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mmselfsup.datasets.deepcluster 源代码
# Copyright (c) OpenMMLab. All rights reserved.
import torch
from .base import BaseDataset
from .builder import DATASETS
from .utils import to_numpy
[文档]@DATASETS.register_module()
class DeepClusterDataset(BaseDataset):
"""Dataset for DC and ODC.
The dataset initializes clustering labels and assigns it during training.
Args:
data_source (dict): Data source defined in
`mmselfsup.datasets.data_sources`.
pipeline (list[dict]): A list of dict, where each element represents
an operation defined in `mmselfsup.datasets.pipelines`.
prefetch (bool, optional): Whether to prefetch data. Defaults to False.
"""
def __init__(self, data_source, pipeline, prefetch=False):
super(DeepClusterDataset, self).__init__(data_source, pipeline,
prefetch)
# init clustering labels
self.clustering_labels = [-1 for _ in range(len(self.data_source))]
def __getitem__(self, idx):
img = self.data_source.get_img(idx)
img = self.pipeline(img)
clustering_label = self.clustering_labels[idx]
if self.prefetch:
img = torch.from_numpy(to_numpy(img))
return dict(img=img, pseudo_label=clustering_label, idx=idx)
def assign_labels(self, labels):
assert len(self.clustering_labels) == len(labels), (
f'Inconsistent length of assigned labels, '
f'{len(self.clustering_labels)} vs {len(labels)}')
self.clustering_labels = labels[:]
def evaluate(self, results, logger=None):
return NotImplemented