Caffe图片训练分类研究、深度学习图片分类
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Caffe图片训练分类研究、深度学习图片分类
转载请注明:http://blog.csdn.net/forest_world
一、NSFW研究
1、安装Docker
http://www.linuxidc.com/Linux/2014-08/105656.htm安装Docker使用apt-get命令:$ apt-get install docker.io创建软连接ln -sf /usr/bin/docker.io /usr/local/bin/dockersudo service docker stopsudo service docker start
2、
sudo docker build -t caffe:cpu https://raw.githubusercontent.com/BVLC/caffe/master/docker/standalone/cpu/DockerfileStep 0 : FROM ubuntu:14.04 ---> 35b394a6f7a2Step 1 : MAINTAINER caffe-maint@googlegroups.com ---> Using cache ---> ca50125d0951Step 2 : RUN apt-get update && apt-get install -y --no-install-recommends build-essential cmake git wget libatlas-base-dev libboost-all-dev libgflags-dev libgoogle-glog-dev libhdf5-serial-dev libleveldb-dev liblmdb-dev libopencv-dev libprotobuf-dev libsnappy-dev protobuf-compiler python-dev python-numpy python-pip python-scipy && rm -rf /var/lib/apt/lists/* ---> Running in d6856e1b4740Ign http://archive.ubuntu.com trusty InReleaseGet:1 http://archive.ubuntu.com trusty-updates InRelease [65.9 kB]Get:2 http://archive.ubuntu.com trusty-security InRelease [65.9 kB]Get:3 http://archive.ubuntu.com trusty Release.gpg [933 B]Get:4 http://archive.ubuntu.com trusty-updates/main Sources [474 kB]Get:5 http://archive.ubuntu.com trusty-updates/main Sources [474 kB]Get:6 http://archive.ubuntu.com trusty-updates/restricted Sources [5247 B]Get:7 http://archive.ubuntu.com trusty-updates/universe Sources [209 kB]Get:8 http://archive.ubuntu.com trusty-updates/main amd64 Packages [1131 kB]......Removing intermediate container d3643cce1d7eStep 7 : ENV PYCAFFE_ROOT $CAFFE_ROOT/python ---> Running in e4e4019889f8 ---> e982c669b99bRemoving intermediate container e4e4019889f8Step 8 : ENV PYTHONPATH $PYCAFFE_ROOT:$PYTHONPATH ---> Running in a9ee4331bbe8 ---> 8a1867b64b5cRemoving intermediate container a9ee4331bbe8Step 9 : ENV PATH $CAFFE_ROOT/build/tools:$PYCAFFE_ROOT:$PATH ---> Running in bc2a271a95bd ---> 864daab5c633Removing intermediate container bc2a271a95bdStep 10 : RUN echo "$CAFFE_ROOT/build/lib" >> /etc/ld.so.conf.d/caffe.conf && ldconfig ---> Running in d0af6f3e69ea ---> fa8b1e810492Removing intermediate container d0af6f3e69eaStep 11 : WORKDIR /workspace ---> Running in ab94152a0a18 ---> 49ffbf2d8fefRemoving intermediate container ab94152a0a18Successfully built 49ffbf2d8fef
3、
git clone https://github.com/yahoo/open_nsfw
$ cd open_nsfw
@ubuntu:~$ git clone https://github.com/yahoo/open_nsfwCloning into 'open_nsfw'...remote: Counting objects: 31, done.remote: Compressing objects: 100% (20/20), done.Unpacking objects: 32% (10/31)
4、
I1012 05:17:23.226325 1 net.cpp:228] relu_stage0_block0 does not need backward computation.I1012 05:17:23.226327 1 net.cpp:228] eltwise_stage0_block0 does not need backward computation.I1012 05:17:23.226331 1 net.cpp:228] scale_stage0_block0_branch2c does not need backward computation.I1012 05:17:23.226333 1 net.cpp:228] bn_stage0_block0_branch2c does not need backward computation.I1012 05:17:23.226336 1 net.cpp:228] conv_stage0_block0_branch2c does not need backward computation.I1012 05:17:23.226339 1 net.cpp:228] relu_stage0_block0_branch2b does not need backward computation.I1012 05:17:23.226342 1 net.cpp:228] scale_stage0_block0_branch2b does not need backward computation.I1012 05:17:23.226346 1 net.cpp:228] bn_stage0_block0_branch2b does not need backward computation.I1012 05:17:23.226348 1 net.cpp:228] conv_stage0_block0_branch2b does not need backward computation.I1012 05:17:23.226351 1 net.cpp:228] relu_stage0_block0_branch2a does not need backward computation.I1012 05:17:23.226354 1 net.cpp:228] scale_stage0_block0_branch2a does not need backward computation.I1012 05:17:23.226356 1 net.cpp:228] bn_stage0_block0_branch2a does not need backward computation.I1012 05:17:23.226359 1 net.cpp:228] conv_stage0_block0_branch2a does not need backward computation.I1012 05:17:23.226362 1 net.cpp:228] scale_stage0_block0_proj_shortcut does not need backward computation.I1012 05:17:23.226366 1 net.cpp:228] bn_stage0_block0_proj_shortcut does not need backward computation.I1012 05:17:23.226368 1 net.cpp:228] conv_stage0_block0_proj_shortcut does not need backward computation.I1012 05:17:23.226372 1 net.cpp:228] pool1_pool1_0_split does not need backward computation.I1012 05:17:23.226374 1 net.cpp:228] pool1 does not need backward computation.I1012 05:17:23.226378 1 net.cpp:228] relu_1 does not need backward computation.I1012 05:17:23.226380 1 net.cpp:228] scale_1 does not need backward computation.I1012 05:17:23.226383 1 net.cpp:228] bn_1 does not need backward computation.I1012 05:17:23.226387 1 net.cpp:228] conv_1 does not need backward computation.I1012 05:17:23.226389 1 net.cpp:228] data does not need backward computation.I1012 05:17:23.226392 1 net.cpp:270] This network produces output probI1012 05:17:23.226526 1 net.cpp:283] Network initialization done.I1012 05:17:23.277700 1 upgrade_proto.cpp:77] Attempting to upgrade batch norm layers using deprecated params: nsfw_model/resnet_50_1by2_nsfw.caffemodelI1012 05:17:23.277819 1 upgrade_proto.cpp:80] Successfully upgraded batch norm layers using deprecated params.I1012 05:17:23.283418 1 net.cpp:761] Ignoring source layer lossNSFW score: 0.000410715758335
二、
#include <caffe/caffe.hpp>#ifdef USE_OPENCV#include <opencv2/core/core.hpp>#include <opencv2/highgui/highgui.hpp>#include <opencv2/imgproc/imgproc.hpp>#endif // USE_OPENCV#include <algorithm>#include <iosfwd>#include <memory>#include <string>#include <utility>#include <vector>#ifdef USE_OPENCVusing namespace caffe; // NOLINT(build/namespaces)using std::string;/* Pair (label, confidence) representing a prediction. */typedef std::pair<string, float> Prediction;class Classifier { public: Classifier(const string& model_file, const string& trained_file, const string& mean_file, const string& label_file); std::vector<Prediction> Classify(const cv::Mat& img, int N = 5); private: void SetMean(const string& mean_file); std::vector<float> Predict(const cv::Mat& img); void WrapInputLayer(std::vector<cv::Mat>* input_channels); void Preprocess(const cv::Mat& img, std::vector<cv::Mat>* input_channels); private: shared_ptr<Net<float> > net_; cv::Size input_geometry_; int num_channels_; cv::Mat mean_; std::vector<string> labels_;};Classifier::Classifier(const string& model_file, const string& trained_file, const string& mean_file, const string& label_file) {#ifdef CPU_ONLY Caffe::set_mode(Caffe::CPU);#else Caffe::set_mode(Caffe::GPU);#endif /* Load the network. */ net_.reset(new Net<float>(model_file, TEST)); net_->CopyTrainedLayersFrom(trained_file); CHECK_EQ(net_->num_inputs(), 1) << "Network should have exactly one input."; CHECK_EQ(net_->num_outputs(), 1) << "Network should have exactly one output."; Blob<float>* input_layer = net_->input_blobs()[0]; num_channels_ = input_layer->channels(); CHECK(num_channels_ == 3 || num_channels_ == 1) << "Input layer should have 1 or 3 channels."; input_geometry_ = cv::Size(input_layer->width(), input_layer->height()); /* Load the binaryproto mean file. */ SetMean(mean_file); /* Load labels. */ std::ifstream labels(label_file.c_str()); CHECK(labels) << "Unable to open labels file " << label_file; string line; while (std::getline(labels, line)) labels_.push_back(string(line)); Blob<float>* output_layer = net_->output_blobs()[0]; CHECK_EQ(labels_.size(), output_layer->channels()) << "Number of labels is different from the output layer dimension.";}static bool PairCompare(const std::pair<float, int>& lhs, const std::pair<float, int>& rhs) { return lhs.first > rhs.first;}/* Return the indices of the top N values of vector v. */static std::vector<int> Argmax(const std::vector<float>& v, int N) { std::vector<std::pair<float, int> > pairs; for (size_t i = 0; i < v.size(); ++i) pairs.push_back(std::make_pair(v[i], static_cast<int>(i))); std::partial_sort(pairs.begin(), pairs.begin() + N, pairs.end(), PairCompare); std::vector<int> result; for (int i = 0; i < N; ++i) result.push_back(pairs[i].second); return result;}/* Return the top N predictions. */std::vector<Prediction> Classifier::Classify(const cv::Mat& img, int N) { std::vector<float> output = Predict(img); N = std::min<int>(labels_.size(), N); std::vector<int> maxN = Argmax(output, N); std::vector<Prediction> predictions; for (int i = 0; i < N; ++i) { int idx = maxN[i]; predictions.push_back(std::make_pair(labels_[idx], output[idx])); } return predictions;}/* Load the mean file in binaryproto format. */void Classifier::SetMean(const string& mean_file) { BlobProto blob_proto; ReadProtoFromBinaryFileOrDie(mean_file.c_str(), &blob_proto); /* Convert from BlobProto to Blob<float> */ Blob<float> mean_blob; mean_blob.FromProto(blob_proto); CHECK_EQ(mean_blob.channels(), num_channels_) << "Number of channels of mean file doesn't match input layer."; /* The format of the mean file is planar 32-bit float BGR or grayscale. */ std::vector<cv::Mat> channels; float* data = mean_blob.mutable_cpu_data(); for (int i = 0; i < num_channels_; ++i) { /* Extract an individual channel. */ cv::Mat channel(mean_blob.height(), mean_blob.width(), CV_32FC1, data); channels.push_back(channel); data += mean_blob.height() * mean_blob.width(); } /* Merge the separate channels into a single image. */ cv::Mat mean; cv::merge(channels, mean); /* Compute the global mean pixel value and create a mean image * filled with this value. */ cv::Scalar channel_mean = cv::mean(mean); mean_ = cv::Mat(input_geometry_, mean.type(), channel_mean);}std::vector<float> Classifier::Predict(const cv::Mat& img) { Blob<float>* input_layer = net_->input_blobs()[0]; input_layer->Reshape(1, num_channels_, input_geometry_.height, input_geometry_.width); /* Forward dimension change to all layers. */ net_->Reshape(); std::vector<cv::Mat> input_channels; WrapInputLayer(&input_channels); Preprocess(img, &input_channels); net_->Forward(); /* Copy the output layer to a std::vector */ Blob<float>* output_layer = net_->output_blobs()[0]; const float* begin = output_layer->cpu_data(); const float* end = begin + output_layer->channels(); return std::vector<float>(begin, end);}/* Wrap the input layer of the network in separate cv::Mat objects * (one per channel). This way we save one memcpy operation and we * don't need to rely on cudaMemcpy2D. The last preprocessing * operation will write the separate channels directly to the input * layer. */void Classifier::WrapInputLayer(std::vector<cv::Mat>* input_channels) { Blob<float>* input_layer = net_->input_blobs()[0]; int width = input_layer->width(); int height = input_layer->height(); float* input_data = input_layer->mutable_cpu_data(); for (int i = 0; i < input_layer->channels(); ++i) { cv::Mat channel(height, width, CV_32FC1, input_data); input_channels->push_back(channel); input_data += width * height; }}void Classifier::Preprocess(const cv::Mat& img, std::vector<cv::Mat>* input_channels) { /* Convert the input image to the input image format of the network. */ cv::Mat sample; if (img.channels() == 3 && num_channels_ == 1) cv::cvtColor(img, sample, cv::COLOR_BGR2GRAY); else if (img.channels() == 4 && num_channels_ == 1) cv::cvtColor(img, sample, cv::COLOR_BGRA2GRAY); else if (img.channels() == 4 && num_channels_ == 3) cv::cvtColor(img, sample, cv::COLOR_BGRA2BGR); else if (img.channels() == 1 && num_channels_ == 3) cv::cvtColor(img, sample, cv::COLOR_GRAY2BGR); else sample = img; cv::Mat sample_resized; if (sample.size() != input_geometry_) cv::resize(sample, sample_resized, input_geometry_); else sample_resized = sample; cv::Mat sample_float; if (num_channels_ == 3) sample_resized.convertTo(sample_float, CV_32FC3); else sample_resized.convertTo(sample_float, CV_32FC1); cv::Mat sample_normalized; cv::subtract(sample_float, mean_, sample_normalized); /* This operation will write the separate BGR planes directly to the * input layer of the network because it is wrapped by the cv::Mat * objects in input_channels. */ cv::split(sample_normalized, *input_channels); CHECK(reinterpret_cast<float*>(input_channels->at(0).data) == net_->input_blobs()[0]->cpu_data()) << "Input channels are not wrapping the input layer of the network.";}int main(int argc, char** argv) { if (argc != 6) { std::cerr << "Usage: " << argv[0] << " deploy.prototxt network.caffemodel" << " mean.binaryproto labels.txt img.jpg" << std::endl; return 1; } ::google::InitGoogleLogging(argv[0]); string model_file = argv[1]; string trained_file = argv[2]; string mean_file = argv[3]; string label_file = argv[4]; Classifier classifier(model_file, trained_file, mean_file, label_file); string file = argv[5]; std::cout << "---------- Prediction for " << file << " ----------" << std::endl; cv::Mat img = cv::imread(file, -1); CHECK(!img.empty()) << "Unable to decode image " << file; std::vector<Prediction> predictions = classifier.Classify(img); /* Print the top N predictions. */ for (size_t i = 0; i < predictions.size(); ++i) { Prediction p = predictions[i]; std::cout << std::fixed << std::setprecision(4) << p.second << " - \"" << p.first << "\"" << std::endl; }}#elseint main(int argc, char** argv) { LOG(FATAL) << "This example requires OpenCV; compile with USE_OPENCV.";}#endif // USE_OPENCV
string model_file ("E:\\ cpp_classification\\caffe.prototxt"); string trained_file ("E:\\ cpp_classification\\caffe.caffemodel"); string mean_file ("E:\\cpp_classification\\mean.binaryproto"); string label_file ("E:\\ cpp_classification\\labels.txt"); Classifier classifier(model_file, trained_file, mean_file, label_file); string file ("E:\\ cpp_classification\\test.jpg");
参考学习资料:
http://m.blog.csdn.net/article/details?id=52443126 基于深度学习的人脸识别系统系列(Caffe+OpenCV+Dlib)——【一】如何在Visual Studio中像使用OpenCV一样使用Caffe
http://mp.weixin.qq.com/s?__biz=MzI1NTE4NTUwOQ==&mid=2650325557&idx=1&sn=362d476d3b3820ea56e4672369565e4f&chksm=f235a53fc5422c2939f76b7e8f5265333f3159b0ec4275fe733d27e7a03f17395b0460a318d2&mpshare=1&scene=1&srcid=1017Le0xZeDhioc9DxPIGNN9#wechat_redirect IJCAI16论文速读:Deep Learning论文选读(上)
http://www.cnblogs.com/carle-09/p/5779304.html 4 .caffe:train_val.prototxt、 solver.prototxt 、 deploy.prototxt( 创建模型与编写配置文件)
http://blog.csdn.net/deeplearninglc007/article/details/40086503 使用Caffe对图片进行训练并分类的简单流程
http://blog.csdn.net/wang4959520/article/details/51841110 将train_val.prototxt 转换成deploy.prototxt
http://blog.csdn.net/hyman_yx/article/details/51732656 Caffe均值文件mean.binaryproto转mean.npy
http://blog.csdn.net/shakevincent/article/details/51694686微软Caffe编译
http://www.cnblogs.com/alexcai/p/5469436.html caffe简易上手指南(二)—— 训练我们自己的数据
http://www.aiuxian.com/article/p-1659539.html 深度学习–如何利用Caffe进行训练ImageNet网络
http://www.th7.cn/system/win/201602/153606.shtml caffe for windows 下使用caffemodel 实现cifar10的图像分类
http://blog.csdn.net/dcxhun3/article/details/52021296 用训练好的caffemodel来进行分类
http://neuralnetworksanddeeplearning.com/chap1.html CHAPTER 1 Using neural nets to recognize handwritten digits
http://www.cnblogs.com/shishupeng/p/5694775.html 深度卷积网络CNN与图像语义分割
本文地址:http://blog.csdn.net/forest_world
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