torchio.data.queue.Queue.__init__() - Code Metrics - Inspection of "Fix many flake8 extensions errors" - fepegar/torchio - Measure and Improve Code Quality continuously with Scrutinizer

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by Fernando

created 2020-09-29 17:03 UTC

torchio.data.queue.Queue.init() A

↳ Parent: torchio.data.queue

Complexity

Conditions

Size

Total Lines	22
Code Lines	21

Duplication

Lines	0
Ratio	0 %

Importance

Changes

Metric	Value
cc	1
eloc	21
nop	9
dl	0
loc	22
rs	9.376
c	0
b	0
f	0

How to fix Many Parameters

import random
import warnings
from itertools import islice
from typing import List, Iterator

from tqdm import trange
from torch.utils.data import Dataset, DataLoader

from .sampler import PatchSampler
from .dataset import SubjectsDataset


class Queue(Dataset):
    r"""Patches queue used for patch-based training.

    Args:
        subjects_dataset: Instance of
            :class:`~torchio.data.dataset.SubjectsDataset`.
        max_length: Maximum number of patches that can be stored in the queue.
            Using a large number means that the queue needs to be filled less
            often, but more CPU memory is needed to store the patches.
        samples_per_volume: Number of patches to extract from each volume.
            A small number of patches ensures a large variability in the queue,
            but training will be slower.
        sampler: A sampler used to extract patches from the volumes.
        num_workers: Number of subprocesses to use for data loading
            (as in :class:`torch.utils.data.DataLoader`).
            ``0`` means that the data will be loaded in the main process.
        shuffle_subjects: If ``True``, the subjects dataset is shuffled at the
            beginning of each epoch, i.e. when all patches from all subjects
            have been processed.
        shuffle_patches: If ``True``, patches are shuffled after filling the
            queue.
        verbose: If ``True``, some debugging messages are printed.

    This sketch can be used to experiment and understand how the queue works.
    In this case, :attr:`shuffle_subjects` is ``False``
    and :attr:`shuffle_patches` is ``True``.

    .. raw:: html

        <embed>
            <iframe style="width: 640px; height: 360px; overflow: hidden;" scrolling="no" frameborder="0" src="https://editor.p5js.org/embed/DZwjZzkkV"></iframe>
        </embed>

    .. note:: :attr:`num_workers` refers to the number of workers used to
        load and transform the volumes. Multiprocessing is not needed to pop
        patches from the queue.

    Example:

    >>> from torch.utils.data import DataLoader
    >>> import torchio
    >>> patch_size = 96
    >>> queue_length = 300
    >>> samples_per_volume = 10
    >>> sampler = torchio.data.UniformSampler(patch_size)
    >>> patches_queue = torchio.Queue(
    ...     subjects_dataset,  # instance of torchio.SubjectsDataset
    ...     queue_length,
    ...     samples_per_volume,
    ...     sampler,
    ...     num_workers=4,
    ...     shuffle_subjects=True,
    ...     shuffle_patches=True,
    ... )
    >>> patches_loader = DataLoader(patches_queue, batch_size=16)
    >>> num_epochs = 20
    >>> for epoch_index in range(num_epochs):
    ...     for patches_batch in patches_loader:
    ...         inputs = patches_batch['image_name'][torchio.DATA]
    ...         targets = patches_batch['targets_name'][torchio.DATA]
    ...         logits = model(inputs)  # model being an instance of torch.nn.Module

    """
    def __init__(
            self,
            subjects_dataset: SubjectsDataset,
            max_length: int,
            samples_per_volume: int,
            sampler: PatchSampler,
            num_workers: int = 0,
            shuffle_subjects: bool = True,
            shuffle_patches: bool = True,
            verbose: bool = False,
            ):
        self.subjects_dataset = subjects_dataset
        self.max_length = max_length
        self.shuffle_subjects = shuffle_subjects
        self.shuffle_patches = shuffle_patches
        self.samples_per_volume = samples_per_volume
        self.sampler = sampler
        self.num_workers = num_workers
        self.verbose = verbose
        self.subjects_iterable = self.get_subjects_iterable()
        self.patches_list: List[dict] = []
        self.num_sampled_patches = 0

    def __len__(self):
        return self.iterations_per_epoch

    def __getitem__(self, _):
        # There are probably more elegant ways of doing this
        if not self.patches_list:
            self._print('Patches list is empty.')
            self.fill()
        sample_patch = self.patches_list.pop()
        self.num_sampled_patches += 1
        return sample_patch

    def __repr__(self):
        attributes = [
            f'max_length={self.max_length}',
            f'num_subjects={self.num_subjects}',
            f'num_patches={self.num_patches}',
            f'samples_per_volume={self.samples_per_volume}',
            f'num_sampled_patches={self.num_sampled_patches}',
            f'iterations_per_epoch={self.iterations_per_epoch}',
        ]
        attributes_string = ', '.join(attributes)
        return f'Queue({attributes_string})'

    def _print(self, *args):
        if self.verbose:
            print(*args)  # noqa: T001

    @property
    def num_subjects(self) -> int:
        return len(self.subjects_dataset)

    @property
    def num_patches(self) -> int:
        return len(self.patches_list)

    @property
    def iterations_per_epoch(self) -> int:
        return self.num_subjects * self.samples_per_volume

    def fill(self) -> None:
        assert self.sampler is not None
        if self.max_length % self.samples_per_volume != 0:
            message = (
                f'Queue length ({self.max_length})'
                ' not divisible by the number of'
                f' patches per volume ({self.samples_per_volume})'
            )
            warnings.warn(message)

        # If there are e.g. 4 subjects and 1 sample per volume and max_length
        # is 6, we just need to load 4 subjects, not 6
        max_num_subjects_for_queue = self.max_length // self.samples_per_volume
        num_subjects_for_queue = min(
            self.num_subjects, max_num_subjects_for_queue)

        self._print(f'Filling queue from {num_subjects_for_queue} subjects...')
        if self.verbose:
            iterable = trange(num_subjects_for_queue, leave=False)
        else:
            iterable = range(num_subjects_for_queue)
        for _ in iterable:
            subject_sample = self.get_next_subject_sample()
            iterable = self.sampler(subject_sample)
            patches = list(islice(iterable, self.samples_per_volume))
            self.patches_list.extend(patches)
        if self.shuffle_patches:
            random.shuffle(self.patches_list)

    def get_next_subject_sample(self) -> dict:
        # A StopIteration exception is expected when the queue is empty
        try:
            subject_sample = next(self.subjects_iterable)
        except StopIteration as exception:
            self._print('Queue is empty:', exception)
            self.subjects_iterable = self.get_subjects_iterable()
            subject_sample = next(self.subjects_iterable)
        return subject_sample

    def get_subjects_iterable(self) -> Iterator:
        # I need a DataLoader to handle parallelism
        # But this loader is always expected to yield single subject samples
        self._print(
            '\nCreating subjects loader with', self.num_workers, 'workers')
        subjects_loader = DataLoader(
            self.subjects_dataset,
            num_workers=self.num_workers,
            collate_fn=lambda x: x[0],
            shuffle=self.shuffle_subjects,
        )
        return iter(subjects_loader)


1			import random
2			import warnings
3			from itertools import islice
4			from typing import List, Iterator
5
6			from tqdm import trange
7			from torch.utils.data import Dataset, DataLoader
8
9			from .sampler import PatchSampler
10			from .dataset import SubjectsDataset
11
12
13			class Queue(Dataset):
14			r"""Patches queue used for patch-based training.
15
16			Args:
17			subjects_dataset: Instance of
18			:class:`~torchio.data.dataset.SubjectsDataset`.
19			max_length: Maximum number of patches that can be stored in the queue.
20			Using a large number means that the queue needs to be filled less
21			often, but more CPU memory is needed to store the patches.
22			samples_per_volume: Number of patches to extract from each volume.
23			A small number of patches ensures a large variability in the queue,
24			but training will be slower.
25			sampler: A sampler used to extract patches from the volumes.
26			num_workers: Number of subprocesses to use for data loading
27			(as in :class:`torch.utils.data.DataLoader`).
28			``0`` means that the data will be loaded in the main process.
29			shuffle_subjects: If ``True``, the subjects dataset is shuffled at the
30			beginning of each epoch, i.e. when all patches from all subjects
31			have been processed.
32			shuffle_patches: If ``True``, patches are shuffled after filling the
33			queue.
34			verbose: If ``True``, some debugging messages are printed.
35
36			This sketch can be used to experiment and understand how the queue works.
37			In this case, :attr:`shuffle_subjects` is ``False``
38			and :attr:`shuffle_patches` is ``True``.
39
40			.. raw:: html
41
42			<embed>
43			<iframe style="width: 640px; height: 360px; overflow: hidden;" scrolling="no" frameborder="0" src="https://editor.p5js.org/embed/DZwjZzkkV"></iframe>
44			</embed>
45
46			.. note:: :attr:`num_workers` refers to the number of workers used to
47			load and transform the volumes. Multiprocessing is not needed to pop
48			patches from the queue.
49
50			Example:
51
52			>>> from torch.utils.data import DataLoader
53			>>> import torchio
54			>>> patch_size = 96
55			>>> queue_length = 300
56			>>> samples_per_volume = 10
57			>>> sampler = torchio.data.UniformSampler(patch_size)
58			>>> patches_queue = torchio.Queue(
59			... subjects_dataset, # instance of torchio.SubjectsDataset
60			... queue_length,
61			... samples_per_volume,
62			... sampler,
63			... num_workers=4,
64			... shuffle_subjects=True,
65			... shuffle_patches=True,
66			... )
67			>>> patches_loader = DataLoader(patches_queue, batch_size=16)
68			>>> num_epochs = 20
69			>>> for epoch_index in range(num_epochs):
70			... for patches_batch in patches_loader:
71			... inputs = patches_batch['image_name'][torchio.DATA]
72			... targets = patches_batch['targets_name'][torchio.DATA]
73			... logits = model(inputs) # model being an instance of torch.nn.Module
74
75			"""
76			def __init__(
77			self,
78			subjects_dataset: SubjectsDataset,
79			max_length: int,
80			samples_per_volume: int,
81			sampler: PatchSampler,
82			num_workers: int = 0,
83			shuffle_subjects: bool = True,
84			shuffle_patches: bool = True,
85			verbose: bool = False,
86			):
87			self.subjects_dataset = subjects_dataset
88			self.max_length = max_length
89			self.shuffle_subjects = shuffle_subjects
90			self.shuffle_patches = shuffle_patches
91			self.samples_per_volume = samples_per_volume
92			self.sampler = sampler
93			self.num_workers = num_workers
94			self.verbose = verbose
95			self.subjects_iterable = self.get_subjects_iterable()
96			self.patches_list: List[dict] = []
97			self.num_sampled_patches = 0
98
99			def __len__(self):
100			return self.iterations_per_epoch
101
102			def __getitem__(self, _):
103			# There are probably more elegant ways of doing this
104			if not self.patches_list:
105			self._print('Patches list is empty.')
106			self.fill()
107			sample_patch = self.patches_list.pop()
108			self.num_sampled_patches += 1
109			return sample_patch
110
111			def __repr__(self):
112			attributes = [
113			f'max_length={self.max_length}',
114			f'num_subjects={self.num_subjects}',
115			f'num_patches={self.num_patches}',
116			f'samples_per_volume={self.samples_per_volume}',
117			f'num_sampled_patches={self.num_sampled_patches}',
118			f'iterations_per_epoch={self.iterations_per_epoch}',
119			]
120			attributes_string = ', '.join(attributes)
121			return f'Queue({attributes_string})'
122
123			def _print(self, *args):
124			if self.verbose:
125			print(*args) # noqa: T001
126
127			@property
128			def num_subjects(self) -> int:
129			return len(self.subjects_dataset)
130
131			@property
132			def num_patches(self) -> int:
133			return len(self.patches_list)
134
135			@property
136			def iterations_per_epoch(self) -> int:
137			return self.num_subjects * self.samples_per_volume
138
139			def fill(self) -> None:
140			assert self.sampler is not None
141			if self.max_length % self.samples_per_volume != 0:
142			message = (
143			f'Queue length ({self.max_length})'
144			' not divisible by the number of'
145			f' patches per volume ({self.samples_per_volume})'
146			)
147			warnings.warn(message)
148
149			# If there are e.g. 4 subjects and 1 sample per volume and max_length
150			# is 6, we just need to load 4 subjects, not 6
151			max_num_subjects_for_queue = self.max_length // self.samples_per_volume
152			num_subjects_for_queue = min(
153			self.num_subjects, max_num_subjects_for_queue)
154
155			self._print(f'Filling queue from {num_subjects_for_queue} subjects...')
156			if self.verbose:
157			iterable = trange(num_subjects_for_queue, leave=False)
158			else:
159			iterable = range(num_subjects_for_queue)
160			for _ in iterable:
161			subject_sample = self.get_next_subject_sample()
162			iterable = self.sampler(subject_sample)
163			patches = list(islice(iterable, self.samples_per_volume))
164			self.patches_list.extend(patches)
165			if self.shuffle_patches:
166			random.shuffle(self.patches_list)
167
168			def get_next_subject_sample(self) -> dict:
169			# A StopIteration exception is expected when the queue is empty
170			try:
171			subject_sample = next(self.subjects_iterable)
172			except StopIteration as exception:
173			self._print('Queue is empty:', exception)
174			self.subjects_iterable = self.get_subjects_iterable()
175			subject_sample = next(self.subjects_iterable)
176			return subject_sample
177
178			def get_subjects_iterable(self) -> Iterator:
179			# I need a DataLoader to handle parallelism
180			# But this loader is always expected to yield single subject samples
181			self._print(
182			'\nCreating subjects loader with', self.num_workers, 'workers')
183			subjects_loader = DataLoader(
184			self.subjects_dataset,
185			num_workers=self.num_workers,
186			collate_fn=lambda x: x[0],
187			shuffle=self.shuffle_subjects,
188			)
189			return iter(subjects_loader)
190

fepegar / torchio

Push — master ( 3c93a7...8f7525 )

torchio.data.queue.Queue.__init__() A

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torchio.data.queue.Queue.init() A