我在visual studio 2015中运行简单的c ++代码将图像传递给我的preTrained caffemodel但是当我想要创建网络时会发生以下错误:
Check failed: registry.count(type) == 1 (0 vs. 1) Unknown layer type: Input (known types: Convolution, Eltwise, LRN, Pooling, Power, Python, ReLU, Sigmoid, Softmax, Split, TanH)
我的原型文件:
name: "DeepID_face"
input: "data_1"
input_dim: 1
input_dim: 3
input_dim: 640
input_dim: 480
layer {
name: "conv1_1"
type: "Convolution"
bottom: "data_1"
top: "conv1_1"
param {
name: "conv1_w"
lr_mult: 1
decay_mult: 1
}
param {
name: "conv1_b"
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 20
kernel_size: 4
stride: 1
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "relu1_1"
type: "ReLU"
bottom: "conv1_1"
top: "conv1_1"
}
layer {
name: "norm1_1"
type: "LRN"
bottom: "conv1_1"
top: "norm1_1"
lrn_param {
local_size: 5
alpha: 0.0001
beta: 0.75
}
}
layer {
name: "pool1_1"
type: "Pooling"
bottom: "norm1_1"
top: "pool1_1"
pooling_param {
pool: MAX
kernel_size: 2
stride: 2
}
}
layer {
name: "conv2_1"
type: "Convolution"
bottom: "pool1_1"
top: "conv2_1"
param {
name: "conv2_w"
lr_mult: 1
decay_mult: 1
}
param {
name: "conv2_b"
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 40
kernel_size: 3
group: 2
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu2_1"
type: "ReLU"
bottom: "conv2_1"
top: "conv2_1"
}
layer {
name: "norm2_1"
type: "LRN"
bottom: "conv2_1"
top: "norm2_1"
lrn_param {
local_size: 5
alpha: 0.0001
beta: 0.75
}
}
layer {
name: "pool2_1"
type: "Pooling"
bottom: "norm2_1"
top: "pool2_1"
pooling_param {
pool: MAX
kernel_size: 2
stride: 2
}
}
layer {
name: "conv3_1"
type: "Convolution"
bottom: "pool2_1"
top: "conv3_1"
param {
name: "conv3_w"
lr_mult: 1
decay_mult: 1
}
param {
name: "conv3_b"
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 60
kernel_size: 3
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "pool3_1"
type: "Pooling"
bottom: "conv3_1"
top: "pool3_1"
pooling_param {
pool: MAX
kernel_size: 2
stride: 2
}
}
layer {
name: "conv4_1"
type: "Convolution"
bottom: "pool3_1"
top: "conv4_1"
param {
name: "conv4_w"
lr_mult: 1
decay_mult: 1
}
param {
name: "conv4_b"
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 80
kernel_size: 2
stride: 2
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer{
name:"flatten_pool3_1"
type:"Flatten"
bottom:"pool3_1"
top:"flatten_pool3_1"
}
layer{
name:"flatten_conv4_1"
type:"Flatten"
bottom:"conv4_1"
top:"flatten_conv4_1"
}
layer{
name:"contact_conv"
type:"Concat"
bottom:"flatten_conv4_1"
bottom:"flatten_pool3_1"
top:"contact_conv"
}
layer {
name: "deepid_1"
type: "InnerProduct"
bottom: "contact_conv"
top: "deepid_1"
param {
name: "fc6_w"
lr_mult: 1
decay_mult: 1
}
param {
name: "fc6_b"
lr_mult: 2
decay_mult: 0
}
inner_product_param {
num_output: 160
weight_filler {
type: "gaussian"
std: 0.005
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "loss"
type: "Softmax"
bottom: "deepid_1"
top: "loss"
}
我希望使用visual studio 2015进行编译的简单代码:
#define USE_OPENCV
#include <cuda_runtime.h>
#include <cstring>
#include <cstdlib>
#include <vector>
#include <string>
#include <iostream>
#include <stdio.h>
#include "caffe/caffe.hpp"
#include "caffe/util/io.hpp"
#include "caffe/blob.hpp"
#include <opencv2\opencv.hpp>
#include <boost/shared_ptr.hpp>
using namespace caffe;
using namespace std;
int main(int argc, char** argv) {
Caffe::set_mode(Caffe::CPU);
caffe::string netS = "C:/Users/127051/Documents/Visual Studio 2015/Projects/C++/Caffe/CaffeTest/x64/Release/net_struct.prototxt";
caffe::string netW = "C:/Users/127051/Documents/Visual Studio 2015/Projects/C++/Caffe/CaffeTest/x64/Release/net_weights.caffemodel";
Datum datum;
cv::Mat img = cv::imread("D:/FEI/All/1-08.jpg");
if (img.empty())
LOG(ERROR) << "Error during file reading";
else
caffe::CVMatToDatum(img, &datum);
//get the net
boost::shared_ptr<Net<float> > net_;
net_.reset(new Net<float>(netS, TEST));
//get trained net
net_->CopyTrainedLayersFrom(netW);
//get the blob
Blob<float>* blob = new Blob<float>(1, datum.channels(), datum.height(), datum.width());
//get the blobproto
BlobProto blob_proto;
blob_proto.set_num(1);
blob_proto.set_channels(datum.channels());
blob_proto.set_height(datum.height());
blob_proto.set_width(datum.width());
const int data_size = datum.channels() * datum.height() * datum.width();
int size_in_datum = std::max<int>(datum.data().size(),
datum.float_data_size());
for (int i = 0; i < size_in_datum; ++i) {
blob_proto.add_data(0.);
}
const string& data = datum.data();
if (data.size() != 0) {
for (int i = 0; i < size_in_datum; ++i) {
blob_proto.set_data(i, blob_proto.data(i) + (uint8_t)data[i]);
}
}
//set data into blob
blob->FromProto(blob_proto);
//fill the vector
vector<Blob<float>*> bottom;
bottom.push_back(blob);
float type = 0.0;
const vector<Blob<float>*>& result = net_->Forward(bottom, &type);
//Here I can use the argmax layer, but for now I do a simple for :)
float max = 0;
float max_i = 0;
for (int i = 0; i < 1000; ++i) {
float value = result[0]->cpu_data()[i];
if (max < value) {
max = value;
max_i = i;
}
}
LOG(ERROR) << "max: " << max << " i " << max_i;
return 0;
}
我也设置了(/ OPT:NOREF)但是没有修复它。也将图层格式更改为:
layer {
name: "data_1"
type: "Input"
top: "data_1"
input_param { shape: { dim: 1 dim: 3 dim: 640 dim: 480 } }
}
但不是固定的。 请帮帮我。
答案 0 :(得分:0)
我最后通过一些更改解决了这个问题,并为这样的caffe源代码添加了一些代码:
如果您想要注册未注册的图层,请执行以下步骤:
1)首先在caffe \ layers评论中的图层源代码中 REGISTER_LAYER_CLASS
2)其次在layer_factory.cpp中添加一些代码,如下面我为输入层编写的代码:
// Get input layer according to engine.
template <typename Dtype>
shared_ptr<Layer<Dtype> > GetInputLayer(const LayerParameter& param) {
int engine = 0;
#ifdef USE_CUDNN
engine = 1;
#endif
if (engine == 0) {
return shared_ptr<Layer<Dtype> >(new InputLayer<Dtype>(param));
#ifdef USE_CUDNN
}
else if (engine == 1) {
return shared_ptr<Layer<Dtype> >(new CuDNNInputLayer<Dtype>(param));
#endif
}
else {
LOG(FATAL) << "Layer " << param.name() << " has unknown engine.";
throw; // Avoids missing return warning
}
}
REGISTER_LAYER_CREATOR(Input, GetInputLayer);
最后重新编译caffe并将看到解决这个问题享受:)。
答案 1 :(得分:0)
有同样的问题。上面接受的答案可能有效,但在某些情况下,它与caffe链接问题有关。我通过在VS项目属性中包含caffe build目录来解决此问题。
Project -> Properties -> C/C++ -> General -> Additional Include Directories -> C:\Projects\caffe\build