从AlexNet到squeezenet

squeezenet出自2016论文SQUEEZENET:ALEXNET-LEVEL ACCURACY WITH 50X FEWER PARAMETERS AND <0.5MB MODEL SIZE，

squeezenet主要提出了FireModule概念，如上图所示，一个FireModule由一个squeeze和一个expand组成，squeeze包含s个1*1的卷积核，expand包含e1个1*1的卷积核，e3个3*3的卷积核，并且满足s<e1+e3。

经过这样的一个替换，使得模型缩小了大概50倍，同时保证了准确性。

 

测试程序：

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) {  argc = 6;  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];*/  /*string model_file = ".//caffenet//deploy.prototxt";  string trained_file = ".//caffenet//bvlc_reference_caffenet.caffemodel";*/  string model_file = ".//AlexNet//deploy.prototxt";  string trained_file = ".//AlexNet//bvlc_alexnet.caffemodel";  /*string model_file = ".\\SqueezeNet_v1.0\\deploy.prototxt";  string trained_file = ".\\SqueezeNet_v1.0\\squeezenet_v1.0.caffemodel";*/  string mean_file = "imagenet_mean.binaryproto";  string label_file = "synset_words.txt";  Classifier classifier(model_file, trained_file, mean_file, label_file);  //string file = argv[5];  string file = "cat.jpg";  //string file = "fish-bike.jpg";  std::cout << "---------- Prediction for "<< file << " ----------" << std::endl;  clock_t start, end;  start = clock();  cv::Mat img = cv::imread(file, -1);  CHECK(!img.empty()) << "Unable to decode image " << file;  std::vector<Prediction> predictions = classifier.Classify(img);  end = (double)(1000 * (clock() - start) / CLOCKS_PER_SEC);  // std::cout << std::fixed << std::setprecision(4) << gp.second <<"  "<<gp.first << endl;  std::cout << "time:" << end <<"ms"<< std::endl;  /* 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;  }  cv::imshow(file, img);  cv::waitKey();}#elseint main(int argc, char** argv) {  LOG(FATAL) << "This example requires OpenCV; compile with USE_OPENCV.";}

模型大小：

AlexNet模型大小：232M

caffeNet模型大小：232M

SqueezeNet_v1.0模型大小：4.76M

SqueezeNet_v1.1模型大小：4.72M

 

 

实验效果：

测试1：

AlexNet:

caffenet:

squeezenet_v1.0:

squeezenet_v1.1:

测试2：

AlexNet:

caffenet:

squeezenet_v1.0:

squeezenet_v1.1:

reference：

https://github.com/BVLC/caffe/tree/master/models

https://github.com/DeepScale/SqueezeNet