Hi all!
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.
My issues:
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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.
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However when I try to run this in a learner I get an error:
learn=unet_learner(dload,resnet18,n_in=256, n_out=1,loss_func=MSELossFlat)
learn.fit_one_cycle(1)
Error:
RuntimeError: expected scalar type Double but found Float
The outputs of the dataloader are floats. Where would this error originate from?