tests.data.test_queue.TestQueue.run_queue() - Code Metrics - Inspection of "Small improvements for Queue" - fepegar/torchio - Measure and Improve Code Quality continuously with Scrutinizer

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Pull Request — master (#423)

by Fernando

created 2021-01-23 11:03 UTC

tests.data.test_queue.TestQueue.run_queue() A

↳ Parent: tests.data.test_queue

Complexity

Conditions

Size

Total Lines	16
Code Lines	15

Duplication

Lines	0
Ratio	0 %

Importance

Changes

Metric	Value
cc	2
eloc	15
nop	3
dl	0
loc	16
rs	9.65
c	0
b	0
f	0

from torch.utils.data import DataLoader
from torchio.data import UniformSampler
from torchio import SubjectsDataset, Queue, DATA
from torchio.utils import create_dummy_dataset
from ..utils import TorchioTestCase


class TestQueue(TorchioTestCase):
    """Tests for `queue` module."""
    def setUp(self):
        super().setUp()
        self.subjects_list = create_dummy_dataset(
            num_images=10,
            size_range=(10, 20),
            directory=self.dir,
            suffix='.nii',
            force=False,
        )

    def run_queue(self, num_workers, **kwargs):
        subjects_dataset = SubjectsDataset(self.subjects_list)
        patch_size = 10
        sampler = UniformSampler(patch_size)
        queue_dataset = Queue(
            subjects_dataset,
            max_length=6,
            samples_per_volume=2,
            sampler=sampler,
            **kwargs,
        )
        _ = str(queue_dataset)
        batch_loader = DataLoader(queue_dataset, batch_size=4)
        for batch in batch_loader:
            _ = batch['one_modality'][DATA]
            _ = batch['segmentation'][DATA]

    def test_queue(self):
        self.run_queue(num_workers=0)

    def test_queue_multiprocessing(self):
        self.run_queue(num_workers=2)

    def test_queue_no_start_background(self):
        self.run_queue(num_workers=0, start_background=False)


1			from torch.utils.data import DataLoader
2			from torchio.data import UniformSampler
3			from torchio import SubjectsDataset, Queue, DATA
4			from torchio.utils import create_dummy_dataset
5			from ..utils import TorchioTestCase
6
7
8			class TestQueue(TorchioTestCase):
9			"""Tests for `queue` module."""
10			def setUp(self):
11			super().setUp()
12			self.subjects_list = create_dummy_dataset(
13			num_images=10,
14			size_range=(10, 20),
15			directory=self.dir,
16			suffix='.nii',
17			force=False,
18			)
19
20			def run_queue(self, num_workers, **kwargs):
21			subjects_dataset = SubjectsDataset(self.subjects_list)
22			patch_size = 10
23			sampler = UniformSampler(patch_size)
24			queue_dataset = Queue(
25			subjects_dataset,
26			max_length=6,
27			samples_per_volume=2,
28			sampler=sampler,
29			**kwargs,
30			)
31			_ = str(queue_dataset)
32			batch_loader = DataLoader(queue_dataset, batch_size=4)
33			for batch in batch_loader:
34			_ = batch['one_modality'][DATA]
35			_ = batch['segmentation'][DATA]
36
37			def test_queue(self):
38			self.run_queue(num_workers=0)
39
40			def test_queue_multiprocessing(self):
41			self.run_queue(num_workers=2)
42
43			def test_queue_no_start_background(self):
44			self.run_queue(num_workers=0, start_background=False)
45

fepegar / torchio

Pull Request — master (#423)

tests.data.test_queue.TestQueue.run_queue() A

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