Class SwinTransformer

segmentation/backbones/swin.py:463–754

Swin Transformer backbone. This backbone is the implementation of `Swin Transformer: Hierarchical Vision Transformer using Shifted Windows `_. Inspiration from https://github.com/microsoft/Swin-Transformer. Args: pretrain_img_size (int

@BACKBONES.register_module()
class SwinTransformer(BaseModule):
    """Swin Transformer backbone.

    This backbone is the implementation of `Swin Transformer:
    Hierarchical Vision Transformer using Shifted
    Windows <https://arxiv.org/abs/2103.14030>`_.
    Inspiration from https://github.com/microsoft/Swin-Transformer.

    Args:
        pretrain_img_size (int | tuple[int]): The size of input image when
            pretrain. Defaults: 224.
        in_channels (int): The num of input channels.
            Defaults: 3.
        embed_dims (int): The feature dimension. Default: 96.
        patch_size (int | tuple[int]): Patch size. Default: 4.
        window_size (int): Window size. Default: 7.
        mlp_ratio (int): Ratio of mlp hidden dim to embedding dim.
            Default: 4.
        depths (tuple[int]): Depths of each Swin Transformer stage.
            Default: (2, 2, 6, 2).
        num_heads (tuple[int]): Parallel attention heads of each Swin
            Transformer stage. Default: (3, 6, 12, 24).
        strides (tuple[int]): The patch merging or patch embedding stride of
            each Swin Transformer stage. (In swin, we set kernel size equal to
            stride.) Default: (4, 2, 2, 2).
        out_indices (tuple[int]): Output from which stages.
            Default: (0, 1, 2, 3).
        qkv_bias (bool, optional): If True, add a learnable bias to query, key,
            value. Default: True
        qk_scale (float | None, optional): Override default qk scale of
            head_dim ** -0.5 if set. Default: None.
        patch_norm (bool): If add a norm layer for patch embed and patch
            merging. Default: True.
        drop_rate (float): Dropout rate. Defaults: 0.
        attn_drop_rate (float): Attention dropout rate. Default: 0.
        drop_path_rate (float): Stochastic depth rate. Defaults: 0.1.
        use_abs_pos_embed (bool): If True, add absolute position embedding to
            the patch embedding. Defaults: False.
        act_cfg (dict): Config dict for activation layer.
            Default: dict(type='LN').
        norm_cfg (dict): Config dict for normalization layer at
            output of backone. Defaults: dict(type='LN').
        with_cp (bool, optional): Use checkpoint or not. Using checkpoint
            will save some memory while slowing down the training speed.
            Default: False.
        pretrained (str, optional): model pretrained path. Default: None.
        frozen_stages (int): Stages to be frozen (stop grad and set eval mode).
            -1 means not freezing any parameters.
        init_cfg (dict, optional): The Config for initialization.
            Defaults to None.
    """

    def __init__(self,
                 pretrain_img_size=224,
                 in_channels=3,
                 embed_dims=96,
                 patch_size=4,
                 window_size=7,