torchvtk.transforms¶

DictTransform¶

class torchvtk.transforms.DictTransform(device=None, apply_on=None, dtype=None)¶

Super Class for the Transforms.

__init__(device=None, apply_on=None, dtype=None)¶

Parameters

apply_on – The keys of the item dictionaries on which the transform should be applied. Defaults to applying to all torch.Tensors
device – The torch.device on which the transformation should be executed. Also valid: “cpu”, “cuda”. Defaults to using whatever comes.
dtype – The torch.dtype to which the data should be converted before the transform. Defaults to using whatever comes..

override_apply_on(apply_on)¶

abstract transform(data)¶: Transformation Method, must be overwritten by every SubClass.

Composite¶

class torchvtk.transforms.Composite(*tfms, apply_on=None, device=None, dtype=None)¶

Bases: torchvtk.transforms.dict_transform.DictTransform

__init__(*tfms, apply_on=None, device=None, dtype=None)¶

Composites multiple transforms together

Parameters

tfms (Callable, DictTransform) – `DictTransform`s or just callable objects that can handle the incoming dict data
apply_on (List of str) – Overrides the apply_on dictionary masks of the given transforms. (Only applies to `DictTransform`s)
device (torch.device, str) – torch.device, ‘cpu’ or ‘cuda’. This overrides the device for all `DictTransform`s.
dtype (torch.dtype) – Overrides the dtype for all `DictTransform`s this composites.

override_apply_on(apply_on)¶

abstract transform(data)¶: Transformation Method, must be overwritten by every SubClass.

Lambda¶

class torchvtk.transforms.Lambda(func, as_list=False, **kwargs)¶

Bases: torchvtk.transforms.dict_transform.DictTransform

__init__(func, as_list=False, **kwargs)¶

Applies a given function, wrapped in a DictTransform

Parameters

func (function) – The function to be executed
as_list (bool) – Wether all inputs specified in apply_on are passed as a list, or as separate items. Defaults to False (separate items).
kwargs – Arguments for DictTransform

override_apply_on(apply_on)¶

transform(items)¶: Transformation Method, must be overwritten by every SubClass.

Noop¶

class torchvtk.transforms.Noop(**kwargs)¶

Bases: torchvtk.transforms.dict_transform.DictTransform

__init__(**kwargs)¶

Just sets device and dtype, does nothing else.

Parameters

device (str, torch.device) – The device to move the tensors on. Defaults to not changing anything
dtype (torch.dtype) – The dtype to move the device to. Defaults to not changing anything
kwargs – Arguments for DictTransform

override_apply_on(apply_on)¶

transform(x)¶: Transformation Method, must be overwritten by every SubClass.

NormalizeMinMax¶

class torchvtk.transforms.NormalizeMinMax(min=0.0, max=1.0, **kwargs)¶

Bases: torchvtk.transforms.dict_transform.DictTransform

__init__(min=0.0, max=1.0, **kwargs)¶

Normalizes tensors to a set min-max range

Parameters

min (float, optional) – New minimum value. Defaults to 0.0.
max (float, optional) – New maximum value. Defaults to 1.0.
kwargs – Arguments for DictTransform

override_apply_on(apply_on)¶

transform(items)¶: Transformation Method, must be overwritten by every SubClass.

NormalizeStandardize¶

class torchvtk.transforms.NormalizeStandardize(mean=0.0, std=1.0, **kwargs)¶

Bases: torchvtk.transforms.dict_transform.DictTransform

__init__(mean=0.0, std=1.0, **kwargs)¶

Normalizes tensors to have a set mean and standard deviation

Parameters

mean (float, optional) – New mean of the sample. Defaults to 0.0.
std (float, optional) – New standard deviation of the sample. Defaults to 1.0.
kwargs – Arguments for DictTransform

override_apply_on(apply_on)¶

transform(items)¶: Transformation Method, must be overwritten by every SubClass.

Crop¶

class torchvtk.transforms.Crop(size=(20, 20, 20), position=0, **kwargs)¶

Bases: torchvtk.transforms.dict_transform.DictTransform

__init__(size=(20, 20, 20), position=0, **kwargs)¶: Crops a tensor size (3-tuple of int): Size of the crop. position (3-tuple of int): Middle point of the cropped region. kwargs: Arguments for DictTransform.

get_center_crop(data, size)¶: Helper method for the crop.

get_crop_around(data, mid, size)¶: Helper method for the crop.

override_apply_on(apply_on)¶

transform(items)¶: Applies the Center Crop.

Resize¶

class torchvtk.transforms.Resize(size, mode='trilinear', is_batch=False, **kwargs)¶

Bases: torchvtk.transforms.dict_transform.DictTransform

__init__(size, mode='trilinear', is_batch=False, **kwargs)¶

Resizes volumes to a given size or by a given factor

Parameters

size (tuple/list or float) – The new spatial dimensions in a tuple or a factor as scalar
mode (str, optional) – Resampling mode. See PyTorch’s torch.nn.functional.interpolate. Defaults to ‘trilinear’.
is_batch (bool) – Wether the data passed in here already has a batch dimension (cannot be inferred if size is given as scalar). Defaults to False.
kwargs – Arguments for DictTransform

override_apply_on(apply_on)¶

transform(items)¶: Transformation Method, must be overwritten by every SubClass.

RandFlip¶

class torchvtk.transforms.RandFlip(flip_probability=0.5, dims=[1, 1, 1], **kwargs)¶

Bases: torchvtk.transforms.dict_transform.DictTransform

Flips dimensions with a given probability. (Random event occurs for each dimension)

__init__(flip_probability=0.5, dims=[1, 1, 1], **kwargs)¶

Flips dimensions of a tensor with a given flip_probability.

Parameters

flip_probability (float) – Probability of a dimension being flipped. Default 0.5.
dims (list of 3 ints) – Dimensions that may be flipped are denoted with a 1, otherwise 0. [1,0,1] would randomly flip a volumes depth and width dimension, while never flipping its height dimension
kwargs – Arguments for DictTransform

override_apply_on(apply_on)¶

transform(items)¶: Transformation Method, must be overwritten by every SubClass.

RandPermute¶

class torchvtk.transforms.RandPermute(permutations=None, **kwargs)¶

Bases: torchvtk.transforms.dict_transform.DictTransform

Chooses one of the 8 random permutations for the volume axes

__init__(permutations=None, **kwargs)¶

Randomly choose one of the given permutations.

Parameters

permutations (list of 3-tuples) – Overrides the list of possible permutations to choose from. The default is [ (0, 1, 2), (0, 2, 1), (1, 0, 2), (1, 2, 0), (2, 0, 1), (2, 1, 0) ]. permutations must be a list or tuple of items that are compatible with torch.permute. Assume 0 to be the first spatial dimension, we account for a possible batch and channel dimension. The permutation will then be chosen at random from the given list/tuple.
kwargs – Arguments for DictTransform

override_apply_on(apply_on)¶

transform(items)¶: Transformation Method, must be overwritten by every SubClass.

GaussianBlur¶

class torchvtk.transforms.GaussianBlur(channels=1, kernel_size=(3, 3, 3), sigma=1, **kwargs)¶

Bases: torchvtk.transforms.dict_transform.DictTransform

__init__(channels=1, kernel_size=(3, 3, 3), sigma=1, **kwargs)¶

Blurs tensors using a Gaussian filter

Parameters

channels (int) – Amount of channels of the input data.
kernel_size (list of int) – Size of the convolution kernel.
sigma (float) – Standard deviation.
kwargs – Arguments for DictTransform

override_apply_on(apply_on)¶

transform(items)¶: Applies the Blur using a 3D Convolution.

GaussianNoise¶

class torchvtk.transforms.GaussianNoise(std_deviation=0.01, mean=0, **kwargs)¶

Bases: torchvtk.transforms.dict_transform.DictTransform

__init__(std_deviation=0.01, mean=0, **kwargs)¶

Adds Gaussian noise to tensors

Parameters

std_deviation (float, tensor) – The variance of the noise
mean (float, tensor) – The mean of the noise.
kwargs – Arguments for DictTransform.

override_apply_on(apply_on)¶

transform(items)¶: Applies the Noise onto the images. Variance is controlled by the noise_variance parameter.