How to addin new Tensorflow layers to TensorRT engine?

AI & Data Science Deep Learning (Training & Inference) TensorRT
1

I have tensorflow freezed model from which TensorRT engine is produced.

I can’t retrain model since I don’t have all those required images.

But Tensorflow process has some post processing layers and I like to add into TensorRT engine.

What would be the best approach?

Can I create plugin layer using TensorRT layers?

Those Tensorflow layers are mostly available in TensorRT as follows.

self.tensor_heatMat_up = tf.image.resize_area(self.tensor_output[:, :, :, :19], self.upsample_size,
                                                      align_corners=False, name='upsample_heatmat')
        self.tensor_pafMat_up = tf.image.resize_area(self.tensor_output[:, :, :, 19:], self.upsample_size,
                                                     align_corners=False, name='upsample_pafmat')
        if trt_bool is True:
            smoother = Smoother({'data': self.tensor_heatMat_up}, 25, 3.0, 19)
        else:
            smoother = Smoother({'data': self.tensor_heatMat_up}, 25, 3.0)
        gaussian_heatMat = smoother.get_output()

        max_pooled_in_tensor = tf.nn.pool(gaussian_heatMat, window_shape=(3, 3), pooling_type='MAX', padding='SAME')
        self.tensor_peaks = tf.where(tf.equal(gaussian_heatMat, max_pooled_in_tensor), gaussian_heatMat,
tf.zeros_like(gaussian_heatMat))

TensorRT has scale for resize_area, conv for Smoother.
Not sure tf.equal in TensorRT.

How to addin those layers to TensorRT?
Possible to use graphsurgeon or UFF model?