fix model manager with various batchsize

algorithm/common_det/cuda/common_det_cuda_impl.cpp

@@ -363,10 +363,16 @@ bool CommonObjectDetectorCUDAImpl::cudaSetup(CommonObjectDetectorBase* base_, bo
   bool with_segmentation = base_->withSegmentation();
   double thrs_conf = base_->getThrsConf();
   double thrs_nms = base_->getThrsNms();
+  std::string model = base_->getModel();
+  int bs = base_->getBatchSize();
+  char bs_c[8];
+  itoa(bs, bs_c, 10);
+  std::string bs_s(bs_c);
 
   std::string engine_fn = get_home() + SV_MODEL_DIR + dataset + ".engine";
   std::vector<std::string> files;
-  _list_dir(get_home() + SV_MODEL_DIR, files, "-online.engine", "Nv-" + dataset + "-yolov5s_c");
+  
+  _list_dir(get_home() + SV_MODEL_DIR, files, "-online.engine", "Nv-" + dataset + "-yolov5" + model + "_b" + bs_s + "_c");
   if (files.size() > 0)
   {
     std::sort(files.rbegin(), files.rend(), _comp_str_lesser);
@@ -376,7 +382,7 @@ bool CommonObjectDetectorCUDAImpl::cudaSetup(CommonObjectDetectorBase* base_, bo
   if (input_w == 1280)
   {
     files.clear();
-    _list_dir(get_home() + SV_MODEL_DIR, files, "-online.engine", "Nv-" + dataset + "-yolov5s6_");
+    _list_dir(get_home() + SV_MODEL_DIR, files, "-online.engine", "Nv-" + dataset + "-yolov5" + model + "6_b" + bs_s + "_c");
     if (files.size() > 0)
     {
       std::sort(files.rbegin(), files.rend(), _comp_str_lesser);
@@ -392,7 +398,7 @@ bool CommonObjectDetectorCUDAImpl::cudaSetup(CommonObjectDetectorBase* base_, bo
     base_->setInputH(640);
     base_->setInputW(640);
     files.clear();
-    _list_dir(get_home() + SV_MODEL_DIR, files, "-online.engine", "Nv-" + dataset + "-yolov5s_seg_");
+    _list_dir(get_home() + SV_MODEL_DIR, files, "-online.engine", "Nv-" + dataset + "-yolov5" + model + "_seg_b" + bs_s + "_c");
     if (files.size() > 0)
     {
       std::sort(files.rbegin(), files.rend(), _comp_str_lesser);

algorithm/common_det/intel/common_det_intel_impl.cpp

@@ -125,10 +125,11 @@ namespace sv
     inpHeight = base_->getInputH();
     inpWidth = base_->getInputW();
     with_segmentation = base_->withSegmentation();
+    std::string model = base_->getModel();
 
     std::string openvino_fn = get_home() + SV_MODEL_DIR + dataset + ".onnx";
     std::vector<std::string> files;
-    _list_dir(get_home() + SV_MODEL_DIR, files, "-online.onnx", "Int-" + dataset + "-yolov5s_c");
+    _list_dir(get_home() + SV_MODEL_DIR, files, "-online.onnx", "Int-" + dataset + "-yolov5" + model + "_c");
     if (files.size() > 0)
     {
       std::sort(files.rbegin(), files.rend(), _comp_str_lesser);
@@ -138,7 +139,7 @@ namespace sv
     if (inpWidth == 1280)
     {
       files.clear();
-      _list_dir(get_home() + SV_MODEL_DIR, files, "-online.onnx", "Int-" + dataset + "-yolov5s6_");
+      _list_dir(get_home() + SV_MODEL_DIR, files, "-online.onnx", "Int-" + dataset + "-yolov5" + model + "6_c");
       if (files.size() > 0)
       {
         std::sort(files.rbegin(), files.rend(), _comp_str_lesser);
@@ -154,7 +155,7 @@ namespace sv
       base_->setInputH(640);
       base_->setInputW(640);
       files.clear();
-      _list_dir(get_home() + SV_MODEL_DIR, files, "-online.onnx", "Int-" + dataset + "-yolov5s_seg_");
+      _list_dir(get_home() + SV_MODEL_DIR, files, "-online.onnx", "Int-" + dataset + "-yolov5" + model + "_seg_c");
       if (files.size() > 0)
       {
         std::sort(files.rbegin(), files.rend(), _comp_str_lesser);

algorithm/sv_algorithm_base.cpp

@@ -916,6 +916,14 @@ void CommonObjectDetectorBase::setInputW(int w_)
 {
   this->_input_w = w_;
 }
+std::string CommonObjectDetectorBase::getModel()
+{
+  return this->_model;
+}
+int CommonObjectDetectorBase::getBatchSize()
+{
+  return this->_batch_size;
+}
 
 void CommonObjectDetectorBase::warmUp()
 {
@@ -1082,6 +1090,8 @@ void CommonObjectDetectorBase::_load()
   this->_thrs_nms = 0.6;
   this->_thrs_conf = 0.4;
   this->_use_width_or_height = 0;
+  this->_batch_size = 1;
+  this->_model = "s";
   
   for (auto i : detector_params_value) {
 
@@ -1089,6 +1099,12 @@ void CommonObjectDetectorBase::_load()
       this->_dataset = i->value.toString();
       std::cout << "dataset: " << this->_dataset << std::endl;
     }
+    else if ("batchSize" == std::string(i->key)) {
+      this->_batch_size = i->value.toNumber();
+    }
+    else if ("model" == std::string(i->key)) {
+      this->_model = i->value.toString();
+    }
     else if ("inputSize" == std::string(i->key)) {
       // std::cout << "inputSize (old, new): " << this->_input_w << ", " << i->value.toNumber() << std::endl;
       this->_input_w = i->value.toNumber();

include/sv_algorithm_base.h

@@ -140,6 +140,8 @@ public:
   double getThrsConf();
   int useWidthOrHeight();
   bool withSegmentation();
+  std::string getModel();
+  int getBatchSize();
 protected:
   virtual bool setupImpl();
   virtual void detectImpl(
@@ -166,6 +168,8 @@ protected:
   double _thrs_conf;
   int _use_width_or_height;
   bool _with_segmentation;
+  std::string _model;
+  int _batch_size;
 };
 
 

params/a-params/sv_algorithm_params.json

@@ -1,11 +1,13 @@
 {
     "CommonObjectDetector": {
         "dataset": "COCO",
+        "model": "s",
+        "batchSize": 1,
         "inputSize": 640,
+        "withSegmentation": true,
         "nmsThrs": 0.6,
         "scoreThrs": 0.4,
         "useWidthOrHeight": 1,
-        "withSegmentation": true,
         "datasetPersonVehicle": {
             "person": [0.5, 1.8],
             "car": [4.1, 1.5],

params/a-params/sv_algorithm_params_1280_wo_mask.json

@@ -1,11 +1,13 @@
 {
     "CommonObjectDetector": {
         "dataset": "COCO",
+        "model": "s",
+        "batchSize": 1,
         "inputSize": 1280,
+        "withSegmentation": false,
         "nmsThrs": 0.6,
         "scoreThrs": 0.4,
         "useWidthOrHeight": 1,
-        "withSegmentation": false,
         "datasetPersonVehicle": {
             "person": [0.5, 1.8],
             "car": [4.1, 1.5],

params/a-params/sv_algorithm_params_640_w_mask.json

@@ -1,11 +1,13 @@
 {
     "CommonObjectDetector": {
         "dataset": "COCO",
+        "model": "s",
+        "batchSize": 1,
         "inputSize": 640,
+        "withSegmentation": true,
         "nmsThrs": 0.6,
         "scoreThrs": 0.4,
         "useWidthOrHeight": 1,
-        "withSegmentation": true,
         "datasetPersonVehicle": {
             "person": [0.5, 1.8],
             "car": [4.1, 1.5],

params/a-params/sv_algorithm_params_640_wo_mask.json

@@ -1,11 +1,13 @@
 {
     "CommonObjectDetector": {
         "dataset": "COCO",
+        "model": "s",
+        "batchSize": 1,
         "inputSize": 640,
+        "withSegmentation": false,
         "nmsThrs": 0.6,
         "scoreThrs": 0.4,
         "useWidthOrHeight": 1,
-        "withSegmentation": false,
         "datasetPersonVehicle": {
             "person": [0.5, 1.8],
             "car": [4.1, 1.5],

params/a-params/sv_algorithm_params_coco_1280.json

@@ -1,11 +1,13 @@
 {
     "CommonObjectDetector": {
         "dataset": "COCO",
+        "model": "s",
+        "batchSize": 1,
         "inputSize": 1280,
+        "withSegmentation": false,
         "nmsThrs": 0.6,
         "scoreThrs": 0.4,
         "useWidthOrHeight": 1,
-        "withSegmentation": false,
         "datasetPersonVehicle": {
             "person": [0.5, 1.8],
             "car": [4.1, 1.5],

params/a-params/sv_algorithm_params_coco_640.json

@@ -1,11 +1,13 @@
 {
     "CommonObjectDetector": {
         "dataset": "COCO",
+        "model": "s",
+        "batchSize": 1,
         "inputSize": 640,
+        "withSegmentation": false,
         "nmsThrs": 0.6,
         "scoreThrs": 0.4,
         "useWidthOrHeight": 1,
-        "withSegmentation": true,
         "datasetPersonVehicle": {
             "person": [0.5, 1.8],
             "car": [4.1, 1.5],

params/a-params/sv_algorithm_params_csrt.json

@@ -1,11 +1,13 @@
 {
     "CommonObjectDetector": {
         "dataset": "COCO",
+        "model": "s",
+        "batchSize": 1,
         "inputSize": 640,
+        "withSegmentation": true,
         "nmsThrs": 0.6,
         "scoreThrs": 0.4,
         "useWidthOrHeight": 1,
-        "withSegmentation": true,
         "datasetPersonVehicle": {
             "person": [0.5, 1.8],
             "car": [4.1, 1.5],

params/a-params/sv_algorithm_params_kcf.json

@@ -1,11 +1,13 @@
 {
     "CommonObjectDetector": {
         "dataset": "COCO",
+        "model": "s",
+        "batchSize": 1,
         "inputSize": 640,
+        "withSegmentation": true,
         "nmsThrs": 0.6,
         "scoreThrs": 0.4,
         "useWidthOrHeight": 1,
-        "withSegmentation": true,
         "datasetPersonVehicle": {
             "person": [0.5, 1.8],
             "car": [4.1, 1.5],

params/a-params/sv_algorithm_params_nano.json

@@ -1,11 +1,13 @@
 {
     "CommonObjectDetector": {
         "dataset": "COCO",
+        "model": "s",
+        "batchSize": 1,
         "inputSize": 640,
+        "withSegmentation": true,
         "nmsThrs": 0.6,
         "scoreThrs": 0.4,
         "useWidthOrHeight": 1,
-        "withSegmentation": true,
         "datasetPersonVehicle": {
             "person": [0.5, 1.8],
             "car": [4.1, 1.5],

params/a-params/sv_algorithm_params_siamrpn.json

@@ -1,11 +1,13 @@
 {
     "CommonObjectDetector": {
         "dataset": "COCO",
+        "model": "s",
+        "batchSize": 1,
         "inputSize": 640,
+        "withSegmentation": true,
         "nmsThrs": 0.6,
         "scoreThrs": 0.4,
         "useWidthOrHeight": 1,
-        "withSegmentation": true,
         "datasetPersonVehicle": {
             "person": [0.5, 1.8],
             "car": [4.1, 1.5],

scripts/model_sync.py

@@ -51,10 +51,16 @@ def main():
             if os.path.exists(model_file):
                 print("[1] Model:", line, "EXIST!")
                 if line.startswith('Nv'):
-                    engine_fn = os.path.splitext(model_file)[0] + '.engine'
+                    net = line.split('-')[2]
-                    online_fn = os.path.splitext(model_file)[0] + '-online.engine'
+                    if len(net.split('_')) == 3:
+                        name, seg, ncls = net.split('_')
+                        engine_fn = os.path.splitext(model_file)[0].replace(net, name + "_" + seg + '_b1_' + ncls) + '.engine'
+                        online_fn = os.path.splitext(model_file)[0].replace(net, name + "_" + seg + '_b1_' + ncls) + '-online.engine'
+                    else:
+                        name, ncls = net.split('_')
+                        engine_fn = os.path.splitext(model_file)[0].replace(net, name + '_b1_' + ncls) + '.engine'
+                        online_fn = os.path.splitext(model_file)[0].replace(net, name + '_b1_' + ncls) + '-online.engine'
                     if not os.path.exists(engine_fn) and not os.path.exists(online_fn):
-                        net = line.split('-')[2]
                         if net.startswith("yolov5"):
                             if len(net.split('_')) == 3:
                                 name, seg, ncls = net.split('_')