def ukbb_sax_transform(self):
train_transform = ts.Compose([
ts.PadNumpy(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.RandomCrop(size=self.patch_size),
ts.TypeCast(['float', 'long'])
])
valid_transform = ts.Compose([
ts.PadNumpy(size=self.scale_size),
ts.ToTensor(),
ts.ChannelsFirst(),
ts.TypeCast(['float', 'float']),
ts.NormalizeMedicPercentile(norm_flag=(True, False)),
ts.SpecialCrop(size=self.patch_size, crop_type=0),
ts.TypeCast(['float', 'long'])
])
return {'train': train_transform, 'valid': valid_transform}
def gsd_pCT_train_transform(self, seed=None):
if seed is None:
seed = np.random.randint(0, 9999) # seed must be an integer for torch
train_transform = ts.Compose([
ts.ToTensor(),
ts.Pad(size=self.scale_size),
ts.TypeCast(['float', 'float']),
RandomFlipTransform(axes=self.flip_axis, flip_probability=self.flip_prob_per_axis, p=self.random_flip_prob,
seed=seed, max_output_channels=self.max_output_channels, prudent=self.prudent),
RandomElasticTransform(max_displacement=self.max_deform,
num_control_points=self.elastic_control_points,
image_interpolation='bspline',
seed=seed, p=self.random_elastic_prob,
max_output_channels=self.max_output_channels, verbose=self.verbose, prudent=self.prudent),
RandomAffineTransform(scales=self.scale_val, degrees=self.rotate_val, translation=self.shift_val,
isotropic=True, default_pad_value=0,
image_interpolation='bspline', seed=seed, p=self.random_affine_prob,
max_output_channels=self.max_output_channels, verbose=self.verbose, prudent=self.prudent),
StandardizeImage(norm_flag=[True, True, True, False]),
RandomNoiseTransform(mean=self.noise_mean, std=self.noise_std, seed=seed, p=self.random_noise_prob,
max_output_channels=self.max_output_channels, prudent=self.prudent),
# Todo eventually add random crop augmentation (fork torchsample and fix the Random Crop bug)
ts.ChannelsFirst(),
ts.TypeCast(['float', 'float'])
])
return train_transform
def gsd_pCT_train_transform(self, seed=None):
if seed is None:
seed = np.random.randint(0, 9999) # seed must be an integer for torch
train_transform = ts.Compose([
ts.ToTensor(),
ts.Pad(size=self.scale_size),
ts.TypeCast(['float', 'float']),
RandomFlipTransform(axes=self.flip_axis, p=self.random_flip_prob, seed=seed, max_output_channels=self.max_output_channels),
RandomElasticTransform(seed=seed, p=self.random_elastic_prob, image_interpolation=Interpolation.BSPLINE, max_displacement=self.max_deform,
num_control_points=self.elastic_control_points, max_output_channels=self.max_output_channels),
RandomAffineTransform(scales = self.scale_val, degrees = (self.rotate_val), isotropic = True, default_pad_value = 0,
image_interpolation = Interpolation.BSPLINE, seed=seed, p=self.random_affine_prob, max_output_channels=self.max_output_channels),
RandomNoiseTransform(p=self.random_noise_prob, std=self.noise_std, seed=seed, max_output_channels=self.max_output_channels),
ts.ChannelsFirst(),
# ts.NormalizeMedicPercentile(norm_flag=(True, False)),
# Todo apply channel wise normalisation
ts.NormalizeMedic(norm_flag=(True, False)),
# Todo eventually add random crop augmentation (fork torchsample and fix the Random Crop bug)
# ts.ChannelsLast(), # seems to be needed for crop
# ts.RandomCrop(size=self.patch_size),
ts.TypeCast(['float', 'long'])
])
return train_transform
def isles2018_valid_transform(self, seed=None):
valid_transform = ts.Compose([
ts.ToTensor(),
ts.Pad(size=self.scale_size),
ts.ChannelsFirst(),
ts.TypeCast(['float', 'long'])
])
return valid_transform
def isles2018_train_transform(self, seed=None):
train_transform = ts.Compose([
ts.ToTensor(),
ts.Pad(size=self.scale_size),
ts.TypeCast(['float', 'float']),
ts.RandomFlip(h=True, v=True, p=self.random_flip_prob),
ts.ChannelsFirst(),
ts.TypeCast(['float', 'long'])
])
return train_transform
def gsd_pCT_valid_transform(self, seed=None):
valid_transform = ts.Compose([
ts.ToTensor(),
ts.Pad(size=self.scale_size),
ts.TypeCast(['float', 'float']),
StandardizeImage(norm_flag=[True, True, True, False]),
ts.ChannelsFirst(),
ts.TypeCast(['float', 'float'])
])
return 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}
def gsd_pCT_valid_transform(self, seed=None):
valid_transform = ts.Compose([
ts.ToTensor(),
ts.Pad(size=self.scale_size),
ts.ChannelsFirst(),
ts.TypeCast(['float', 'float']),
# ts.NormalizeMedicPercentile(norm_flag=(True, False)),
ts.NormalizeMedic(norm_flag=(True, False)),
# ts.ChannelsLast(),
# ts.SpecialCrop(size=self.patch_size, crop_type=0),
ts.TypeCast(['float', 'long'])
])
return valid_transform
def gsd_pCT_transform(self):
'''
Data augmentation transformations for the Geneva Stroke dataset (pCT maps)
:return:
'''
train_transform = ts.Compose([
ts.ToTensor(),
ts.Pad(size=self.scale_size),
ts.TypeCast(['float', 'float']),
ts.RandomFlip(h=True, v=True, p=self.random_flip_prob),
# Todo Random Affine doesn't support channels --> try newer version of torchsample or torchvision
# ts.RandomAffine(rotation_range=self.rotate_val, translation_range=self.shift_val,
# zoom_range=self.scale_val, interp=('bilinear', 'nearest')),
ts.ChannelsFirst(),
#ts.NormalizeMedicPercentile(norm_flag=(True, False)),
# Todo apply channel wise normalisation
ts.NormalizeMedic(norm_flag=(True, False)),
# Todo fork torchsample and fix the Random Crop bug
# ts.ChannelsLast(), # seems to be needed for crop
# ts.RandomCrop(size=self.patch_size),
ts.TypeCast(['float', 'long'])
])
valid_transform = ts.Compose([
ts.ToTensor(),
ts.Pad(size=self.scale_size),
ts.ChannelsFirst(),
ts.TypeCast(['float', 'float']),
#ts.NormalizeMedicPercentile(norm_flag=(True, False)),
ts.NormalizeMedic(norm_flag=(True, False)),
# ts.ChannelsLast(),
# ts.SpecialCrop(size=self.patch_size, crop_type=0),
ts.TypeCast(['float', 'long'])
])
# train_transform = ts.Compose([
# ts.ToTensor(),
# ts.Pad(size=self.scale_size),
# ts.ChannelsFirst(),
# ts.TypeCast(['float', 'long'])
# ])
# valid_transform = ts.Compose([
# ts.ToTensor(),
# ts.Pad(size=self.scale_size),
# ts.ChannelsFirst(),
# ts.TypeCast(['float', 'long'])
#
# ])
# train_transform = tf.Compose([
# tf.Pad(1),
# tf.Lambda(lambda a: a.permute(3, 0, 1, 2)),
# tf.Lambda(lambda a: a.float()),
# ])
# valid_transform = tf.Compose([
# tf.Pad(1),
# tf.Lambda(lambda a: a.permute(3, 0, 1, 2)),
# tf.Lambda(lambda a: a.float()),
#
# ])
return {'train': train_transform, 'valid': valid_transform}
