.. role:: hidden :class: hidden-section .. module:: mmpretrain.models mmpretrain.models =================================== The ``models`` package contains several sub-packages for addressing the different components of a model. - :mod:`~mmpretrain.models.classifiers`: The top-level module which defines the whole process of a classification model. - :mod:`~mmpretrain.models.selfsup`: The top-level module which defines the whole process of a self-supervised learning model. - :mod:`~mmpretrain.models.backbones`: Usually a feature extraction network, e.g., ResNet, MobileNet. - :mod:`~mmpretrain.models.necks`: The component between backbones and heads, e.g., GlobalAveragePooling. - :mod:`~mmpretrain.models.heads`: The component for specific tasks. In MMClassification, we provides heads for classification. - :mod:`~mmpretrain.models.losses`: Loss functions. - :mod:`~mmpretrain.models.utils`: Some helper functions and common components used in various networks. - :mod:`~mmpretrain.models.utils.data_preprocessor`: The component before model to preprocess the inputs, e.g., ClsDataPreprocessor. - :ref:`components`: Common components used in various networks. - :ref:`helpers`: Helper functions. Build Functions --------------- .. autosummary:: :toctree: generated :nosignatures: build_classifier build_backbone build_neck build_head build_loss .. module:: mmpretrain.models.classifiers Classifiers ------------------ .. autosummary:: :toctree: generated :nosignatures: BaseClassifier ImageClassifier TimmClassifier HuggingFaceClassifier .. module:: mmpretrain.models.selfsup Self-supervised Algorithms -------------------------- .. _selfsup_algorithms: .. autosummary:: :toctree: generated :nosignatures: BaseSelfSupervisor BEiT BYOL BarlowTwins CAE DenseCL EVA MAE MILAN MaskFeat MixMIM MoCo MoCoV3 SimCLR SimMIM SimSiam SwAV .. _selfsup_backbones: Some of above algorithms modified the backbone module to adapt the extra inputs like ``mask``, and here is the a list of these **modified backbone** modules. .. autosummary:: :toctree: generated :nosignatures: BEiTPretrainViT CAEPretrainViT MAEViT MILANViT MaskFeatViT MixMIMPretrainTransformer MoCoV3ViT SimMIMSwinTransformer .. _target_generators: Some self-supervise algorithms need an external **target generator** to generate the optimization target. Here is a list of target generators. .. autosummary:: :toctree: generated :nosignatures: VQKD DALLEEncoder HOGGenerator CLIPGenerator .. module:: mmpretrain.models.backbones Backbones ------------------ .. autosummary:: :toctree: generated :nosignatures: AlexNet BEiTViT CSPDarkNet CSPNet CSPResNeXt CSPResNet Conformer ConvMixer ConvNeXt DaViT DeiT3 DenseNet DistilledVisionTransformer EdgeNeXt EfficientFormer EfficientNet EfficientNetV2 HRNet HorNet InceptionV3 LeNet5 LeViT MViT MlpMixer MobileNetV2 MobileNetV3 MobileOne MobileViT PCPVT PoolFormer PyramidVig RegNet RepLKNet RepMLPNet RepVGG Res2Net ResNeSt ResNeXt ResNet ResNetV1c ResNetV1d ResNet_CIFAR RevVisionTransformer SEResNeXt SEResNet SVT ShuffleNetV1 ShuffleNetV2 SwinTransformer SwinTransformerV2 T2T_ViT TIMMBackbone TNT VAN VGG Vig VisionTransformer XCiT .. module:: mmpretrain.models.necks Necks ------------------ .. autosummary:: :toctree: generated :nosignatures: BEiTV2Neck CAENeck ClsBatchNormNeck DenseCLNeck GeneralizedMeanPooling GlobalAveragePooling HRFuseScales LinearNeck MAEPretrainDecoder MILANPretrainDecoder MixMIMPretrainDecoder MoCoV2Neck NonLinearNeck SimMIMLinearDecoder SwAVNeck .. module:: mmpretrain.models.heads Heads ------------------ .. autosummary:: :toctree: generated :nosignatures: ArcFaceClsHead BEiTV1Head BEiTV2Head CAEHead CSRAClsHead ClsHead ConformerHead ContrastiveHead DeiTClsHead EfficientFormerClsHead LatentCrossCorrelationHead LatentPredictHead LeViTClsHead LinearClsHead MAEPretrainHead MIMHead MixMIMPretrainHead MoCoV3Head MultiLabelClsHead MultiLabelLinearClsHead MultiTaskHead SimMIMHead StackedLinearClsHead SwAVHead VigClsHead VisionTransformerClsHead .. module:: mmpretrain.models.losses Losses ------------------ .. autosummary:: :toctree: generated :nosignatures: AsymmetricLoss CAELoss CosineSimilarityLoss CrossCorrelationLoss CrossEntropyLoss FocalLoss LabelSmoothLoss PixelReconstructionLoss SeesawLoss SwAVLoss .. module:: mmpretrain.models.utils models.utils ------------ This package includes some helper functions and common components used in various networks. .. _components: Common Components ^^^^^^^^^^^^^^^^^ .. autosummary:: :toctree: generated :nosignatures: ConditionalPositionEncoding CosineEMA HybridEmbed InvertedResidual LayerScale MultiheadAttention PatchEmbed PatchMerging SELayer ShiftWindowMSA WindowMSA WindowMSAV2 .. _helpers: Helper Functions ^^^^^^^^^^^^^^^^ .. autosummary:: :toctree: generated :nosignatures: channel_shuffle is_tracing make_divisible resize_pos_embed resize_relative_position_bias_table to_ntuple