int NumSequenceMatches(const TransformationParameter transform_param,
const Datum& datum, Phase phase) {
// Get crop sequence with Caffe seed 1701.
DataTransformer<Dtype> transformer(transform_param, phase);
const int crop_size = transform_param.crop_size();
Caffe::set_random_seed(seed_);
transformer.InitRand();
Blob<Dtype> blob(1, datum.channels(), datum.height(), datum.width());
if (transform_param.crop_size() > 0) {
blob.Reshape(1, datum.channels(), crop_size, crop_size);
}
vector<vector<Dtype> > crop_sequence;
for (int iter = 0; iter < this->num_iter_; ++iter) {
vector<Dtype> iter_crop_sequence;
transformer.Transform(datum, &blob);
for (int j = 0; j < blob.count(); ++j) {
iter_crop_sequence.push_back(blob.cpu_data()[j]);
}
crop_sequence.push_back(iter_crop_sequence);
}
// Check if the sequence differs from the previous
int num_sequence_matches = 0;
for (int iter = 0; iter < this->num_iter_; ++iter) {
vector<Dtype> iter_crop_sequence = crop_sequence[iter];
transformer.Transform(datum, &blob);
for (int j = 0; j < blob.count(); ++j) {
num_sequence_matches += (crop_sequence[iter][j] == blob.cpu_data()[j]);
}
}
return num_sequence_matches;
}
DataTransformer<Dtype>::DataTransformer(const TransformationParameter& param,
Phase phase)
: param_(param), phase_(phase) {
// check if we want to use mean_file
if (param_.has_mean_file()) {
CHECK_EQ(param_.mean_value_size(), 0) <<
"Cannot specify mean_file and mean_value at the same time";
const string& mean_file = param.mean_file();
if (Caffe::root_solver()) {
LOG(INFO) << "Loading mean file from: " << mean_file;
}
if ( mean_file.substr(mean_file.length()-3,3) == string("jpg") || mean_file.substr(mean_file.length()-3,3) == string("bmp") ) {
cv::Mat mean_img;
if ( param.mean_color() ) {
mean_img = cv::imread(mean_file.c_str(), CV_LOAD_IMAGE_COLOR);
data_mean_.Reshape(1, mean_img.channels(), mean_img.rows, mean_img.cols);
int mean_data_index = 0;
for ( int k = 0; k < 3; ++k )
for ( int r = 0; r < mean_img.rows; ++r )
for ( int c = 0; c < mean_img.cols; ++c )
data_mean_.mutable_cpu_data()[mean_data_index++] = mean_img.at<cv::Vec3b>(r,c)[k];
} else {
mean_img = cv::imread(mean_file.c_str(), CV_LOAD_IMAGE_GRAYSCALE);
data_mean_.Reshape(1, mean_img.channels(), mean_img.rows, mean_img.cols);
int mean_data_index = 0;
for ( int r = 0; r < mean_img.rows; ++r )
for ( int c = 0; c < mean_img.cols; ++c )
data_mean_.mutable_cpu_data()[mean_data_index++] = mean_img.at<unsigned char>(r,c);
}
} else {
BlobProto blob_proto;
ReadProtoFromBinaryFileOrDie(mean_file.c_str(), &blob_proto);
data_mean_.FromProto(blob_proto);
}
}
offset_w_ = 0;
offset_h_ = 0;
apply_mirror_ = false;
// check if we want to use mean_value
if (param_.mean_value_size() > 0) {
CHECK(param_.has_mean_file() == false) <<
"Cannot specify mean_file and mean_value at the same time";
for (int c = 0; c < param_.mean_value_size(); ++c) {
mean_values_.push_back(param_.mean_value(c));
}
}
}
DataTransformer<Dtype>::DataTransformer(const TransformationParameter& param,
Phase phase)
: param_(param), phase_(phase) {
// check if we want to use mean_file
if (param_.has_mean_file()) {
CHECK_EQ(param_.mean_value_size(), 0) <<
"Cannot specify mean_file and mean_value at the same time";
const string& mean_file = param.mean_file();
LOG(INFO) << "Loading mean file from: " << mean_file;
BlobProto blob_proto;
ReadProtoFromBinaryFileOrDie(mean_file.c_str(), &blob_proto);
data_mean_.FromProto(blob_proto);
}
// check if we want to use mean_value
if (param_.mean_value_size() > 0) {
CHECK(param_.has_mean_file() == false) <<
"Cannot specify mean_file and mean_value at the same time";
for (int c = 0; c < param_.mean_value_size(); ++c) {
mean_values_.push_back(param_.mean_value(c));
}
}
}
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;
}
