Batched inference for cpp_classification

Example usage: ./build/examples/cpp_classification/classification.bin models/bvlc_reference_caffenet/deploy.prototxt \ models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel \ data/ilsvrc12/imagenet_mean.binaryproto \ data/ilsvrc12/synset_words.txt \ images/1.jpg images/2.jpg images/3.jpg images/4.jpg images/5.jpg

Author: flx42

examples/cpp_classification/classification.cpp

@@ -25,14 +25,14 @@ class Classifier {
  const string& mean_file,
  const string& label_file);
 
- std::vector<Prediction> Classify(const cv::Mat& img, int N = 5);
+ std::vector<std::vector<Prediction> > Classify(const std::vector<cv::Mat>& imgs, int N = 5);
 
  private:
  void SetMean(const string& mean_file);
 
- std::vector<float> Predict(const cv::Mat& img);
+ std::vector<std::vector<float> > Predict(const std::vector<cv::Mat>& imgs);
 
- void WrapInputLayer(std::vector<cv::Mat>* input_channels);
+ void WrapInputLayer(std::vector<cv::Mat>* input_channels, int n);
 
  void Preprocess(const cv::Mat& img,
  std::vector<cv::Mat>* input_channels);
@@ -102,18 +102,24 @@ static std::vector<int> Argmax(const std::vector<float>& v, int N) {
 }
 
 /* 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;
+std::vector<std::vector<Prediction> > Classifier::Classify(const std::vector<cv::Mat>& imgs, int N) {
+ std::vector<std::vector<float> > outputs = Predict(imgs);
+
+ std::vector<std::vector<Prediction> > all_predictions;
+ for (int j = 0; j < outputs.size(); ++j) {
+ std::vector<float> output = outputs[j];
+
+ 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]));
+ }
+ all_predictions.push_back(predictions);
+ }
+
+ return all_predictions;
 }
 
 /* Load the mean file in binaryproto format. */
@@ -147,42 +153,48 @@ void Classifier::SetMean(const string& mean_file) {
  mean_ = cv::Mat(input_geometry_, mean.type(), channel_mean);
 }
 
-std::vector<float> Classifier::Predict(const cv::Mat& img) {
+std::vector<std::vector<float> > Classifier::Predict(const std::vector<cv::Mat>& imgs) {
  Blob<float>* input_layer = net_->input_blobs()[0];
- input_layer->Reshape(1, num_channels_,
+ input_layer->Reshape(imgs.size(), 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);
-
+ for (int i = 0; i < imgs.size(); ++i) {
+  std::vector<cv::Mat> input_channels;
+ WrapInputLayer(&input_channels, i);
+  Preprocess(imgs[i], &input_channels);
+ }
  net_->ForwardPrefilled();
 
- /* Copy the output layer to a std::vector */
+ std::vector<std::vector<float> > outputs;
+
  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);
+ for (int i = 0; i < output_layer->num(); ++i) {
+ const float* begin = output_layer->cpu_data() + i * output_layer->channels();
+ const float* end = begin + output_layer->channels();
+ /* Copy the output layer to a std::vector */
+ outputs.push_back(std::vector<float>(begin, end));
+ }
+ return outputs;
 }
 
 /* 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) {
+void Classifier::WrapInputLayer(std::vector<cv::Mat>* input_channels, int n) {
  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;
+ int channels = input_layer->channels();
+ float* input_data = input_layer->mutable_cpu_data() + n * width * height * channels;
+ for (int i = 0; i < channels; ++i) {
+ cv::Mat channel(height, width, CV_32FC1, input_data);
+ input_channels->push_back(channel);
+ input_data += width * height;
  }
 }
 
@@ -221,13 +233,15 @@ void Classifier::Preprocess(const cv::Mat& img,
  * 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) {
+ if (argc < 6) {
  std::cerr << "Usage: " << argv[0]
  << " deploy.prototxt network.caffemodel"
  << " mean.binaryproto labels.txt img.jpg" << std::endl;
@@ -242,20 +256,27 @@ int main(int argc, char** argv) {
  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);
+ std::vector<cv::Mat> imgs;
+ for (int i = 5; i < argc; ++i)
+ {
+ cv::Mat img = cv::imread(argv[i], -1);
+ CHECK(!img.empty()) << "Unable to decode image " << argv[i];
+  imgs.push_back(img);
+ }
+ std::vector<std::vector<Prediction> > all_predictions = classifier.Classify(imgs);
 
  /* 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;
+ for (size_t i = 0; i < all_predictions.size(); ++i) {
+ std::cout << "---------- Prediction for "
+ << argv[5 + i] << " ----------" << std::endl;
+
+ std::vector<Prediction>& predictions = all_predictions[i];
+ for (size_t j = 0; j < predictions.size(); ++j) {
+ Prediction p = predictions[j];
+ std::cout << std::fixed << std::setprecision(4) << p.second << " - \""
+ << p.first << "\"" << std::endl;
+ }
+ std::cout << std::endl;
  }
 }
 #else