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CAE

class mmpretrain.models.selfsup.CAE(backbone, neck, head, target_generator=None, base_momentum=0.0, data_preprocessor=None, init_cfg=None)[source]

CAE.

Implementation of Context Autoencoder for Self-Supervised Representation Learning.

Parameters:
  • backbone (dict) – Config dict for module of backbone.

  • neck (dict) – Config dict for module of neck.

  • head (dict) – Config dict for module of head functions.

  • target_generator – (dict, optional): The target_generator module to generate targets for self-supervised learning optimization, such as HOG, extracted features from other modules(DALL-E, CLIP), etc.

  • base_momentum (float) – The base momentum coefficient for the target network. Defaults to 0.0.

  • data_preprocessor (dict, optional) – The config for preprocessing input data. If None or no specified type, it will use “SelfSupDataPreprocessor” as type. See SelfSupDataPreprocessor for more details. Defaults to None.

  • init_cfg (Union[List[dict], dict], optional) – Config dict for weight initialization. Defaults to None.

init_weights()[source]

Initialize weights.

loss(inputs, data_samples, **kwargs)[source]

The forward function in training.

Parameters:
  • inputs (List[torch.Tensor]) – The input images.

  • data_samples (List[DataSample]) – All elements required during the forward function.

Returns:

A dictionary of loss components.

Return type:

Dict[str, torch.Tensor]

momentum_update()[source]

Momentum update of the teacher network.

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