- class mmpretrain.models.selfsup.DenseCL(backbone, neck, head, queue_len=65536, feat_dim=128, momentum=0.001, loss_lambda=0.5, pretrained=None, data_preprocessor=None, init_cfg=None)¶
Implementation of Dense Contrastive Learning for Self-Supervised Visual Pre-Training. Borrowed from the authors’ code: https://github.com/WXinlong/DenseCL. The loss_lambda warmup is in engine/hooks/densecl_hook.py.
backbone (dict) – Config dict for module of backbone.
neck (dict) – Config dict for module of deep features to compact feature vectors.
head (dict) – Config dict for module of head functions.
queue_len (int) – Number of negative keys maintained in the queue. Defaults to 65536.
feat_dim (int) – Dimension of compact feature vectors. Defaults to 128.
momentum (float) – Momentum coefficient for the momentum-updated encoder. Defaults to 0.999.
loss_lambda (float) – Loss weight for the single and dense contrastive loss. Defaults to 0.5.
pretrained (str, optional) – The pretrained checkpoint path, support local path and remote path. Defaults to None.
data_preprocessor (dict, optional) – The config for preprocessing input data. If None or no specified type, it will use “SelfSupDataPreprocessor” as type. See
SelfSupDataPreprocessorfor more details. Defaults to None.