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