def cmr_3d_sax_transform(self): train_transform = ts.Compose([ ts.Pad(size=self.scale_size), ts.ToTensor(), ts.ChannelsFirst(), ts.TypeCast(['float', 'float']), ts.RandomFlip(h=True, v=True, p=self.random_flip_prob), ts.RandomAffine(rotation_range=self.rotate_val, translation_range=self.shift_val, zoom_range=self.scale_val, interp=('bilinear', 'nearest')), #ts.NormalizeMedicPercentile(norm_flag=(True, False)), ts.NormalizeMedic(norm_flag=(True, False)), ts.ChannelsLast(), ts.AddChannel(axis=0), ts.RandomCrop(size=self.patch_size), ts.TypeCast(['float', 'long']) ]) valid_transform = ts.Compose([ ts.Pad(size=self.scale_size), ts.ToTensor(), ts.ChannelsFirst(), ts.TypeCast(['float', 'float']), #ts.NormalizeMedicPercentile(norm_flag=(True, False)), ts.NormalizeMedic(norm_flag=(True, False)), ts.ChannelsLast(), ts.AddChannel(axis=0), ts.SpecialCrop(size=self.patch_size, crop_type=0), ts.TypeCast(['float', 'long']) ]) return {'train': train_transform, 'valid': valid_transform}
def test_3d_sax_transform(self): test_transform = ts.Compose([ ts.PadFactorNumpy(factor=self.division_factor), ts.ToTensor(), ts.ChannelsFirst(), ts.TypeCast(['float']), #ts.NormalizeMedicPercentile(norm_flag=True), ts.NormalizeMedic(norm_flag=True), ts.ChannelsLast(), ts.AddChannel(axis=0), ]) return {'test': test_transform}