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Unverified Commit 709b6667 authored by Adrià Arrufat's avatar Adrià Arrufat Committed by GitHub
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Avoid redundant computations in the YOLO loss (#2453)

parent a41b3d7c
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Showing with 4 additions and 3 deletions
dlib/dnn/loss.h
View file @ 709b6667
...@@ -3614,7 +3614,6 @@ namespace dlib ...@@ -3614,7 +3614,6 @@ namespace dlib
std::vector<yolo_rect>& dets std::vector<yolo_rect>& dets
) )
{ {
DLIB_CASSERT(sub.sample_expansion_factor() == 1, sub.sample_expansion_factor());
const auto& anchors = options.anchors.at(tag_id<TAG_TYPE>::id); const auto& anchors = options.anchors.at(tag_id<TAG_TYPE>::id);
const tensor& output_tensor = layer<TAG_TYPE>(sub).get_output(); const tensor& output_tensor = layer<TAG_TYPE>(sub).get_output();
DLIB_CASSERT(static_cast<size_t>(output_tensor.k()) == anchors.size() * (options.labels.size() + 5)); DLIB_CASSERT(static_cast<size_t>(output_tensor.k()) == anchors.size() * (options.labels.size() + 5));
...@@ -3672,7 +3671,6 @@ namespace dlib ...@@ -3672,7 +3671,6 @@ namespace dlib
double& loss double& loss
) )
{ {
DLIB_CASSERT(sub.sample_expansion_factor() == 1, sub.sample_expansion_factor());
const tensor& output_tensor = layer<TAG_TYPE>(sub).get_output(); const tensor& output_tensor = layer<TAG_TYPE>(sub).get_output();
const auto& anchors = options.anchors.at(tag_id<TAG_TYPE>::id); const auto& anchors = options.anchors.at(tag_id<TAG_TYPE>::id);
DLIB_CASSERT(static_cast<size_t>(output_tensor.k()) == anchors.size() * (options.labels.size() + 5)); DLIB_CASSERT(static_cast<size_t>(output_tensor.k()) == anchors.size() * (options.labels.size() + 5));
...@@ -3684,6 +3682,7 @@ namespace dlib ...@@ -3684,6 +3682,7 @@ namespace dlib
tensor& grad = layer<TAG_TYPE>(sub).get_gradient_input(); tensor& grad = layer<TAG_TYPE>(sub).get_gradient_input();
DLIB_CASSERT(input_tensor.num_samples() == grad.num_samples()); DLIB_CASSERT(input_tensor.num_samples() == grad.num_samples());
DLIB_CASSERT(input_tensor.num_samples() == output_tensor.num_samples()); DLIB_CASSERT(input_tensor.num_samples() == output_tensor.num_samples());
const double input_area = input_tensor.nr() * input_tensor.nc();
float* g = grad.host(); float* g = grad.host();
// Compute the objectness loss for all grid cells // Compute the objectness loss for all grid cells
...@@ -3781,7 +3780,7 @@ namespace dlib ...@@ -3781,7 +3780,7 @@ namespace dlib
const double th = truth_box.rect.height() / (anchors[a].height + truth_box.rect.height()); const double th = truth_box.rect.height() / (anchors[a].height + truth_box.rect.height());
// Scale regression error according to the truth size // Scale regression error according to the truth size
const double scale_box = 2 - truth_box.rect.area() / (input_tensor.nr() * input_tensor.nc()); const double scale_box = 2 - truth_box.rect.area() / input_area;
// Compute the gradient for the box coordinates // Compute the gradient for the box coordinates
g[x_idx] = options.lambda_box * scale_box * (out_data[x_idx] - tx); g[x_idx] = options.lambda_box * scale_box * (out_data[x_idx] - tx);
...@@ -3835,6 +3834,7 @@ namespace dlib ...@@ -3835,6 +3834,7 @@ namespace dlib
double adjust_threshold = 0.25 double adjust_threshold = 0.25
) const ) const
{ {
DLIB_CASSERT(sub.sample_expansion_factor() == 1, sub.sample_expansion_factor());
std::vector<yolo_rect> dets_accum; std::vector<yolo_rect> dets_accum;
std::vector<yolo_rect> final_dets; std::vector<yolo_rect> final_dets;
for (long i = 0; i < input_tensor.num_samples(); ++i) for (long i = 0; i < input_tensor.num_samples(); ++i)
...@@ -3868,6 +3868,7 @@ namespace dlib ...@@ -3868,6 +3868,7 @@ namespace dlib
) const ) const
{ {
DLIB_CASSERT(input_tensor.num_samples() != 0); DLIB_CASSERT(input_tensor.num_samples() != 0);
DLIB_CASSERT(sub.sample_expansion_factor() == 1, sub.sample_expansion_factor());
double loss = 0; double loss = 0;
for (long i = 0; i < input_tensor.num_samples(); ++i) for (long i = 0; i < input_tensor.num_samples(); ++i)
{ {
......
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