How to add custom bilinear resize or relu6? - DeeplabV3+ with Tensorrt

Hi,
i try to run deeplabv3+ (mobilenet encoder) with tensorrt. But there are custom layers like bilinearUpsampling or Relu6 who are not supoorted and i cant run the uff parser. I cant find a good example how to implement custom layers in TensorRT like bilinearUpsampling or Relu6. Is there a support or how can i implement these custom layers? I read about transform to deconv layers instead - but this will enlarge my network?

.pb File Relu6 Layer:

node {
  name: "Conv_Relu6/Relu6"
  op: "Relu6"
  input: "Conv_BN/cond/Merge"
  attr {
    key: "T"
    value {
      type: DT_FLOAT
    }
  }
}

.pb File BilinearUpsampling Layer:

node {
  name: "bilinear_upsampling_4/resize_images/ResizeBilinear"
  op: "ResizeBilinear"
  input: "activation_8/Relu"
  input: "bilinear_upsampling_4/resize_images/size"
  attr {
    key: "T"
    value {
      type: DT_FLOAT
    }
  }
  attr {
    key: "align_corners"
    value {
      b: false
    }
  }
}

Thanks for your help in advance!

Neither of those are on the support matrix (https://docs.nvidia.com/deeplearning/sdk/tensorrt-support-matrix/index.html) but you might want to look at the developer guide (https://docs.nvidia.com/deeplearning/sdk/tensorrt-developer-guide/index.html#using_custom_layer) for details on implementing custom layers.