void BaseShufflingDataLayer<Dtype>::Forward_cpu(
const vector<Blob<Dtype>*>& bottom, vector<Blob<Dtype>*>* top) {
int batch_size = this->layer_param_.shuffling_data_param().batch_size();
int offset = 0;
int N = prefetch_data_[current_buffer_].num();
int dim = prefetch_data_[current_buffer_].count() / N;
while (batch_size - offset) {
if (current_row_ == N) {
JoinPrefetchThread();
current_row_ = 0;
current_buffer_ = 1 - current_buffer_;
CreatePrefetchThread();
}
int avail = std::min(batch_size-offset, N - current_row_);
caffe_copy(avail * dim, prefetch_data_[current_buffer_].cpu_data() +
prefetch_data_[current_buffer_].offset(current_row_),
(*top)[0]->mutable_cpu_data() + (*top)[0]->offset(offset));
if (this->output_labels_) {
dim = prefetch_label_[current_buffer_].count() / N;
caffe_copy(avail * dim, prefetch_label_[current_buffer_].cpu_data() +
prefetch_label_[current_buffer_].offset(current_row_),
(*top)[1]->mutable_cpu_data() + (*top)[1]->offset(offset));
}
current_row_ += avail;
offset += avail;
}
}
void BasePrefetchingDataLayer<Dtype>::Forward_gpu(
const vector<Blob<Dtype>*>& bottom,
const vector<Blob<Dtype>*>& top) {
// First, join the thread
JoinPrefetchThread();
// Reshape to loaded data.
top[0]->Reshape(
this->prefetch_data_.num(),
this->prefetch_data_.channels(),
this->prefetch_data_.height(),
this->prefetch_data_.width());
// Copy the data
caffe_copy(
prefetch_data_.count(),
prefetch_data_.cpu_data(),
top[0]->mutable_gpu_data());
if (this->output_labels_) {
caffe_copy(
prefetch_label_.count(),
prefetch_label_.cpu_data(),
top[1]->mutable_gpu_data());
}
// Start a new prefetch thread
CreatePrefetchThread();
}
void BasePrefetchingDataLayer<Dtype>::Forward_cpu(
const vector<Blob<Dtype>*>& bottom, vector<Blob<Dtype>*>* top) {
// First, join the thread
JoinPrefetchThread();
// Copy the data
caffe_copy(prefetch_data_.count(), prefetch_data_.cpu_data(),
(*top)[0]->mutable_cpu_data());
if (this->output_labels_) {
caffe_copy(prefetch_label_.count(), prefetch_label_.cpu_data(),
(*top)[1]->mutable_cpu_data());
}
// Start a new prefetch thread
CreatePrefetchThread();
}
Dtype VolumeDataLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
vector<Blob<Dtype>*>* top) {
// First, join the thread
JoinPrefetchThread();
// Copy the data
caffe_copy(prefetch_data_->count(), prefetch_data_->cpu_data(),
(*top)[0]->mutable_cpu_data());
if (output_labels_) {
caffe_copy(prefetch_label_->count(), prefetch_label_->cpu_data(),
(*top)[1]->mutable_cpu_data());
}
// Start a new prefetch thread
CreatePrefetchThread();
return Dtype(0.);
}
void BasePrefetchingDataLayer<Dtype>::Forward_cpu(
const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top) {
// First, join the thread
JoinPrefetchThread();
DLOG(INFO) << "Thread joined";
// Copy the data
caffe_copy(prefetch_data_.count(), prefetch_data_.cpu_data(),
top[0]->mutable_cpu_data());
DLOG(INFO) << "Prefetch copied";
if (this->output_labels_) {
caffe_copy(prefetch_label_.count(), prefetch_label_.cpu_data(),
top[1]->mutable_cpu_data());
}
// Start a new prefetch thread
DLOG(INFO) << "CreatePrefetchThread";
CreatePrefetchThread();
}
void VolumeDataLayer<Dtype>::SetUp(const vector<Blob<Dtype>*>& bottom,
vector<Blob<Dtype>*>* top) {
CHECK_EQ(bottom.size(), 0) << "Data Layer takes no input blobs.";
CHECK_GE(top->size(), 1) << "Data Layer takes at least one blob as output.";
CHECK_LE(top->size(), 2) << "Data Layer takes at most two blobs as output.";
if (top->size() == 1) {
output_labels_ = false;
} else {
output_labels_ = true;
}
// Initialize the leveldb
leveldb::DB* db_temp;
leveldb::Options options;
options.create_if_missing = false;
options.max_open_files = 100;
LOG(INFO) << "Opening leveldb " << this->layer_param_.data_param().source();
leveldb::Status status = leveldb::DB::Open(
options, this->layer_param_.data_param().source(), &db_temp);
CHECK(status.ok()) << "Failed to open leveldb "
<< this->layer_param_.data_param().source() << std::endl
<< status.ToString();
db_.reset(db_temp);
iter_.reset(db_->NewIterator(leveldb::ReadOptions()));
iter_->SeekToFirst();
// Check if we would need to randomly skip a few data points
if (this->layer_param_.data_param().rand_skip()) {
unsigned int skip = caffe_rng_rand() %
this->layer_param_.data_param().rand_skip();
LOG(INFO) << "Skipping first " << skip << " data points.";
while (skip-- > 0) {
iter_->Next();
if (!iter_->Valid()) {
iter_->SeekToFirst();
}
}
}
// Read a data point, and use it to initialize the top blob.
VolumeDatum datum;
datum.ParseFromString(iter_->value().ToString());
// image
int crop_size = this->layer_param_.data_param().crop_size();
if (crop_size > 0) {
(*top)[0]->Reshape(this->layer_param_.data_param().batch_size(),
datum.channels(), datum.length(), crop_size, crop_size);
prefetch_data_.reset(new Blob<Dtype>(
this->layer_param_.data_param().batch_size(), datum.channels(), datum.length(),
crop_size, crop_size));
} else {
(*top)[0]->Reshape(
this->layer_param_.data_param().batch_size(), datum.channels(), datum.length(),
datum.height(), datum.width());
prefetch_data_.reset(new Blob<Dtype>(
this->layer_param_.data_param().batch_size(), datum.channels(), datum.length(),
datum.height(), datum.width()));
}
LOG(INFO) << "output data size: " << (*top)[0]->num() << ","
<< (*top)[0]->channels() << "," << (*top)[0]->length() << "," << (*top)[0]->height() << ","
<< (*top)[0]->width();
// label
if (output_labels_) {
(*top)[1]->Reshape(this->layer_param_.data_param().batch_size(), 1, 1, 1, 1);
prefetch_label_.reset(
new Blob<Dtype>(this->layer_param_.data_param().batch_size(), 1, 1, 1, 1));
}
// datum size
datum_channels_ = datum.channels();
datum_length_ = datum.length();
datum_height_ = datum.height();
datum_width_ = datum.width();
datum_size_ = datum.channels() * datum.length() * datum.height() * datum.width();
CHECK_GT(datum_height_, crop_size);
CHECK_GT(datum_width_, crop_size);
// check if we want to have mean
if (this->layer_param_.data_param().has_mean_file()) {
const string& mean_file = this->layer_param_.data_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_EQ(data_mean_.num(), 1);
CHECK_EQ(data_mean_.channels(), datum_channels_);
CHECK_EQ(data_mean_.length(), datum_length_);
CHECK_EQ(data_mean_.height(), datum_height_);
CHECK_EQ(data_mean_.width(), datum_width_);
} else {
// Simply initialize an all-empty mean.
data_mean_.Reshape(1, datum_channels_, datum_length_, datum_height_, datum_width_);
}
// Now, start the prefetch thread. Before calling prefetch, we make two
// cpu_data calls so that the prefetch thread does not accidentally make
// simultaneous cudaMalloc calls when the main thread is running. In some
// GPUs this seems to cause failures if we do not so.
prefetch_data_->mutable_cpu_data();
if (output_labels_) {
prefetch_label_->mutable_cpu_data();
}
data_mean_.cpu_data();
DLOG(INFO) << "Initializing prefetch";
CreatePrefetchThread();
DLOG(INFO) << "Prefetch initialized.";
}
