Deep_Classifier::Deep_Classifier(const string& model_file,
const string& trained_file,
const string& mean_file,
const string& label_file) {
Caffe::set_mode(Caffe::GPU);
/* Load the network. */
net_.reset(new Net<float>(model_file, TEST));
net_->CopyTrainedLayersFrom(trained_file);
Blob<float>* input_layer = net_->input_blobs()[0];
num_channels_ = input_layer->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());
string line;
while (std::getline(labels, line))
labels_.push_back(string(line));
Blob<float>* output_layer = net_->output_blobs()[0];
}
Classifier::Classifier(const std::string& model_file, const std::string& trained_file, const std::string& mean_file) {
#ifdef CPU_ONLY
Caffe::set_mode(Caffe::CPU);
#else
caffe::Caffe::set_mode(caffe::Caffe::GPU);
#endif
/* Load the network. */
net_.reset(new caffe::Net<float>(model_file, caffe::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.";
caffe::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.";
*/
}
void Classifier::init(const std::string& model_def,
const std::string& trained_weights,
const std::string& mean_file,
const std::string& label_file,
const int &gpu_id) {
if(gpu_id>=0) {
caffe::Caffe::set_mode(caffe::Caffe::GPU);
caffe::Caffe::SetDevice(gpu_id);
}
else
caffe::Caffe::set_mode(caffe::Caffe::CPU);
/* Load the network. */
net_.reset(new caffe::Net<float>(model_def, caffe::TEST));
net_->CopyTrainedLayersFrom(trained_weights);
CHECK_EQ(net_->num_inputs(), 1) << "Network should have exactly one input.";
CHECK_EQ(net_->num_outputs(), 1) << "Network should have exactly one output.";
caffe::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. */
if(mean_file!="")
SetMean(mean_file);
else {
mean_ = cv::Mat::zeros(input_geometry_, CV_MAKE_TYPE(CV_32F, num_channels_));
}
/* Load labels. */
caffe::Blob<float>* output_layer = net_->output_blobs()[0];
if(label_file!="") {
std::ifstream labels(label_file.c_str());
CHECK(labels) << "Unable to open labels file " << label_file;
std::string line;
while (std::getline(labels, line))
labels_.push_back(std::string(line));
CHECK_EQ(labels_.size(), output_layer->channels())
<< "Number of labels is different from the output layer dimension.";
}
else {
for (int i = 0; i < output_layer->channels(); ++i) {
std::stringstream ss;
ss << i;
labels_.push_back(ss.str());
}
}
is_ready = true;
}
