Method load_batch

src/caffe/layers/data_layer.cpp:82–131

// This function is called on prefetch thread
template<typename Dtype>
void DataLayer<Dtype>::load_batch(Batch<Dtype>* batch) {
  CPUTimer batch_timer;
  batch_timer.Start();
  double read_time = 0;
  double trans_time = 0;
  CPUTimer timer;
  CHECK(batch->data_.count());
  CHECK(this->transformed_data_.count());
  const int batch_size = this->layer_param_.data_param().batch_size();

  Datum datum;
  for (int item_id = 0; item_id < batch_size; ++item_id) {
    timer.Start();
    while (Skip()) {
      Next();
    }
    datum.ParseFromString(cursor_->value());
    read_time += timer.MicroSeconds();

    if (item_id == 0) {
      // Reshape according to the first datum of each batch
      // on single input batches allows for inputs of varying dimension.
      // Use data_transformer to infer the expected blob shape from datum.
      vector<int> top_shape = this->data_transformer_->InferBlobShape(datum);
      this->transformed_data_.Reshape(top_shape);
      // Reshape batch according to the batch_size.
      top_shape[0] = batch_size;
      batch->data_.Reshape(top_shape);
    }

    // Apply data transformations (mirror, scale, crop...)
    timer.Start();
    int offset = batch->data_.offset(item_id);
    Dtype* top_data = batch->data_.mutable_cpu_data();
    this->transformed_data_.set_cpu_data(top_data + offset);
    this->data_transformer_->Transform(datum, &(this->transformed_data_));
    // Copy label.
    if (this->output_labels_) {
      Dtype* top_label = batch->label_.mutable_cpu_data();
      top_label[item_id] = datum.label();
    }
    trans_time += timer.MicroSeconds();
    Next();
  }
  timer.Stop();
  batch_timer.Stop();
  DLOG(INFO) << "Prefetch batch: " << batch_timer.MilliSeconds() << " ms.";
  DLOG(INFO) << "     Read time: " << read_time / 1000 << " ms.";
  DLOG(INFO) << "Transform time: " << trans_time / 1000 << " ms.";
}

INSTANTIATE_CLASS(DataLayer);
REGISTER_LAYER_CLASS(Data);