CODEWITHSUNDEEP
tensorflowconv-neural-networkvgg-netstyle-transfer
wandadars
wandadars

Reputation: 1183

Large input image limitations for VGG19 transfer learning

I'm using the Tensorflow (using the Keras API) in Python 3.0. I'm using the VGG19 pre-trained network to perform style transfer on an Nvidia RTX 2070.

The largest input image that I have is 4500x4500 pixels (I have removed the fully-connected layers in the VGG19 to allow for a fully-convolutional network that handles arbitrary image sizes.) If it helps, my batch size is just 1 image at a time currently.

1.) Is there an option for parallelizing the evaluation of the model on the image input given that I am not training the model, but just passing data through the pre-trained model?

2.) Is there any increase in capacity for handling larger images in going from 1 GPU to 2 GPUs? Is there a way for the memory to be shared across the GPUs?

I'm unsure if larger images make my GPU compute-bound or memory-bound. I'm speculating that it's a compute issue, which is what started my search for parallel CNN evaluation discussions. I've seen some papers on tiling methods that seem to allow for larger images

Upvotes: 0

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