optional)
- (Optional) Implement the GPU versions
Forward_gpu
andBackward_gpu
inlayers/your_layer.cu
. - If needed, declare parameters in
proto/caffe.proto
, using (and then incrementing) the "next available layer-specific ID" declared in a comment abovemessage LayerParameter
- Instantiate and register your layer in your cpp file with the macro provided in
layer_factory.hpp
. Assuming that you have a new layerMyAwesomeLayer
, you can achieve it with the following command:
INSTANTIATE_CLASS(MyAwesomeLayer);
REGISTER_LAYER_CLASS(MyAwesome);
- Note that you should put the registration code in your own cpp file, so your implementation of a layer is self-contained.
- Optionally, you can also register a Creator if your layer has multiple engines. For an example on how to define a creator function and register it, see
GetConvolutionLayer
incaffe/layer_factory.cpp
. - Write tests in
test/test_your_layer.cpp
. Usetest/test_gradient_check_util.hpp
to check that your Forward and Backward implementations are in numerical agreement.
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
(or Backward_gpu
) together with the definition of your layer in include/caffe/your_layer.hpp
that looks like:
virtual void Backward_cpu(const vector<Blob<Dtype>*>& top, const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
NOT_IMPLEMENTED;
}
The NOT_IMPLEMENTED
macro (defined in common.hpp
) throws an error log saying "Not implemented yet". For examples, look at the accuracy layer (accuracy_layer.hpp
) and threshold layer (threshold_layer.hpp
) definitions.