input_norm_and_resized_node vs input_node

[JadBatmobile]

Hello,

I am curious what the difference between the input_node and the input_norm_and_resized_node are?

I see in deploy_cpp that netTF.cpp utilizes input_node, and netTRT.cpp utilizes input_norm_and_resized_node.

[JadBatmobile]

i see that one is raw image, and the other is resized and normalized, but why is it necessary that TRT accepts the processed version, and TF the raw version?

[tano297]

Unfortunately some of the operators between the input and the norm_resized version are not supported by tensorRT. At least at the time that I built the inference pipeline. If you want to try to change that and it works with newer tensorRT versions, I'm more than happy accepting a pull request :)

[JadBatmobile]

I understand, which tensorRT version are you using? it is not specified in the README

[JadBatmobile]

Also, in bonnet.py, lines 52 - 62, i see img_pl being resized with tf.image.resize_images, then transposed with tf.transpose, then normalized with a regular python subtraction and division ( - 128 / 128 ). Do you recall which of those operations specifically was not running in TensorRT?

[tano297]

It was definitely the resizing. And in older versions of tensorRT also division was a problem given some nomenclature and API problems. The division seems to be supported in tensorRT5, but resizing I don't think. In my computer I'm currently running tensorRT3 and in my jetson AGX tensorRT5, and it's working for both in the current state

[JadBatmobile]

Okay, thank you for the speedy responses