torchio.data.sampler.uniform

Complexity

Total Complexity

5

Size/Duplication

Total Lines	34
Duplicated Lines	0 %

Importance

Changes

0

2 Methods

Rating	Name	Duplication	Size	Complexity
A	UniformSampler.get_probability_map()	0	2	1
A	UniformSampler._generate_patches()	0	16	4

import torch
from ...data.subject import Subject
from .sampler import RandomSampler
from typing import Generator
import numpy as np


class UniformSampler(RandomSampler):
    """Randomly extract patches from a volume with uniform probability.

    Args:
        patch_size: See :class:`~torchio.data.PatchSampler`.
    """

    def get_probability_map(self, subject: Subject) -> torch.Tensor:
        return torch.ones(1, *subject.spatial_shape)

    def _generate_patches(
            self,
            subject: Subject,
            num_patches: int = None,
            ) -> Generator[Subject, None, None]:
        valid_range = subject.spatial_shape - self.patch_size
        patches_left = num_patches if num_patches is not None else True
        while patches_left:
            index_ini = [
                torch.randint(x + 1, (1,)).item()
                for x in valid_range
            ]
            index_ini_array = np.asarray(index_ini)
            yield self.extract_patch(subject, index_ini_array)
            if num_patches is not None:
                patches_left -= 1