Swin Transformer V2: Scaling Up Capacity and Resolution

Unofficial PyTorch reimplementation of the paper Swin Transformer V2: Scaling Up Capacity and Resolution by Ze Liu, Han Hu et al. (Microsoft Research Asia).

This repository includes a pure PyTorch implementation of the Swin Transformer V2.

The official Swin Transformer V1 implementation is available here. Currently (10.01.2022), an official implementation of the Swin Transformer V2 is not publicly available.


You can simply install the Swin Transformer V2 implementation as a Python package by using pip.

pip install git+https://github.com/ChristophReich1996/Involution

Alternatively, you can clone the repository and use the implementation in swin_transformer_v2 directly in your project.


This implementation provides the configurations reported in the paper (SwinV2-T, SwinV2-S, etc.). You can build the model by calling the corresponding function. Please note that the Swin Transformer V2 (SwinTransformerV2 class) implementation returns the feature maps of each stage of the network (List[torch.Tensor]). If you want to use this implementation for image classification simply wrap this model and take the final feature map.

from swin_transformer_v2 import SwinTransformerV2

from swin_transformer_v2 import swin_transformer_v2_t, swin_transformer_v2_s, swin_transformer_v2_b, \
    swin_transformer_v2_l, swin_transformer_v2_h, swin_transformer_v2_g

# SwinV2-T
swin_transformer: SwinTransformerV2 = swin_transformer_v2_t(in_channels=3,
                                                            input_resolution=(256, 256),

If you want to change the resolution and/or the window size for fine-tuning or inference pleas use the update_resolution method.

# Change resolution and window size of the model
swin_transformer.update_resolution(new_window_size=16, new_input_resolution=(512, 512))

In case you want to use a custom configuration you can use the SwinTransformerV2 class. The constructor method takes the following parameters.

Parameter Description Type
in_channels Number of input channels int
depth Depth of the stage (number of layers) int
downscale If true input is downsampled (see Fig. 3 or V1 paper) bool
input_resolution Input resolution Tuple[int, int]
number_of_heads Number of attention heads to be utilized int
window_size Window size to be utilized int
shift_size Shifting size to be used int
ff_feature_ratio Ratio of the hidden dimension in the FFN to the input channels int
dropout Dropout in input mapping float
dropout_attention Dropout rate of attention map float
dropout_path Dropout in main path float
use_checkpoint If true checkpointing is utilized bool
sequential_self_attention If true sequential self-attention is performed bool

This file includes a full example how to use this implementation.


This is a very experimental implementation based on the Swin Transformer V2 paper and the . Since an official implementation of the Swin Transformer V2 is not yet published, it is not possible to say to which extent this implementation might differ from the original one. If you have any issues with this implementation please raise an issue.


    title={{Swin Transformer V2: Scaling Up Capacity and Resolution}},
    author={Liu, Ze and Hu, Han and Lin, Yutong and Yao, Zhuliang and Xie, Zhenda and Wei, Yixuan and Ning, Jia and Cao, 
            Yue and Zhang, Zheng and Dong, Li and others},
    journal={arXiv preprint arXiv:2111.09883},


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