Caffe 增加自定义 Layer 及其 ProtoBuffer 参数[转自：卜居https://blog.csdn.net/kkk584520/article/details/52721838]
在飞驰的列车上，无法入眠。外面阴雨绵绵，思绪被拉扯到天边。
翻看之前聊天，想起还欠一个读者一篇博客。
于是花了点时间整理一下之前学习 Caffe 时增加自定义 Layer 及自定义 ProtoBuffer 参数的简单例程，希望对初学者有借鉴意义。
博客内容基于新书《深度学习：21 天实战 Caffe》，书中课后习题答案欢迎读者留言讨论。以下进入正文。
在使用 Caffe 过程中经常会有这样的需求：已有 Layer 不符合我的应用场景；我需要这样这样的功能，原版代码没有实现；或者已经实现但效率太低，我有更好的实现。
方案一：简单粗暴的解法——偷天换日
如果你对 ConvolutionLayer 的实现不满意，那就直接改这两个文件：$CAFFE_ROOT/include/caffe/layers/conv_layer.hpp 和 $CAFFE_ROOT/src/caffe/layers/conv_layer.cpp 或 conv_layer.cu ，将 im2col + gemm 替换为你自己的实现（比如基于 winograd 算法的实现）。
优点：快速迭代，不需要对 Caffe 框架有过多了解，糙快狠准。
缺点：代码难维护，不能 merge 到 caffe master branch，容易给使用代码的人带来困惑（效果和 #define TRUE false 差不多）。
方案二：稍微温柔的解法——千人千面
和方案一类似，只是通过预编译宏来确定使用哪种实现。例如可以保留 ConvolutionLayer 默认实现，同时在代码中增加如下段：
-
#ifdef SWITCH_MY_IMPLEMENTATION
-
// 你的实现代码
-
#else
-
// 默认代码
-
#endif
这样可以在需要使用该 Layer 的代码中，增加宏定义：
#define SWITCH_MY_IMPLEMENTATION
就可以使用你的实现。而未定义该宏的代码，仍然使用原版实现。
优点：可以在新旧实现代码之间灵活切换；
缺点：每次切换需要重新编译；
方案三：优雅转身——山路十八弯
同一个功能的 Layer 有不同实现，希望能灵活切换又不需要重新编译代码，该如何实现？
这时不得不使用 ProtoBuffer 工具了。
首先，要把你的实现，要像正常的 Layer 类一样，分解为声明部分和实现部分，分别放在 .hpp 与 .cpp、.cu 中。Layer 名称要起一个能区别于原版实现的新名称。.hpp 文件置于 $CAFFE_ROOT/include/caffe/layers/，而 .cpp 和 .cu 置于 $CAFFE_ROOT/src/caffe/layers/，这样你在 $CAFFE_ROOT 下执行 make 编译时，会自动将这些文件加入构建过程，省去了手动设置编译选项的繁琐流程。
其次，在 $CAFFE_ROOT/src/caffe/proto/caffe.proto 中，增加新 LayerParameter 选项，这样你在编写 train.prototxt 或者 test.prototxt 或者 deploy.prototxt 时就能把新 Layer 的描述写进去，便于修改网络结构和替换其他相同功能的 Layer 了。
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下面给出一个实际案例，走一遍方案三的流程。
这里我们实现一个新 Layer，名称为 AllPassLayer，顾名思义就是全通 Layer，“全通”借鉴于信号处理中的全通滤波器，将信号无失真地从输入转到输出。
虽然这个 Layer 并没有什么卵用，但是在这个基础上增加你的处理是非常简单的事情。另外也是出于实验考虑，全通层的 Forward/Backward 函数非常简单不需要读者有任何高等数学和求导的背景知识。读者使用该层时可以插入到任何已有网络中，而不会影响训练、预测的准确性。
首先看头文件：
-
#ifndef CAFFE_ALL_PASS_LAYER_HPP_
-
#define CAFFE_ALL_PASS_LAYER_HPP_
-
-
#include
-
-
#include "caffe/blob.hpp"
-
#include "caffe/layer.hpp"
-
#include "caffe/proto/caffe.pb.h"
-
-
#include "caffe/layers/neuron_layer.hpp"
-
-
namespace caffe {
-
template <
typename Dtype>
-
class AllPassLayer :
public NeuronLayer
{
-
public:
-
explicit AllPassLayer(const LayerParameter& param)
-
: NeuronLayer
(param) {}
-
-
virtual inline const char* type() const {
return
"AllPass"; }
-
-
protected:
-
-
virtual void Forward_cpu(const vector
*>& bottom,
-
const
vector
*>& top);
-
virtual void Forward_gpu(const vector
*>& bottom,
-
const
vector
*>& top);
-
virtual void Backward_cpu(const vector
*>& top,
-
const
vector<
bool>& propagate_down,
const
vector
*>& bottom);
-
virtual void Backward_gpu(const vector
*>& top,
-
const
vector<
bool>& propagate_down,
const
vector
*>& bottom);
-
};
-
-
}
// namespace caffe
-
-
#endif // CAFFE_ALL_PASS_LAYER_HPP_
再看源文件：
-
#include
-
#include
-
-
#include "caffe/layers/all_pass_layer.hpp"
-
-
#include
-
using
namespace
std;
-
#define DEBUG_AP(str) cout<
-
namespace caffe {
-
-
template <
typename Dtype>
-
void AllPassLayer
::Forward_cpu(
const
vector*>& bottom,
-
const
vector
*>& top) {
-
const Dtype* bottom_data = bottom[
0]->cpu_data();
-
Dtype* top_data = top[
0]->mutable_cpu_data();
-
const
int count = bottom[
0]->count();
-
for (
int i =
0; i < count; ++i) {
-
top_data[i] = bottom_data[i];
-
}
-
DEBUG_AP(
"Here is All Pass Layer, forwarding.");
-
DEBUG_AP(
this->layer_param_.all_pass_param().key());
-
}
-
-
template <
typename Dtype>
-
void AllPassLayer
::Backward_cpu(
const
vector*>& top,
-
const
vector<
bool>& propagate_down,
-
const
vector
*>& bottom) {
-
if (propagate_down[
0]) {
-
const Dtype* bottom_data = bottom[
0]->cpu_data();
-
const Dtype* top_diff = top[
0]->cpu_diff();
-
Dtype* bottom_diff = bottom[
0]->mutable_cpu_diff();
-
const
int count = bottom[
0]->count();
-
for (
int i =
0; i < count; ++i) {
-
bottom_diff[i] = top_diff[i];
-
}
-
}
-
DEBUG_AP(
"Here is All Pass Layer, backwarding.");
-
DEBUG_AP(
this->layer_param_.all_pass_param().key());
-
}
-
-
-
#ifdef CPU_ONLY
-
STUB_GPU(AllPassLayer);
-
#endif
-
-
INSTANTIATE_CLASS(AllPassLayer);
-
REGISTER_LAYER_CLASS(AllPass);
-
}
// namespace caffe
-
时间考虑，我没有实现 GPU 模式的 forward、backward，故本文例程仅支持 CPU_ONLY 模式。
编辑 caffe.proto，找到 LayerParameter 描述，增加一项：
-
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 on which bottoms the backpropagation should be 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 PoolingParameter pooling_param =
121;
-
optional PowerParameter power_param =
122;
-
optional PReLUParameter prelu_param =
131;
-
optional PythonParameter python_param =
130;
-
optional ReductionParameter reduction_param =
136;
-
optional ReLUParameter relu_param =
123;
-
optional ReshapeParameter reshape_param =
133;
-
optional ScaleParameter scale_param =
142;
-
optional SigmoidParameter sigmoid_param =
124;
-
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 AllPassParameter all_pass_param =
155;
-
}
注意新增数字不要和以前的 Layer 数字重复。
仍然在 caffe.proto 中，增加 AllPassParameter 声明，位置任意。我设定了一个参数，可以用于从 prototxt 中读取预设值。
-
message AllPassParameter {
-
optional
float key =
1 [
default =
0];
-
}
在 cpp 代码中，通过
this->layer_param_.all_pass_param().key()
在 $CAFFE_ROOT 下执行 make clean，然后重新 make all。要想一次编译成功，务必规范代码，对常见错误保持敏锐的嗅觉并加以避免。
万事具备，只欠 prototxt 了。
不难，我们写个最简单的 deploy.prototxt，不需要 data layer 和 softmax layer，just for fun。
-
name:
"AllPassTest"
-
layer {
-
name:
"data"
-
type:
"Input"
-
top:
"data"
-
input_param { shape: { dim:
10 dim:
3 dim:
227 dim:
227 } }
-
}
-
layer {
-
name:
"ap"
-
type:
"AllPass"
-
bottom:
"data"
-
top:
"conv1"
-
all_pass_param {
-
key:
12.88
-
}
-
}
注意，这里的 type ：后面写的内容，应该是你在 .hpp 中声明的新类 class name 去掉 Layer 后的名称。
上面设定了 key 这个参数的预设值为 12.88，嗯，你想到了刘翔对不对。
为了检验该 Layer 是否能正常创建和执行 forward, backward，我们运行 caffe time 命令并指定刚刚实现的 prototxt ：
$ ./build/tools/caffe.bin time -model deploy.prototxt
I1002 02:03:41.667682 1954701312 caffe.cpp:312] Use CPU.
I1002 02:03:41.671360 1954701312 net.cpp:49] Initializing net from parameters:
name: "AllPassTest"
state {
phase: TRAIN
}
layer {
name: "data"
type: "Input"
top: "data"
input_param {
shape {
dim: 10
dim: 3
dim: 227
dim: 227
}
}
}
layer {
name: "ap"
type: "AllPass"
bottom: "data"
top: "conv1"
all_pass_param {
key: 12.88
}
}
I1002 02:03:41.671463 1954701312 layer_factory.hpp:77] Creating layer data
I1002 02:03:41.671484 1954701312 net.cpp:91] Creating Layer data
I1002 02:03:41.671499 1954701312 net.cpp:399] data -> data
I1002 02:03:41.671555 1954701312 net.cpp:141] Setting up data
I1002 02:03:41.671566 1954701312 net.cpp:148] Top shape: 10 3 227 227 (1545870)
I1002 02:03:41.671592 1954701312 net.cpp:156] Memory required for data: 6183480
I1002 02:03:41.671605 1954701312 layer_factory.hpp:77] Creating layer ap
I1002 02:03:41.671620 1954701312 net.cpp:91] Creating Layer ap
I1002 02:03:41.671630 1954701312 net.cpp:425] ap <- data
I1002 02:03:41.671644 1954701312 net.cpp:399] ap -> conv1
I1002 02:03:41.671663 1954701312 net.cpp:141] Setting up ap
I1002 02:03:41.671674 1954701312 net.cpp:148] Top shape: 10 3 227 227 (1545870)
I1002 02:03:41.671685 1954701312 net.cpp:156] Memory required for data: 12366960
I1002 02:03:41.671695 1954701312 net.cpp:219] ap does not need backward computation.
I1002 02:03:41.671705 1954701312 net.cpp:219] data does not need backward computation.
I1002 02:03:41.671710 1954701312 net.cpp:261] This network produces output conv1
I1002 02:03:41.671720 1954701312 net.cpp:274] Network initialization done.
I1002 02:03:41.671746 1954701312 caffe.cpp:320] Performing Forward
Here is All Pass Layer, forwarding.
12.88
I1002 02:03:41.679689 1954701312 caffe.cpp:325] Initial loss: 0
I1002 02:03:41.679714 1954701312 caffe.cpp:326] Performing Backward
I1002 02:03:41.679738 1954701312 caffe.cpp:334] *** Benchmark begins ***
I1002 02:03:41.679746 1954701312 caffe.cpp:335] Testing for 50 iterations.
Here is All Pass Layer, forwarding.
12.88
Here is All Pass Layer, backwarding.
12.88
I1002 02:03:41.681139 1954701312 caffe.cpp:363] Iteration: 1 forward-backward time: 1 ms.
Here is All Pass Layer, forwarding.
12.88
Here is All Pass Layer, backwarding.
12.88
I1002 02:03:41.682394 1954701312 caffe.cpp:363] Iteration: 2 forward-backward time: 1 ms.
Here is All Pass Layer, forwarding.
12.88
Here is All Pass Layer, backwarding.
12.88
I1002 02:03:41.683653 1954701312 caffe.cpp:363] Iteration: 3 forward-backward time: 1 ms.
Here is All Pass Layer, forwarding.
12.88
Here is All Pass Layer, backwarding.
12.88
I1002 02:03:41.685096 1954701312 caffe.cpp:363] Iteration: 4 forward-backward time: 1 ms.
Here is All Pass Layer, forwarding.
12.88
Here is All Pass Layer, backwarding.
12.88
I1002 02:03:41.686326 1954701312 caffe.cpp:363] Iteration: 5 forward-backward time: 1 ms.
Here is All Pass Layer, forwarding.
12.88
Here is All Pass Layer, backwarding.
12.88
I1002 02:03:41.687713 1954701312 caffe.cpp:363] Iteration: 6 forward-backward time: 1 ms.
Here is All Pass Layer, forwarding.
12.88
Here is All Pass Layer, backwarding.
12.88
I1002 02:03:41.689038 1954701312 caffe.cpp:363] Iteration: 7 forward-backward time: 1 ms.
Here is All Pass Layer, forwarding.
12.88
Here is All Pass Layer, backwarding.
12.88
I1002 02:03:41.690251 1954701312 caffe.cpp:363] Iteration: 8 forward-backward time: 1 ms.
Here is All Pass Layer, forwarding.
12.88
Here is All Pass Layer, backwarding.
12.88
I1002 02:03:41.691548 1954701312 caffe.cpp:363] Iteration: 9 forward-backward time: 1 ms.
Here is All Pass Layer, forwarding.
12.88
Here is All Pass Layer, backwarding.
12.88
I1002 02:03:41.692805 1954701312 caffe.cpp:363] Iteration: 10 forward-backward time: 1 ms.
Here is All Pass Layer, forwarding.
12.88
Here is All Pass Layer, backwarding.
12.88
I1002 02:03:41.694056 1954701312 caffe.cpp:363] Iteration: 11 forward-backward time: 1 ms.
Here is All Pass Layer, forwarding.
12.88
Here is All Pass Layer, backwarding.
12.88
I1002 02:03:41.695264 1954701312 caffe.cpp:363] Iteration: 12 forward-backward time: 1 ms.
Here is All Pass Layer, forwarding.
12.88
Here is All Pass Layer, backwarding.
12.88
I1002 02:03:41.696761 1954701312 caffe.cpp:363] Iteration: 13 forward-backward time: 1 ms.
Here is All Pass Layer, forwarding.
12.88
Here is All Pass Layer, backwarding.
12.88
I1002 02:03:41.698225 1954701312 caffe.cpp:363] Iteration: 14 forward-backward time: 1 ms.
Here is All Pass Layer, forwarding.
12.88
Here is All Pass Layer, backwarding.
12.88
I1002 02:03:41.699653 1954701312 caffe.cpp:363] Iteration: 15 forward-backward time: 1 ms.
Here is All Pass Layer, forwarding.
12.88
Here is All Pass Layer, backwarding.
12.88
I1002 02:03:41.700945 1954701312 caffe.cpp:363] Iteration: 16 forward-backward time: 1 ms.
Here is All Pass Layer, forwarding.
12.88
Here is All Pass Layer, backwarding.
12.88
I1002 02:03:41.702761 1954701312 caffe.cpp:363] Iteration: 17 forward-backward time: 1 ms.
Here is All Pass Layer, forwarding.
12.88
Here is All Pass Layer, backwarding.
12.88
I1002 02:03:41.704056 1954701312 caffe.cpp:363] Iteration: 18 forward-backward time: 1 ms.
Here is All Pass Layer, forwarding.
12.88
Here is All Pass Layer, backwarding.
12.88
I1002 02:03:41.706471 1954701312 caffe.cpp:363] Iteration: 19 forward-backward time: 2 ms.
Here is All Pass Layer, forwarding.
12.88
Here is All Pass Layer, backwarding.
12.88
I1002 02:03:41.708784 1954701312 caffe.cpp:363] Iteration: 20 forward-backward time: 2 ms.
Here is All Pass Layer, forwarding.
12.88
Here is All Pass Layer, backwarding.
12.88
I1002 02:03:41.710043 1954701312 caffe.cpp:363] Iteration: 21 forward-backward time: 1 ms.
Here is All Pass Layer, forwarding.
12.88
Here is All Pass Layer, backwarding.
12.88
I1002 02:03:41.711272 1954701312 caffe.cpp:363] Iteration: 22 forward-backward time: 1 ms.
Here is All Pass Layer, forwarding.
12.88
Here is All Pass Layer, backwarding.
12.88
I1002 02:03:41.712528 1954701312 caffe.cpp:363] Iteration: 23 forward-backward time: 1 ms.
Here is All Pass Layer, forwarding.
12.88
Here is All Pass Layer, backwarding.
12.88
I1002 02:03:41.713964 1954701312 caffe.cpp:363] Iteration: 24 forward-backward time: 1 ms.
Here is All Pass Layer, forwarding.
12.88
Here is All Pass Layer, backwarding.
12.88
I1002 02:03:41.715248 1954701312 caffe.cpp:363] Iteration: 25 forward-backward time: 1 ms.
Here is All Pass Layer, forwarding.
12.88
Here is All Pass Layer, backwarding.
12.88
I1002 02:03:41.716487 1954701312 caffe.cpp:363] Iteration: 26 forward-backward time: 1 ms.
Here is All Pass Layer, forwarding.
12.88
Here is All Pass Layer, backwarding.
12.88
I1002 02:03:41.717725 1954701312 caffe.cpp:363] Iteration: 27 forward-backward time: 1 ms.
Here is All Pass Layer, forwarding.
12.88
Here is All Pass Layer, backwarding.
12.88
I1002 02:03:41.718962 1954701312 caffe.cpp:363] Iteration: 28 forward-backward time: 1 ms.
Here is All Pass Layer, forwarding.
12.88
Here is All Pass Layer, backwarding.
12.88
I1002 02:03:41.720289 1954701312 caffe.cpp:363] Iteration: 29 forward-backward time: 1 ms.
Here is All Pass Layer, forwarding.
12.88
Here is All Pass Layer, backwarding.
12.88
I1002 02:03:41.721837 1954701312 caffe.cpp:363] Iteration: 30 forward-backward time: 1 ms.
Here is All Pass Layer, forwarding.
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Here is All Pass Layer, backwarding.
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I1002 02:03:41.723042 1954701312 caffe.cpp:363] Iteration: 31 forward-backward time: 1 ms.
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I1002 02:03:41.725587 1954701312 caffe.cpp:363] Iteration: 33 forward-backward time: 1 ms.
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I1002 02:03:41.726771 1954701312 caffe.cpp:363] Iteration: 34 forward-backward time: 1 ms.
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I1002 02:03:41.728013 1954701312 caffe.cpp:363] Iteration: 35 forward-backward time: 1 ms.
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I1002 02:03:41.729249 1954701312 caffe.cpp:363] Iteration: 36 forward-backward time: 1 ms.
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I1002 02:03:41.730716 1954701312 caffe.cpp:363] Iteration: 37 forward-backward time: 1 ms.
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I1002 02:03:41.732275 1954701312 caffe.cpp:363] Iteration: 38 forward-backward time: 1 ms.
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I1002 02:03:41.733809 1954701312 caffe.cpp:363] Iteration: 39 forward-backward time: 1 ms.
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I1002 02:03:41.735049 1954701312 caffe.cpp:363] Iteration: 40 forward-backward time: 1 ms.
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I1002 02:03:41.737144 1954701312 caffe.cpp:363] Iteration: 41 forward-backward time: 2 ms.
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I1002 02:03:41.739090 1954701312 caffe.cpp:363] Iteration: 42 forward-backward time: 1 ms.
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I1002 02:03:41.741575 1954701312 caffe.cpp:363] Iteration: 43 forward-backward time: 2 ms.
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I1002 02:03:41.743450 1954701312 caffe.cpp:363] Iteration: 44 forward-backward time: 1 ms.
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I1002 02:03:41.744732 1954701312 caffe.cpp:363] Iteration: 45 forward-backward time: 1 ms.
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I1002 02:03:41.745970 1954701312 caffe.cpp:363] Iteration: 46 forward-backward time: 1 ms.
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I1002 02:03:41.747185 1954701312 caffe.cpp:363] Iteration: 47 forward-backward time: 1 ms.
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I1002 02:03:41.748430 1954701312 caffe.cpp:363] Iteration: 48 forward-backward time: 1 ms.
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I1002 02:03:41.749826 1954701312 caffe.cpp:363] Iteration: 49 forward-backward time: 1 ms.
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I1002 02:03:41.751124 1954701312 caffe.cpp:363] Iteration: 50 forward-backward time: 1 ms.
I1002 02:03:41.751147 1954701312 caffe.cpp:366] Average time per layer:
I1002 02:03:41.751157 1954701312 caffe.cpp:369] data forward: 0.00108 ms.
I1002 02:03:41.751183 1954701312 caffe.cpp:372] data backward: 0.001 ms.
I1002 02:03:41.751194 1954701312 caffe.cpp:369] ap forward: 1.37884 ms.
I1002 02:03:41.751205 1954701312 caffe.cpp:372] ap backward: 0.01156 ms.
I1002 02:03:41.751220 1954701312 caffe.cpp:377] Average Forward pass: 1.38646 ms.
I1002 02:03:41.751231 1954701312 caffe.cpp:379] Average Backward pass: 0.0144 ms.
I1002 02:03:41.751240 1954701312 caffe.cpp:381] Average Forward-Backward: 1.42 ms.
I1002 02:03:41.751250 1954701312 caffe.cpp:383] Total Time: 71 ms.
I1002 02:03:41.751260 1954701312 caffe.cpp:384] *** Benchmark ends ***可见该 Layer 可以正常创建、加载预设参数、执行 forward、backward 函数。
实际上对于算法 Layer，还要写 Test Case 保证功能正确。由于我们选择了极为简单的全通 Layer，故这一步可以省去。我这里偷点懒，您省点阅读时间。
感谢各位读者提出的宝贵建议和意见，这些都是无价的有监督学习数据集，是激励我不断 update 的 back prop 源动力。
祝各位同学国庆快乐！