Add mobilenetv2ssdlite512 test.
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
@@ -103,6 +103,9 @@ target_link_libraries(test_yolo3_flir tkDNN)
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add_executable(test_mobilenetv2ssd tests/mobilenetv2ssd/mobilenetv2ssd.cpp)
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target_link_libraries(test_mobilenetv2ssd tkDNN)
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add_executable(test_mobilenetv2ssd512 tests/mobilenetv2ssd512/mobilenetv2ssd512.cpp)
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target_link_libraries(test_mobilenetv2ssd512 tkDNN)
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add_executable(test_resnet101 tests/resnet101/resnet101.cpp)
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target_link_libraries(test_resnet101 tkDNN)
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+1
-1
@@ -44,7 +44,7 @@ int main(int argc, char *argv[]) {
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cnet.init(net);
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break;
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case 'm':
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mbnet.init(net);
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mbnet.init(net, 512, 81);
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break;
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default:
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FatalError("Network type not allowed (3rd parameter)\n");
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@@ -15,31 +15,31 @@
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struct SSDSpec
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{
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int feature_size = 0;
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int featureSize = 0;
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int shrinkage = 0;
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int box_width = 0;
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int box_height = 0;
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int boxWidth = 0;
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int boxHeight = 0;
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int ratio1 = 0;
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int ratio2 = 0;
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SSDSpec() {}
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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),
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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),
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ratio1(ratio1), ratio2(ratio2) {}
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void setAll(int feature_size, int shrinkage, int box_width, int box_height, int ratio1, int ratio2)
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{
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this->feature_size = feature_size;
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this->featureSize = feature_size;
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this->shrinkage = shrinkage;
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this->box_width = box_width;
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this->box_height = box_height;
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this->boxWidth = box_width;
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this->boxHeight = box_height;
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this->ratio1 = ratio1;
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this->ratio2 = ratio2;
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}
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void print()
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{
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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;
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std::cout << "fsize: " << featureSize << "\tshrinkage: " << shrinkage << "\t box W:" << boxWidth << "\tbox H: " << boxHeight << "\t x ratio:" << ratio1 << "\t y ratio:" << ratio2 << std::endl;
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}
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};
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@@ -54,14 +54,12 @@ class MobilenetDetection
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private:
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tk::dnn::NetworkRT *netRT = nullptr;
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int classes = 21;
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float iou_threshold = 0.45;
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float center_variance = 0.1;
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float size_variance = 0.2;
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float conf_thresh = 0.4;
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int input_h = 300;
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int input_w = 300;
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int image_size = 300;
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int classes;
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float iouThreshold = 0.45;
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float centerVariance = 0.1;
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float sizeVariance = 0.2;
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float confThresh = 0.4;
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int imageSize;
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float *priors = nullptr;
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int n_priors = 0;
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@@ -91,7 +89,7 @@ private:
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float get_color2(int c, int x, int max);
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cv::Scalar colors[256];
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std::vector<std::string> voc_class_name;
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std::vector<std::string> classesNames;
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public:
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// keep track of inference times (ms)
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@@ -101,7 +99,7 @@ public:
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MobilenetDetection() {}
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~MobilenetDetection() {}
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void init(std::string tensor_path);
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void init(std::string tensor_path, int input_size, int n_classes);
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cv::Mat draw();
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void update(cv::Mat &img);
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};
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+77
-27
@@ -15,7 +15,7 @@ void MobilenetDetection::generate_ssd_priors(const SSDSpec *specs, const int n_s
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n_priors = 0;
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for (int i = 0; i < n_specs; i++)
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{
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n_priors += specs[i].feature_size * specs[i].feature_size * 6;
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n_priors += specs[i].featureSize * specs[i].featureSize * 6;
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}
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// std::cout<<"n priors: "<<n_priors<<std::endl;
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@@ -28,18 +28,18 @@ void MobilenetDetection::generate_ssd_priors(const SSDSpec *specs, const int n_s
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int min, max;
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for (int i = 0; i < n_specs; i++)
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{
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scale = (float)image_size / (float)specs[i].shrinkage;
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min = specs[i].box_height > specs[i].box_width ? specs[i].box_width : specs[i].box_height;
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max = specs[i].box_height < specs[i].box_width ? specs[i].box_width : specs[i].box_height;
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for (int j = 0; j < specs[i].feature_size; j++)
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scale = (float)imageSize / (float)specs[i].shrinkage;
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min = specs[i].boxHeight > specs[i].boxWidth ? specs[i].boxWidth : specs[i].boxHeight;
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max = specs[i].boxHeight < specs[i].boxWidth ? specs[i].boxWidth : specs[i].boxHeight;
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for (int j = 0; j < specs[i].featureSize; j++)
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{
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for (int k = 0; k < specs[i].feature_size; k++)
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for (int k = 0; k < specs[i].featureSize; k++)
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{
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//small sized square box
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size = min;
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x_center = (k + 0.5f) / scale;
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y_center = (j + 0.5f) / scale;
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h = w = (float)size / (float)image_size;
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h = w = (float)size / (float)imageSize;
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priors[i_prio * N_COORDS + 0] = x_center;
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priors[i_prio * N_COORDS + 1] = y_center;
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@@ -49,7 +49,7 @@ void MobilenetDetection::generate_ssd_priors(const SSDSpec *specs, const int n_s
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//big sized square box
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size = sqrt(max * min);
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h = w = (float)size / (float)image_size;
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h = w = (float)size / (float)imageSize;
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priors[i_prio * N_COORDS + 0] = x_center;
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priors[i_prio * N_COORDS + 1] = y_center;
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@@ -59,7 +59,7 @@ void MobilenetDetection::generate_ssd_priors(const SSDSpec *specs, const int n_s
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//change h/w ratio of the small sized box
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size = min;
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h = w = size / (float)image_size;
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h = w = size / (float)imageSize;
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ratio = sqrt(specs[i].ratio1);
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priors[i_prio * N_COORDS + 0] = x_center;
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priors[i_prio * N_COORDS + 1] = y_center;
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@@ -103,15 +103,15 @@ void MobilenetDetection::generate_ssd_priors(const SSDSpec *specs, const int n_s
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}
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}
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void MobilenetDetection::convert_locatios_to_boxes_and_center(float *priors, const int n_priors, float *locations, const float center_variance, const float size_variance)
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void MobilenetDetection::convert_locatios_to_boxes_and_center(float *priors, const int n_priors, float *locations, const float centerVariance, const float sizeVariance)
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{
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float cur_x, cur_y;
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for (int i = 0; i < n_priors; i++)
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{
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locations[i * N_COORDS + 0] = locations[i * N_COORDS + 0] * center_variance * priors[i * N_COORDS + 2] + priors[i * N_COORDS + 0];
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locations[i * N_COORDS + 1] = locations[i * N_COORDS + 1] * center_variance * priors[i * N_COORDS + 3] + priors[i * N_COORDS + 1];
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locations[i * N_COORDS + 2] = exp(locations[i * N_COORDS + 2] * size_variance) * priors[i * N_COORDS + 2];
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locations[i * N_COORDS + 3] = exp(locations[i * N_COORDS + 3] * size_variance) * priors[i * N_COORDS + 3];
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locations[i * N_COORDS + 0] = locations[i * N_COORDS + 0] * centerVariance * priors[i * N_COORDS + 2] + priors[i * N_COORDS + 0];
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locations[i * N_COORDS + 1] = locations[i * N_COORDS + 1] * centerVariance * priors[i * N_COORDS + 3] + priors[i * N_COORDS + 1];
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locations[i * N_COORDS + 2] = exp(locations[i * N_COORDS + 2] * sizeVariance) * priors[i * N_COORDS + 2];
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locations[i * N_COORDS + 3] = exp(locations[i * N_COORDS + 3] * sizeVariance) * priors[i * N_COORDS + 3];
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cur_x = locations[i * N_COORDS + 0];
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cur_y = locations[i * N_COORDS + 1];
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@@ -221,16 +221,38 @@ float MobilenetDetection::get_color2(int c, int x, int max)
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return r;
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}
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void MobilenetDetection::init(std::string tensor_path)
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void MobilenetDetection::init(std::string tensor_path, int input_size, int n_classes)
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{
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this->imageSize = input_size;
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this->classes = n_classes;
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const int n_SSDSpec = 6;
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SSDSpec specs[6];
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specs[0].setAll(19, 16, 60, 105, 2, 3);
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specs[1].setAll(10, 32, 105, 150, 2, 3);
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specs[2].setAll(5, 64, 150, 195, 2, 3);
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specs[3].setAll(3, 100, 195, 240, 2, 3);
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specs[4].setAll(2, 150, 240, 285, 2, 3);
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specs[5].setAll(1, 300, 285, 330, 2, 3);
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if(input_size == 300)
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{
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specs[0].setAll(19, 16, 60, 105, 2, 3);
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specs[1].setAll(10, 32, 105, 150, 2, 3);
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specs[2].setAll(5, 64, 150, 195, 2, 3);
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specs[3].setAll(3, 100, 195, 240, 2, 3);
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specs[4].setAll(2, 150, 240, 285, 2, 3);
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specs[5].setAll(1, 300, 285, 330, 2, 3);
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}
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else if(input_size == 512)
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{
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specs[0].setAll(32, 16, 60, 105, 2, 3);
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specs[1].setAll(16, 32, 105, 150, 2, 3);
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specs[2].setAll(8, 64, 150, 195, 2, 3);
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specs[3].setAll(4, 100, 195, 240, 2, 3);
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specs[4].setAll(2, 150, 240, 285, 2, 3);
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specs[5].setAll(1, 300, 285, 330, 2, 3);
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}
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else
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{
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FatalError("Input size for mobilenet not supported");
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}
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generate_ssd_priors(specs, n_SSDSpec);
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@@ -242,7 +264,7 @@ void MobilenetDetection::init(std::string tensor_path)
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locations_h = (float *)malloc(N_COORDS * n_priors * sizeof(float));
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confidences_h = (float *)malloc(n_priors * classes * sizeof(float));
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dim = tk::dnn::dataDim_t(1, 3, input_w, input_h, 1);
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dim = tk::dnn::dataDim_t(1, 3, imageSize, imageSize, 1);
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for (int c = 0; c < classes; c++)
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{
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@@ -253,11 +275,39 @@ void MobilenetDetection::init(std::string tensor_path)
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colors[c] = cv::Scalar(int(255.0 * b), int(255.0 * g), int(255.0 * r));
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}
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const char *voc_class_name_[] = {
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if(classes == 21)
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{
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const char *classes_names_[] = {
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"BACKGROUND", "aeroplane", "bicycle", "bird", "boat", "bottle", "bus",
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"car", "cat", "chair", "cow", "diningtable", "dog", "horse", "motorbike",
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"person", "pottedplant", "sheep", "sofa", "train", "tvmonitor"};
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voc_class_name = std::vector<std::string>(voc_class_name_, std::end(voc_class_name_));
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classesNames = std::vector<std::string>(classes_names_, std::end(classes_names_));
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}
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else if (classes == 81)
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{
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const char *classes_names_[] = {
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"BACKGROUND", "person" , "bicycle" , "car" , "motorbike" , "aeroplane" , "bus" ,
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"train" , "truck" , "boat" , "traffic light" , "fire hydrant" , "stop sign" ,
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"parking meter" , "bench" , "bird" , "cat" , "dog" , "horse" , "sheep" , "cow" ,
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"elephant" , "bear" , "zebra" , "giraffe" , "backpack" , "umbrella" , "handbag" ,
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"tie" , "suitcase" , "frisbee" , "skis" , "snowboard" , "sports ball" , "kite" ,
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"baseball bat" , "baseball glove" , "skateboard" , "surfboard" , "tennis racket" ,
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"bottle" , "wine glass" , "cup" , "fork" , "knife" , "spoon" , "bowl" , "banana" ,
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"apple" , "sandwich" , "orange" , "broccoli" , "carrot" , "hot dog" , "pizza" ,
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"donut" , "cake" , "chair" , "sofa" , "pottedplant" , "bed" , "diningtable" ,
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"toilet" , "tvmonitor" , "laptop" , "mouse" , "remote" , "keyboard" ,
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"cell phone" , "microwave" , "oven" , "toaster" , "sink" , "refrigerator" ,
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"book" , "clock" , "vase" , "scissors" , "teddy bear" , "hair drier" , "toothbrush"};
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classesNames = std::vector<std::string>(classes_names_, std::end(classes_names_));
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}
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else
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{
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FatalError("Number of classes not supported for mobilenet");
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}
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}
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cv::Mat MobilenetDetection::draw()
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@@ -266,7 +316,7 @@ cv::Mat MobilenetDetection::draw()
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for (size_t i = 0; i < detected.size(); i++)
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{
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b = detected[i];
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std::string det_class = voc_class_name[b.cl];
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std::string det_class = classesNames[b.cl];
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cv::rectangle(origImg, cv::Point(b.x, b.y), cv::Point(b.w, b.h), colors[b.cl], 2);
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// draw label
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cv::Size textSize = getTextSize(det_class, cv::FONT_HERSHEY_SIMPLEX, fontScale, thickness, &baseline);
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@@ -319,8 +369,8 @@ void MobilenetDetection::update(cv::Mat &img)
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checkCuda(cudaMemcpy(locations_h, loc, N_COORDS * n_priors * sizeof(float), cudaMemcpyDeviceToHost));
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//postprocess
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convert_locatios_to_boxes_and_center(priors, n_priors, locations_h, center_variance, size_variance);
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detected = postprocess(locations_h, confidences_h, n_priors, conf_thresh, classes, iou_threshold, sz.width, sz.height);
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convert_locatios_to_boxes_and_center(priors, n_priors, locations_h, centerVariance, sizeVariance);
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detected = postprocess(locations_h, confidences_h, n_priors, confThresh, classes, iouThreshold, sz.width, sz.height);
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TIMER_STOP
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stats.push_back(t_ns);
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@@ -0,0 +1,540 @@
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#include <iostream>
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#include "tkdnn.h"
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const char *output_bin1 = "../tests/mobilenetv2ssd512/debug/classification_headers-5.bin";
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const char *output_bin2 = "../tests/mobilenetv2ssd512/debug/regression_headers-5.bin";
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const char *input_bin = "../tests/mobilenetv2ssd512/debug/input.bin";
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const char *conv0_bin = "../tests/mobilenetv2ssd512/layers/base_net-0-0.bin";
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const char *inverted_residual1[] = {
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"../tests/mobilenetv2ssd512/layers/base_net-1-conv-0.bin",
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"../tests/mobilenetv2ssd512/layers/base_net-1-conv-3.bin"};
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const char *inverted_residual2[] = {
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"../tests/mobilenetv2ssd512/layers/base_net-2-conv-0.bin",
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"../tests/mobilenetv2ssd512/layers/base_net-2-conv-3.bin",
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"../tests/mobilenetv2ssd512/layers/base_net-2-conv-6.bin"};
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const char *inverted_residual3[] = {
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"../tests/mobilenetv2ssd512/layers/base_net-3-conv-0.bin",
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"../tests/mobilenetv2ssd512/layers/base_net-3-conv-3.bin",
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"../tests/mobilenetv2ssd512/layers/base_net-3-conv-6.bin"};
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const char *inverted_residual4[] = {
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"../tests/mobilenetv2ssd512/layers/base_net-4-conv-0.bin",
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"../tests/mobilenetv2ssd512/layers/base_net-4-conv-3.bin",
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"../tests/mobilenetv2ssd512/layers/base_net-4-conv-6.bin"};
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const char *inverted_residual5[] = {
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"../tests/mobilenetv2ssd512/layers/base_net-5-conv-0.bin",
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"../tests/mobilenetv2ssd512/layers/base_net-5-conv-3.bin",
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"../tests/mobilenetv2ssd512/layers/base_net-5-conv-6.bin"};
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const char *inverted_residual6[] = {
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"../tests/mobilenetv2ssd512/layers/base_net-6-conv-0.bin",
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"../tests/mobilenetv2ssd512/layers/base_net-6-conv-3.bin",
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"../tests/mobilenetv2ssd512/layers/base_net-6-conv-6.bin"};
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const char *inverted_residual7[] = {
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"../tests/mobilenetv2ssd512/layers/base_net-7-conv-0.bin",
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"../tests/mobilenetv2ssd512/layers/base_net-7-conv-3.bin",
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"../tests/mobilenetv2ssd512/layers/base_net-7-conv-6.bin"};
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const char *inverted_residual8[] = {
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"../tests/mobilenetv2ssd512/layers/base_net-8-conv-0.bin",
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"../tests/mobilenetv2ssd512/layers/base_net-8-conv-3.bin",
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"../tests/mobilenetv2ssd512/layers/base_net-8-conv-6.bin"};
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const char *inverted_residual9[] = {
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"../tests/mobilenetv2ssd512/layers/base_net-9-conv-0.bin",
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"../tests/mobilenetv2ssd512/layers/base_net-9-conv-3.bin",
|
||||
"../tests/mobilenetv2ssd512/layers/base_net-9-conv-6.bin"};
|
||||
const char *inverted_residual10[] = {
|
||||
"../tests/mobilenetv2ssd512/layers/base_net-10-conv-0.bin",
|
||||
"../tests/mobilenetv2ssd512/layers/base_net-10-conv-3.bin",
|
||||
"../tests/mobilenetv2ssd512/layers/base_net-10-conv-6.bin"};
|
||||
const char *inverted_residual11[] = {
|
||||
"../tests/mobilenetv2ssd512/layers/base_net-11-conv-0.bin",
|
||||
"../tests/mobilenetv2ssd512/layers/base_net-11-conv-3.bin",
|
||||
"../tests/mobilenetv2ssd512/layers/base_net-11-conv-6.bin"};
|
||||
const char *inverted_residual12[] = {
|
||||
"../tests/mobilenetv2ssd512/layers/base_net-12-conv-0.bin",
|
||||
"../tests/mobilenetv2ssd512/layers/base_net-12-conv-3.bin",
|
||||
"../tests/mobilenetv2ssd512/layers/base_net-12-conv-6.bin"};
|
||||
const char *inverted_residual13[] = {
|
||||
"../tests/mobilenetv2ssd512/layers/base_net-13-conv-0.bin",
|
||||
"../tests/mobilenetv2ssd512/layers/base_net-13-conv-3.bin",
|
||||
"../tests/mobilenetv2ssd512/layers/base_net-13-conv-6.bin"};
|
||||
const char *inverted_residual14[] = {
|
||||
"../tests/mobilenetv2ssd512/layers/base_net-14-conv-0.bin",
|
||||
"../tests/mobilenetv2ssd512/layers/base_net-14-conv-3.bin",
|
||||
"../tests/mobilenetv2ssd512/layers/base_net-14-conv-6.bin"};
|
||||
const char *inverted_residual15[] = {
|
||||
"../tests/mobilenetv2ssd512/layers/base_net-15-conv-0.bin",
|
||||
"../tests/mobilenetv2ssd512/layers/base_net-15-conv-3.bin",
|
||||
"../tests/mobilenetv2ssd512/layers/base_net-15-conv-6.bin"};
|
||||
const char *inverted_residual16[] = {
|
||||
"../tests/mobilenetv2ssd512/layers/base_net-16-conv-0.bin",
|
||||
"../tests/mobilenetv2ssd512/layers/base_net-16-conv-3.bin",
|
||||
"../tests/mobilenetv2ssd512/layers/base_net-16-conv-6.bin"};
|
||||
const char *inverted_residual17[] = {
|
||||
"../tests/mobilenetv2ssd512/layers/base_net-17-conv-0.bin",
|
||||
"../tests/mobilenetv2ssd512/layers/base_net-17-conv-3.bin",
|
||||
"../tests/mobilenetv2ssd512/layers/base_net-17-conv-6.bin"};
|
||||
|
||||
const char *conv18 = "../tests/mobilenetv2ssd512/layers/base_net-18-0.bin";
|
||||
|
||||
const char *extras0[] = {
|
||||
"../tests/mobilenetv2ssd512/layers/extras-0-conv-0.bin",
|
||||
"../tests/mobilenetv2ssd512/layers/extras-0-conv-3.bin",
|
||||
"../tests/mobilenetv2ssd512/layers/extras-0-conv-6.bin"};
|
||||
const char *extras1[] = {
|
||||
"../tests/mobilenetv2ssd512/layers/extras-1-conv-0.bin",
|
||||
"../tests/mobilenetv2ssd512/layers/extras-1-conv-3.bin",
|
||||
"../tests/mobilenetv2ssd512/layers/extras-1-conv-6.bin"};
|
||||
const char *extras2[] = {
|
||||
"../tests/mobilenetv2ssd512/layers/extras-2-conv-0.bin",
|
||||
"../tests/mobilenetv2ssd512/layers/extras-2-conv-3.bin",
|
||||
"../tests/mobilenetv2ssd512/layers/extras-2-conv-6.bin"};
|
||||
const char *extras3[] = {
|
||||
"../tests/mobilenetv2ssd512/layers/extras-3-conv-0.bin",
|
||||
"../tests/mobilenetv2ssd512/layers/extras-3-conv-3.bin",
|
||||
"../tests/mobilenetv2ssd512/layers/extras-3-conv-6.bin"};
|
||||
|
||||
const char *classification_header0[] = {
|
||||
"../tests/mobilenetv2ssd512/layers/classification_headers-0-0.bin",
|
||||
"../tests/mobilenetv2ssd512/layers/classification_headers-0-3.bin"};
|
||||
const char *classification_header1[] = {
|
||||
"../tests/mobilenetv2ssd512/layers/classification_headers-1-0.bin",
|
||||
"../tests/mobilenetv2ssd512/layers/classification_headers-1-3.bin"};
|
||||
const char *classification_header2[] = {
|
||||
"../tests/mobilenetv2ssd512/layers/classification_headers-2-0.bin",
|
||||
"../tests/mobilenetv2ssd512/layers/classification_headers-2-3.bin"};
|
||||
const char *classification_header3[] = {
|
||||
"../tests/mobilenetv2ssd512/layers/classification_headers-3-0.bin",
|
||||
"../tests/mobilenetv2ssd512/layers/classification_headers-3-3.bin"};
|
||||
const char *classification_header4[] = {
|
||||
"../tests/mobilenetv2ssd512/layers/classification_headers-4-0.bin",
|
||||
"../tests/mobilenetv2ssd512/layers/classification_headers-4-3.bin"};
|
||||
|
||||
const char *classification_header5 = "../tests/mobilenetv2ssd512/layers/classification_headers-5.bin";
|
||||
|
||||
const char *regression_header0[] = {
|
||||
"../tests/mobilenetv2ssd512/layers/regression_headers-0-0.bin",
|
||||
"../tests/mobilenetv2ssd512/layers/regression_headers-0-3.bin"};
|
||||
const char *regression_header1[] = {
|
||||
"../tests/mobilenetv2ssd512/layers/regression_headers-1-0.bin",
|
||||
"../tests/mobilenetv2ssd512/layers/regression_headers-1-3.bin"};
|
||||
const char *regression_header2[] = {
|
||||
"../tests/mobilenetv2ssd512/layers/regression_headers-2-0.bin",
|
||||
"../tests/mobilenetv2ssd512/layers/regression_headers-2-3.bin"};
|
||||
const char *regression_header3[] = {
|
||||
"../tests/mobilenetv2ssd512/layers/regression_headers-3-0.bin",
|
||||
"../tests/mobilenetv2ssd512/layers/regression_headers-3-3.bin"};
|
||||
const char *regression_header4[] = {
|
||||
"../tests/mobilenetv2ssd512/layers/regression_headers-4-0.bin",
|
||||
"../tests/mobilenetv2ssd512/layers/regression_headers-4-3.bin"};
|
||||
|
||||
const char *regression_header5 = "../tests/mobilenetv2ssd512/layers/regression_headers-5.bin";
|
||||
|
||||
|
||||
int main()
|
||||
{
|
||||
|
||||
// downloadWeightsifDoNotExist(input_bin, "./tests/mobilenetv2ssd512");
|
||||
|
||||
int classes = 81;
|
||||
|
||||
// Network layout
|
||||
tk::dnn::dataDim_t dim(1, 3, 512, 512, 1);
|
||||
tk::dnn::Network net(dim);
|
||||
|
||||
tk::dnn::Conv2d conv1(&net, 32, 3, 3, 2, 2, 1, 1, conv0_bin, true);
|
||||
tk::dnn::Activation relu3(&net, CUDNN_ACTIVATION_RELU);
|
||||
|
||||
//Inverted Residual 1
|
||||
|
||||
tk::dnn::Conv2d conv2(&net, 32, 3, 3, 1, 1, 1, 1, inverted_residual1[0], true, false, false, 32);
|
||||
tk::dnn::Activation relu5(&net, CUDNN_ACTIVATION_RELU);
|
||||
tk::dnn::Conv2d conv3(&net, 16, 1, 1, 1, 1, 0, 0, inverted_residual1[1], true);
|
||||
|
||||
//Inverted Residual 2
|
||||
tk::dnn::Conv2d ir_2_conv1(&net, 96, 1, 1, 1, 1, 0, 0, inverted_residual2[0], true);
|
||||
tk::dnn::Activation relu_2_1(&net, CUDNN_ACTIVATION_RELU);
|
||||
tk::dnn::Conv2d ir_2_conv2(&net, 96, 3, 3, 2, 2, 1, 1, inverted_residual2[1], true, false, false, 96);
|
||||
tk::dnn::Activation relu_2_2(&net, CUDNN_ACTIVATION_RELU);
|
||||
tk::dnn::Conv2d ir_2_conv3(&net, 24, 1, 1, 1, 1, 0, 0, inverted_residual2[2], true);
|
||||
|
||||
//Inverted Residual 3
|
||||
tk::dnn::Layer *last = &ir_2_conv3;
|
||||
tk::dnn::Conv2d ir_3_conv1(&net, 144, 1, 1, 1, 1, 0, 0, inverted_residual3[0], true);
|
||||
tk::dnn::Activation relu_3_1(&net, CUDNN_ACTIVATION_RELU);
|
||||
tk::dnn::Conv2d ir_3_conv2(&net, 144, 3, 3, 1, 1, 1, 1, inverted_residual3[1], true, false, false, 144);
|
||||
tk::dnn::Activation relu_3_2(&net, CUDNN_ACTIVATION_RELU);
|
||||
tk::dnn::Conv2d ir_3_conv3(&net, 24, 1, 1, 1, 1, 0, 0, inverted_residual3[2], true);
|
||||
|
||||
tk::dnn::Shortcut s3_0(&net, last);
|
||||
// //Inverted Residual 4
|
||||
tk::dnn::Conv2d ir_4_conv1(&net, 144, 1, 1, 1, 1, 0, 0, inverted_residual4[0], true);
|
||||
tk::dnn::Activation relu_4_1(&net, CUDNN_ACTIVATION_RELU);
|
||||
tk::dnn::Conv2d ir_4_conv2(&net, 144, 3, 3, 2, 2, 1, 1, inverted_residual4[1], true, false, false, 144);
|
||||
tk::dnn::Activation relu_4_2(&net, CUDNN_ACTIVATION_RELU);
|
||||
tk::dnn::Conv2d ir_4_conv3(&net, 32, 1, 1, 1, 1, 0, 0, inverted_residual4[2], true);
|
||||
|
||||
// // //Inverted Residual 5
|
||||
last = &ir_4_conv3;
|
||||
tk::dnn::Conv2d ir_5_conv1(&net, 192, 1, 1, 1, 1, 0, 0, inverted_residual5[0], true);
|
||||
tk::dnn::Activation relu_5_1(&net, CUDNN_ACTIVATION_RELU);
|
||||
tk::dnn::Conv2d ir_5_conv2(&net, 192, 3, 3, 1, 1, 1, 1, inverted_residual5[1], true, false, false, 192);
|
||||
tk::dnn::Activation relu_5_2(&net, CUDNN_ACTIVATION_RELU);
|
||||
tk::dnn::Conv2d ir_5_conv3(&net, 32, 1, 1, 1, 1, 0, 0, inverted_residual5[2], true);
|
||||
|
||||
tk::dnn::Shortcut s5_0(&net, last);
|
||||
// // // //Inverted Residual 6
|
||||
last = &s5_0;
|
||||
tk::dnn::Conv2d ir_6_conv1(&net, 192, 1, 1, 1, 1, 0, 0, inverted_residual6[0], true);
|
||||
tk::dnn::Activation relu_6_1(&net, CUDNN_ACTIVATION_RELU);
|
||||
tk::dnn::Conv2d ir_6_conv2(&net, 192, 3, 3, 1, 1, 1, 1, inverted_residual6[1], true, false, false, 192);
|
||||
tk::dnn::Activation relu_6_2(&net, CUDNN_ACTIVATION_RELU);
|
||||
tk::dnn::Conv2d ir_6_conv3(&net, 32, 1, 1, 1, 1, 0, 0, inverted_residual6[2], true);
|
||||
|
||||
tk::dnn::Shortcut s6_0(&net, last);
|
||||
//Inverted Residual 7
|
||||
tk::dnn::Conv2d ir_7_conv1(&net, 192, 1, 1, 1, 1, 0, 0, inverted_residual7[0], true);
|
||||
tk::dnn::Activation relu_7_1(&net, CUDNN_ACTIVATION_RELU);
|
||||
tk::dnn::Conv2d ir_7_conv2(&net, 192, 3, 3, 2, 2, 1, 1, inverted_residual7[1], true, false, false, 192);
|
||||
tk::dnn::Activation relu_7_2(&net, CUDNN_ACTIVATION_RELU);
|
||||
tk::dnn::Conv2d ir_7_conv3(&net, 64, 1, 1, 1, 1, 0, 0, inverted_residual7[2], true);
|
||||
|
||||
// //Inverted Residual 8
|
||||
last = &ir_7_conv3;
|
||||
tk::dnn::Conv2d ir_8_conv1(&net, 384, 1, 1, 1, 1, 0, 0, inverted_residual8[0], true);
|
||||
tk::dnn::Activation relu_8_1(&net, CUDNN_ACTIVATION_RELU);
|
||||
tk::dnn::Conv2d ir_8_conv2(&net, 384, 3, 3, 1, 1, 1, 1, inverted_residual8[1], true, false, false, 384);
|
||||
tk::dnn::Activation relu_8_2(&net, CUDNN_ACTIVATION_RELU);
|
||||
tk::dnn::Conv2d ir_8_conv3(&net, 64, 1, 1, 1, 1, 0, 0, inverted_residual8[2], true);
|
||||
|
||||
tk::dnn::Shortcut s8_0(&net, last);
|
||||
//Inverted Residual 9
|
||||
last = &s8_0;
|
||||
tk::dnn::Conv2d ir_9_conv1(&net, 384, 1, 1, 1, 1, 0, 0, inverted_residual9[0], true);
|
||||
tk::dnn::Activation relu_9_1(&net, CUDNN_ACTIVATION_RELU);
|
||||
tk::dnn::Conv2d ir_9_conv2(&net, 384, 3, 3, 1, 1, 1, 1, inverted_residual9[1], true, false, false, 384);
|
||||
tk::dnn::Activation relu_9_2(&net, CUDNN_ACTIVATION_RELU);
|
||||
tk::dnn::Conv2d ir_9_conv3(&net, 64, 1, 1, 1, 1, 0, 0, inverted_residual9[2], true);
|
||||
|
||||
tk::dnn::Shortcut s9_0(&net, last);
|
||||
//Inverted Residual 10
|
||||
last = &s9_0;
|
||||
tk::dnn::Conv2d ir_10_conv1(&net, 384, 1, 1, 1, 1, 0, 0, inverted_residual10[0], true);
|
||||
tk::dnn::Activation relu_10_1(&net, CUDNN_ACTIVATION_RELU);
|
||||
tk::dnn::Conv2d ir_10_conv2(&net, 384, 3, 3, 1, 1, 1, 1, inverted_residual10[1], true, false, false, 384);
|
||||
tk::dnn::Activation relu_10_2(&net, CUDNN_ACTIVATION_RELU);
|
||||
tk::dnn::Conv2d ir_10_conv3(&net, 64, 1, 1, 1, 1, 0, 0, inverted_residual10[2], true);
|
||||
|
||||
tk::dnn::Shortcut s10_0(&net, last);
|
||||
//Inverted Residual 11
|
||||
tk::dnn::Conv2d ir_11_conv1(&net, 384, 1, 1, 1, 1, 0, 0, inverted_residual11[0], true);
|
||||
tk::dnn::Activation relu_11_1(&net, CUDNN_ACTIVATION_RELU);
|
||||
tk::dnn::Conv2d ir_11_conv2(&net, 384, 3, 3, 1, 1, 1, 1, inverted_residual11[1], true, false, false, 384);
|
||||
tk::dnn::Activation relu_11_2(&net, CUDNN_ACTIVATION_RELU);
|
||||
tk::dnn::Conv2d ir_11_conv3(&net, 96, 1, 1, 1, 1, 0, 0, inverted_residual11[2], true);
|
||||
|
||||
last = &ir_11_conv3;
|
||||
//Inverted Residual 12
|
||||
tk::dnn::Conv2d ir_12_conv1(&net, 576, 1, 1, 1, 1, 0, 0, inverted_residual12[0], true);
|
||||
tk::dnn::Activation relu_12_1(&net, CUDNN_ACTIVATION_RELU);
|
||||
tk::dnn::Conv2d ir_12_conv2(&net, 576, 3, 3, 1, 1, 1, 1, inverted_residual12[1], true, false, false, 576);
|
||||
tk::dnn::Activation relu_12_2(&net, CUDNN_ACTIVATION_RELU);
|
||||
tk::dnn::Conv2d ir_12_conv3(&net, 96, 1, 1, 1, 1, 0, 0, inverted_residual12[2], true);
|
||||
|
||||
tk::dnn::Shortcut s12_0(&net, last);
|
||||
last = &s12_0;
|
||||
//Inverted Residual 13
|
||||
tk::dnn::Conv2d ir_13_conv1(&net, 576, 1, 1, 1, 1, 0, 0, inverted_residual13[0], true);
|
||||
tk::dnn::Activation relu_13_1(&net, CUDNN_ACTIVATION_RELU);
|
||||
tk::dnn::Conv2d ir_13_conv2(&net, 576, 3, 3, 1, 1, 1, 1, inverted_residual13[1], true, false, false, 576);
|
||||
tk::dnn::Activation relu_13_2(&net, CUDNN_ACTIVATION_RELU);
|
||||
tk::dnn::Conv2d ir_13_conv3(&net, 96, 1, 1, 1, 1, 0, 0, inverted_residual13[2], true);
|
||||
|
||||
tk::dnn::Shortcut s13_0(&net, last);
|
||||
// //Inverted Residual 14
|
||||
tk::dnn::Conv2d ir_14_conv1(&net, 576, 1, 1, 1, 1, 0, 0, inverted_residual14[0], true);
|
||||
tk::dnn::Activation relu_14_1(&net, CUDNN_ACTIVATION_RELU);
|
||||
tk::dnn::Conv2d ir_14_conv2(&net, 576, 3, 3, 2, 2, 1, 1, inverted_residual14[1], true, false, false, 576);
|
||||
tk::dnn::Activation relu_14_2(&net, CUDNN_ACTIVATION_RELU);
|
||||
tk::dnn::Conv2d ir_14_conv3(&net, 160, 1, 1, 1, 1, 0, 0, inverted_residual14[2], true);
|
||||
|
||||
// //Inverted Residual 15
|
||||
last = &ir_14_conv3;
|
||||
tk::dnn::Conv2d ir_15_conv1(&net, 960, 1, 1, 1, 1, 0, 0, inverted_residual15[0], true);
|
||||
tk::dnn::Activation relu_15_1(&net, CUDNN_ACTIVATION_RELU);
|
||||
tk::dnn::Conv2d ir_15_conv2(&net, 960, 3, 3, 1, 1, 1, 1, inverted_residual15[1], true, false, false, 960);
|
||||
tk::dnn::Activation relu_15_2(&net, CUDNN_ACTIVATION_RELU);
|
||||
tk::dnn::Conv2d ir_15_conv3(&net, 160, 1, 1, 1, 1, 0, 0, inverted_residual15[2], true);
|
||||
|
||||
tk::dnn::Shortcut s15_0(&net, last);
|
||||
//Inverted Residual 16
|
||||
last = &s15_0;
|
||||
tk::dnn::Conv2d ir_16_conv1(&net, 960, 1, 1, 1, 1, 0, 0, inverted_residual16[0], true);
|
||||
tk::dnn::Activation relu_16_1(&net, CUDNN_ACTIVATION_RELU);
|
||||
tk::dnn::Conv2d ir_16_conv2(&net, 960, 3, 3, 1, 1, 1, 1, inverted_residual16[1], true, false, false, 960);
|
||||
tk::dnn::Activation relu_16_2(&net, CUDNN_ACTIVATION_RELU);
|
||||
tk::dnn::Conv2d ir_16_conv3(&net, 160, 1, 1, 1, 1, 0, 0, inverted_residual16[2], true);
|
||||
|
||||
tk::dnn::Shortcut s16_0(&net, last);
|
||||
//Inverted Residual 17
|
||||
tk::dnn::Conv2d ir_17_conv1(&net, 960, 1, 1, 1, 1, 0, 0, inverted_residual17[0], true);
|
||||
tk::dnn::Activation relu_17_1(&net, CUDNN_ACTIVATION_RELU);
|
||||
tk::dnn::Conv2d ir_17_conv2(&net, 960, 3, 3, 1, 1, 1, 1, inverted_residual17[1], true, false, false, 960);
|
||||
tk::dnn::Activation relu_17_2(&net, CUDNN_ACTIVATION_RELU);
|
||||
tk::dnn::Conv2d ir_17_conv3(&net, 320, 1, 1, 1, 1, 0, 0, inverted_residual17[2], true);
|
||||
|
||||
//Conv 18
|
||||
tk::dnn::Conv2d ir_18_conv1(&net, 1280, 1, 1, 1, 1, 0, 0, conv18, true);
|
||||
tk::dnn::Activation relu_18_1(&net, CUDNN_ACTIVATION_RELU);
|
||||
tk::dnn::Layer *header_1[1] = {&relu_18_1};
|
||||
|
||||
// //extras Inverted Residual 0
|
||||
tk::dnn::Conv2d e_0_conv1(&net, 256, 1, 1, 1, 1, 0, 0, extras0[0], true);
|
||||
tk::dnn::Activation e_relu_0_1(&net, CUDNN_ACTIVATION_RELU);
|
||||
tk::dnn::Conv2d e_0_conv2(&net, 256, 3, 3, 2, 2, 1, 1, extras0[1], true, false, false, 256);
|
||||
tk::dnn::Activation e_relu_0_2(&net, CUDNN_ACTIVATION_RELU);
|
||||
tk::dnn::Conv2d e_0_conv3(&net, 512, 1, 1, 1, 1, 0, 0, extras0[2], true);
|
||||
tk::dnn::Layer *header_2[1] = {&e_0_conv3};
|
||||
|
||||
// //extras Inverted Residual 1
|
||||
tk::dnn::Conv2d e_1_conv1(&net, 128, 1, 1, 1, 1, 0, 0, extras1[0], true);
|
||||
tk::dnn::Activation e_relu_1_1(&net, CUDNN_ACTIVATION_RELU);
|
||||
tk::dnn::Conv2d e_1_conv2(&net, 128, 3, 3, 2, 2, 1, 1, extras1[1], true, false, false, 128);
|
||||
tk::dnn::Activation e_relu_1_2(&net, CUDNN_ACTIVATION_RELU);
|
||||
tk::dnn::Conv2d e_1_conv3(&net, 256, 1, 1, 1, 1, 0, 0, extras1[2], true);
|
||||
tk::dnn::Layer *header_3[1] = {&e_1_conv3};
|
||||
|
||||
//extras Inverted Residual 2
|
||||
tk::dnn::Conv2d e_2_conv1(&net, 128, 1, 1, 1, 1, 0, 0, extras2[0], true);
|
||||
tk::dnn::Activation e_relu_2_1(&net, CUDNN_ACTIVATION_RELU);
|
||||
tk::dnn::Conv2d e_2_conv2(&net, 128, 3, 3, 2, 2, 1, 1, extras2[1], true, false, false, 128);
|
||||
tk::dnn::Activation e_relu_2_2(&net, CUDNN_ACTIVATION_RELU);
|
||||
tk::dnn::Conv2d e_2_conv3(&net, 256, 1, 1, 1, 1, 0, 0, extras2[2], true);
|
||||
tk::dnn::Layer *header_4[1] = {&e_2_conv3};
|
||||
|
||||
//extras Inverted Residual 3
|
||||
tk::dnn::Conv2d e_3_conv1(&net, 64, 1, 1, 1, 1, 0, 0, extras3[0], true);
|
||||
tk::dnn::Activation e_relu_3_1(&net, CUDNN_ACTIVATION_RELU);
|
||||
tk::dnn::Conv2d e_3_conv2(&net, 64, 3, 3, 2, 2, 1, 1, extras3[1], true, false, false, 64);
|
||||
tk::dnn::Activation e_relu_3_2(&net, CUDNN_ACTIVATION_RELU);
|
||||
tk::dnn::Conv2d e_3_conv3(&net, 64, 1, 1, 1, 1, 0, 0, extras3[2], true);
|
||||
tk::dnn::Layer *header_5[1] = {&e_3_conv3};
|
||||
|
||||
// classification header 0
|
||||
tk::dnn::Layer *header_0[1] = {&relu_14_1};
|
||||
tk::dnn::Route rout_ch_0(&net, header_0, 1);
|
||||
tk::dnn::Conv2d ch_0_conv1(&net, 576, 3, 3, 1, 1, 1, 1, classification_header0[0], true, false, false, 576, true);
|
||||
tk::dnn::Activation ch_relu_0_1(&net, CUDNN_ACTIVATION_CLIPPED_RELU, 6);
|
||||
tk::dnn::Conv2d ch_0_conv2(&net, 486, 1, 1, 1, 1, 0, 0, classification_header0[1], false);
|
||||
tk::dnn::Layer *conf0[1] = {&ch_0_conv2};
|
||||
|
||||
// // classification header 1
|
||||
tk::dnn::Route rout_ch_1(&net, header_1, 1);
|
||||
tk::dnn::Conv2d ch_1_conv1(&net, 1280, 3, 3, 1, 1, 1, 1, classification_header1[0], true, false, false, 1280, true);
|
||||
tk::dnn::Activation ch_relu_1_1(&net, CUDNN_ACTIVATION_CLIPPED_RELU, 6);
|
||||
tk::dnn::Conv2d ch_1_conv2(&net, 486, 1, 1, 1, 1, 0, 0, classification_header1[1], false);
|
||||
tk::dnn::Layer *conf1[1] = {&ch_1_conv2};
|
||||
|
||||
// //classification header 2
|
||||
tk::dnn::Route rout_ch_2(&net, header_2, 1);
|
||||
tk::dnn::Conv2d ch_2_conv1(&net, 512, 3, 3, 1, 1, 1, 1, classification_header2[0], true, false, false, 512, true);
|
||||
tk::dnn::Activation ch_relu_2_1(&net, CUDNN_ACTIVATION_CLIPPED_RELU, 6);
|
||||
tk::dnn::Conv2d ch_2_conv2(&net, 486, 1, 1, 1, 1, 0, 0, classification_header2[1], false);
|
||||
tk::dnn::Layer *conf2[1] = {&ch_2_conv2};
|
||||
|
||||
// //classification header 3
|
||||
tk::dnn::Route rout_ch_3(&net, header_3, 1);
|
||||
tk::dnn::Conv2d ch_3_conv1(&net, 256, 3, 3, 1, 1, 1, 1, classification_header3[0], true, false, false, 256, true);
|
||||
tk::dnn::Activation ch_relu_3_1(&net, CUDNN_ACTIVATION_CLIPPED_RELU, 6);
|
||||
tk::dnn::Conv2d ch_3_conv2(&net, 486, 1, 1, 1, 1, 0, 0, classification_header3[1], false);
|
||||
tk::dnn::Layer *conf3[1] = {&ch_3_conv2};
|
||||
|
||||
// //classification header 4
|
||||
tk::dnn::Route rout_ch_4(&net, header_4, 1);
|
||||
tk::dnn::Conv2d ch_4_conv1(&net, 256, 3, 3, 1, 1, 1, 1, classification_header4[0], true, false, false, 256, true);
|
||||
tk::dnn::Activation ch_relu_4_1(&net, CUDNN_ACTIVATION_CLIPPED_RELU, 6);
|
||||
tk::dnn::Conv2d ch_4_conv2(&net, 486, 1, 1, 1, 1, 0, 0, classification_header4[1], false);
|
||||
tk::dnn::Layer *conf4[1] = {&ch_4_conv2};
|
||||
|
||||
// //classification header 5
|
||||
tk::dnn::Route rout_ch_5(&net, header_5, 1);
|
||||
tk::dnn::Conv2d ch_5_conv(&net, 486, 1, 1, 1, 1, 0, 0, classification_header5, false, false, true);
|
||||
tk::dnn::Layer *conf5[1] = {&ch_5_conv};
|
||||
|
||||
//regression header 0
|
||||
tk::dnn::Route rout_rh_0(&net, header_0, 1);
|
||||
tk::dnn::Conv2d rh_0_conv1(&net, 576, 3, 3, 1, 1, 1, 1, regression_header0[0], true, false, false, 576, true);
|
||||
tk::dnn::Activation rh_relu_0_1(&net, CUDNN_ACTIVATION_CLIPPED_RELU, 6);
|
||||
tk::dnn::Conv2d rh_0_conv2(&net, 24, 1, 1, 1, 1, 0, 0, regression_header0[1], false);
|
||||
tk::dnn::Layer *loc0[1] = {&rh_0_conv2};
|
||||
|
||||
// //regression header 1
|
||||
tk::dnn::Route rout_rh_1(&net, header_1, 1);
|
||||
tk::dnn::Conv2d rh_1_conv1(&net, 1280, 3, 3, 1, 1, 1, 1, regression_header1[0], true, false, false, 1280, true);
|
||||
tk::dnn::Activation rh_relu_1_1(&net, CUDNN_ACTIVATION_CLIPPED_RELU, 6);
|
||||
tk::dnn::Conv2d rh_1_conv2(&net, 24, 1, 1, 1, 1, 0, 0, regression_header1[1], false);
|
||||
tk::dnn::Layer *loc1[1] = {&rh_1_conv2};
|
||||
|
||||
//regression header 2
|
||||
tk::dnn::Route rout_rh_2(&net, header_2, 1);
|
||||
tk::dnn::Conv2d rh_2_conv1(&net, 512, 3, 3, 1, 1, 1, 1, regression_header2[0], true, false, false, 512, true);
|
||||
tk::dnn::Activation rh_relu_2_1(&net, CUDNN_ACTIVATION_CLIPPED_RELU, 6);
|
||||
tk::dnn::Conv2d rh_2_conv2(&net, 24, 1, 1, 1, 1, 0, 0, regression_header2[1], false);
|
||||
tk::dnn::Layer *loc2[1] = {&rh_2_conv2};
|
||||
|
||||
//regression header 3
|
||||
tk::dnn::Route rout_rh_3(&net, header_3, 1);
|
||||
tk::dnn::Conv2d rh_3_conv1(&net, 256, 3, 3, 1, 1, 1, 1, regression_header3[0], true, false, false, 256, true);
|
||||
tk::dnn::Activation rh_relu_3_1(&net, CUDNN_ACTIVATION_CLIPPED_RELU, 6);
|
||||
tk::dnn::Conv2d rh_3_conv2(&net, 24, 1, 1, 1, 1, 0, 0, regression_header3[1], false);
|
||||
tk::dnn::Layer *loc3[1] = {&rh_3_conv2};
|
||||
|
||||
//regression header 4
|
||||
|
||||
tk::dnn::Route rout_rh_4(&net, header_4, 1);
|
||||
tk::dnn::Conv2d rh_4_conv1(&net, 256, 3, 3, 1, 1, 1, 1, regression_header4[0], true, false, false, 256, true);
|
||||
tk::dnn::Activation rh_relu_4_1(&net, CUDNN_ACTIVATION_CLIPPED_RELU, 6);
|
||||
tk::dnn::Conv2d rh_4_conv2(&net, 24, 1, 1, 1, 1, 0, 0, regression_header4[1], false);
|
||||
tk::dnn::Layer *loc4[1] = {&rh_4_conv2};
|
||||
|
||||
//regression header 5
|
||||
tk::dnn::Route rout_rh_5(&net, header_5, 1);
|
||||
tk::dnn::Conv2d rh_5_conv(&net, 24, 1, 1, 1, 1, 0, 0, regression_header5, false, false, true);
|
||||
tk::dnn::Layer *loc5[1] = {&rh_5_conv};
|
||||
|
||||
last = &rh_5_conv;
|
||||
|
||||
//flatten all confidence
|
||||
tk::dnn::Route r_conf_0(&net, conf0, 1);
|
||||
tk::dnn::Flatten fl_c_0(&net);
|
||||
tk::dnn::Route r_conf_1(&net, conf1, 1);
|
||||
tk::dnn::Flatten fl_c_1(&net);
|
||||
tk::dnn::Route r_conf_2(&net, conf2, 1);
|
||||
tk::dnn::Flatten fl_c_2(&net);
|
||||
tk::dnn::Route r_conf_3(&net, conf3, 1);
|
||||
tk::dnn::Flatten fl_c_3(&net);
|
||||
tk::dnn::Route r_conf_4(&net, conf4, 1);
|
||||
tk::dnn::Flatten fl_c_4(&net);
|
||||
tk::dnn::Route r_conf_5(&net, conf5, 1);
|
||||
tk::dnn::Flatten fl_c_5(&net);
|
||||
|
||||
// //flatten all locations
|
||||
tk::dnn::Route r_loc_0(&net, loc0, 1);
|
||||
tk::dnn::Flatten fl_l_0(&net);
|
||||
tk::dnn::Route r_loc_1(&net, loc1, 1);
|
||||
tk::dnn::Flatten fl_l_1(&net);
|
||||
tk::dnn::Route r_loc_2(&net, loc2, 1);
|
||||
tk::dnn::Flatten fl_l_2(&net);
|
||||
tk::dnn::Route r_loc_3(&net, loc3, 1);
|
||||
tk::dnn::Flatten fl_l_3(&net);
|
||||
tk::dnn::Route r_loc_4(&net, loc4, 1);
|
||||
tk::dnn::Flatten fl_l_4(&net);
|
||||
tk::dnn::Route r_loc_5(&net, loc5, 1);
|
||||
tk::dnn::Flatten fl_l_5(&net);
|
||||
|
||||
// //concat confidence + softmax
|
||||
tk::dnn::Layer *confidences[6] = {&fl_c_0, &fl_c_1, &fl_c_2, &fl_c_3, &fl_c_4, &fl_c_5};
|
||||
tk::dnn::Route rout_conf(&net, confidences, 6);
|
||||
tk::dnn::dataDim_t olddim_c = net.layers[net.num_layers - 1]->output_dim;
|
||||
tk::dnn::dataDim_t dim_resh(1, olddim_c.c * olddim_c.h * olddim_c.w / classes, classes, 1, 1);
|
||||
|
||||
tk::dnn::Reshape reshape_conf1(&net, dim_resh);
|
||||
tk::dnn::Flatten fl_l_6(&net);
|
||||
tk::dnn::dataDim_t newdim_c(1, classes, olddim_c.c * olddim_c.h * olddim_c.w / classes, 1, 1);
|
||||
|
||||
tk::dnn::Reshape reshape_conf2(&net, newdim_c);
|
||||
|
||||
tk::dnn::Softmax sm_1(&net, &newdim_c, true);
|
||||
// tk::dnn::Flatten fl_l_7(&net);
|
||||
// tk::dnn::Reshape reshape_conf3(&net,dim_resh, true);
|
||||
tk::dnn::Layer *conf = &sm_1;
|
||||
|
||||
//concat locations
|
||||
tk::dnn::Layer *locations[6] = {&fl_l_0, &fl_l_1, &fl_l_2, &fl_l_3, &fl_l_4, &fl_l_5};
|
||||
tk::dnn::Route rout_loc(&net, locations, 6);
|
||||
tk::dnn::dataDim_t olddim_l = net.layers[net.num_layers - 1]->output_dim;
|
||||
tk::dnn::dataDim_t newdim_l(1, olddim_l.c * olddim_l.h * olddim_l.w / 4, 1, 4, 1);
|
||||
tk::dnn::Reshape reshape_loc(&net, newdim_l, true);
|
||||
tk::dnn::Layer *loc = &reshape_loc;
|
||||
|
||||
// Load input
|
||||
dnnType *data;
|
||||
dnnType *input_h;
|
||||
readBinaryFile(input_bin, dim.tot(), &input_h, &data);
|
||||
//printDeviceVector(64, data, true);
|
||||
|
||||
//print network model
|
||||
net.print();
|
||||
|
||||
// convert network to tensorRT
|
||||
tk::dnn::NetworkRT netRT(&net, "mobilenetv2ssd512.rt");
|
||||
|
||||
tk::dnn::dataDim_t dim1 = dim; //input dim
|
||||
printCenteredTitle(" CUDNN inference ", '=', 30);
|
||||
{
|
||||
dim1.print();
|
||||
TIMER_START
|
||||
net.infer(dim1, data);
|
||||
TIMER_STOP
|
||||
dim1.print();
|
||||
}
|
||||
|
||||
dnnType *cudnn_out1 = conf5[0]->dstData;
|
||||
tk::dnn::dataDim_t out_dim1 = conf5[0]->output_dim;
|
||||
dnnType *cudnn_out2 = loc5[0]->dstData;
|
||||
tk::dnn::dataDim_t out_dim2 = loc5[0]->output_dim;
|
||||
|
||||
tk::dnn::dataDim_t dim2 = dim;
|
||||
printCenteredTitle(" TENSORRT inference ", '=', 30);
|
||||
{
|
||||
dim2.print();
|
||||
TIMER_START
|
||||
netRT.infer(dim2, data);
|
||||
TIMER_STOP
|
||||
dim2.print();
|
||||
}
|
||||
|
||||
dnnType *rt_out1 = (dnnType *)netRT.buffersRT[1];
|
||||
dnnType *rt_out2 = (dnnType *)netRT.buffersRT[2];
|
||||
dnnType *rt_out3 = (dnnType *)netRT.buffersRT[3];
|
||||
dnnType *rt_out4 = (dnnType *)netRT.buffersRT[4];
|
||||
|
||||
printCenteredTitle(std::string(" RESNET CHECK RESULTS ").c_str(), '=', 30);
|
||||
dnnType *out1, *out1_h;
|
||||
int odim1 = out_dim1.tot();
|
||||
readBinaryFile(output_bin1, odim1, &out1_h, &out1);
|
||||
|
||||
dnnType *out2, *out2_h;
|
||||
int odim2 = out_dim2.tot();
|
||||
readBinaryFile(output_bin2, odim2, &out2_h, &out2);
|
||||
std::cout << "CUDNN vs correct" << std::endl;
|
||||
checkResult(odim1, cudnn_out1, out1);
|
||||
checkResult(odim2, cudnn_out2, out2);
|
||||
|
||||
std::cout << "TRT vs correct" << std::endl;
|
||||
checkResult(odim1, rt_out1, out1);
|
||||
checkResult(odim2, rt_out2, out2);
|
||||
|
||||
std::cout << "CUDNN vs TRT " << std::endl;
|
||||
checkResult(odim1, cudnn_out1, rt_out1);
|
||||
checkResult(odim2, cudnn_out2, rt_out2);
|
||||
|
||||
std::cout << "---------------------------------------------------" << std::endl;
|
||||
std::cout << "Confidence CUDNN" << std::endl;
|
||||
printDeviceVector(64, conf->dstData, true);
|
||||
std::cout << "Locations CUDNN" << std::endl;
|
||||
printDeviceVector(64, loc->dstData, true);
|
||||
std::cout << "---------------------------------------------------" << std::endl;
|
||||
|
||||
std::cout << "Confidence tensorRT" << std::endl;
|
||||
printDeviceVector(64, rt_out3, true);
|
||||
std::cout << "Locations tensorRT" << std::endl;
|
||||
printDeviceVector(64, rt_out4, true);
|
||||
std::cout << "---------------------------------------------------" << std::endl;
|
||||
|
||||
std::cout << "CUDNN vs TRT " << std::endl;
|
||||
checkResult(conf->output_dim.tot(), conf->dstData, rt_out3);
|
||||
checkResult(loc->output_dim.tot(), loc->dstData, rt_out4);
|
||||
|
||||
return 0;
|
||||
}
|
||||
Reference in New Issue
Block a user