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

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by Fernando

created 2020-10-27 16:27 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 .subject import Subject
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:

    >>> import torch
    >>> import torchio as tio
    >>> from torch.utils.data import DataLoader
    >>> patch_size = 96
    >>> queue_length = 300
    >>> samples_per_volume = 10
    >>> sampler = tio.data.UniformSampler(patch_size)
    >>> subject = tio.datasets.Colin27()
    >>> subjects_dataset = tio.SubjectsDataset(10 * [subject])
    >>> patches_queue = tio.Queue(
    ...     subjects_dataset,
    ...     queue_length,
    ...     samples_per_volume,
    ...     sampler,
    ...     num_workers=4,
    ... )
    >>> patches_loader = DataLoader(patches_queue, batch_size=16)
    >>> num_epochs = 2
    >>> model = torch.nn.Identity()
    >>> for epoch_index in range(num_epochs):
    ...     for patches_batch in patches_loader:
    ...         inputs = patches_batch['t1'][tio.DATA]  # key 't1' is in subject
    ...         targets = patches_batch['brain'][tio.DATA]  # key 'brain' is in subject
    ...         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 = self.get_next_subject()
            iterable = self.sampler(subject)
            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(self) -> Subject:
        # A StopIteration exception is expected when the queue is empty
        try:
            subject = next(self.subjects_iterable)
        except StopIteration as exception:
            self._print('Queue is empty:', exception)
            self.subjects_iterable = self.get_subjects_iterable()
            subject = next(self.subjects_iterable)
        return subject

    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 .subject import Subject
10			from .sampler import PatchSampler
11			from .dataset import SubjectsDataset
12
13
14			class Queue(Dataset):
15			r"""Patches queue used for patch-based training.
16
17			Args:
18			subjects_dataset: Instance of
19			:class:`~torchio.data.dataset.SubjectsDataset`.
20			max_length: Maximum number of patches that can be stored in the queue.
21			Using a large number means that the queue needs to be filled less
22			often, but more CPU memory is needed to store the patches.
23			samples_per_volume: Number of patches to extract from each volume.
24			A small number of patches ensures a large variability in the queue,
25			but training will be slower.
26			sampler: A sampler used to extract patches from the volumes.
27			num_workers: Number of subprocesses to use for data loading
28			(as in :class:`torch.utils.data.DataLoader`).
29			``0`` means that the data will be loaded in the main process.
30			shuffle_subjects: If ``True``, the subjects dataset is shuffled at the
31			beginning of each epoch, i.e. when all patches from all subjects
32			have been processed.
33			shuffle_patches: If ``True``, patches are shuffled after filling the
34			queue.
35			verbose: If ``True``, some debugging messages are printed.
36
37			This sketch can be used to experiment and understand how the queue works.
38			In this case, :attr:`shuffle_subjects` is ``False``
39			and :attr:`shuffle_patches` is ``True``.
40
41			.. raw:: html
42
43			<embed>
44			<iframe style="width: 640px; height: 360px; overflow: hidden;" scrolling="no" frameborder="0" src="https://editor.p5js.org/embed/DZwjZzkkV"></iframe>
45			</embed>
46
47			.. note:: :attr:`num_workers` refers to the number of workers used to
48			load and transform the volumes. Multiprocessing is not needed to pop
49			patches from the queue.
50
51			Example:
52
53			>>> import torch
54			>>> import torchio as tio
55			>>> from torch.utils.data import DataLoader
56			>>> patch_size = 96
57			>>> queue_length = 300
58			>>> samples_per_volume = 10
59			>>> sampler = tio.data.UniformSampler(patch_size)
60			>>> subject = tio.datasets.Colin27()
61			>>> subjects_dataset = tio.SubjectsDataset(10 * [subject])
62			>>> patches_queue = tio.Queue(
63			... subjects_dataset,
64			... queue_length,
65			... samples_per_volume,
66			... sampler,
67			... num_workers=4,
68			... )
69			>>> patches_loader = DataLoader(patches_queue, batch_size=16)
70			>>> num_epochs = 2
71			>>> model = torch.nn.Identity()
72			>>> for epoch_index in range(num_epochs):
73			... for patches_batch in patches_loader:
74			... inputs = patches_batch['t1'][tio.DATA] # key 't1' is in subject
75			... targets = patches_batch['brain'][tio.DATA] # key 'brain' is in subject
76			... logits = model(inputs) # model being an instance of torch.nn.Module
77
78			"""
79			def __init__(
80			self,
81			subjects_dataset: SubjectsDataset,
82			max_length: int,
83			samples_per_volume: int,
84			sampler: PatchSampler,
85			num_workers: int = 0,
86			shuffle_subjects: bool = True,
87			shuffle_patches: bool = True,
88			verbose: bool = False,
89			):
90			self.subjects_dataset = subjects_dataset
91			self.max_length = max_length
92			self.shuffle_subjects = shuffle_subjects
93			self.shuffle_patches = shuffle_patches
94			self.samples_per_volume = samples_per_volume
95			self.sampler = sampler
96			self.num_workers = num_workers
97			self.verbose = verbose
98			self.subjects_iterable = self.get_subjects_iterable()
99			self.patches_list: List[dict] = []
100			self.num_sampled_patches = 0
101
102			def __len__(self):
103			return self.iterations_per_epoch
104
105			def __getitem__(self, _):
106			# There are probably more elegant ways of doing this
107			if not self.patches_list:
108			self._print('Patches list is empty.')
109			self.fill()
110			sample_patch = self.patches_list.pop()
111			self.num_sampled_patches += 1
112			return sample_patch
113
114			def __repr__(self):
115			attributes = [
116			f'max_length={self.max_length}',
117			f'num_subjects={self.num_subjects}',
118			f'num_patches={self.num_patches}',
119			f'samples_per_volume={self.samples_per_volume}',
120			f'num_sampled_patches={self.num_sampled_patches}',
121			f'iterations_per_epoch={self.iterations_per_epoch}',
122			]
123			attributes_string = ', '.join(attributes)
124			return f'Queue({attributes_string})'
125
126			def _print(self, *args):
127			if self.verbose:
128			print(*args) # noqa: T001
129
130			@property
131			def num_subjects(self) -> int:
132			return len(self.subjects_dataset)
133
134			@property
135			def num_patches(self) -> int:
136			return len(self.patches_list)
137
138			@property
139			def iterations_per_epoch(self) -> int:
140			return self.num_subjects * self.samples_per_volume
141
142			def fill(self) -> None:
143			assert self.sampler is not None
144			if self.max_length % self.samples_per_volume != 0:
145			message = (
146			f'Queue length ({self.max_length})'
147			' not divisible by the number of'
148			f' patches per volume ({self.samples_per_volume})'
149			)
150			warnings.warn(message)
151
152			# If there are e.g. 4 subjects and 1 sample per volume and max_length
153			# is 6, we just need to load 4 subjects, not 6
154			max_num_subjects_for_queue = self.max_length // self.samples_per_volume
155			num_subjects_for_queue = min(
156			self.num_subjects, max_num_subjects_for_queue)
157
158			self._print(f'Filling queue from {num_subjects_for_queue} subjects...')
159			if self.verbose:
160			iterable = trange(num_subjects_for_queue, leave=False)
161			else:
162			iterable = range(num_subjects_for_queue)
163			for _ in iterable:
164			subject = self.get_next_subject()
165			iterable = self.sampler(subject)
166			patches = list(islice(iterable, self.samples_per_volume))
167			self.patches_list.extend(patches)
168			if self.shuffle_patches:
169			random.shuffle(self.patches_list)
170
171			def get_next_subject(self) -> Subject:
172			# A StopIteration exception is expected when the queue is empty
173			try:
174			subject = next(self.subjects_iterable)
175			except StopIteration as exception:
176			self._print('Queue is empty:', exception)
177			self.subjects_iterable = self.get_subjects_iterable()
178			subject = next(self.subjects_iterable)
179			return subject
180
181			def get_subjects_iterable(self) -> Iterator:
182			# I need a DataLoader to handle parallelism
183			# But this loader is always expected to yield single subject samples
184			self._print(
185			'\nCreating subjects loader with', self.num_workers, 'workers')
186			subjects_loader = DataLoader(
187			self.subjects_dataset,
188			num_workers=self.num_workers,
189			collate_fn=lambda x: x[0],
190			shuffle=self.shuffle_subjects,
191			)
192			return iter(subjects_loader)
193

fepegar / torchio

Push — master ( b06930...3ddbe5 )

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

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