// Load the mean file in binaryproto format.
int DeepFeatureExtractor::SetMean(
const std::string& meanfile)
{
BlobProto blob_proto;
ReadProtoFromBinaryFileOrDie(meanfile.c_str(), &blob_proto);
// Convert from BlobProto to Blob<float>
Blob<float> meanblob;
meanblob.FromProto(blob_proto);
CHECK_EQ(meanblob.channels(), m_num_channels)
<< "Number of channels of mean file doesn't match input layer.";
// The format of the mean file is planar 32-bit float BGR or grayscale.
std::vector<cv::Mat> channels;
float* data = meanblob.mutable_cpu_data();
for (unsigned int i = 0; i < m_num_channels; ++i) {
// Extract an individual channel.
cv::Mat channel(meanblob.height(), meanblob.width(), CV_32FC1, data);
channels.push_back(channel);
data += meanblob.height() * meanblob.width();
}
// Merge the separate channels into a single image.
cv::Mat mean;
cv::merge(channels, mean);
// Compute the global mean pixel value and create a mean image
// filled with this value.
cv::Scalar channel_mean = cv::mean(mean);
m_mean = cv::Mat(m_input_geometry, mean.type(), channel_mean);
return 0;
}
void CompactDataLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
vector<Blob<Dtype>*>* top) {
if (top->size() == 1) {
this->output_labels_ = false;
} else {
this->output_labels_ = true;
}
DataLayerSetUp(bottom, top);
// The subclasses should setup the datum channels, height and width
CHECK_GT(this->datum_channels_, 0);
CHECK_GT(this->datum_height_, 0);
CHECK_GT(this->datum_width_, 0);
CHECK(this->transform_param_.crop_size() > 0);
CHECK_GE(this->datum_height_, this->transform_param_.crop_size());
CHECK_GE(this->datum_width_, this->transform_param_.crop_size());
int crop_size = this->transform_param_.crop_size();
// check if we want to have mean
if (transform_param_.has_mean_file()) {
//CHECK(this->transform_param_.has_mean_file());
this->data_mean_.Reshape(1, this->datum_channels_, crop_size, crop_size);
const string& mean_file = this->transform_param_.mean_file();
LOG(INFO) << "Loading mean file from" << mean_file;
BlobProto blob_proto;
ReadProtoFromBinaryFileOrDie(mean_file.c_str(), &blob_proto);
this->data_mean_.FromProto(blob_proto);
Blob<Dtype> tmp;
tmp.FromProto(blob_proto);
const Dtype* src_data = tmp.cpu_data();
Dtype* dst_data = this->data_mean_.mutable_cpu_data();
CHECK_EQ(tmp.num(), 1);
CHECK_EQ(tmp.channels(), this->datum_channels_);
CHECK_GE(tmp.height(), crop_size);
CHECK_GE(tmp.width(), crop_size);
int w_off = (tmp.width() - crop_size) / 2;
int h_off = (tmp.height() - crop_size) / 2;
for (int c = 0; c < this->datum_channels_; c++) {
for (int h = 0; h < crop_size; h++) {
for (int w = 0; w < crop_size; w++) {
int src_idx = (c * tmp.height() + h + h_off) * tmp.width() + w + w_off;
int dst_idx = (c * crop_size + h) * crop_size + w;
dst_data[dst_idx] = src_data[src_idx];
}
}
}
} else {
// Simply initialize an all-empty mean.
this->data_mean_.Reshape(1, this->datum_channels_, crop_size, crop_size);
}
this->mean_ = this->data_mean_.cpu_data();
this->data_transformer_.InitRand();
this->prefetch_data_.mutable_cpu_data();
if (this->output_labels_) {
this->prefetch_label_.mutable_cpu_data();
}
DLOG(INFO) << "Initializing prefetch";
this->CreatePrefetchThread();
DLOG(INFO) << "Prefetch initialized.";
}
CaffeModel (fs::path const& dir, int batch)
: CaffeSetMode(mode),
net((dir/"caffe.model").native(), TEST)
{
BOOST_VERIFY(batch >= 1);
//CHECK_EQ(net.num_inputs(), 1) << "Network should have exactly one input: " << net.num_inputs();
input_blob = net.input_blobs()[0];
shape[0] = batch;
shape[1] = input_blob->shape(1);
CHECK(shape[1] == 3 || shape[1] == 1)
<< "Input layer should have 1 or 3 channels." << shape[1];
net.CopyTrainedLayersFrom((dir/"caffe.params").native());
// resize to required batch size
shape[2] = input_blob->shape(2);
shape[3] = input_blob->shape(3);
input_blob->Reshape(shape[0], shape[1], shape[2], shape[3]);
net.Reshape();
// set mean file
means[0] = means[1] = means[2] = 0;
fs::path mean_file = dir / "caffe.mean";
fs::ifstream test(mean_file);
if (test) {
BlobProto blob_proto;
// check old format
if (ReadProtoFromBinaryFile(mean_file.native(), &blob_proto)) {
/* Convert from BlobProto to Blob<float> */
Blob<float> meanblob;
meanblob.FromProto(blob_proto);
CHECK_EQ(meanblob.channels(), channels())
<< "Number of channels of mean file doesn't match input layer.";
/* The format of the mean file is planar 32-bit float BGR or grayscale. */
vector<cv::Mat> mats;
float* data = meanblob.mutable_cpu_data();
for (int i = 0; i < channels(); ++i) {
/* Extract an individual channel. */
cv::Mat channel(meanblob.height(), meanblob.width(), CV_32FC1, data);
mats.push_back(channel);
data += meanblob.height() * meanblob.width();
}
/* Merge the separate channels into a single image. */
cv::Mat merged;
cv::merge(mats, merged);
cv::Scalar channel_mean = cv::mean(merged);
//mean = cv::Mat(input_height, input_width, merged.type(), channel_mean);
means[0] = means[1] = means[2] = channel_mean[0];
if (channels() > 1) {
means[1] = channel_mean[1];
means[2] = channel_mean[2];
}
}
// if not proto format, then the mean file is just a bunch of textual numbers
else {
test >> means[0];
means[1] = means[2] = means[0];
test >> means[1];
test >> means[2];
}
}
{
fs::ifstream is(dir/"blobs");
string blob;
CHECK(is) << "cannot open blobs file.";
while (is >> blob) {
output_blobs.push_back(net.blob_by_name(blob));
}
}
}
