InfogainLossLayerTest()
: blob_bottom_data_(new Blob<Dtype>(4, 2, 1, 5, 2)),
blob_bottom_label_(new Blob<Dtype>(4, 2, 1, 1, 2)),
blob_bottom_infogain_(new Blob<Dtype>(1, 1, 1, 5, 5)),
blob_top_loss_(new Blob<Dtype>()),
blob_top_prob_(new Blob<Dtype>()),
inner_(2), outer_(4*2), num_labels_(5) {
Caffe::set_random_seed(1701);
FillerParameter filler_param;
filler_param.set_min(-0.5);
filler_param.set_max(2.0);
UniformFiller<Dtype> filler(filler_param);
filler.Fill(this->blob_bottom_data_);
blob_bottom_vec_.push_back(blob_bottom_data_);
for (int i = 0; i < blob_bottom_label_->count(); ++i) {
blob_bottom_label_->mutable_cpu_data()[i] =
caffe_rng_rand() % num_labels_;
}
blob_bottom_vec_.push_back(blob_bottom_label_);
filler_param.set_min(0.1);
filler_param.set_max(2.0);
UniformFiller<Dtype> infogain_filler(filler_param);
infogain_filler.Fill(this->blob_bottom_infogain_);
blob_bottom_vec_.push_back(blob_bottom_infogain_);
blob_top_vec_.push_back(blob_top_loss_);
blob_top_vec_.push_back(blob_top_prob_);
}
void InfogainLossLayerTestForwardPerformance(int num_images, int num_channels, int im_width, int im_height) {
typedef typename TypeParam::Dtype Dtype;
LayerParameter layer_param;
InfogainLossLayer<Dtype> layer(layer_param);
blob_bottom_data_->Reshape(num_images, num_channels, 1, 1);
blob_bottom_label_->Reshape(num_images, 1, 1, 1);
blob_bottom_infogain_->Reshape(1, 1, num_channels, num_channels);
FillerParameter filler_param;
UniformFiller<Dtype> filler(filler_param);
filler.Fill(this->blob_bottom_data_);
for (int i = 0; i < blob_bottom_label_->count(); ++i) {
blob_bottom_label_->mutable_cpu_data()[i] = caffe_rng_rand() % 5;
}
filler_param.set_min(0.1);
filler_param.set_max(2.0);
UniformFiller<Dtype> infogain_filler(filler_param);
infogain_filler.Fill(this->blob_bottom_infogain_);
blob_bottom_vec_.clear();
blob_bottom_vec_.push_back(blob_bottom_data_);
blob_bottom_vec_.push_back(blob_bottom_label_);
blob_bottom_vec_.push_back(blob_bottom_infogain_);
layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
#if defined(USE_CUDA) || defined(USE_OPENCL)
blob_bottom_data_->mutable_gpu_data();
blob_bottom_data_->mutable_gpu_diff();
blob_bottom_label_->mutable_gpu_data();
blob_bottom_label_->mutable_gpu_diff();
blob_bottom_infogain_->mutable_gpu_data();
blob_bottom_infogain_->mutable_gpu_diff();
blob_top_loss_->mutable_gpu_data();
blob_top_loss_->mutable_gpu_diff();
#endif
record r;
r.type = std::string(typeid(Dtype).name());
r.num_images = num_images;
r.num_channels = num_channels;
r.img_width = im_width;
r.img_height = im_height;
BENCH(r, {
layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_)
;
});
InfogainLossLayerTest()
: blob_bottom_data_(new Blob<Dtype>(10, 5, 1, 1)),
blob_bottom_label_(new Blob<Dtype>(10, 1, 1, 1)),
blob_bottom_infogain_(new Blob<Dtype>(1, 1, 5, 5)),
blob_top_loss_(new Blob<Dtype>()) {
Caffe::set_random_seed(1701);
FillerParameter filler_param;
PositiveUnitballFiller<Dtype> filler(filler_param);
filler.Fill(this->blob_bottom_data_);
blob_bottom_vec_.push_back(blob_bottom_data_);
for (int i = 0; i < blob_bottom_label_->count(); ++i) {
blob_bottom_label_->mutable_cpu_data()[i] = caffe_rng_rand() % 5;
}
blob_bottom_vec_.push_back(blob_bottom_label_);
filler_param.set_min(0.1);
filler_param.set_max(2.0);
UniformFiller<Dtype> infogain_filler(filler_param);
infogain_filler.Fill(this->blob_bottom_infogain_);
blob_bottom_vec_.push_back(blob_bottom_infogain_);
blob_top_vec_.push_back(blob_top_loss_);
}