mmcls.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：定义完整分类模型的顶级模块。

• backbones：用于特征提取的主干网络结构，如 ResNet、MobileNet。

• necks：位于主干网络和头部网络之间的过渡层，如 GlobalAveragePooling。

• heads：用于特定任务的头部网络。在 MMClassification 中，我们提供了若干用于分类任务的头部网络。

• losses：损失函数

• utils：一些辅助函数，或是在多个网络中出现的公共模块。

  • data_preprocessor：对网络的输入进行预处理的模块，如 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.

Backbones

AlexNet	AlexNet backbone.
BEiT	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.
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.
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.
XCiT	XCiT backbone.

Necks

GlobalAveragePooling	Global Average Pooling neck.
GeneralizedMeanPooling	Generalized Mean Pooling neck.
HRFuseScales	Fuse feature map of multiple scales in HRNet.

Heads

ClsHead	Classification head.
LinearClsHead	Linear classifier head.
StackedLinearClsHead	Classifier head with several hidden fc layer and a output fc layer.
VisionTransformerClsHead	Vision Transformer classifier head.
EfficientFormerClsHead	EfficientFormer classifier head.
DeiTClsHead	Distilled Vision Transformer classifier head.
ConformerHead	Linear classifier head.
ArcFaceClsHead	ArcFace classifier head.
MultiLabelClsHead	Classification head for multilabel task.
MultiLabelLinearClsHead	Linear classification head for multilabel task.
CSRAClsHead	Class-specific residual attention classifier head.

Losses

CrossEntropyLoss	Cross entropy loss.
LabelSmoothLoss	Initializer for the label smoothed cross entropy loss.
FocalLoss	Focal loss.
AsymmetricLoss	asymmetric loss.
SeesawLoss	Implementation of seesaw loss.

models.utils

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

Common Components

InvertedResidual	Inverted Residual Block.
SELayer	Squeeze-and-Excitation 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.
ShiftWindowMSA	Shift Window Multihead Self-Attention Module.
MultiheadAttention	Multi-head Attention Module.
ConditionalPositionEncoding	The Conditional Position Encoding (CPE) module.
PatchEmbed	Image to Patch Embedding.
PatchMerging	Merge patch feature map.
HybridEmbed	CNN Feature Map Embedding.
LayerScale	LayerScale layer.

Helper Functions

channel_shuffle	Channel Shuffle operation.
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.
is_tracing	Determine whether the model is called during the tracing of code with torch.jit.trace.