Inference#

Here is an example that uses a grid sampler and aggregator to perform dense inference across a 3D image using patches:

>>> import torch
>>> import torch.nn as nn
>>> import torchio as tio
>>> patch_overlap = 4, 4, 4  # or just 4
>>> patch_size = 88, 88, 60
>>> subject = tio.datasets.Colin27()
>>> subject
Colin27(Keys: ('t1', 'head', 'brain'); images: 3)
>>> grid_sampler = tio.inference.GridSampler(
...     subject,
...     patch_size,
...     patch_overlap,
... )
>>> aggregator = tio.inference.GridAggregator(grid_sampler)
>>> model = nn.Identity().eval()
...         input_tensor = patches_batch['t1'][tio.DATA]
...         locations = patches_batch[tio.LOCATION]
...         logits = model(input_tensor)
...         labels = logits.argmax(dim=tio.CHANNELS_DIMENSION, keepdim=True)
...         outputs = labels
>>> output_tensor = aggregator.get_output_tensor()


Grid sampler#

GridSampler#

class torchio.data.GridSampler(subject: = None, patch_size: Optional[Union[int, Tuple[int, int, int]]] = None, patch_overlap: Union[int, Tuple[int, int, int]] = (0, 0, 0), padding_mode: = None)[source]#

Bases: PatchSampler

Extract patches across a whole volume.

Grid samplers are useful to perform inference using all patches from a volume. It is often used with a GridAggregator.

Parameters:
• subject – Instance of Subject from which patches will be extracted. This argument should only be used before instantiating a GridAggregator, or to precompute the number of patches that would be generated from a subject.

• patch_size – Tuple of integers $$(w, h, d)$$ to generate patches of size $$w \times h \times d$$. If a single number $$n$$ is provided, $$w = h = d = n$$. This argument is mandatory (it is a keyword argument for backward compatibility).

• patch_overlap – Tuple of even integers $$(w_o, h_o, d_o)$$ specifying the overlap between patches for dense inference. If a single number $$n$$ is provided, $$w_o = h_o = d_o = n$$.

• padding_mode – Same as padding_mode in Pad. If None, the volume will not be padded before sampling and patches at the border will not be cropped by the aggregator. Otherwise, the volume will be padded with $$\left(\frac{w_o}{2}, \frac{h_o}{2}, \frac{d_o}{2} \right)$$ on each side before sampling. If the sampler is passed to a GridAggregator, it will crop the output to its original size.

Example

>>> import torchio as tio
>>> sampler = tio.GridSampler(patch_size=88)
>>> colin = tio.datasets.Colin27()
>>> for i, patch in enumerate(sampler(colin)):
...     patch.t1.save(f'patch_{i}.nii.gz')
...
>>> # To figure out the number of patches beforehand:
>>> sampler = tio.GridSampler(subject=colin, patch_size=88)
>>> len(sampler)
8


Note

Adapted from NiftyNet. See this NiftyNet tutorial for more information about patch based sampling. Note that patch_overlap is twice border in NiftyNet tutorial.

Grid aggregator#

GridAggregator#

class torchio.data.GridAggregator(sampler: GridSampler, overlap_mode: str = 'crop')[source]#

Aggregate patches for dense inference.

This class is typically used to build a volume made of patches after inference of batches extracted by a GridSampler.

Parameters:
• sampler – Instance of GridSampler used to extract the patches.

• overlap_mode – If 'crop', the overlapping predictions will be cropped. If 'average', the predictions in the overlapping areas will be averaged with equal weights. If 'hann', the predictions in the overlapping areas will be weighted with a Hann window function. See the grid aggregator tests for a raw visualization of the three modes.

Note

• batch_tensor – 5D tensor, typically the output of a convolutional neural network, e.g. batch['image'][torchio.DATA].
• locations – 2D tensor with shape $$(B, 6)$$ representing the patch indices in the original image. They are typically extracted using batch[torchio.LOCATION].