caffe增加自己的layer实战（下-续1）--caffe学习（13）

接上篇：caffe增加自己的layer实战（下）–caffe学习（12）
构造完函数后我们就要进入proto目录。编辑caffe.proto文件，构造我们的video_data_layer的输入参数。
找到：message LayerParameter {
里面有很多类似：
optional PythonParameter python_param = 130;
我们要为自己的video_data_layer添加一个optional
如：

optional VideoDataParameter video_data_param = 140;

要保证后面的140不要与这里面的其他数字重复，保证是唯一的。
然后对这个输入参数写对应的message：
像这样：

message VideoDataParameter{  // Specify the data source.  optional string source = 1;  // Specify the batch size.  optional uint32 batch_size = 4;  // The rand_skip variable is for the data layer to skip a few data points  // to avoid all asynchronous sgd clients to start at the same point. The skip  // point would be set as rand_skip * rand(0,1). Note that rand_skip should not  // be larger than the number of keys in the leveldb.  optional uint32 rand_skip = 7 [default = 0];  // Whether or not ImageLayer should shuffle the list of files at every epoch.  optional bool shuffle = 8 [default = false];  // It will also resize images if new_height or new_width are not zero.  optional uint32 new_height = 9 [default = 0];  optional uint32 new_width = 10 [default = 0];  optional uint32 new_length = 11 [default = 1];  optional uint32 num_segments = 12 [default = 1];  // DEPRECATED. See TransformationParameter. For data pre-processing, we can do  // simple scaling and subtracting the data mean, if provided. Note that the  // mean subtraction is always carried out before scaling.  optional float scale = 2 [default = 1];  optional string mean_file = 3;  // DEPRECATED. See TransformationParameter. Specify if we would like to randomly  // crop an image.  optional uint32 crop_size = 5 [default = 0];  // DEPRECATED. See TransformationParameter. Specify if we want to randomly mirror  // data.  optional bool mirror = 6 [default = false];  enum Modality {    RGB = 0;    FLOW = 1;  }  optional Modality modality = 13 [default = FLOW];  // the name pattern for frame images,  // for RGB modality it is default to "img_%04d.jpg", for FLOW "flow_x_%04d" and "flow_y_%04d"  optional string name_pattern = 14;  // The type of input  optional bool encoded = 15 [default = false];}

现在开始在就是对输入prototxt参数进行类型定义和默认值。
现在全部所需都完成了。
进入caffe目录
make clean
make all -j8
make install


更新，
最后也是最容易忽视的一点，在 Layer 工厂注册新 Layer 加工函数，不然在你运行过程中可能会报如下错误：

F1002 01:51:22.656038 1954701312 layer_factory.hpp:81] Check failed: registry.count(type) == 1 (0 vs. 1) Unknown layer type: AllPass (known types: AbsVal, Accuracy, ArgMax, BNLL, BatchNorm, BatchReindex, Bias, Concat, ContrastiveLoss, Convolution, Crop, Data, Deconvolution, Dropout, DummyData, ELU, Eltwise, Embed, EuclideanLoss, Exp, Filter, Flatten, HDF5Data, HDF5Output, HingeLoss, Im2col, ImageData, InfogainLoss, InnerProduct, Input, LRN, Log, MVN, MemoryData, MultinomialLogisticLoss, PReLU, Pooling, Power, ReLU, Reduction, Reshape, SPP, Scale, Sigmoid, SigmoidCrossEntropyLoss, Silence, Slice, Softmax, SoftmaxWithLoss, Split, TanH, Threshold, Tile, WindowData) *** Check failure stack trace: *** @ 0x10243154e google::LogMessage::Fail() @ 0x102430c53 google::LogMessage::SendToLog() @ 0x1024311a9 google::LogMessage::Flush() @ 0x1024344d7 google::LogMessageFatal::~LogMessageFatal() @ 0x10243183b google::LogMessageFatal::~LogMessageFatal() @ 0x102215356 caffe::LayerRegistry<>::CreateLayer() @ 0x102233ccf caffe::Net<>::Init() @ 0x102235996 caffe::Net<>::Net() @ 0x102118d8b time() @ 0x102119c9a main @ 0x7fff851285ad start @ 0x4 (unknown) Abort trap: 6

主要错误提示是：Check failed: registry.count(type) == 1 (0 vs. 1) Unknown layer type: VideoDataLayer
解决办法：

全部成功以后
新建一个train.prototxt文件，用最简单的LeNet网络做测试。

name: "LeNet"layer {  name: "data"  type: "VideoData"  top: "data"  top: "label"  video_data_param {    source: "data/hmdb51_rgb_train_split_1.txt"    batch_size: 32    new_length: 1    num_segments: 3    modality: RGB    shuffle: true    name_pattern: "img_%05d.jpg"  }  transform_param{    crop_size: 224    mirror: true    fix_crop: true    more_fix_crop: true    multi_scale: true    max_distort: 1    scale_ratios:[1,.875,.75, .66]    is_flow: false    mean_value: [104, 117, 123, 104, 117, 123, 104, 117, 123]  }  include: { phase: TRAIN }}layer { name: "reshape_data" type: "Reshape" bottom: "data" top: "reshape_data" reshape_param { shape { dim: -1 dim: 3 dim: 224 dim: 224 } } }layer {  name: "conv1"  type: "Convolution"  bottom: "data"  top: "conv1"  param {    lr_mult: 1  }  param {    lr_mult: 2  }  convolution_param {    num_output: 20    kernel_size: 5    stride: 1    weight_filler {      type: "xavier"    }    bias_filler {      type: "constant"    }  }}layer {  name: "pool1"  type: "Pooling"  bottom: "conv1"  top: "pool1"  pooling_param {    pool: MAX    kernel_size: 2    stride: 2  }}layer {  name: "conv2"  type: "Convolution"  bottom: "pool1"  top: "conv2"  param {    lr_mult: 1  }  param {    lr_mult: 2  }  convolution_param {    num_output: 50    kernel_size: 5    stride: 1    weight_filler {      type: "xavier"    }    bias_filler {      type: "constant"    }  }}layer {  name: "pool2"  type: "Pooling"  bottom: "conv2"  top: "pool2"  pooling_param {    pool: MAX    kernel_size: 2    stride: 2  }}layer {  name: "ip1"  type: "InnerProduct"  bottom: "pool2"  top: "ip1"  param {    lr_mult: 1  }  param {    lr_mult: 2  }  inner_product_param {    num_output: 500    weight_filler {      type: "xavier"    }    bias_filler {      type: "constant"    }  }}layer {  name: "relu1"  type: "ReLU"  bottom: "ip1"  top: "ip1"}layer {  name: "ip2"  type: "InnerProduct"  bottom: "ip1"  top: "ip2"  param {    lr_mult: 1  }  param {    lr_mult: 2  }  inner_product_param {    num_output: 10    weight_filler {      type: "xavier"    }    bias_filler {      type: "constant"    }  }}layer {  name: "prob"  type: "Softmax"  bottom: "ip2"  top: "prob"}

上面的source: "data/hmdb51_rgb_train_split_1.txt"

文件内容如下：

data/Sara_beim_Rauchen_-_Volume_3_smoke_h_cm_np1_fr_med_0 77 0

这里我们只拿一个视频进行测试
数据在链接：http://pan.baidu.com/s/1eSMHquu 密码：06e4