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Add mobilenetv2ssdlite512 test.

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Works for CuDNN, not for tensorRT.
Modified channels in second convolution for classification headers from 126 to 486, when changing size from 300 to 512.
Added size 512 SSD specs and support to COCO dataset (81 classes, first BACKGROUND due to repo for training).
Refactoring class names.

Signed-off-by: Micaela Verucchi <micaelaverucchi@gmail.com>
This commit is contained in:
Micaela Verucchi
2020-03-17 10:35:02 +01:00
parent 9c25d15ff2
commit 1d388ca51a
5 changed files with 637 additions and 46 deletions
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include/tkDNN/MobilenetDetection.h
+16 -18
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@@ -15,31 +15,31 @@
struct SSDSpec
{
int feature_size = 0;
int featureSize = 0;
int shrinkage = 0;
int box_width = 0;
int box_height = 0;
int boxWidth = 0;
int boxHeight = 0;
int ratio1 = 0;
int ratio2 = 0;
SSDSpec() {}
SSDSpec(int feature_size, int shrinkage, int box_width, int box_height, int ratio1, int ratio2) : feature_size(feature_size), shrinkage(shrinkage), box_width(box_width), box_height(box_height),
SSDSpec(int feature_size, int shrinkage, int box_width, int box_height, int ratio1, int ratio2) : featureSize(feature_size), shrinkage(shrinkage), boxWidth(box_width), boxHeight(box_height),
ratio1(ratio1), ratio2(ratio2) {}
void setAll(int feature_size, int shrinkage, int box_width, int box_height, int ratio1, int ratio2)
{
this->feature_size = feature_size;
this->featureSize = feature_size;
this->shrinkage = shrinkage;
this->box_width = box_width;
this->box_height = box_height;
this->boxWidth = box_width;
this->boxHeight = box_height;
this->ratio1 = ratio1;
this->ratio2 = ratio2;
}
void print()
{
std::cout << "fsize: " << feature_size << "\tshrinkage: " << shrinkage << "\t box W:" << box_width << "\tbox H: " << box_height << "\t x ratio:" << ratio1 << "\t y ratio:" << ratio2 << std::endl;
std::cout << "fsize: " << featureSize << "\tshrinkage: " << shrinkage << "\t box W:" << boxWidth << "\tbox H: " << boxHeight << "\t x ratio:" << ratio1 << "\t y ratio:" << ratio2 << std::endl;
}
};
@@ -54,14 +54,12 @@ class MobilenetDetection
private:
tk::dnn::NetworkRT *netRT = nullptr;
int classes = 21;
float iou_threshold = 0.45;
float center_variance = 0.1;
float size_variance = 0.2;
float conf_thresh = 0.4;
int input_h = 300;
int input_w = 300;
int image_size = 300;
int classes;
float iouThreshold = 0.45;
float centerVariance = 0.1;
float sizeVariance = 0.2;
float confThresh = 0.4;
int imageSize;
float *priors = nullptr;
int n_priors = 0;
@@ -91,7 +89,7 @@ private:
float get_color2(int c, int x, int max);
cv::Scalar colors[256];
std::vector<std::string> voc_class_name;
std::vector<std::string> classesNames;
public:
// keep track of inference times (ms)
@@ -101,7 +99,7 @@ public:
MobilenetDetection() {}
~MobilenetDetection() {}
void init(std::string tensor_path);
void init(std::string tensor_path, int input_size, int n_classes);
cv::Mat draw();
void update(cv::Mat &img);
};