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Simplification of flow

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This commit is contained in:
Tadas Baltrusaitis
2017-08-15 18:06:12 +01:00
parent f8519bf385
commit f4e73cc23f
1 changed files with 67 additions and 64 deletions
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131
lib/local/LandmarkDetector/src/FaceDetectorMTCNN.cpp
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@@ -136,6 +136,62 @@ CNN::CNN(const CNN& other) : cnn_layer_types(other.cnn_layer_types), cnn_max_poo
}
}
void fully_connected(std::vector<cv::Mat_<float> >& outputs, const std::vector<cv::Mat_<float> >& input_maps, cv::Mat_<float> weights, cv::Mat_<float> biases)
{
if (input_maps.size() > 1)
{
// Concatenate all the maps
cv::Size orig_size = input_maps[0].size();
cv::Mat_<float> input_concat = input_maps[0].t();
input_concat = input_concat.reshape(0, 1);
for (size_t in = 1; in < input_maps.size(); ++in)
{
cv::Mat_<float> add = input_maps[in].t();
add = add.reshape(0, 1);
cv::vconcat(input_concat, add, input_concat);
}
// Treat the input as separate feature maps
if (input_concat.rows == weights.rows)
{
input_concat = input_concat.t() * weights;
// Add biases
for (size_t k = 0; k < biases.rows; ++k)
{
input_concat.col(k) = input_concat.col(k) + biases.at<float>(k);
}
outputs.clear();
// Resize and add as output
for (size_t k = 0; k < biases.rows; ++k)
{
cv::Mat_<float> reshaped = input_concat.col(k).clone();
reshaped = reshaped.reshape(1, orig_size.width).t();
outputs.push_back(reshaped);
}
}
else
{
// Flatten the input
input_concat = input_concat.reshape(0, 1);
input_concat = input_concat * weights + biases.t();
outputs.clear();
outputs.push_back(input_concat.t());
}
}
else
{
cv::Mat out = input_maps[0].t() * weights + biases.t();
outputs.clear();
outputs.push_back(out);
}
}
void max_pooling(std::vector<cv::Mat_<float> >& outputs, const std::vector<cv::Mat_<float> >& input_maps, int stride_x, int stride_y, int kernel_size_x, int kernel_size_y)
{
vector<cv::Mat_<float> > outputs_sub;
@@ -299,68 +355,8 @@ std::vector<cv::Mat_<float>> CNN::Inference(const cv::Mat& input_img)
if (layer_type == 2)
{
if(input_maps.size() > 1)
{
// Concatenate all the maps
cv::Size orig_size = input_maps[0].size();
cv::Mat_<float> input_concat = input_maps[0].t();
input_concat = input_concat.reshape(0, 1);
for (size_t in = 1; in < input_maps.size(); ++in)
{
cv::Mat_<float> add = input_maps[in].t();
add = add.reshape(0, 1);
cv::vconcat(input_concat, add, input_concat);
}
// Treat the input as separate feature maps
if(input_concat.rows == cnn_fully_connected_layers_weights[fully_connected_layer].rows)
{
input_concat = input_concat.t() * cnn_fully_connected_layers_weights[fully_connected_layer];
// Add biases
for (size_t k = 0; k < cnn_fully_connected_layers_biases[fully_connected_layer].rows; ++k)
{
input_concat.col(k) = input_concat.col(k) + cnn_fully_connected_layers_biases[fully_connected_layer].at<float>(k);
}
outputs.clear();
// Resize and add as output
for (size_t k = 0; k < cnn_fully_connected_layers_biases[fully_connected_layer].rows; ++k)
{
cv::Mat_<float> reshaped = input_concat.col(k).clone();
reshaped = reshaped.reshape(1, orig_size.width).t();
outputs.push_back(reshaped);
}
}
else
{
// Flatten the input
input_concat = input_concat.reshape(0, 1);
input_concat = input_concat * cnn_fully_connected_layers_weights[fully_connected_layer] + cnn_fully_connected_layers_biases[fully_connected_layer].t();
outputs.clear();
outputs.push_back(input_concat.t());
}
}
else
{
cv::Mat out = input_maps[0].t() * cnn_fully_connected_layers_weights[fully_connected_layer] + cnn_fully_connected_layers_biases[fully_connected_layer].t();
outputs.clear();
outputs.push_back(out);
}
fully_connected(outputs, input_maps, cnn_fully_connected_layers_weights[fully_connected_layer], cnn_fully_connected_layers_biases[fully_connected_layer]);
fully_connected_layer++;
//float diff = 0.0;
//for (size_t k = 0; k < input_maps.size(); ++k)
//{
// diff += cv::mean(cv::abs(outputs[k] - outs[k]))[0];
//}
//cout << diff << endl;
}
if (layer_type == 3) // PReLU
{
@@ -392,6 +388,14 @@ std::vector<cv::Mat_<float>> CNN::Inference(const cv::Mat& input_img)
outputs.push_back(pos + neg);
}
//float diff = 0.0;
//for (size_t k = 0; k < outs.size(); ++k)
//{
// diff += cv::mean(cv::abs(outputs[k] - outs[k]))[0];
//}
//cout << diff << endl;
prelu_layer++;
}
if (layer_type == 4)
@@ -407,9 +411,8 @@ std::vector<cv::Mat_<float>> CNN::Inference(const cv::Mat& input_img)
}
}
// Set the outputs of this layer to inputs of the next
input_maps = outputs;
// Set the outputs of this layer to inputs of the next one
input_maps = outputs;
}