diff --git a/include/tkDNN/NetworkRT.h b/include/tkDNN/NetworkRT.h
index 146ea4c..6457068 100644
--- a/include/tkDNN/NetworkRT.h
+++ b/include/tkDNN/NetworkRT.h
@@ -98,7 +98,6 @@ public:
     nvinfer1::IResizeLayer* convert_layer(nvinfer1::ITensor *input, Upsample *l);
     nvinfer1::ILayer* convert_layer(nvinfer1::ITensor *input, DeformConv2d *l);
     nvinfer1::ILayer* convert_layer(nvinfer1::ITensor *input,Padding *l);
-    nvinfer1::ILayer* convert_layer(nvinfer1::ITensor* input,BatchNorm *l);
     nvinfer1::ILayer* convert_layer(nvinfer1::ITensor* input,MulAdd *l);
 
 #if NV_TENSORRT_MAJOR > 5 && NV_TENSORRT_MAJOR < 8
diff --git a/src/NetworkRT.cpp b/src/NetworkRT.cpp
index 71e45ed..9177ef2 100644
--- a/src/NetworkRT.cpp
+++ b/src/NetworkRT.cpp
@@ -277,8 +277,6 @@ ILayer* NetworkRT::convert_layer(ITensor *input, Layer *l) {
         return convert_layer(input, (DeformConv2d*) l);
     if(type == LAYER_PADDING)
         return convert_layer(input, (Padding*) l);
-    if(type == LAYER_BATCHNORM)
-        return convert_layer(input,(BatchNorm*) l);
     if(type == LAYER_MULADD)
         return convert_layer(input,(MulAdd*) l);
 
@@ -411,38 +409,6 @@ ILayer* NetworkRT::convert_layer(ITensor *input, Conv2d *l) {
     return lRT;
 }
 
-ILayer* NetworkRT::convert_layer(ITensor *input,BatchNorm *l){
-    void *bias_b, *power_b, *mean_b, *variance_b, *scales_b;
-    if(dtRT == DataType::kHALF) {
-        bias_b     = l->bias16_h;
-        power_b    = l->power16_h;
-        mean_b     = l->mean16_h;
-        variance_b = l->variance16_h;
-        scales_b   = l->scales16_h;
-    } else {
-        bias_b     = l->bias_h;
-        power_b    = l->power_h;
-        mean_b     = l->mean_h;
-        variance_b = l->variance_h;
-        scales_b   = l->scales_h;
-    }
-    Weights power{dtRT, power_b, l->outputs};
-    Weights shift{dtRT, mean_b, l->outputs};
-    Weights scale{dtRT, variance_b, l->outputs};
-
-    IScaleLayer *lRT = networkRT->addScale(*input, ScaleMode::kCHANNEL,
-                    shift, scale, power);
-    checkNULL(lRT);
-    Weights shift2{dtRT, bias_b, l->outputs};
-    Weights scale2{dtRT, scales_b, l->outputs};
-    IScaleLayer *lRT2 = networkRT->addScale(*lRT->getOutput(0), ScaleMode::kCHANNEL,
-                    shift2, scale2, power);
-    checkNULL(lRT2);
-
-    return lRT2;
-
-}
-
 ILayer* NetworkRT::convert_layer(ITensor *input,MulAdd *l){
 
     void *power_b, *shift_b, *scales_b;