Affine transformations on CPU are already pretty fast, but GPU-accelerated transformations are useful in CPU-constrained environments like Google Colab where a GPU is available but only 2 mediocre CPUs which can lead to a bottleneck in the data pipeline.
Some open questions:
- Would the existing transforms work out-of-the box on
CuArrays (i.e. does ImageTransformations.warp[!] work on CuArrays) ? What about for masks, i.e. integer arrays? Otherwise, what would be necessary to implement that?
- Performance-wise, do the transforms need to be applied to a whole batch at once or is it as fast to apply them to samples individually? Former would require first resizing images to the same size and wrapping in a
Batch wrapper item.
Can someone who has experience with image transformations on GPU chime in?
@jsamaroo
Affine transformations on CPU are already pretty fast, but GPU-accelerated transformations are useful in CPU-constrained environments like Google Colab where a GPU is available but only 2 mediocre CPUs which can lead to a bottleneck in the data pipeline.
Some open questions:
CuArrays (i.e. doesImageTransformations.warp[!]work onCuArrays) ? What about for masks, i.e. integer arrays? Otherwise, what would be necessary to implement that?Batchwrapper item.Can someone who has experience with image transformations on GPU chime in?
@jsamaroo