lbzhang · October 12, 2017 10:29 · Jun 17, 2014
diff --git a/C++ Predict with caffe b/C++ Predict with caffe
@@ -0,0 +1,96 @@
+#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"
+
+using namespace caffe;
+using namespace std;
+
+int main(int argc, char** argv) {
+
+  if (argc < 4 || argc > 6) {
+    LOG(ERROR) << "test_net net_proto pretrained_net_proto iterations "
+        << "[CPU/GPU] [Device ID]";
+    return 1;
+  }
+  Caffe::set_phase(Caffe::TEST);
+
+  //Setting CPU or GPU
+  if (argc >= 5 && strcmp(argv[4], "GPU") == 0) {
+    Caffe::set_mode(Caffe::GPU);
+    int device_id = 0;
+    if (argc == 6) {
+      device_id = atoi(argv[5]);
+    }
+    Caffe::SetDevice(device_id);
+    LOG(ERROR) << "Using GPU #" << device_id;
+  } else {
+    LOG(ERROR) << "Using CPU";
+    Caffe::set_mode(Caffe::CPU);
+  }
+
+  //get the net
+  Net<float> caffe_test_net(argv[1]);
+  //get trained net
+  caffe_test_net.CopyTrainedLayersFrom(argv[2]);
+
+  //get datum
+  Datum datum;
+  if (!ReadImageToDatum("./cat.png", 1, 227, 227, &datum)) {
+    LOG(ERROR) << "Error during file reading";
+  }
+
+  //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 =  caffe_test_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;
+}