mmselfsup/openselfsup/datasets/contrastive.py
2020-12-20 22:30:11 +08:00

34 lines
1.2 KiB
Python

import torch
from PIL import Image
from .registry import DATASETS
from .base import BaseDataset
from .utils import to_numpy
@DATASETS.register_module
class ContrastiveDataset(BaseDataset):
"""Dataset for contrastive learning methods that forward
two views of the image at a time (MoCo, SimCLR).
"""
def __init__(self, data_source, pipeline, prefetch=False):
data_source['return_label'] = False
super(ContrastiveDataset, self).__init__(data_source, pipeline, prefetch)
def __getitem__(self, idx):
img = self.data_source.get_sample(idx)
assert isinstance(img, Image.Image), \
'The output from the data source must be an Image, got: {}. \
Please ensure that the list file does not contain labels.'.format(
type(img))
img1 = self.pipeline(img)
img2 = self.pipeline(img)
if self.prefetch:
img1 = torch.from_numpy(to_numpy(img1))
img2 = torch.from_numpy(to_numpy(img2))
img_cat = torch.cat((img1.unsqueeze(0), img2.unsqueeze(0)), dim=0)
return dict(img=img_cat)
def evaluate(self, scores, keyword, logger=None, **kwargs):
raise NotImplemented