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A big PReLU speedup by making it in place and iterating smarter over it.

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Tadas Baltrusaitis
2017-08-23 22:25:28 +01:00
parent a2a3bd08c2
commit a25da69550
1 changed files with 34 additions and 17 deletions
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51
lib/local/LandmarkDetector/src/FaceDetectorMTCNN.cpp
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@@ -149,34 +149,49 @@ CNN::CNN(const CNN& other) : cnn_layer_types(other.cnn_layer_types), cnn_max_poo
}
}
void PReLU(std::vector<cv::Mat_<float> >& outputs, const std::vector<cv::Mat_<float> >& input_maps, cv::Mat_<float> prelu_weights)
void PReLU(std::vector<cv::Mat_<float> >& input_output_maps, cv::Mat_<float> prelu_weights)
{
outputs.clear();
if (input_maps.size() > 1)
if (input_output_maps.size() > 1)
{
for (size_t k = 0; k < input_maps.size(); ++k)
int h = input_output_maps[0].rows;
int w = input_output_maps[0].cols;
size_t size_in = h * w;
for (size_t k = 0; k < input_output_maps.size(); ++k)
{
// Apply the PReLU
cv::Mat_<float> pos;
cv::threshold(input_maps[k], pos, 0, 0, cv::THRESH_TOZERO);
cv::Mat_<float> neg;
cv::threshold(input_maps[k], neg, 0, 0, cv::THRESH_TOZERO_INV);
outputs.push_back(pos + neg * prelu_weights.at<float>(k));
auto iter = input_output_maps[k].begin();
float neg_mult = prelu_weights.at<float>(k);
for(size_t i = 0; i < size_in; ++i)
{
float in_val = *iter;
// The prelu step
*iter++ = in_val >= 0 ? in_val : in_val * neg_mult;
}
}
}
else
{
cv::Mat_<float> pos(input_maps[0].size(), 0.0);
cv::Mat_<float> neg(input_maps[0].size(), 0.0);
int w = input_output_maps[0].cols;
for (size_t k = 0; k < prelu_weights.rows; ++k)
{
// Apply the PReLU
cv::threshold(input_maps[0].row(k), pos.row(k), 0, 0, cv::THRESH_TOZERO);
cv::threshold(input_maps[0].row(k), neg.row(k), 0, 0, cv::THRESH_TOZERO_INV);
neg.row(k) = neg.row(k) * prelu_weights.at<float>(k);
auto iter = input_output_maps[0].row(k).begin();
float neg_mult = prelu_weights.at<float>(k);
for (size_t i = 0; i < w; ++i)
{
float in_val = *iter;
// Apply the PReLU
*iter++ = in_val >= 0 ? in_val : in_val * neg_mult;
}
}
outputs.push_back(pos + neg);
}
@@ -699,7 +714,9 @@ std::vector<cv::Mat_<float>> CNN::Inference(const cv::Mat& input_img, bool direc
}
if (layer_type == 3) // PReLU
{
PReLU(outputs, input_maps, cnn_prelu_layer_weights[prelu_layer]);
// In place prelu computation
PReLU(input_maps, cnn_prelu_layer_weights[prelu_layer]);
outputs = input_maps;
prelu_layer++;
}
if (layer_type == 4)