Caffe 层创建失败 - 层类型未知
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【中文标题】Caffe 层创建失败 - 层类型未知【英文标题】：Caffe layer creation failure - Unknown layer type 【发布时间】：2017-03-24 07:35:22 【问题描述】：我在 Visual Studio 2015 中运行简单的 C++ 代码将图像传递给我的 preTrained caffemodel，但是当我想创建网络时会出现此错误：
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 prototxt 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"
以及我想用 Visual Studio 2015 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) 但没有修复它。还将图层格式更改为：
layer
name: "data_1"
type: "Input"
top: "data_1"
input_param shape: dim: 1 dim: 3 dim: 640 dim: 480
但不固定。 请帮帮我。
【问题讨论】：
您需要将top: "data" 更改为top: "data_1"，因为您的第一个conv 层底部是data_1 和dim: 3 内层。
【参考方案1】：
我终于通过一些更改解决了这个问题，并在 caffe 源代码中添加了一些代码，如下所示：
如果您想注册未注册的图层，请执行以下步骤：
1) 首先在 caffe\layers 注释中层源代码 REGISTER_LAYER_CLASS
2) 其次，在 layer_factory.cpp 中添加一些代码，如下所示，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 会看到解决了这个问题，享受:)。
【讨论】：
【参考方案2】：有同样的问题。以上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 构建目录来解决它。
Project -> Properties -> C/C++ -> General -> Additional Include Directories -> C:\Projects\caffe\build
【讨论】：
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