I am trying to create a UNET image to image regression model. my data is 256 channel inputs regressing to a 1 channel output. My data is stored as MATLAB .mat files. I was initially going to convert to numpy arrays and store them but realised that I could read in from the matfiles directly using loadmat from scipy.
Am I using the datablock and TransformBlocks correctly?
dblock = DataBlock(blocks = (TransformBlock, TransformBlock),
get_items = glob.glob, get_x = x_func, get_y = y_func, splitter = RandomSplitter(), item_tfms=None)
dsets = dblock.dataloaders(path+"*.mat")
This seems to be fine and returns correctly shaped and matched data.
However when I try to run this in a learner I get an error:
RuntimeError: expected scalar type Double but found Float
The outputs of the dataloader are floats. Where would this error originate from?