mmpretrain.models

models 包中包含了若干子包，分别对应神经网络中不同的组件。

• classifiers: The top-level module which defines the whole process of a classification model.

• selfsup: The top-level module which defines the whole process of a self-supervised learning model.

• retrievers: The top-level module which defines the whole process of a retrieval model.

• backbones: Usually a feature extraction network, e.g., ResNet, MobileNet.

• necks: The component between backbones and heads, e.g., GlobalAveragePooling.

• heads: The component for specific tasks.

• losses: Loss functions.

• peft: The PEFT (Parameter-Efficient Fine-Tuning) module, e.g. LoRAModel.

• utils: Some helper functions and common components used in various networks.

  • data_preprocessor: The component before model to preprocess the inputs, e.g., ClsDataPreprocessor.

  • Common Components：多个网络共用的一些公共模块。

  • Helper Functions：模型中用到的辅助函数。

Build Functions

build_classifier	Build classifier.
build_backbone	Build backbone.
build_neck	Build neck.
build_head	Build head.
build_loss	Build loss.

Classifiers

BaseClassifier	Base class for classifiers.
ImageClassifier	Image classifiers for supervised classification task.
TimmClassifier	Image classifiers for pytorch-image-models (timm) model.
HuggingFaceClassifier	Image classifiers for HuggingFace model.

Self-supervised Algorithms

BaseSelfSupervisor	BaseModel for Self-Supervised Learning.
BEiT	BEiT v1/v2.
BYOL	BYOL.
BarlowTwins	BarlowTwins.
CAE	CAE.
DenseCL	DenseCL.
EVA	EVA.
iTPN	iTPN.
MAE	MAE.
MILAN	MILAN.
MaskFeat	MaskFeat.
MixMIM	MixMIM.
MoCo	MoCo.
MoCoV3	MoCo v3.
SimCLR	SimCLR.
SimMIM	SimMIM.
SimSiam	SimSiam.
SparK	Implementation of SparK.
SwAV	SwAV.

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.

BEiTPretrainViT	Vision Transformer for BEiT pre-training.
CAEPretrainViT	Vision Transformer for CAE pre-training and the implementation is based on BEiTViT.
iTPNHiViT	HiViT for iTPN pre-training.
MAEHiViT	HiViT for MAE pre-training.
MAEViT	Vision Transformer for MAE pre-training.
MILANViT	Vision Transformer for MILAN pre-training.
MaskFeatViT	Vision Transformer for MaskFeat pre-training.
MixMIMPretrainTransformer	MixMIM backbone for MixMIM pre-training.
MoCoV3ViT	Vision Transformer for MoCoV3 pre-training.
SimMIMSwinTransformer	Swin Transformer for SimMIM pre-training.

Some self-supervise algorithms need an external target generator to generate the optimization target. Here is a list of target generators.

VQKD	Vector-Quantized Knowledge Distillation.
DALLEEncoder	DALL-E Encoder for feature extraction.
HOGGenerator	Generate HOG feature for images.
CLIPGenerator	Get the features and attention from the last layer of CLIP.

Retrievers

BaseRetriever	Base class for retriever.
ImageToImageRetriever	Image To Image Retriever for supervised retrieval task.

Multi-Modality Algorithms

Blip2Caption	BLIP2 Caption.
Blip2Retrieval	BLIP2 Retriever.
Blip2VQA	BLIP2 VQA.
BlipCaption	BLIP Caption.
BlipGrounding	BLIP Grounding.
BlipNLVR	BLIP NLVR.
BlipRetrieval	BLIP Retriever.
BlipVQA	BLIP VQA.
Flamingo	The Open Flamingo model for multiple tasks.
OFA	The OFA model for multiple tasks.
MiniGPT4	The multi-modality model of MiniGPT-4.
Llava	The LLaVA model for multiple tasks.
Otter	The Otter model for multiple tasks.

Backbones

AlexNet	AlexNet backbone.
BEiTViT	Backbone for BEiT.
CSPDarkNet	CSP-Darknet backbone used in YOLOv4.
CSPNet	The abstract CSP Network class.
CSPResNeXt	CSP-ResNeXt backbone.
CSPResNet	CSP-ResNet backbone.
Conformer	Conformer backbone.
ConvMixer	ConvMixer.
ConvNeXt	ConvNeXt v1&v2 backbone.
DaViT	DaViT.
DeiT3	DeiT3 backbone.
DenseNet	DenseNet.
DistilledVisionTransformer	Distilled Vision Transformer.
EdgeNeXt	EdgeNeXt.
EfficientFormer	EfficientFormer.
EfficientNet	EfficientNet backbone.
EfficientNetV2	EfficientNetV2 backbone.
HiViT	HiViT.
HRNet	HRNet backbone.
HorNet	HorNet backbone.
InceptionV3	Inception V3 backbone.
LeNet5	LeNet5 backbone.
LeViT	LeViT backbone.
MViT	Multi-scale ViT v2.
MlpMixer	Mlp-Mixer backbone.
MobileNetV2	MobileNetV2 backbone.
MobileNetV3	MobileNetV3 backbone.
MobileOne	MobileOne backbone.
MobileViT	MobileViT backbone.
PCPVT	The backbone of Twins-PCPVT.
PoolFormer	PoolFormer.
PyramidVig	Pyramid Vision GNN backbone.
RegNet	RegNet backbone.
RepLKNet	RepLKNet backbone.
RepMLPNet	RepMLPNet backbone.
RepVGG	RepVGG backbone.
Res2Net	Res2Net backbone.
ResNeSt	ResNeSt backbone.
ResNeXt	ResNeXt backbone.
ResNet	ResNet backbone.
ResNetV1c	ResNetV1c backbone.
ResNetV1d	ResNetV1d backbone.
ResNet_CIFAR	ResNet backbone for CIFAR.
RevVisionTransformer	Reversible Vision Transformer.
SEResNeXt	SEResNeXt backbone.
SEResNet	SEResNet backbone.
SVT	The backbone of Twins-SVT.
ShuffleNetV1	ShuffleNetV1 backbone.
ShuffleNetV2	ShuffleNetV2 backbone.
SparseResNet	ResNet with sparse module conversion function.
SparseConvNeXt	ConvNeXt with sparse module conversion function.
SwinTransformer	Swin Transformer.
SwinTransformerV2	Swin Transformer V2.
T2T_ViT	Tokens-to-Token Vision Transformer (T2T-ViT)
TIMMBackbone	Wrapper to use backbones from timm library.
TNT	Transformer in Transformer.
VAN	Visual Attention Network.
VGG	VGG backbone.
Vig	Vision GNN backbone.
VisionTransformer	Vision Transformer.
ViTSAM	Vision Transformer as image encoder used in SAM.
XCiT	XCiT backbone.
ViTEVA02	EVA02 Vision Transformer.

Necks

BEiTV2Neck	Neck for BEiTV2 Pre-training.
CAENeck	Neck for CAE Pre-training.
ClsBatchNormNeck	Normalize cls token across batch before head.
DenseCLNeck	The non-linear neck of DenseCL.
GeneralizedMeanPooling	Generalized Mean Pooling neck.
GlobalAveragePooling	Global Average Pooling neck.
HRFuseScales	Fuse feature map of multiple scales in HRNet.
LinearNeck	Linear neck with Dimension projection.
MAEPretrainDecoder	Decoder for MAE Pre-training.
MILANPretrainDecoder	Prompt decoder for MILAN.
MixMIMPretrainDecoder	Decoder for MixMIM Pretraining.
MoCoV2Neck	The non-linear neck of MoCo v2: fc-relu-fc.
NonLinearNeck	The non-linear neck.
SimMIMLinearDecoder	Linear Decoder For SimMIM pretraining.
SwAVNeck	The non-linear neck of SwAV: fc-bn-relu-fc-normalization.
iTPNPretrainDecoder	The neck module of iTPN (transformer pyramid network).
SparKLightDecoder	The decoder for SparK, which upsamples the feature maps.

Heads

ArcFaceClsHead	ArcFace classifier head.
BEiTV1Head	Head for BEiT v1 Pre-training.
BEiTV2Head	Head for BEiT v2 Pre-training.
CAEHead	Head for CAE Pre-training.
CSRAClsHead	Class-specific residual attention classifier head.
ClsHead	Classification head.
ConformerHead	Linear classifier head.
ContrastiveHead	Head for contrastive learning.
DeiTClsHead	Distilled Vision Transformer classifier head.
EfficientFormerClsHead	EfficientFormer classifier head.
LatentCrossCorrelationHead	Head for latent feature cross correlation.
LatentPredictHead	Head for latent feature prediction.
LeViTClsHead
LinearClsHead	Linear classifier head.
MAEPretrainHead	Head for MAE Pre-training.
MIMHead	Pre-training head for Masked Image Modeling.
MixMIMPretrainHead	Head for MixMIM Pre-training.
MoCoV3Head	Head for MoCo v3 Pre-training.
MultiLabelClsHead	Classification head for multilabel task.
MultiLabelLinearClsHead	Linear classification head for multilabel task.
MultiTaskHead	Multi task head.
SimMIMHead	Head for SimMIM Pre-training.
StackedLinearClsHead	Classifier head with several hidden fc layer and a output fc layer.
SwAVHead	Head for SwAV Pre-training.
VigClsHead	The classification head for Vision GNN.
VisionTransformerClsHead	Vision Transformer classifier head.
iTPNClipHead	Head for iTPN Pre-training using Clip.
SparKPretrainHead	Pre-training head for SparK.

Losses

AsymmetricLoss	asymmetric loss.
CAELoss	Loss function for CAE.
CosineSimilarityLoss	Cosine similarity loss function.
CrossCorrelationLoss	Cross correlation loss function.
CrossEntropyLoss	Cross entropy loss.
FocalLoss	Focal loss.
LabelSmoothLoss	Initializer for the label smoothed cross entropy loss.
PixelReconstructionLoss	Loss for the reconstruction of pixel in Masked Image Modeling.
SeesawLoss	Implementation of seesaw loss.
SwAVLoss	The Loss for SwAV.

PEFT

LoRAModel

Implements LoRA in a module.

models.utils

This package includes some helper functions and common components used in various networks.

Common Components

ConditionalPositionEncoding	The Conditional Position Encoding (CPE) module.
CosineEMA	CosineEMA is implemented for updating momentum parameter, used in BYOL, MoCoV3, etc.
HybridEmbed	CNN Feature Map Embedding.
InvertedResidual	Inverted Residual Block.
LayerScale	LayerScale layer.
MultiheadAttention	Multi-head Attention Module.
PatchEmbed	Image to Patch Embedding.
PatchMerging	Merge patch feature map.
SELayer	Squeeze-and-Excitation Module.
ShiftWindowMSA	Shift Window Multihead Self-Attention Module.
WindowMSA	Window based multi-head self-attention (W-MSA) module with relative position bias.
WindowMSAV2	Window based multi-head self-attention (W-MSA) module with relative position bias.

Helper Functions

channel_shuffle	Channel Shuffle operation.
is_tracing	Determine whether the model is called during the tracing of code with torch.jit.trace.
make_divisible	Make divisible function.
resize_pos_embed	Resize pos_embed weights.
resize_relative_position_bias_table	Resize relative position bias table.
to_ntuple	A to_tuple function generator.