void MILDataLayer<Dtype>::load_batch(Batch<Dtype>* batch) {
CPUTimer timer;
timer.Start();
CHECK(batch->data_.count());
//Dtype* top_data = this->prefetch_data_.mutable_cpu_data();
//Dtype* top_label = this->prefetch_label_.mutable_cpu_data();
Dtype* top_data = batch->data_.mutable_cpu_data();
Dtype* top_label = batch->label_.mutable_cpu_data();
const int img_size = this->transform_param_.crop_size();
const int channels = this->layer_param_.mil_data_param().channels();
const int scale = this->transform_param_.scale();
const bool mirror = this->transform_param_.mirror();
const int images_per_batch = this->layer_param_.mil_data_param().images_per_batch();
const int n_classes = this->layer_param_.mil_data_param().n_classes();
const int num_scales = this->layer_param_.mil_data_param().num_scales();
const float scale_factor = this->layer_param_.mil_data_param().scale_factor();
// zero out batch
//caffe_set(this->prefetch_data_.count(), Dtype(0), top_data);
caffe_set(batch->data_.count(), Dtype(0), top_data);
int item_id;
for(int i_image = 0; i_image < images_per_batch; i_image++){
// Sample which image to read
unsigned int index = counter_; counter_ = counter_ + 1;
const unsigned int rand_index = this->PrefetchRand();
if(this->layer_param_.mil_data_param().randomize())
index = rand_index;
// LOG(INFO) << index % this->num_images_ << ", " << this->num_images_;
pair<string, string> p = this->image_database_[index % this->num_images_];
string im_name = p.first;
string full_im_name = p.second;
cv::Mat cv_img = cv::imread(full_im_name, CV_LOAD_IMAGE_COLOR);
if (!cv_img.data) {
LOG(ERROR) << "Could not open or find file " << full_im_name;
return;
}
//REVIEW ktran: do not hardcode dataset name (or its prefix "/labels-")
//REVIEW ktran: also do not use deep dataset name so that we don't have to modify the core caffe code
//(ref: https://github.com/BVLC/caffe/commit/a0787631a27ca6478f70341462aafdcf35dabb19)
hdf5_load_nd_dataset(this->label_file_id_, string("/labels-"+im_name).c_str(), 4, 4, &this->label_blob_);
const Dtype* label = label_blob_.mutable_cpu_data();
CHECK_EQ(label_blob_.width(), 1) << "Expected width of label to be 1." ;
CHECK_EQ(label_blob_.height(), n_classes) << "Expected height of label to be " << n_classes;
CHECK_EQ(label_blob_.channels(), 1) << "Expected channels of label to be 1." ;
CHECK_EQ(label_blob_.num(), 1) << "Expected num of label to be 1." ;
float img_size_i = img_size;
for(int i_scales = 0; i_scales < num_scales; i_scales++){
// Resize such that the image is of size img_size, img_size
item_id = i_image*num_scales + i_scales;
// LOG(INFO) << "MIL Data Layer: scale: " << (int) round(img_size_i);
cv::Mat cv_cropped_img = Transform_IDL(cv_img, static_cast<int>(round(img_size_i)), mirror);
for (int c = 0; c < channels; ++c) {
for (int h = 0; h < cv_cropped_img.rows; ++h) {
for (int w = 0; w < cv_cropped_img.cols; ++w) {
Dtype pixel =
static_cast<Dtype>(cv_cropped_img.at<cv::Vec3b>(h, w)[c]);
top_data[((item_id * channels + c) * img_size + h)
* img_size + w]
= (pixel - static_cast<Dtype>(mean_value_[c]))*scale;
}
}
}
img_size_i = std::max(static_cast<float>(1.), img_size_i*scale_factor);
}
for(int i_label = 0; i_label < n_classes; i_label++){
top_label[i_image*n_classes + i_label] =
label[i_label];
}
}
timer.Stop();
DLOG(INFO) << "Prefetch batch: " << timer.MilliSeconds() << " ms.";
}
