torchio.data.sampler.uniform.UniformSampler.__call__() - Code Metrics - Inspection of "Faster implementation for UniformSampler" - fepegar/torchio - Measure and Improve Code Quality continuously with Scrutinizer

Passed

Pull Request — master (#296)

by David

created 2020-09-12 10:06 UTC

UniformSampler.call() A

↳ Parent: torchio.data.sampler.uniform

Complexity

Conditions

Size

Total Lines	14
Code Lines	9

Duplication

Lines	0
Ratio	0 %

Importance

Changes

Metric	Value
cc	2
eloc	9
nop	2
dl	0
loc	14
rs	9.95
c	0
b	0
f	0

import torch
from ...data.subject import Subject
from ...torchio import TypePatchSize
from .sampler import RandomSampler
from typing import Optional, Tuple, Generator


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

    Args:
        patch_size: See :py:class:`~torchio.data.PatchSampler`.
    """
    def __init__(self, patch_size: TypePatchSize):
        super().__init__(patch_size)

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

    def __call__(self, sample: Subject) -> Generator[Subject, None, None]:

        sample.check_consistent_spatial_shape()

        if np.any(self.patch_size > sample.spatial_shape):

            message = (
                f'Patch size {tuple(self.patch_size)} cannot be'
                f' larger than image size {tuple(sample.spatial_shape)}'
            )
            raise RuntimeError(message)

        valid_range = sample.spatial_shape - self.patch_size
        corners = np.asarray([torch.randint(x+1,(1,)).item() for x in valid_range])
        yield self.extract_patch(sample, corners)


1			import torch
2			from ...data.subject import Subject
3			from ...torchio import TypePatchSize
4			from .sampler import RandomSampler
5			from typing import Optional, Tuple, Generator
6
7
8			class UniformSampler(RandomSampler):
9			"""Randomly extract patches from a volume with uniform probability.
10
11			Args:
12			patch_size: See :py:class:`~torchio.data.PatchSampler`.
13			"""
14			def __init__(self, patch_size: TypePatchSize):
15			super().__init__(patch_size)
16
17			def get_probability_map(self, sample: Subject) -> torch.Tensor:
18			return torch.ones(1, *sample.spatial_shape)
19
20			def __call__(self, sample: Subject) -> Generator[Subject, None, None]:
21
22			sample.check_consistent_spatial_shape()
23
24			if np.any(self.patch_size > sample.spatial_shape):
			0 ignored issues – show Comprehensibility Best Practice introduced 2020-09-12 10:07 UTC by Report Bug Copy Issue Report The variable `np` does not seem to be defined. Loading history...
25			message = (
26			f'Patch size {tuple(self.patch_size)} cannot be'
27			f' larger than image size {tuple(sample.spatial_shape)}'
28			)
29			raise RuntimeError(message)
30
31			valid_range = sample.spatial_shape - self.patch_size
32			corners = np.asarray([torch.randint(x+1,(1,)).item() for x in valid_range])
33			yield self.extract_patch(sample, corners)
34

fepegar / torchio

Pull Request — master (#296)

UniformSampler.__call__() A

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UniformSampler.call() A