标签：memorydatalayer 深度学习 caffe 内存中加载数据
最近在搞caffe的应用，因为很多时候我们需要进行服务器来进行特征的抽取，所以我们需要很将单张图片丢入caffe的网络进行一次传递，这样就诞生了一个从内存中如何加载数据进入caffe的需求，这里我直接贴出代码来先：
#include <boost/make_shared.hpp>
// these need to be included after boost on OS X
#include <string> // NOLINT(build/include_order)
#include <vector> // NOLINT(build/include_order)
#include <fstream> // NOLINT
#include "caffe/caffe.hpp"
#include <opencv.hpp>
static void CheckFile(const std::string& filename) {
std::ifstream f(filename.c_str());
if (!f.good()) {
f.close();
throw std::runtime_error("Could not open file " + filename);
}
f.close();
}
template <typename Dtype>
caffe::Net<Dtype>* Net_Init_Load(
std::string param_file, std::string pretrained_param_file, caffe::Phase phase)
{
CheckFile(param_file);
CheckFile(pretrained_param_file);
caffe::Net<Dtype>* net(new caffe::Net<Dtype>(param_file,phase));
net->CopyTrainedLayersFrom(pretrained_param_file,0);
return net;
}
#define NetF float
int main()
{
cv::Mat src1;
src1 = cv::imread("test.png");
cv::Mat rszimage;
//// The mean file image size is 256x256, need to resize the input image to 256x256
cv::resize(src1, rszimage, cv::Size(227, 227));
std::vector<cv::Mat> dv = { rszimage }; // image is a cv::Mat, as I'm using #1416
std::vector<int> dvl = { 0 };
caffe::Datum data;
caffe::ReadFileToDatum("D:/work/DestImage/crop/CH0005-00-0019/00028.png", &data);
caffe::Net<NetF>* _net = Net_Init_Load<NetF>("deploy_Test.prototxt", "bvlc_alexnet.caffemodel", caffe::TEST);
caffe::MemoryDataLayer<NetF> *m_layer_ = (caffe::MemoryDataLayer<NetF> *)_net->layers()[0].get();
m_layer_->AddMatVector(dv, dvl);
/*float loss = 0.0;
std::vector<caffe::Blob<float>*> results = _net->ForwardPrefilled(&loss);*/
int end_ind = _net->layers().size();
std::vector<caffe::Blob<NetF>*> input_vec;
_net->Forward(input_vec);
boost::shared_ptr<caffe::Blob<NetF>> outPool5 = _net->blob_by_name("pool5");
std::cout << outPool5->shape()[0] << std::endl;
std::cout << outPool5->shape()[1] << std::endl;
std::cout << outPool5->shape()[2] << std::endl;
std::cout << outPool5->shape()[3] << std::endl;
std::cout << outPool5->num() << std::endl;
std::cout << outPool5->channels() << std::endl;
std::cout << outPool5->width() << std::endl;
std::cout << outPool5->height() << std::endl;
std::cout << outPool5->data_at(0, 0, 0, 0) << std::endl;
std::cout << outPool5->data_at(0, 0, 1, 1) << std::endl;
std::cout << outPool5->data_at(0, 95, 5, 5) << std::endl;
const NetF* pstart = outPool5->cpu_data();
std::cout << m_layer_->width() << std::endl;
return 0;
}
然后是配置文件：
name: "CaffeNet"
layers
{
name: "data"
type: MEMORY_DATA
top: "data"
top: "label"
memory_data_param
{
batch_size: 1
channels: 3
height: 227
width: 227
}
transform_param
{
crop_size: 227
mirror: false
#mean_file:"imagenet_mean.binaryproto"
mean_value: 104
mean_value: 117
mean_value: 123
}
}
layers {
name: "`"
type: CONVOLUTION
bottom: "data"
top: "conv1"
blobs_lr: 1
blobs_lr: 2
weight_decay: 1
weight_decay: 0
convolution_param {
num_output: 96
kernel_size: 11
stride: 4
}
}
layers {
name: "relu1"
type: RELU
bottom: "conv1"
top: "conv1"
}
layers {
name: "pool1"
type: POOLING
bottom: "conv1"
top: "pool1"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layers {
name: "norm1"
type: LRN
bottom: "pool1"
top: "norm1"
lrn_param {
local_size: 5
alpha: 0.0001
beta: 0.75
}
}
layers {
name: "conv2"
type: CONVOLUTION
bottom: "norm1"
top: "conv2"
blobs_lr: 1
blobs_lr: 2
weight_decay: 1
weight_decay: 0
convolution_param {
num_output: 256
pad: 2
kernel_size: 5
group: 2
}
}
layers {
name: "relu2"
type: RELU
bottom: "conv2"
top: "conv2"
}
layers {
name: "pool2"
type: POOLING
bottom: "conv2"
top: "pool2"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layers {
name: "norm2"
type: LRN
bottom: "pool2"
top: "norm2"
lrn_param {
local_size: 5
alpha: 0.0001
beta: 0.75
}
}
layers {
name: "conv3"
type: CONVOLUTION
bottom: "norm2"
top: "conv3"
blobs_lr: 1
blobs_lr: 2
weight_decay: 1
weight_decay: 0
convolution_param {
num_output: 384
pad: 1
kernel_size: 3
}
}
layers {
name: "relu3"
type: RELU
bottom: "conv3"
top: "conv3"
}
layers {
name: "conv4"
type: CONVOLUTION
bottom: "conv3"
top: "conv4"
blobs_lr: 1
blobs_lr: 2
weight_decay: 1
weight_decay: 0
convolution_param {
num_output: 384
pad: 1
kernel_size: 3
group: 2
}
}
layers {
name: "relu4"
type: RELU
bottom: "conv4"
top: "conv4"
}
layers {
name: "conv5"
type: CONVOLUTION
bottom: "conv4"
top: "conv5"
blobs_lr: 1
blobs_lr: 2
weight_decay: 1
weight_decay: 0
convolution_param {
num_output: 256
pad: 1
kernel_size: 3
group: 2
}
}
layers {
name: "relu5"
type: RELU
bottom: "conv5"
top: "conv5"
}
layers {
name: "pool5"
type: POOLING
bottom: "conv5"
top: "pool5"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layers {
name: "fc6"
type: INNER_PRODUCT
bottom: "pool5"
top: "fc6"
blobs_lr: 1
blobs_lr: 2
weight_decay: 1
weight_decay: 0
inner_product_param {
num_output: 4096
}
}
layers {
name: "relu6"
type: RELU
bottom: "fc6"
top: "fc6"
}
layers {
name: "drop6"
type: DROPOUT
bottom: "fc6"
top: "fc6"
dropout_param {
dropout_ratio: 0.5
}
}
layers {
name: "fc7"
type: INNER_PRODUCT
bottom: "fc6"
top: "fc7"
blobs_lr: 1
blobs_lr: 2
weight_decay: 1
weight_decay: 0
inner_product_param {
num_output: 4096
}
}
layers {
name: "relu7"
type: RELU
bottom: "fc7"
top: "fc7"
}
layers {
name: "drop7"
type: DROPOUT
bottom: "fc7"
top: "fc7"
dropout_param {
dropout_ratio: 0.5
}
}
layers {
name: "fc8"
type: INNER_PRODUCT
bottom: "fc7"
top: "fc8"
blobs_lr: 1
blobs_lr: 2
weight_decay: 1
weight_decay: 0
inner_product_param {
num_output: 1000
}
}
layers
{
name: "prob"
type: SOFTMAX
bottom: "fc8"
top: "prob"
}
layers
{
name: "output"
type: ARGMAX
bottom: "prob"
top: "output"
}
我的模型使用的是alexnet，例子是用来抽取一个图片在pool5那一层的特征。这样大家使用这个例子可以利用caffe的任意模型抽取任意图片的特征。
版权声明：本文为博主原创文章，未经博主允许不得转载。
caffe使用MemoryDataLayer从内存中加载数据
标签：memorydatalayer 深度学习 caffe 内存中加载数据
原文地址：http://blog.csdn.net/cparent/article/details/47008485
