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https://github.com/shicai/Caffe_Manual
Caffe使用教程
https://github.com/shicai/Caffe_Manual
Last synced: 3 months ago
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Caffe使用教程
- Host: GitHub
- URL: https://github.com/shicai/Caffe_Manual
- Owner: shicai
- Created: 2015-08-06T12:15:41.000Z (almost 9 years ago)
- Default Branch: master
- Last Pushed: 2015-11-19T07:51:56.000Z (over 8 years ago)
- Last Synced: 2024-01-22T01:30:41.429Z (5 months ago)
- Language: C++
- Homepage: http://weibo.com/shicaiyang
- Size: 36.1 KB
- Stars: 351
- Watchers: 31
- Forks: 201
- Open Issues: 8
-
Metadata Files:
- Readme: ReadMe.md
Lists
- awesome-android-cpp - shicai/Caffe_Manual - Caffe使用教程 (TODO scan for Android support in followings)
README
##Caffe使用教程
by Shicai Yang（[@星空下的巫师](http://weibo.com/shicaiyang)）on 2015/08/06
### 初始化网络
#include "caffe/caffe.hpp"
#include
#include
using namespace caffe;
char *proto = "H:\\Models\\Caffe\\deploy.prototxt"; /* 加载CaffeNet的配置 */
Phase phase = TEST; /* or TRAIN */
Caffe::set_mode(Caffe::CPU);
// Caffe::set_mode(Caffe::GPU);
// Caffe::SetDevice(0);
//! Note: 后文所有提到的net，都是这个net
boost::shared_ptr< Net > net(new caffe::Net(proto, phase));
### 加载已训练好的模型
char *model = "H:\\Models\\Caffe\\bvlc_reference_caffenet.caffemodel";
net->CopyTrainedLayersFrom(model);
### 读取模型中的每层的结构配置参数
char *model = "H:\\Models\\Caffe\\bvlc_reference_caffenet.caffemodel";
NetParameter param;
ReadNetParamsFromBinaryFileOrDie(model, ¶m);
int num_layers = param.layer_size();
for (int i = 0; i < num_layers; ++i)
{
// 结构配置参数:name，type，kernel size，pad，stride等
LOG(ERROR) << "Layer " << i << ":" << param.layer(i).name() << "\t" << param.layer(i).type();
if (param.layer(i).type() == "Convolution")
{
ConvolutionParameter conv_param = param.layer(i).convolution_param();
LOG(ERROR) << "\t\tkernel size: " << conv_param.kernel_size()
<< ", pad: " << conv_param.pad()
<< ", stride: " << conv_param.stride();
}
}
### 读取图像均值
char *mean_file = "H:\\Models\\Caffe\\imagenet_mean.binaryproto";
Blob image_mean;
BlobProto blob_proto;
const float *mean_ptr;
unsigned int num_pixel;
bool succeed = ReadProtoFromBinaryFile(mean_file, &blob_proto);
if (succeed)
{
image_mean.FromProto(blob_proto);
num_pixel = image_mean.count(); /* NCHW=1x3x256x256=196608 */
mean_ptr = (const float *) image_mean.cpu_data();
}
### 根据指定数据，前向传播网络
//! Note: data_ptr指向已经处理好（去均值的，符合网络输入图像的长宽和Batch Size）的数据
void caffe_forward(boost::shared_ptr< Net > & net, float *data_ptr)
{
Blob* input_blobs = net->input_blobs()[0];
switch (Caffe::mode())
{
case Caffe::CPU:
memcpy(input_blobs->mutable_cpu_data(), data_ptr,
sizeof(float) * input_blobs->count());
break;
case Caffe::GPU:
cudaMemcpy(input_blobs->mutable_gpu_data(), data_ptr,
sizeof(float) * input_blobs->count(), cudaMemcpyHostToDevice);
break;
default:
LOG(FATAL) << "Unknown Caffe mode.";
}
net->ForwardPrefilled();
}
### 根据Feature层的名字获取其在网络中的Index
//! Note: Net的Blob是指，每个层的输出数据，即Feature Maps
// char *query_blob_name = "conv1";
unsigned int get_blob_index(boost::shared_ptr< Net > & net, char *query_blob_name)
{
std::string str_query(query_blob_name);
vector< string > const & blob_names = net->blob_names();
for( unsigned int i = 0; i != blob_names.size(); ++i )
{
if( str_query == blob_names[i] )
{
return i;
}
}
LOG(FATAL) << "Unknown blob name: " << str_query;
}
### 读取网络指定Feature层数据
//! Note: 根据CaffeNet的deploy.prototxt文件，该Net共有15个Blob，从data一直到prob
char *query_blob_name = "conv1"; /* data, conv1, pool1, norm1, fc6, prob, etc */
unsigned int blob_id = get_blob_index(net, query_blob_name);
boost::shared_ptr > blob = net->blobs()[blob_id];
unsigned int num_data = blob->count(); /* NCHW=10x96x55x55 */
const float *blob_ptr = (const float *) blob->cpu_data();
### 根据文件列表，获取特征，并存为二进制文件
详见**get_features.cpp**文件：
> 主要包括三个步骤
- 生成文件列表，格式与训练用的类似，每行一个图像
包括文件全路径、空格、标签（没有的话，可以置0）
- 根据train_val或者deploy的prototxt，改写生成feat.prototxt
主要是将输入层改为image_data层，最后加上prob和argmax（为了输出概率和Top1/5预测标签）
- 根据指定参数，运行程序后会生成若干个二进制文件，可以用MATLAB读取数据，进行分析
### 根据Layer的名字获取其在网络中的Index
//! Note: Layer包括神经网络所有层，比如，CaffeNet共有23层
// char *query_layer_name = "conv1";
unsigned int get_layer_index(boost::shared_ptr< Net > & net, char *query_layer_name)
{
std::string str_query(query_layer_name);
vector< string > const & layer_names = net->layer_names();
for( unsigned int i = 0; i != layer_names.size(); ++i )
{
if( str_query == layer_names[i] )
{
return i;
}
}
LOG(FATAL) << "Unknown layer name: " << str_query;
}
### 读取指定Layer的权重数据
//! Note: 不同于Net的Blob是Feature Maps，Layer的Blob是指Conv和FC等层的Weight和Bias
char *query_layer_name = "conv1";
const float *weight_ptr, *bias_ptr;
unsigned int layer_id = get_layer_index(net, query_layer_name);
boost::shared_ptr > layer = net->layers()[layer_id];
std::vector >> blobs = layer->blobs();
if (blobs.size() > 0)
{
weight_ptr = (const float *) blobs[0]->cpu_data();
bias_ptr = (const float *) blobs[1]->cpu_data();
}
//! Note: 训练模式下，读取指定Layer的梯度数据，与此相似，唯一的区别是将cpu_data改为cpu_diff
### 修改某层的Weight数据
const float* data_ptr; /* 指向待写入数据的指针， 源数据指针*/
float* weight_ptr = NULL; /* 指向网络中某层权重的指针，目标数据指针*/
unsigned int data_size; /* 待写入的数据量 */
char *layer_name = "conv1"; /* 需要修改的Layer名字 */
unsigned int layer_id = get_layer_index(net, query_layer_name);
boost::shared_ptr > blob = net->layers()[layer_id]->blobs()[0];
CHECK(data_size == blob->count());
switch (Caffe::mode())
{
case Caffe::CPU:
weight_ptr = blob->mutable_cpu_data();
break;
case Caffe::GPU:
weight_ptr = blob->mutable_gpu_data();
break;
default:
LOG(FATAL) << "Unknown Caffe mode";
}
caffe_copy(blob->count(), data_ptr, weight_ptr);
//! Note: 训练模式下，手动修改指定Layer的梯度数据，与此相似
// mutable_cpu_data改为mutable_cpu_diff，mutable_gpu_data改为mutable_gpu_diff
### 保存新的模型
char* weights_file = "bvlc_reference_caffenet_new.caffemodel";
NetParameter net_param;
net->ToProto(&net_param, false);
WriteProtoToBinaryFile(net_param, weights_file);