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# Copyright (c) OpenMMLab. All rights reserved.
import copy
import numpy as np
from mmengine.dataset import BaseDataset, force_full_init
from mmcls.registry import DATASETS
@DATASETS.register_module()
class KFoldDataset:
"""A wrapper of dataset for K-Fold cross-validation.
K-Fold cross-validation divides all the samples in groups of samples,
called folds, of almost equal sizes. And we use k-1 of folds to do training
and use the fold left to do validation.
Args:
dataset (:obj:`mmengine.dataset.BaseDataset` | dict): The dataset to be
divided
fold (int): The fold used to do validation. Defaults to 0.
num_splits (int): The number of all folds. Defaults to 5.
test_mode (bool): Use the training dataset or validation dataset.
Defaults to False.
seed (int, optional): The seed to shuffle the dataset before splitting.
If None, not shuffle the dataset. Defaults to None.
"""
def __init__(self,
dataset,
fold=0,
num_splits=5,
test_mode=False,
seed=None):
if isinstance(dataset, dict):
self.dataset = DATASETS.build(dataset)
# Init the dataset wrapper lazily according to the dataset setting.
lazy_init = dataset.get('lazy_init', False)
elif isinstance(dataset, BaseDataset):
self.dataset = dataset
else:
raise TypeError(f'Unsupported dataset type {type(dataset)}.')
self._metainfo = getattr(self.dataset, 'metainfo', {})
self.fold = fold
self.num_splits = num_splits
self.test_mode = test_mode
self.seed = seed
self._fully_initialized = False
if not lazy_init:
self.full_init()
@property
def metainfo(self) -> dict:
"""Get the meta information of ``self.dataset``.
Returns:
dict: Meta information of the dataset.
"""
# Prevent `self._metainfo` from being modified by outside.
return copy.deepcopy(self._metainfo)
def full_init(self):
"""fully initialize the dataset."""
if self._fully_initialized:
return
self.dataset.full_init()
ori_len = len(self.dataset)
indices = list(range(ori_len))
if self.seed is not None:
rng = np.random.default_rng(self.seed)
rng.shuffle(indices)
test_start = ori_len * self.fold // self.num_splits
test_end = ori_len * (self.fold + 1) // self.num_splits
if self.test_mode:
indices = indices[test_start:test_end]
else:
indices = indices[:test_start] + indices[test_end:]
self._ori_indices = indices
self.dataset = self.dataset.get_subset(indices)
self._fully_initialized = True
@force_full_init
def _get_ori_dataset_idx(self, idx: int) -> int:
"""Convert global idx to local index.
Args:
idx (int): Global index of ``KFoldDataset``.
Returns:
int: The original index in the whole dataset.
"""
return self._ori_indices[idx]
@force_full_init
def get_data_info(self, idx: int) -> dict:
"""Get annotation by index.
Args:
idx (int): Global index of ``KFoldDataset``.
Returns:
dict: The idx-th annotation of the datasets.
"""
return self.dataset.get_data_info(idx)
@force_full_init
def __len__(self):
return len(self.dataset)
@force_full_init
def __getitem__(self, idx):
return self.dataset[idx]
@force_full_init
def get_cat_ids(self, idx):
return self.dataset.get_cat_ids(idx)
@force_full_init
def get_gt_labels(self):
return self.dataset.get_gt_labels()
@property
def CLASSES(self):
"""Return all categories names."""
return self._metainfo.get('classes', None)
@property
def class_to_idx(self):
"""Map mapping class name to class index.
Returns:
dict: mapping from class name to class index.
"""
return {cat: i for i, cat in enumerate(self.CLASSES)}
def __repr__(self):
"""Print the basic information of the dataset.
Returns:
str: Formatted string.
"""
head = 'Dataset ' + self.__class__.__name__
body = []
type_ = 'test' if self.test_mode else 'training'
body.append(f'Type: \t{type_}')
body.append(f'Seed: \t{self.seed}')
def ordinal(n):
# Copy from https://codegolf.stackexchange.com/a/74047
suffix = 'tsnrhtdd'[(n // 10 % 10 != 1) * (n % 10 < 4) * n % 10::4]
return f'{n}{suffix}'
body.append(
f'Fold: \t{ordinal(self.fold+1)} of {self.num_splits}-fold')
if self._fully_initialized:
body.append(f'Number of samples: \t{self.__len__()}')
else:
body.append("Haven't been initialized")
if self.CLASSES is not None:
body.append(f'Number of categories: \t{len(self.CLASSES)}')
else:
body.append('The `CLASSES` meta info is not set.')
body.append(
f'Original dataset type:\t{self.dataset.__class__.__name__}')
lines = [head] + [' ' * 4 + line for line in body]
return '\n'.join(lines)
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