torchio.data.queue.Queue.__init__() - Code Metrics - Inspection of "Make sampler copy non image fields (#167)" - fepegar/torchio - Measure and Improve Code Quality continuously with Scrutinizer

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by Fernando

created 2020-05-26 09:45 UTC

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

↳ Parent: torchio.data.queue

Complexity

Conditions

Size

Total Lines	24
Code Lines	23

Duplication

Lines	0
Ratio	0 %

Importance

Changes

Metric	Value
cc	1
eloc	23
nop	10
dl	0
loc	24
rs	9.328
c	0
b	0
f	0

How to fix Many Parameters

import random
import warnings
from typing import List, Iterator
from itertools import islice
from tqdm import trange
from torch.utils.data import Dataset, DataLoader
from .. import TypeTuple
from .dataset import ImagesDataset
from .sampler import ImageSampler


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

    Args:
        subjects_dataset: Instance of
            :class:`~torchio.data.dataset.ImagesDataset`.
        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 RAM 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.
        patch_size: Tuple of integers :math:`(d, h, w)` to generate patches
            of size :math:`d \times h \times w`.
            If a single number :math:`n` is provided,
            :math:`d = h = w = n`.
        sampler_class: An instance of :class:`~torchio.data.datasetampler` used
            to define the patches sampling strategy.
        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.

    .. 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
    >>> patches_queue = torchio.Queue(
    ...     subjects_dataset=subjects_dataset,  # instance of torchio.ImagesDataset
    ...     max_length=300,
    ...     samples_per_volume=10,
    ...     patch_size=96,
    ...     sampler_class=torchio.sampler.ImageSampler,
    ...     num_workers=4,
    ...     shuffle_subjects=True,
    ...     shuffle_patches=True,
    ... )
    >>> patches_loader = DataLoader(patches_queue, batch_size=4)
    >>> 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 is some torch.nn.Module

    """
    def __init__(
            self,
            subjects_dataset: ImagesDataset,
            max_length: int,
            samples_per_volume: int,
            patch_size: TypeTuple,
            sampler_class: ImageSampler,
            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_class = sampler_class
        self.patch_size = patch_size
        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)

    @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_class is not None
        assert self.patch_size 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()
            sampler = self.sampler_class(subject_sample, self.patch_size)
            samples = list(islice(sampler, self.samples_per_volume))
            self.patches_list.extend(samples)
        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 typing import List, Iterator
4			from itertools import islice
5			from tqdm import trange
6			from torch.utils.data import Dataset, DataLoader
7			from .. import TypeTuple
8			from .dataset import ImagesDataset
9			from .sampler import ImageSampler
10
11
12			class Queue(Dataset):
13			r"""Patches queue used for patch-based training.
14
15			Args:
16			subjects_dataset: Instance of
17			:class:`~torchio.data.dataset.ImagesDataset`.
18			max_length: Maximum number of patches that can be stored in the queue.
19			Using a large number means that the queue needs to be filled less
20			often, but more RAM is needed to store the patches.
21			samples_per_volume: Number of patches to extract from each volume.
22			A small number of patches ensures a large variability in the queue,
23			but training will be slower.
24			patch_size: Tuple of integers :math:`(d, h, w)` to generate patches
25			of size :math:`d \times h \times w`.
26			If a single number :math:`n` is provided,
27			:math:`d = h = w = n`.
28			sampler_class: An instance of :class:`~torchio.data.datasetampler` used
29			to define the patches sampling strategy.
30			num_workers: Number of subprocesses to use for data loading
31			(as in :class:`torch.utils.data.DataLoader`).
32			``0`` means that the data will be loaded in the main process.
33			shuffle_subjects: If ``True``, the subjects dataset is shuffled at the
34			beginning of each epoch, i.e. when all patches from all subjects
35			have been processed.
36			shuffle_patches: If ``True``, patches are shuffled after filling the
37			queue.
38			verbose: If ``True``, some debugging messages are printed.
39
40			.. note:: :attr:`num_workers` refers to the number of workers used to
41			load and transform the volumes. Multiprocessing is not needed to pop
42			patches from the queue.
43
44			Example:
45
46			>>> from torch.utils.data import DataLoader
47			>>> import torchio
48			>>> patches_queue = torchio.Queue(
49			... subjects_dataset=subjects_dataset, # instance of torchio.ImagesDataset
50			... max_length=300,
51			... samples_per_volume=10,
52			... patch_size=96,
53			... sampler_class=torchio.sampler.ImageSampler,
54			... num_workers=4,
55			... shuffle_subjects=True,
56			... shuffle_patches=True,
57			... )
58			>>> patches_loader = DataLoader(patches_queue, batch_size=4)
59			>>> num_epochs = 20
60			>>> for epoch_index in range(num_epochs):
61			... for patches_batch in patches_loader:
62			... inputs = patches_batch['image_name'][torchio.DATA]
63			... targets = patches_batch['targets_name'][torchio.DATA]
64			... logits = model(inputs) # model is some torch.nn.Module
65
66			"""
67			def __init__(
68			self,
69			subjects_dataset: ImagesDataset,
70			max_length: int,
71			samples_per_volume: int,
72			patch_size: TypeTuple,
73			sampler_class: ImageSampler,
74			num_workers: int = 0,
75			shuffle_subjects: bool = True,
76			shuffle_patches: bool = True,
77			verbose: bool = False,
78			):
79			self.subjects_dataset = subjects_dataset
80			self.max_length = max_length
81			self.shuffle_subjects = shuffle_subjects
82			self.shuffle_patches = shuffle_patches
83			self.samples_per_volume = samples_per_volume
84			self.sampler_class = sampler_class
85			self.patch_size = patch_size
86			self.num_workers = num_workers
87			self.verbose = verbose
88			self.subjects_iterable = self.get_subjects_iterable()
89			self.patches_list: List[dict] = []
90			self.num_sampled_patches = 0
91
92			def __len__(self):
93			return self.iterations_per_epoch
94
95			def __getitem__(self, _):
96			# There are probably more elegant ways of doing this
97			if not self.patches_list:
98			self.print('Patches list is empty.')
99			self.fill()
100			sample_patch = self.patches_list.pop()
101			self.num_sampled_patches += 1
102			return sample_patch
103
104			def __repr__(self):
105			attributes = [
106			f'max_length={self.max_length}',
107			f'num_subjects={self.num_subjects}',
108			f'num_patches={self.num_patches}',
109			f'samples_per_volume={self.samples_per_volume}',
110			f'num_sampled_patches={self.num_sampled_patches}',
111			f'iterations_per_epoch={self.iterations_per_epoch}',
112			]
113			attributes_string = ', '.join(attributes)
114			return f'Queue({attributes_string})'
115
116			def print(self, *args):
117			if self.verbose:
118			print(*args)
119
120			@property
121			def num_subjects(self) -> int:
122			return len(self.subjects_dataset)
123
124			@property
125			def num_patches(self) -> int:
126			return len(self.patches_list)
127
128			@property
129			def iterations_per_epoch(self) -> int:
130			return self.num_subjects * self.samples_per_volume
131
132			def fill(self) -> None:
133			assert self.sampler_class is not None
134			assert self.patch_size is not None
135			if self.max_length % self.samples_per_volume != 0:
136			message = (
137			f'Queue length ({self.max_length})'
138			' not divisible by the number of'
139			f' patches per volume ({self.samples_per_volume})'
140			)
141			warnings.warn(message)
142
143			# If there are e.g. 4 subjects and 1 sample per volume and max_length
144			# is 6, we just need to load 4 subjects, not 6
145			max_num_subjects_for_queue = self.max_length // self.samples_per_volume
146			num_subjects_for_queue = min(
147			self.num_subjects, max_num_subjects_for_queue)
148
149			self.print(f'Filling queue from {num_subjects_for_queue} subjects...')
150			if self.verbose:
151			iterable = trange(num_subjects_for_queue, leave=False)
152			else:
153			iterable = range(num_subjects_for_queue)
154			for _ in iterable:
155			subject_sample = self.get_next_subject_sample()
156			sampler = self.sampler_class(subject_sample, self.patch_size)
157			samples = list(islice(sampler, self.samples_per_volume))
158			self.patches_list.extend(samples)
159			if self.shuffle_patches:
160			random.shuffle(self.patches_list)
161
162			def get_next_subject_sample(self) -> dict:
163			# A StopIteration exception is expected when the queue is empty
164			try:
165			subject_sample = next(self.subjects_iterable)
166			except StopIteration as exception:
167			self.print('Queue is empty:', exception)
168			self.subjects_iterable = self.get_subjects_iterable()
169			subject_sample = next(self.subjects_iterable)
170			return subject_sample
171
172			def get_subjects_iterable(self) -> Iterator:
173			# I need a DataLoader to handle parallelism
174			# But this loader is always expected to yield single subject samples
175			self.print(
176			'\nCreating subjects loader with', self.num_workers, 'workers')
177			subjects_loader = DataLoader(
178			self.subjects_dataset,
179			num_workers=self.num_workers,
180			collate_fn=lambda x: x[0],
181			shuffle=self.shuffle_subjects,
182			)
183			return iter(subjects_loader)
184

fepegar / torchio

Push — master ( 7b848f...cac223 )

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

Complexity

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Duplication

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How to fix Many Parameters

Many Parameters

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