def data_prep_function_valid(x, p_transform=p_transform, **kwargs):
#take a patch in the middle of the chip
x = x.astype(np.float32)
x = data_transforms.channel_norm(x,
img_stats=channel_norm_stats,
percentiles=[.1, 99.9],
no_channels=4)
return x
def data_prep_function_train(x,
p_transform=p_transform,
p_augmentation=p_augmentation,
**kwargs):
x = x.astype(np.float32)
x = data_transforms.perturb(x, p_augmentation, p_transform['patch_size'],
rng)
x = data_transforms.channel_norm(x,
img_stats=channel_norm_stats,
percentiles=[.1, 99.9],
no_channels=4)
return x
