DeepStream 7.1 + Fixes + New model output format

nvdsinfer_custom_impl_Yolo/layers/batchnorm_layer.cpp

@@ -21,6 +21,11 @@ batchnormLayer(int layerIdx, std::map<std::string, std::string>& block, std::vec
   int filters = std::stoi(block.at("filters"));
   std::string activation = block.at("activation");
 
+  float eps = 1.0e-5;
+  if (block.find("eps") != block.end()) {
+    eps = std::stof(block.at("eps"));
+  }
+
   std::vector<float> bnBiases;
   std::vector<float> bnWeights;
   std::vector<float> bnRunningMean;
@@ -39,7 +44,7 @@ batchnormLayer(int layerIdx, std::map<std::string, std::string>& block, std::vec
     ++weightPtr;
   }
   for (int i = 0; i < filters; ++i) {
-    bnRunningVar.push_back(sqrt(weights[weightPtr] + 1.0e-5));
+    bnRunningVar.push_back(sqrt(weights[weightPtr] + eps));
     ++weightPtr;
   }
 
@@ -47,18 +52,25 @@ batchnormLayer(int layerIdx, std::map<std::string, std::string>& block, std::vec
   nvinfer1::Weights shift {nvinfer1::DataType::kFLOAT, nullptr, size};
   nvinfer1::Weights scale {nvinfer1::DataType::kFLOAT, nullptr, size};
   nvinfer1::Weights power {nvinfer1::DataType::kFLOAT, nullptr, size};
+
   float* shiftWt = new float[size];
-  for (int i = 0; i < size; ++i)
+  for (int i = 0; i < size; ++i) {
     shiftWt[i] = bnBiases.at(i) - ((bnRunningMean.at(i) * bnWeights.at(i)) / bnRunningVar.at(i));
+  }
   shift.values = shiftWt;
+
   float* scaleWt = new float[size];
-  for (int i = 0; i < size; ++i)
+  for (int i = 0; i < size; ++i) {
     scaleWt[i] = bnWeights.at(i) / bnRunningVar[i];
+  }
   scale.values = scaleWt;
+
   float* powerWt = new float[size];
-  for (int i = 0; i < size; ++i)
+  for (int i = 0; i < size; ++i) {
     powerWt[i] = 1.0;
+  }
   power.values = powerWt;
+
   trtWeights.push_back(shift);
   trtWeights.push_back(scale);
   trtWeights.push_back(power);