在caffe中只编译一层文件

Question

当我们在caffe中开发了一个新层new_layer.cpp，new_layer.cu，new.layer.hpp，我们要重新编译caffe，是否可以只编译这个新层文件，比如对原始库的更新，或者我们必须再次重新编译整个库？有人可以给我提示吗？

添加：实际上我从github上的某人的实现中下载了图层文件：https://github.com/farmingyard/ShuffleNet有.cpp.cu.hpp个文件，我把.cpp 和 .cu 在 caffe/src/caffe/layers 中，把 .hpp 放在 caffe/include/caffe/layers/ 中，然后在 caffe.proto 中添加：

message LayerParameter {
...
optional ShuffleChannelParameter shuffle_channel_param = 164;
}

还有：

message ShuffleChannelParameter {
  optional uint32 group = 1[default = 1]; // The number of group
}

在 proto 文件的末尾，然后我调用 make clean 然后在 caffe 根目录中调用 make all，没有错误，我检查了

行

CXX src/caffe/layers/shuffle_channel_layer.cpp

和

NVCC src/caffe/layers/shuffle_channel_layer.cu

and（我不知道这是否意味着proto文件已经被重新编译）

CXX .build_release/src/caffe/proto/caffe.pb.cc

然后 make proto，得到：

make: Nothing to be done for proto

然后我调用make pycaffe，没有错误提示，编译成功。然后我用作者写的example prototxt file for a sample network using the new layer，然后出现如下错误提示：

Message type "caffe.LayerParameter" has no field named "shuffle_channel_param".

完整 LayerParameter:

// NOTE
// Update the next available ID when you add a new LayerParameter field.
//
// LayerParameter next available layer-specific ID: 147 (last added: recurrent_param)
message LayerParameter {
  optional string name = 1; // the layer name
  optional string type = 2; // the layer type
  repeated string bottom = 3; // the name of each bottom blob
  repeated string top = 4; // the name of each top blob

  // The train / test phase for computation.
  optional Phase phase = 10;

  // The amount of weight to assign each top blob in the objective.
  // Each layer assigns a default value, usually of either 0 or 1,
  // to each top blob.
  repeated float loss_weight = 5;

  // Specifies training parameters (multipliers on global learning constants,
  // and the name and other settings used for weight sharing).
  repeated ParamSpec param = 6;

  // The blobs containing the numeric parameters of the layer.
  repeated BlobProto blobs = 7;

  // Specifies whether to backpropagate to each bottom. If unspecified,
  // Caffe will automatically infer whether each input needs backpropagation
  // to compute parameter gradients. If set to true for some inputs,
  // backpropagation to those inputs is forced; if set false for some inputs,
  // backpropagation to those inputs is skipped.
  //
  // The size must be either 0 or equal to the number of bottoms.
  repeated bool propagate_down = 11;

  // Rules controlling whether and when a layer is included in the network,
  // based on the current NetState.  You may specify a non-zero number of rules
  // to include OR exclude, but not both.  If no include or exclude rules are
  // specified, the layer is always included.  If the current NetState meets
  // ANY (i.e., one or more) of the specified rules, the layer is
  // included/excluded.
  repeated NetStateRule include = 8;
  repeated NetStateRule exclude = 9;

  // Parameters for data pre-processing.
  optional TransformationParameter transform_param = 100;

  // Parameters shared by loss layers.
  optional LossParameter loss_param = 101;

  // Layer type-specific parameters.
  //
  // Note: certain layers may have more than one computational engine
  // for their implementation. These layers include an Engine type and
  // engine parameter for selecting the implementation.
  // The default for the engine is set by the ENGINE switch at compile-time.
  optional AccuracyParameter accuracy_param = 102;
  optional ArgMaxParameter argmax_param = 103;
  optional BatchNormParameter batch_norm_param = 139;
  optional BiasParameter bias_param = 141;
  optional ConcatParameter concat_param = 104;
  optional ContrastiveLossParameter contrastive_loss_param = 105;
  optional ConvolutionParameter convolution_param = 106;
  optional CropParameter crop_param = 144;
  optional DataParameter data_param = 107;
  optional DropoutParameter dropout_param = 108;
  optional DummyDataParameter dummy_data_param = 109;
  optional EltwiseParameter eltwise_param = 110;
  optional ELUParameter elu_param = 140;
  optional EmbedParameter embed_param = 137;
  optional ExpParameter exp_param = 111;
  optional FlattenParameter flatten_param = 135;
  optional HDF5DataParameter hdf5_data_param = 112;
  optional HDF5OutputParameter hdf5_output_param = 113;
  optional HingeLossParameter hinge_loss_param = 114;
  optional ImageDataParameter image_data_param = 115;
  optional InfogainLossParameter infogain_loss_param = 116;
  optional InnerProductParameter inner_product_param = 117;
  optional InputParameter input_param = 143;
  optional LogParameter log_param = 134;
  optional LRNParameter lrn_param = 118;
  optional MemoryDataParameter memory_data_param = 119;
  optional MVNParameter mvn_param = 120;
  optional ParameterParameter parameter_param = 145;
  optional PoolingParameter pooling_param = 121;
  optional PowerParameter power_param = 122;
  optional PReLUParameter prelu_param = 131;
  optional PythonParameter python_param = 130;
  optional RecurrentParameter recurrent_param = 146;
  optional ReductionParameter reduction_param = 136;
  optional ReLUParameter relu_param = 123;
  optional ReshapeParameter reshape_param = 133;
  optional ROIPoolingParameter roi_pooling_param = 8266711;
  optional ScaleParameter scale_param = 142;
  optional SigmoidParameter sigmoid_param = 124;
  optional SmoothL1LossParameter smooth_l1_loss_param = 8266712;
  optional SoftmaxParameter softmax_param = 125;
  optional SPPParameter spp_param = 132;
  optional SliceParameter slice_param = 126;
  optional TanHParameter tanh_param = 127;
  optional ThresholdParameter threshold_param = 128;
  optional TileParameter tile_param = 138;
  optional WindowDataParameter window_data_param = 129;
  optional ShuffleChannelParameter shuffle_channel_param = 164;
}

Answer 1

当使用 make 编译时，make 知道它已经编译了哪些源代码以及自上次构建以来发生了什么变化。如果您只进行本地更改，make 只会 compile/link 更改的源及其影响的内容（对头文件的更改可能需要编译 #include 此头文件的其他源).
也就是说，您不需要做任何特别的事情，只需继续使用 make.
如果您 make clean 删除所有已编译的对象并强制 make 从头开始重新编译整个项目。

在caffe中只编译一层文件

Compile only one layer files in caffe

c++

makefile

build

caffe