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}