CaffeFeatExtractor<Dtype>::CaffeFeatExtractor(string _caffemodel_file,
string _prototxt_file, int _resizeWidth, int _resizeHeight,
string _blob_names,
string _compute_mode,
int _device_id,
bool _timing) {
// Setup the GPU or the CPU mode for Caffe
if (strcmp(_compute_mode.c_str(), "GPU") == 0 || strcmp(_compute_mode.c_str(), "gpu") == 0) {
std::cout << "CaffeFeatExtractor::CaffeFeatExtractor(): using GPU" << std::endl;
gpu_mode = true;
device_id = _device_id;
Caffe::CheckDevice(device_id);
std::cout << "CaffeFeatExtractor::CaffeFeatExtractor(): using device_id = " << device_id << std::endl;
Caffe::set_mode(Caffe::GPU);
Caffe::SetDevice(device_id);
// Optional: to check that the GPU is working properly...
Caffe::DeviceQuery();
} else
{
std::cout << "CaffeFeatExtractor::CaffeFeatExtractor(): using CPU" << std::endl;
gpu_mode = false;
device_id = -1;
Caffe::set_mode(Caffe::CPU);
}
// Assign specified .caffemodel and .prototxt files
caffemodel_file = _caffemodel_file;
prototxt_file = _prototxt_file;
// Network creation using the specified .prototxt
feature_extraction_net = boost::make_shared<Net<Dtype> > (prototxt_file, caffe::TEST);
// Network initialization using the specified .caffemodel
feature_extraction_net->CopyTrainedLayersFrom(caffemodel_file);
// Mean image initialization
mean_width = 0;
mean_height = 0;
mean_channels = 0;
caffe::shared_ptr<MemoryDataLayer<Dtype> > memory_data_layer = boost::dynamic_pointer_cast<caffe::MemoryDataLayer<Dtype> >(feature_extraction_net->layers()[0]);
TransformationParameter tp = memory_data_layer->layer_param().transform_param();
if (tp.has_mean_file())
{
const string& mean_file = tp.mean_file();
std::cout << "CaffeFeatExtractor::CaffeFeatExtractor(): loading mean file from " << mean_file << std::endl;
BlobProto blob_proto;
ReadProtoFromBinaryFileOrDie(mean_file.c_str(), &blob_proto);
Blob<Dtype> data_mean;
data_mean.FromProto(blob_proto);
mean_channels = data_mean.channels();
mean_width = data_mean.width();
mean_height = data_mean.height();
} else if (tp.mean_value_size()>0)
{
const int b = tp.mean_value(0);
const int g = tp.mean_value(1);
const int r = tp.mean_value(2);
mean_channels = tp.mean_value_size();
mean_width = _resizeWidth;
mean_height = _resizeHeight;
std::cout << "CaffeFeatExtractor::CaffeFeatExtractor(): B " << b << " G " << g << " R " << r << std::endl;
std::cout << "CaffeFeatExtractor::CaffeFeatExtractor(): resizing anysotropically to " << " W: " << mean_width << " H: " << mean_height << std::endl;
}
else
{
std::cout << "CaffeFeatExtractor::CaffeFeatExtractor(): Error: neither mean file nor mean value in prototxt!" << std::endl;
}
// Check that requested blobs exist
boost::split(blob_names, _blob_names, boost::is_any_of(","));
for (size_t i = 0; i < blob_names.size(); i++) {
if (!feature_extraction_net->has_blob(blob_names[i]))
{
std::cout << "CaffeFeatExtractor::CaffeFeatExtractor(): unknown feature blob name " << blob_names[i] << " in the network " << prototxt_file << std::endl;
}
}
// Initialize timing flag
timing = _timing;
}
