I’m trying to port over a caffe model from Python & Caffe to the Jetson tx2 with TensorRT.
This model does gaze estimation.
You can see a collobaratory notebook here with it working:
I’m following the examples in jetson-inference which are super helpful. One thing I’m wondering, is that in the python/caffe code, there is a step that does:
transformer = caffe.io.Transformer({layer_name: net.blobs['data'].data.shape})
transformer.set_transpose(layer_name, (2,0,1)) # move image channels to outermost dimension
transformer.set_mean(layer_name, mean_image) # subtract the dataset-mean value in each channel
How can I do this in the context of the jetson-inference examples in c++?
Does the function cudaPreImageNetMean
take care of moving image channels to the outermost dimension?