def cmr_3d_sax_transform(self): train_transform = ts.Compose([ #ts.PadArray(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.SpecialCrop(size=self.patch_size, crop_type=0), #ts.RandomCrop(size=self.patch_size), ts.TypeCast(['float', 'long']) ]) valid_transform = ts.Compose([ #ts.PadArray(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 ultrasound_transform(self): train_transform = ts.Compose([ts.ToTensor(), ts.TypeCast(['float']), ts.AddChannel(axis=0), ts.SpecialCrop(self.patch_size,0), ts.RandomFlip(h=True, v=False, p=self.random_flip_prob), ts.RandomAffine(rotation_range=self.rotate_val, translation_range=self.shift_val, zoom_range=self.scale_val, interp=('bilinear')), ts.StdNormalize(), ]) valid_transform = ts.Compose([ts.ToTensor(), ts.TypeCast(['float']), ts.AddChannel(axis=0), ts.SpecialCrop(self.patch_size,0), ts.StdNormalize(), ]) return {'train': train_transform, 'valid': valid_transform}