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Performance improvement by pre-allocating im2col matrices instead of re-allocating the memory every iteration.

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Tadas Baltrusaitis
2018-05-04 17:43:39 +01:00
parent 4f42fd9ac5
commit 5d0467dfd0
7 changed files with 89 additions and 62 deletions
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36
lib/local/LandmarkDetector/src/CEN_patch_expert.cpp
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@@ -503,15 +503,15 @@ void LandmarkDetector::interpolationMatrix(cv::Mat_<float>& mapMatrix, int respo
}
//===========================================================================
void CEN_patch_expert::ResponseSparse(const cv::Mat_<float> &area_of_interest, cv::Mat_<float> &response, cv::Mat_<float>& mapMatrix)
void CEN_patch_expert::ResponseSparse(const cv::Mat_<float> &area_of_interest, cv::Mat_<float> &response, cv::Mat_<float>& mapMatrix, cv::Mat_<float>& im2col_prealloc)
{
int response_height = area_of_interest.rows - height + 1;
int response_width = area_of_interest.cols - width + 1;
// Extract im2col but in a sparse way and contrast normalize
im2colBiasSparseContrastNorm(area_of_interest, width, height, response);
response = response.t();
im2colBiasSparseContrastNorm(area_of_interest, width, height, im2col_prealloc);
response = im2col_prealloc.t();
for (size_t layer = 0; layer < activation_function.size(); ++layer)
{
@@ -532,7 +532,7 @@ void CEN_patch_expert::ResponseSparse(const cv::Mat_<float> &area_of_interest, c
// The above is a faster version of this, by calling the fortran version directly
//cblas_sgemm(CblasColMajor, CblasNoTrans, CblasNoTrans, resp.cols, weight.rows, weight.cols, 1, m1, resp.cols, m2, weight.cols, 0.0, m3, resp.cols);
// Adding the bias (bit ugly, but the fastest way to do this)
response = resp_blas;
@@ -558,7 +558,7 @@ void CEN_patch_expert::ResponseSparse(const cv::Mat_<float> &area_of_interest, c
// Iterate over the data directly
float* data = (float*)response.data;
for (size_t counter = 0; counter < resp_size; ++counter)
{
float in = *data;
@@ -572,15 +572,15 @@ void CEN_patch_expert::ResponseSparse(const cv::Mat_<float> &area_of_interest, c
}
}
response = response * mapMatrix;
response = response * mapMatrix;
response = response.t();
response = response.reshape(1, response_height);
response = response.t();
}
//===========================================================================
void CEN_patch_expert::ResponseSparse_mirror(const cv::Mat_<float> &area_of_interest, cv::Mat_<float> &response, cv::Mat_<float>& mapMatrix)
void CEN_patch_expert::ResponseSparse_mirror(const cv::Mat_<float> &area_of_interest, cv::Mat_<float> &response, cv::Mat_<float>& mapMatrix, cv::Mat_<float>& im2col_prealloc)
{
int response_height = area_of_interest.rows - height + 1;
@@ -589,9 +589,9 @@ void CEN_patch_expert::ResponseSparse_mirror(const cv::Mat_<float> &area_of_inte
cv::flip(area_of_interest, area_of_interest, 1);
// Extract im2col but in a sparse way and contrast normalize
im2colBiasSparseContrastNorm(area_of_interest, width, height, response);
im2colBiasSparseContrastNorm(area_of_interest, width, height, im2col_prealloc);
response = response.t();
response = im2col_prealloc.t();
for (size_t layer = 0; layer < activation_function.size(); ++layer)
{
@@ -660,7 +660,7 @@ void CEN_patch_expert::ResponseSparse_mirror(const cv::Mat_<float> &area_of_inte
cv::flip(response, response, 1);
}
void CEN_patch_expert::ResponseSparse_mirror_joint(const cv::Mat_<float> &area_of_interest_left, const cv::Mat_<float> &area_of_interest_right, cv::Mat_<float> &response_left, cv::Mat_<float> &response_right, cv::Mat_<float>& mapMatrix)
void CEN_patch_expert::ResponseSparse_mirror_joint(const cv::Mat_<float> &area_of_interest_left, const cv::Mat_<float> &area_of_interest_right, cv::Mat_<float> &response_left, cv::Mat_<float> &response_right, cv::Mat_<float>& mapMatrix, cv::Mat_<float>& im2col_prealloc_left, cv::Mat_<float>& im2col_prealloc_right)
{
int response_height = area_of_interest_left.rows - height + 1;
int response_width = area_of_interest_left.cols - width + 1;
@@ -668,13 +668,11 @@ void CEN_patch_expert::ResponseSparse_mirror_joint(const cv::Mat_<float> &area_o
cv::flip(area_of_interest_right, area_of_interest_right, 1);
// Extract im2col but in a sparse way and contrast normalize
cv::Mat_<float> resp_l;
im2colBiasSparseContrastNorm(area_of_interest_left, width, height, resp_l);
cv::Mat_<float> resp_r;
im2colBiasSparseContrastNorm(area_of_interest_right, width, height, resp_r);
im2colBiasSparseContrastNorm(area_of_interest_left, width, height, im2col_prealloc_left);
im2colBiasSparseContrastNorm(area_of_interest_right, width, height, im2col_prealloc_right);
cv::Mat_<float> response;
cv::vconcat(resp_l, resp_r, response);
cv::vconcat(im2col_prealloc_left, im2col_prealloc_right, response);
response = response.t();
@@ -730,8 +728,8 @@ void CEN_patch_expert::ResponseSparse_mirror_joint(const cv::Mat_<float> &area_o
for (size_t counter = 0; counter < resp_size; ++counter)
{
float in = *data;
*data++ = 1.0 / (1.0 + std::exp(-(in)));
float in = *data;
*data++ = 1.0 / (1.0 + std::exp(-(in)));
}*/
}