clinicadl.infer.PatchesToImageInferer¶

class clinicadl.infer.PatchesToImageInferer(patch_size: int | tuple[int, int, int], overlap: float | tuple[float, float, float] = 0.25, avg_mode: str | AveragingMode = AveragingMode.CONSTANT, sigma_scale: float = 0.125, batch_size: int = 1, postprocessing: Sequence[Callable[[DataPointT], DataPointT] | TransformConfig] | None = None, postprocessing_on_cpu: bool = False, output_name: str = 'output', output_type: str | OutputType = OutputType.TENSOR)[source]¶

Splits a 3D volume into 3D patches, passes them in a 3D neural network, and merges the outputs in a 3D output volume.

Adapted from monai.inferers.SlidingWindowInferer.

Parameters:

patch_size (Union[int, tuple[int, int, int]]) – The size of the patches. If a single value is passed, the same patch size will be used for the three spatial dimensions.
overlap (Union[float, tuple[float, float, float]], default=0.0) – A float in \([0.0, 1.0)\) that defines relative patch overlap in each dimension. If a single value is passed, the same overlap will be used for the three spatial dimensions.
avg_mode (AveragingMode, default="constant") –
How to average the outputs when patches overlap.
- "constant": gives equal weight to all predictions;
- "gaussian": gives less weight to the prediction on edges of patches.
sigma_scale (float, default=0.125) – The standard deviation coefficient of the Gaussian window when avg_mode is "gaussian". The actual sigma is sigma_scale * patch_size.
batch_size (int, default=1) –
The size of the batch passed to the neural network. If you pass a batch of images to the inferer, this batch will be rearranged to match batch_size.

E.g., if a batch of 2 images is passed, with 3 patches in each image, and batch_size=4, then the first batch passed to the neural network will contain the three patches of the first image, and the first patch of the second.
postprocessing (Optional[Sequence[TransformOrConfig]], default=None) – To apply postprocessing transformations (e.g. activations) after the pass forward in the neural network. Accepted transforms are functions that take as input a DataPoint and return a DataPoint, or configuration classes.
postprocessing_on_cpu (bool, default=False) – Whether to necessarily apply postprocessing on CPU. If False, postprocessing will be applied on the device where are the data and the neural network.
output_name (str, default="output") –
The name the give to the output in the DataPoint.

Important

If you postprocessing transform comes from clinicadl.transforms.config, do not forget to specify include=["<output_name>"] to apply the postprocessing to the output of the neural network.
output_type (OutputType, default="tensor") –
Determines the data type of the output:
- "image": the output will be converted to a torchio.ScalarImage;
- "mask": the output will be converted to a torchio.LabelMap;
- "tensor": the output will remain a torch.Tensor.

Examples

import torch
from clinicadl.infer import PatchesToImageInferer
from clinicadl.data.structures.examples import Colin27DataPoint
from clinicadl.networks.nn import ConvEncoder

net = AutoEncoder(in_shape=(1, 64, 64, 64), latent_size=16, conv_args={"channels": [2]})
datapoint = Colin27DataPoint()
inferer = PatchesToImageInferer(patch_size=64, batch_size=16, overlap=1/5)

>>> datapoint.image.shape
(1, 181, 217, 181)
>>> with torch.no_grad(): out = inferer(datapoint, net)
>>> out["output"].shape
(1, 181, 217, 181)