Swin Transformer 代码学习笔记(目标检测)

        本文主要针对目标检测部分的代码。

源码地址：GitHub - SwinTransformer/Swin-Transformer-Object-Detection: This is an official implementation for "Swin Transformer: Hierarchical Vision Transformer using Shifted Windows" on Object Detection and Instance Segmentation.

 论文地址：https://arxiv.org/abs/2103.14030

         开始之前，先上一下swin transformer 结构图

        首先从模型训练开始，训练模型py文件位于项目根目录/tools/train.py，该文件中整体结构简单，仅有一个main函数。为了方便程序运行，我直接在配置项中将config配置成

\configs\swin\mask_rcnn_swin_tiny_patch4_window7_mstrain_480-800_adamw_1x_coco.py

        main函数中主要关注161行后的代码，以下是代码片段

    model = build_detector(
        cfg.model,
        train_cfg=cfg.get('train_cfg'),
        test_cfg=cfg.get('test_cfg'))

    datasets = [build_dataset(cfg.data.train)]
    if len(cfg.workflow) == 2:
        val_dataset = copy.deepcopy(cfg.data.val)
        val_dataset.pipeline = cfg.data.train.pipeline
        datasets.append(build_dataset(val_dataset))
    if cfg.checkpoint_config is not None:
        # save mmdet version, config file content and class names in
        # checkpoints as meta data
        cfg.checkpoint_config.meta = dict(
            mmdet_version=__version__ + get_git_hash()[:7],
            CLASSES=datasets[0].CLASSES)
    # add an attribute for visualization convenience
    model.CLASSES = datasets[0].CLASSES
    train_detector(
        model,
        datasets,
        cfg,
        distributed=distributed,
        validate=(not args.no_validate),
        timestamp=timestamp,
        meta=meta)

        这部分主要是构建模型，构建数据集，模型训练函数

        这里用的Mask RCNN结构，构建模型的时候，会分别构建如下文件夹中的相应组件：

mmdet/models/detectors/mask_rcnn.py 中的Mask RCNN类

mmdet/models/detectors/two_stage.py 中的TwoStageDetector类

mmdet/models/backbones/swin_transformer.py 中的SwinTransformer类（算法关键）

mmdet/models/necks/fpn.py 中的FPN类

mmdet/models/dense_heads/rpn_head.py 中的RPNHead类，其中还会构建各种损失函数和一些功能组件

mmdet/models/roi_heads/base_roi_head.py 中的BaseRoIHead 类

mmdet/models/roi_heads/bbox_heads/convfc_bbox_head.py 中的ConvFCBBoxHead类

mmdet/models/losses/cross_entropy_loss.py 中的CrossEntropyLoss类

mmdet/models/losses/smooth_l1_loss.py 中的L1Loss类

mmdet/core/bbox/assigners/max_iou_assigner.py 中的MaxIoUAssigner类

mmdet/core/bbox/samplers/random_sampler.py中的RandomSampler类

         构建完的模型：

MaskRCNN(
  (backbone): SwinTransformer(
    (patch_embed): PatchEmbed(
      (proj): Conv2d(3, 96, kernel_size=(4, 4), stride=(4, 4))
      (norm): LayerNorm((96,), eps=1e-05, elementwise_affine=True)
    )
    (pos_drop): Dropout(p=0.0, inplace=False)
    (layers): ModuleList(
      (0): BasicLayer(
        (blocks): ModuleList(
          (0): SwinTransformerBlock(
            (norm1): LayerNorm((96,), eps=1e-05, elementwise_affine=True)
            (attn): WindowAttention(
              (qkv): Linear(in_features=96, out_features=288, bias=True)
              (attn_drop): Dropout(p=0.0, inplace=False)
              (proj): Linear(in_features=96, out_features=96, bias=True)
              (proj_drop): Dropout(p=0.0, inplace=False)
              (softmax): Softmax(dim=-1)
            )
            (drop_path): Identity()
            (norm2): LayerNorm((96,), eps=1e-05, elementwise_affine=True)
            (mlp): Mlp(
              (fc1): Linear(in_features=96, out_features=384, bias=True)
              (act): GELU()
              (fc2): Linear(in_features=384, out_features=96, bias=True)
              (drop): Dropout(p=0.0, inplace=False)
            )
          )
          (1): SwinTransformerBlock(
            (norm1): LayerNorm((96,), eps=1e-05, elementwise_affine=True)
            (attn): WindowAttention(
              (qkv): Linear(in_features=96, out_features=288, bias=True)
              (attn_drop): Dropout(p=0.0, inplace=False)
              (proj): Linear(in_features=96, out_features=96, bias=True)
              (proj_drop): Dropout(p=0.0, inplace=False)
              (softmax): Softmax(dim=-1)
            )
            (drop_path): DropPath()
            (norm2): LayerNorm((96,), eps=1e-05, elementwise_affine=True)
            (mlp): Mlp(
              (fc1): Linear(in_features=96, out_features=384, bias=True)
              (act): GELU()
              (fc2): Linear(in_features=384, out_features=96, bias=True)
              (drop): Dropout(p=0.0, inplace=False)
            )
          )
        )
        (downsample): PatchMerging(
          (reduction): Linear(in_features=384, out_features=192, bias=False)
          (norm): LayerNorm((384,), eps=1e-05, elementwise_affine=True)
        )
      )
      (1): BasicLayer(
        (blocks): ModuleList(
          (0): SwinTransformerBlock(
            (norm1): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
            (attn): WindowAttention(
              (qkv): Linear(in_features=192, out_features=576, bias=True)
              (attn_drop): Dropout(p=0.0, inplace=False)
              (proj): Linear(in_features=192, out_features=192, bias=True)
              (proj_drop): Dropout(p=0.0, inplace=False)
              (softmax): Softmax(dim=-1)
            )
            (drop_path): DropPath()
            (norm2): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
            (mlp): Mlp(
              (fc1): Linear(in_features=192, out_features=768, bias=True)
              (act): GELU()
              (fc2): Linear(in_features=768, out_features=192, bias=True)
              (drop): Dropout(p=0.0, inplace=False)
            )
          )
          (1): SwinTransformerBlock(
            (norm1): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
            (attn): WindowAttention(
              (qkv): Linear(in_features=192, out_features=576, bias=True)
              (attn_drop): Dropout(p=0.0, inplace=False)
              (proj): Linear(in_features=192, out_features=192, bias=True)
              (proj_drop): Dropout(p=0.0, inplace=False)
              (softmax): Softmax(dim=-1)
            )
            (drop_path): DropPath()
            (norm2): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
            (mlp): Mlp(
              (fc1): Linear(in_features=192, out_features=768, bias=True)
              (act): GELU()
              (fc2): Linear(in_features=768, out_features=192, bias=True)
              (drop): Dropout(p=0.0, inplace=False)
            )
          )
        )
        (downsample): PatchMerging(
          (reduction): Linear(in_features=768, out_features=384, bias=False)
          (norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
        )
      )
      (2): BasicLayer(
        (blocks): ModuleList(
          (0): SwinTransformerBlock(
            (norm1): LayerNorm((384,), eps=1e-05, elementwise_affine=True)
            (attn): WindowAttention(
              (qkv): Linear(in_features=384, out_features=1152, bias=True)
              (attn_drop): Dropout(p=0.0, inplace=False)
              (proj): Linear(in_features=384, out_features=384, bias=True)
              (proj_drop): Dropout(p=0.0, inplace=False)
              (softmax): Softmax(dim=-1)
            )
            (drop_path): DropPath()
            (norm2): LayerNorm((384,), eps=1e-05, elementwise_affine=True)
            (mlp): Mlp(
              (fc1): Linear(in_features=384, out_features=1536, bias=True)
              (act): GELU()
              (fc2): Linear(in_features=1536, out_features=384, bias=True)
              (drop): Dropout(p=0.0, inplace=False)
            )
          )
          (1): SwinTransformerBlock(
            (norm1): LayerNorm((384,), eps=1e-05, elementwise_affine=True)
            (attn): WindowAttention(
              (qkv): Linear(in_features=384, out_features=1152, bias=True)
              (attn_drop): Dropout(p=0.0, inplace=False)
              (proj): Linear(in_features=384, out_features=384, bias=True)
              (proj_drop): Dropout(p=0.0, inplace=False)
              (softmax): Softmax(dim=-1)
            )
            (drop_path): DropPath()
            (norm2): LayerNorm((384,), eps=1e-05, elementwise_affine=True)
            (mlp): Mlp(
              (fc1): Linear(in_features=384, out_features=1536, bias=True)
              (act): GELU()
              (fc2): Linear(in_features=1536, out_features=384, bias=True)
              (drop): Dropout(p=0.0, inplace=False)
            )
          )
          (2): SwinTransformerBlock(
            (norm1): LayerNorm((384,), eps=1e-05, elementwise_affine=True)
            (attn): WindowAttention(
              (qkv): Linear(in_features=384, out_features=1152, bias=True)
              (attn_drop): Dropout(p=0.0, inplace=False)
              (proj): Linear(in_features=384, out_features=384, bias=True)
              (proj_drop): Dropout(p=0.0, inplace=False)
              (softmax): Softmax(dim=-1)
            )
            (drop_path): DropPath()
            (norm2): LayerNorm((384,), eps=1e-05, elementwise_affine=True)
            (mlp): Mlp(
              (fc1): Linear(in_features=384, out_features=1536, bias=True)
              (act): GELU()
              (fc2): Linear(in_features=1536, out_features=384, bias=True)
              (drop): Dropout(p=0.0, inplace=False)
            )
          )
          (3): SwinTransformerBlock(
            (norm1): LayerNorm((384,), eps=1e-05, elementwise_affine=True)
            (attn): WindowAttention(
              (qkv): Linear(in_features=384, out_features=1152, bias=True)
              (attn_drop): Dropout(p=0.0, inplace=False)
              (proj): Linear(in_features=384, out_features=384, bias=True)
              (proj_drop): Dropout(p=0.0, inplace=False)
              (softmax): Softmax(dim=-1)
            )
            (drop_path): DropPath()
            (norm2): LayerNorm((384,), eps=1e-05, elementwise_affine=True)
            (mlp): Mlp(
              (fc1): Linear(in_features=384, out_features=1536, bias=True)
              (act): GELU()
              (fc2): Linear(in_features=1536, out_features=384, bias=True)
              (drop): Dropout(p=0.0, inplace=False)
            )
          )
          (4): SwinTransformerBlock(
            (norm1): LayerNorm((384,), eps=1e-05, elementwise_affine=True)
            (attn): WindowAttention(
              (qkv): Linear(in_features=384, out_features=1152, bias=True)
              (attn_drop): Dropout(p=0.0, inplace=False)
              (proj): Linear(in_features=384, out_features=384, bias=True)
              (proj_drop): Dropout(p=0.0, inplace=False)
              (softmax): Softmax(dim=-1)
            )
            (drop_path): DropPath()
            (norm2): LayerNorm((384,), eps=1e-05, elementwise_affine=True)
            (mlp): Mlp(
              (fc1): Linear(in_features=384, out_features=1536, bias=True)
              (act): GELU()
              (fc2): Linear(in_features=1536, out_features=384, bias=True)
              (drop): Dropout(p=0.0, inplace=False)
            )
          )
          (5): SwinTransformerBlock(
            (norm1): LayerNorm((384,), eps=1e-05, elementwise_affine=True)
            (attn): WindowAttention(
              (qkv): Linear(in_features=384, out_features=1152, bias=True)
              (attn_drop): Dropout(p=0.0, inplace=False)
              (proj): Linear(in_features=384, out_features=384, bias=True)
              (proj_drop): Dropout(p=0.0, inplace=False)
              (softmax): Softmax(dim=-1)
            )
            (drop_path): DropPath()
            (norm2): LayerNorm((384,), eps=1e-05, elementwise_affine=True)
            (mlp): Mlp(
              (fc1): Linear(in_features=384, out_features=1536, bias=True)
              (act): GELU()
              (fc2): Linear(in_features=1536, out_features=384, bias=True)
              (drop): Dropout(p=0.0, inplace=False)
            )
          )
        )
        (downsample): PatchMerging(
          (reduction): Linear(in_features=1536, out_features=768, bias=False)
          (norm): LayerNorm((1536,), eps=1e-05, elementwise_affine=True)
        )
      )
      (3): BasicLayer(
        (blocks): ModuleList(
          (0): SwinTransformerBlock(
            (norm1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
            (attn): WindowAttention(
              (qkv): Linear(in_features=768, out_features=2304, bias=True)
              (attn_drop): Dropout(p=0.0, inplace=False)
              (proj): Linear(in_features=768, out_features=768, bias=True)
              (proj_drop): Dropout(p=0.0, inplace=False)
              (softmax): Softmax(dim=-1)
            )
            (drop_path): DropPath()
            (norm2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
            (mlp): Mlp(
              (fc1): Linear(in_features=768, out_features=3072, bias=True)
              (act): GELU()
              (fc2): Linear(in_features=3072, out_features=768, bias=True)
              (drop): Dropout(p=0.0, inplace=False)
            )
          )
          (1): SwinTransformerBlock(
            (norm1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
            (attn): WindowAttention(
              (qkv): Linear(in_features=768, out_features=2304, bias=True)
              (attn_drop): Dropout(p=0.0, inplace=False)
              (proj): Linear(in_features=768, out_features=768, bias=True)
              (proj_drop): Dropout(p=0.0, inplace=False)
              (softmax): Softmax(dim=-1)
            )
            (drop_path): DropPath()
            (norm2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
            (mlp): Mlp(
              (fc1): Linear(in_features=768, out_features=3072, bias=True)
              (act): GELU()
              (fc2): Linear(in_features=3072, out_features=768, bias=True)
              (drop): Dropout(p=0.0, inplace=False)
            )
          )
        )
      )
    )
    (norm0): LayerNorm((96,), eps=1e-05, elementwise_affine=True)
    (norm1): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
    (norm2): LayerNorm((384,), eps=1e-05, elementwise_affine=True)
    (norm3): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
  )
  (neck): FPN(
    (lateral_convs): ModuleList(
      (0): ConvModule(
        (conv): Conv2d(96, 256, kernel_size=(1, 1), stride=(1, 1))
      )
      (1): ConvModule(
        (conv): Conv2d(192, 256, kernel_size=(1, 1), stride=(1, 1))
      )
      (2): ConvModule(
        (conv): Conv2d(384, 256, kernel_size=(1, 1), stride=(1, 1))
      )
      (3): ConvModule(
        (conv): Conv2d(768, 256, kernel_size=(1, 1), stride=(1, 1))
      )
    )
    (fpn_convs): ModuleList(
      (0): ConvModule(
        (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      )
      (1): ConvModule(
        (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      )
      (2): ConvModule(
        (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      )
      (3): ConvModule(
        (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      )
    )
  )
  (rpn_head): RPNHead(
    (loss_cls): CrossEntropyLoss()
    (loss_bbox): L1Loss()
    (rpn_conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (rpn_cls): Conv2d(256, 3, kernel_size=(1, 1), stride=(1, 1))
    (rpn_reg): Conv2d(256, 12, kernel_size=(1, 1), stride=(1, 1))
  )
  (roi_head): StandardRoIHead(
    (bbox_roi_extractor): SingleRoIExtractor(
      (roi_layers): ModuleList(
        (0): RoIAlign(output_size=(7, 7), spatial_scale=0.25, sampling_ratio=0, pool_mode=avg, aligned=True, use_torchvision=False)
        (1): RoIAlign(output_size=(7, 7), spatial_scale=0.125, sampling_ratio=0, pool_mode=avg, aligned=True, use_torchvision=False)
        (2): RoIAlign(output_size=(7, 7), spatial_scale=0.0625, sampling_ratio=0, pool_mode=avg, aligned=True, use_torchvision=False)
        (3): RoIAlign(output_size=(7, 7), spatial_scale=0.03125, sampling_ratio=0, pool_mode=avg, aligned=True, use_torchvision=False)
      )
    )
    (bbox_head): Shared2FCBBoxHead(
      (loss_cls): CrossEntropyLoss()
      (loss_bbox): L1Loss()
      (fc_cls): Linear(in_features=1024, out_features=21, bias=True)
      (fc_reg): Linear(in_features=1024, out_features=80, bias=True)
      (shared_convs): ModuleList()
      (shared_fcs): ModuleList(
        (0): Linear(in_features=12544, out_features=1024, bias=True)
        (1): Linear(in_features=1024, out_features=1024, bias=True)
      )
      (cls_convs): ModuleList()
      (cls_fcs): ModuleList()
      (reg_convs): ModuleList()
      (reg_fcs): ModuleList()
      (relu): ReLU(inplace=True)
    )
  )
)

        backbone部分的就是swin transformer的精髓，下图是mask rcnn的结构图

        Swin Transformer就是用transformer块替换了图中CNN的结构，作为特征采集器。扯了这么多，是时候进入正题了，代码的关键算法都位于mmdet/models/backbones/swin_transformer.py文件中。主体位于SwinTransformer类中。

class SwinTransformer(nn.Module):
    """ Swin Transformer backbone.
        A PyTorch impl of : `Swin Transformer: Hierarchical Vision Transformer using Shifted Windows`  -
          https://arxiv.org/pdf/2103.14030

    Args:
        pretrain_img_size (int): Input image size for training the pretrained model,
            used in absolute postion embedding. Default 224.
        patch_size (int | tuple(int)): Patch size. Default: 4.
        in_chans (int): Number of input image channels. Default: 3.
        embed_dim (int): Number of linear projection output channels. Default: 96.
        depths (tuple[int]): Depths of each Swin Transformer stage.
        num_heads (tuple[int]): Number of attention head of each stage.
        window_size (int): Window size. Default: 7.
        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4.
        qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: True
        qk_scale (float): Override default qk scale of head_dim ** -0.5 if set.
        drop_rate (float): Dropout rate.
        attn_drop_rate (float): Attention dropout rate. Default: 0.
        drop_path_rate (float): Stochastic depth rate. Default: 0.2.
        norm_layer (nn.Module): Normalization layer. Default: nn.LayerNorm.
        ape (bool): If True, add absolute position embedding to the patch embedding. Default: False.
        patch_norm (bool): If True, add normalization after patch embedding. Default: True.
        out_indices (Sequence[int]): Output from which stages.
        frozen_stages (int): Stages to be frozen (stop grad and set eval mode).
            -1 means not freezing any parameters.
        use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.
    """

    def __init__(self,
                 pretrain_img_size=224,
                 patch_size=4,
                 in_chans=3,
                 embed_dim=96,
                 depths=[2, 2, 6, 2],
                 num_heads=[3, 6, 12, 24],
                 window_size=7,
                 mlp_ratio=4.,
                 qkv_bias=True,
                 qk_scale=None,
                 drop_rate=0.,
                 attn_drop_rate=0.,
                 drop_path_rate=0.2,
                 norm_layer=nn.LayerNorm,
                 ape=False,
                 patch_norm=True,
                 out_indices=(0, 1, 2, 3),
                 frozen_stages=-1,
                 use_checkpoint=False):
        super().__init__()

        self.pretrain_img_size = pretrain_img_size
        self.num_layers = len(depths)
        self.embed_dim = embed_dim
        self.ape = ape
        self.patch_norm = patch_norm
        self.out_indices = out_indices
        self.frozen_stages = frozen_stages

        # split image into non-overlapping patches
        self.patch_embed = PatchEmbed(
            patch_size=patch_size, in_chans=in_chans, embed_dim=embed_dim,
            norm_layer=norm_layer if self.patch_norm else None)

        # absolute position embedding
        if self.ape:
            pretrain_img_size = to_2tuple(pretrain_img_size)
            patch_size = to_2tuple(patch_size)
            patches_resolution = [pretrain_img_size[0] // patch_size[0], pretrain_img_size[1] // patch_size[1]]

            # 对应网络结构中的 linear embedding 网络结构
            self.absolute_pos_embed = nn.Parameter(torch.zeros(1, embed_dim, patches_resolution[0], patches_resolution[1]))
            # 绝对位置编码参数初始化
            trunc_normal_(self.absolute_pos_embed, std=.02)

        self.pos_drop = nn.Dropout(p=drop_rate)

        # stochastic depth
        # 给网络层数每层设置随机dropout rate
        dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))]  # stochastic depth decay rule

        # build layers
        self.layers = nn.ModuleList()
        # 构建四层网络结构
        # mlp_ratio Ratio of mlp hidden dim to embedding dim.
        # downsample 下采样 前三个block 会进行下采样 第四个block 不会在进行下采样
        for i_layer in range(self.num_layers):
            layer = BasicLayer(
                dim=int(embed_dim * 2 ** i_layer),
                depth=depths[i_layer],
                num_heads=num_heads[i_layer],
                window_size=window_size,
                mlp_ratio=mlp_ratio,
                qkv_bias=qkv_bias,
                qk_scale=qk_scale,
                drop=drop_rate,
                attn_drop=attn_drop_rate,
                drop_path=dpr[sum(depths[:i_layer]):sum(depths[:i_layer + 1])],
                norm_layer=norm_layer,
                downsample=PatchMerging if (i_layer < self.num_layers - 1) else None,
                use_checkpoint=use_checkpoint)
            self.layers.append(layer)

        num_features = [int(embed_dim * 2 ** i) for i in range(self.num_layers)]
        self.num_features = num_features

        # add a norm layer for each output
        for i_layer in out_indices:
            layer = norm_layer(num_features[i_layer])
            layer_name = f'norm{i_layer}'
            self.add_module(layer_name, layer)

        self._freeze_stages()

    def _freeze_stages(self):
        if self.frozen_stages >= 0:
            self.patch_embed.eval()
            for param in self.patch_embed.parameters():
                param.requires_grad = False

        if self.frozen_stages >= 1 and self.ape:
            self.absolute_pos_embed.requires_grad = False

        if self.frozen_stages >= 2:
            self.pos_drop.eval()
            for i in range(0, self.frozen_stages - 1):
                m = self.layers[i]
                m.eval()
                for param in m.parameters():
                    param.requires_grad = False

    def init_weights(self, pretrained=None):
        """Initialize the weights in backbone.

        Args:
            pretrained (str, optional): Path to pre-trained weights.
                Defaults to None.
        """

        def _init_weights(m):
            if isinstance(m, nn.Linear):
                trunc_normal_(m.weight, std=.02)
                if isinstance(m, nn.Linear) and m.bias is not None:
                    nn.init.constant_(m.bias, 0)
            elif isinstance(m, nn.LayerNorm):
                nn.init.constant_(m.bias, 0)
                nn.init.constant_(m.weight, 1.0)

        if isinstance(pretrained, str):
            self.apply(_init_weights)
            logger = get_root_logger()
            load_checkpoint(self, pretrained, strict=False, logger=logger)
        elif pretrained is None:
            self.apply(_init_weights)
        else:
            raise TypeError('pretrained must be a str or None')

    def forward(self, x):
        """Forward function."""
        x = self.patch_embed(x)

        Wh, Ww = x.size(2), x.size(3)
        if self.ape:
            # interpolate the position embedding to the corresponding size
            absolute_pos_embed = F.interpolate(self.absolute_pos_embed, size=(Wh, Ww), mode='bicubic')
            x = (x + absolute_pos_embed).flatten(2).transpose(1, 2)  # B Wh*Ww C
        else:
            x = x.flatten(2).transpose(1, 2)
        x = self.pos_drop(x)

        outs = []
        for i in range(self.num_layers):
            layer = self.layers[i]
            x_out, H, W, x, Wh, Ww = layer(x, Wh, Ww)

            if i in self.out_indices:
                norm_layer = getattr(self, f'norm{i}')
                x_out = norm_layer(x_out)

                out = x_out.view(-1, H, W, self.num_features[i]).permute(0, 3, 1, 2).contiguous()
                outs.append(out)

        return tuple(outs)

    def train(self, mode=True):
        """Convert the model into training mode while keep layers freezed."""
        super(SwinTransformer, self).train(mode)
        self._freeze_stages()

        为了方便将img_scale设置为[(224,224)]，此时的数据集中的图片会进行resize，并且将短边padding成32的倍数，按最长边与224的比例缩放最短边，即 x=短边 / (500/224)，x即为缩放后的实际长度，比如输入时500*287（w*h）的图片，缩放后的短边长为287 * 224 / 500 = 129，由于输入的图像尺寸需要是32的倍数，此时需要向上取整，所以padding后的短边长为160。
        吃了贫穷的亏，电脑只有单卡3080ti，batch size设置为2，使用未修改的img_scale训练coco数据集时还经常贴着最大显存跑，生怕训练的时候爆显存。。。所以单卡训练batch size就设置成2吧，每个batch中的数据会根据边长最大的图片再进行一次padding，比如其中一张第一次padding后大小为(160, 224, 3)，另一张为(192, 224, 3)，那么最终输入网络的数据尺寸为[2, 3, 192, 224] (B,C,H,W)

        之后将会以输入[2, 3, 192, 224]为例进行讲解，按网络结构的顺序来。

        PatchEmbed 通过一个size为4，stride为4的2d卷积来达到图像缩小4倍，并将维度升到embed_dim，PatchEmbed可以简单理解为包含了网络结构中的patch partition和linear embedding，所以embed_dim为输入transformer block的维度而不是论文中的48。

class PatchEmbed(nn.Module):
    """ Image to Patch Embedding

    Args:
        patch_size (int): Patch token size. Default: 4.
        in_chans (int): Number of input image channels. Default: 3.
        embed_dim (int): Number of linear projection output channels. Default: 96.
        norm_layer (nn.Module, optional): Normalization layer. Default: None
    """

    def __init__(self, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None):
        super().__init__()
        patch_size = to_2tuple(patch_size)
        self.patch_size = patch_size

        self.in_chans = in_chans
        self.embed_dim = embed_dim
        # 用2d卷积实现图像缩小四倍 kernel_size = stride = patch_size = 4
        self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size)
        if norm_layer is not None:
            self.norm = norm_layer(embed_dim)
        else:
            self.norm = None

    def forward(self, x):
        """Forward function."""
        # padding
        _, _, H, W = x.size()
        if W % self.patch_size[1] != 0:
            x = F.pad(x, (0, self.patch_size[1] - W % self.patch_size[1]))
        if H % self.patch_size[0] != 0:
            x = F.pad(x, (0, 0, 0, self.patch_size[0] - H % self.patch_size[0]))

        x = self.proj(x)  # B C Wh Ww
        if self.norm is not None:
            # x的shape变化 B, C, H, W --> B, C, h, w --> B, C, h * w --> B, h * w, c
            # x shape 为 [2, 96, 48, 56]
            Wh, Ww = x.size(2), x.size(3)
            # x shape 为 [2, 2688, 96]
            x = x.flatten(2).transpose(1, 2)
            x = self.norm(x)
            # x shape 为 [2, 96, 48, 56]
            x = x.transpose(1, 2).view(-1, self.embed_dim, Wh, Ww)

        return x

        经过PatchEmbed后的输出维度为[2, 96, 48, 56]，之后会再经过一个x.flatten(2).transpose(1, 2)将输入维度转换为transformer block能够接收的输入，即[2, 2688, 96]。

        接下来是BasicLayer

class BasicLayer(nn.Module):
    """ A basic Swin Transformer layer for one stage.

    Args:
        dim (int): Number of feature channels
        depth (int): Depths of this stage.
        num_heads (int): Number of attention head.
        window_size (int): Local window size. Default: 7.
        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4.
        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.
        drop (float, optional): Dropout rate. Default: 0.0
        attn_drop (float, optional): Attention dropout rate. Default: 0.0
        drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0
        norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
        downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None
        use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.
    """

    def __init__(self,
                 dim,
                 depth,
                 num_heads,
                 window_size=7,
                 mlp_ratio=4.,
                 qkv_bias=True,
                 qk_scale=None,
                 drop=0.,
                 attn_drop=0.,
                 drop_path=0.,
                 norm_layer=nn.LayerNorm,
                 downsample=None,
                 use_checkpoint=False):
        super().__init__()
        self.window_size = window_size
        self.shift_size = window_size // 2
        self.depth = depth
        self.use_checkpoint = use_checkpoint

        # build blocks
        # 序号为偶数的block进行W-MSA，奇数进行SW-MSA
        # 这样让输出特征包含local window attention和跨窗口的 window attention
        self.blocks = nn.ModuleList([
            SwinTransformerBlock(
                dim=dim,
                num_heads=num_heads,
                window_size=window_size,
                shift_size=0 if (i % 2 == 0) else window_size // 2,
                mlp_ratio=mlp_ratio,
                qkv_bias=qkv_bias,
                qk_scale=qk_scale,
                drop=drop,
                attn_drop=attn_drop,
                drop_path=drop_path[i] if isinstance(drop_path, list) else drop_path,
                norm_layer=norm_layer)
            for i in range(depth)])

        # patch merging layer
        # 只有前三个block执行patch merging，最后一个block不会执行 patch merging
        if downsample is not None:
            self.downsample = downsample(dim=dim, norm_layer=norm_layer)
        else:
            self.downsample = None

    def forward(self, x, H, W):
        """ Forward function.

        Args:
            x: Input feature, tensor size (B, H*W, C).
            H, W: Spatial resolution of the input feature.
        """

        # calculate attention mask for SW-MSA
        Hp = int(np.ceil(H / self.window_size)) * self.window_size
        Wp = int(np.ceil(W / self.window_size)) * self.window_size
        img_mask = torch.zeros((1, Hp, Wp, 1), device=x.device)  # 1 Hp Wp 1
        h_slices = (slice(0, -self.window_size),
                    slice(-self.window_size, -self.shift_size),
                    slice(-self.shift_size, None))
        w_slices = (slice(0, -self.window_size),
                    slice(-self.window_size, -self.shift_size),
                    slice(-self.shift_size, None))
        cnt = 0
        for h in h_slices:
            for w in w_slices:
                img_mask[:, h, w, :] = cnt
                cnt += 1

        mask_windows = window_partition(img_mask, self.window_size)  # nW, window_size, window_size, 1
        mask_windows = mask_windows.view(-1, self.window_size * self.window_size)
        attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2)
        attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill(attn_mask == 0, float(0.0))

        for blk in self.blocks:
            blk.H, blk.W = H, W
            if self.use_checkpoint:
                x = checkpoint.checkpoint(blk, x, attn_mask)
            else:
                x = blk(x, attn_mask)
        if self.downsample is not None:
            x_down = self.downsample(x, H, W)
            Wh, Ww = (H + 1) // 2, (W + 1) // 2
            return x, H, W, x_down, Wh, Ww
        else:
            return x, H, W, x, H, W

        BasicLayer构建了一个stage的swin transformer基本结构，包含了带窗（SW-MSA）和不带窗（W-MSA）的transformer block以及一个PatchMerging，可以理解为网络结构图中的swin transformer block + patch merging。

        在训练的过程中序号为偶数的block进行W-MSA，奇数进行SW-MSA，比如说第一个transformer block 有一个W-MSA和一个SW-MSA，先计算W-MSA，再计算SW-MSA，这样让输出特征包含local window attention和跨窗口的 window attention，而patch merging layer 仅在前三个block执行。接下来把这部分代码拆分出来解读。

def window_partition(x, window_size):
    """
    Args:
        x: (B, H, W, C)
        window_size (int): window size

    Returns:
        windows: (num_windows*B, window_size, window_size, C)
    """
    # B, H, W, C = x.shape
    # x = x.view(B, H // window_size, window_size, W // window_size, window_size, C)
    # windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C)

    H, W = x.shape
    x = x.view(H // window_size, window_size, W // window_size, window_size)
    windows = x.permute(0, 2, 1, 3).contiguous().view(-1,window_size, window_size)
    return windows


Hp = 14
Wp = 14
window_size = 7
shift_size = window_size //2
# img_mask = torch.zeros((1, Hp, Wp, 1))  # 1 Hp Wp 1
img_mask = torch.zeros((Hp, Wp))
h_slices = (slice(0, -window_size),
            slice(-window_size, -shift_size),
            slice(-shift_size, None))
w_slices = (slice(0, -window_size),
            slice(-window_size, -shift_size),
            slice(-shift_size, None))
cnt = 0
for h in h_slices:
    for w in w_slices:
        # img_mask[:, h, w, :] = cnt
        img_mask[h, w] = cnt
        cnt += 1

print(img_mask)
# tensor([[0., 0., 0., 0., 0., 0., 0.,| 1., 1., 1., 1., 2., 2., 2.],
#         [0., 0., 0., 0., 0., 0., 0.,| 1., 1., 1., 1., 2., 2., 2.],
#         [0., 0., 0., 0., 0., 0., 0.,| 1., 1., 1., 1., 2., 2., 2.],
#         [0., 0., 0., 0., 0., 0., 0.,| 1., 1., 1., 1., 2., 2., 2.],
#         [0., 0., 0., 0., 0., 0., 0.,| 1., 1., 1., 1., 2., 2., 2.],
#         [0., 0., 0., 0., 0., 0., 0.,| 1., 1., 1., 1., 2., 2., 2.],
#         [0., 0., 0., 0., 0., 0., 0.,| 1., 1., 1., 1., 2., 2., 2.],
#          - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
#         [3., 3., 3., 3., 3., 3., 3.,| 4., 4., 4., 4., 5., 5., 5.],
#         [3., 3., 3., 3., 3., 3., 3.,| 4., 4., 4., 4., 5., 5., 5.],
#         [3., 3., 3., 3., 3., 3., 3.,| 4., 4., 4., 4., 5., 5., 5.],
#         [3., 3., 3., 3., 3., 3., 3.,| 4., 4., 4., 4., 5., 5., 5.],
#         [6., 6., 6., 6., 6., 6., 6.,| 7., 7., 7., 7., 8., 8., 8.],
#         [6., 6., 6., 6., 6., 6., 6.,| 7., 7., 7., 7., 8., 8., 8.],
#         [6., 6., 6., 6., 6., 6., 6.,| 7., 7., 7., 7., 8., 8., 8.]])

mask_windows = window_partition(img_mask, window_size)  # nW, window_size, window_size, 1
mask_windows = mask_windows.view(-1, window_size * window_size)
attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2)
attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill(attn_mask == 0, float(0.0))

        这部分代码是用于生成mask的，源码中的维度比较高，不是很直观，这里将其降为后输出成低维张量，就很直观了，张量的高宽设置为window_size的两倍，这样正好能够完整的看出其中张量做了什么操作，shift_size保持不变，打印出的img_mask可以看出，mask大体上分为四个部分。
        window_partition函数则是将img_mask按照每个部分展开，即将张量分成N个[window_size，window_size]的小窗张量，此时的张量shape为[4,7,7]。之后view成[4，49]，再在扩充对应的维度，再相减，张量中不为0的填充为-100，最后得到的attn_mask的shape为[4,49,49]，这里的尺寸就和后面自注意力中的大小对应上了。

        具体形式如下图

         其中：

# 白色色块
# [0., 0., 0., 0., 0., 0., 0.,
#  0., 0., 0., 0., 0., 0., 0.,
#  0., 0., 0., 0., 0., 0., 0.,
#  0., 0., 0., 0., 0., 0., 0.,
#  0., 0., 0., 0., 0., 0., 0.,
#  0., 0., 0., 0., 0., 0., 0.,
#  0., 0., 0., 0., 0., 0., 0.],

# 蓝色色块
# [   0.,    0.,    0.,    0., -100., -100., -100.,
#     0.,    0.,    0.,    0., -100., -100., -100.,
#     0.,    0.,    0.,    0., -100., -100., -100.,
#     0.,    0.,    0.,    0., -100., -100., -100.,
#  -100., -100., -100., -100.,    0.,    0.,    0.,
#  -100., -100., -100., -100.,    0.,    0.,    0.,
#  -100., -100., -100., -100.,    0.,    0.,    0.,],

# 黄色色块
# [-100., -100., -100., -100., -100., -100., -100.,
#  -100., -100., -100., -100., -100., -100., -100.,
#  -100., -100., -100., -100., -100., -100., -100.,
#  -100., -100., -100., -100., -100., -100., -100.,
#  -100., -100., -100., -100., -100., -100., -100.,
#  -100., -100., -100., -100., -100., -100., -100.,
#  -100., -100., -100., -100., -100., -100., -100.,]

        实际上输出的attn_mask的shape为[56,49,49]，其中的56=(Hp / window_size) * (Wp / window_size)

        之后就是transformer block

class SwinTransformerBlock(nn.Module):
    """ Swin Transformer Block.

    Args:
        dim (int): Number of input channels.
        num_heads (int): Number of attention heads.
        window_size (int): Window size.
        shift_size (int): Shift size for SW-MSA.
        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
        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.
        drop (float, optional): Dropout rate. Default: 0.0
        attn_drop (float, optional): Attention dropout rate. Default: 0.0
        drop_path (float, optional): Stochastic depth rate. Default: 0.0
        act_layer (nn.Module, optional): Activation layer. Default: nn.GELU
        norm_layer (nn.Module, optional): Normalization layer.  Default: nn.LayerNorm
    """

    def __init__(self, dim, num_heads, window_size=7, shift_size=0,
                 mlp_ratio=4., qkv_bias=True, qk_scale=None, drop=0., attn_drop=0., drop_path=0.,
                 act_layer=nn.GELU, norm_layer=nn.LayerNorm):
        super().__init__()
        self.dim = dim
        self.num_heads = num_heads
        # window_size默认大小 7
        self.window_size = window_size
        # 进行 SW-MSA shift-size 7//2=3
        # 进行 W-MSA shift-size 0
        self.shift_size = shift_size
        # multi self attention 最后神经网络的隐藏层的维度的倍率
        self.mlp_ratio = mlp_ratio
        assert 0 <= self.shift_size < self.window_size, "shift_size must in 0-window_size"

        self.norm1 = norm_layer(dim)
        # local window multi head self attention
        self.attn = WindowAttention(
            dim, window_size=to_2tuple(self.window_size), num_heads=num_heads,
            qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop)

        self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
        self.norm2 = norm_layer(dim)
        mlp_hidden_dim = int(dim * mlp_ratio)
        self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)

        self.H = None
        self.W = None

    def forward(self, x, mask_matrix):
        """ Forward function.

        Args:
            x: Input feature, tensor size (B, H*W, C).
            H, W: Spatial resolution of the input feature.
            mask_matrix: Attention mask for cyclic shift.
        """
        # x的shape为[2,2688,96]
        B, L, C = x.shape
        H, W = self.H, self.W
        assert L == H * W, "input feature has wrong size"

        shortcut = x
        # 进行LN，再将x展开为[2,48,56,96]
        x = self.norm1(x)
        x = x.view(B, H, W, C)

        # pad feature maps to multiples of window size
        # 此处需要根据窗的大小对特征图进行pad操作，pad之后的shape为[2,49,56,96]
        pad_l = pad_t = 0
        pad_r = (self.window_size - W % self.window_size) % self.window_size
        pad_b = (self.window_size - H % self.window_size) % self.window_size
        x = F.pad(x, (0, 0, pad_l, pad_r, pad_t, pad_b))
        _, Hp, Wp, _ = x.shape

        # cyclic shift
        if self.shift_size > 0:
            # 如果是进行 sw-msa 将数据进行变换
            shifted_x = torch.roll(x, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2))
            attn_mask = mask_matrix
        else:
            shifted_x = x
            attn_mask = None

        # partition windows
        # x_windows 的shape为[112,7,7,96]
        x_windows = window_partition(shifted_x, self.window_size)  # nW*B, window_size, window_size, C
        x_windows = x_windows.view(-1, self.window_size * self.window_size, C)  # nW*B, window_size*window_size, C

        # W-MSA/SW-MSA
        attn_windows = self.attn(x_windows, mask=attn_mask)  # nW*B, window_size*window_size, C

        # merge windows
        attn_windows = attn_windows.view(-1, self.window_size, self.window_size, C)
        # shifted_x的shape为[2,49,56,96]
        shifted_x = window_reverse(attn_windows, self.window_size, Hp, Wp)  # B H' W' C

        # reverse cyclic shift
        if self.shift_size > 0:
            x = torch.roll(shifted_x, shifts=(self.shift_size, self.shift_size), dims=(1, 2))
        else:
            x = shifted_x

        if pad_r > 0 or pad_b > 0:
            # 映射回输入时的大小
            x = x[:, :H, :W, :].contiguous()

        x = x.view(B, H * W, C)

        # FFN
        x = shortcut + self.drop_path(x)
        x = x + self.drop_path(self.mlp(self.norm2(x)))

        return x

        SwinTransformerBlock由LN，W-MSA/SW-MSA以及MLP组成。在W-MSA中输入为[2,2688,96]，通过window_partition函数，将输入划分为window_size大小的窗，此时的shape为[112,7,7,96]，输入自注意力层时打平成[112,49,96]，其中自注意力层就是标准的自注意力结构，这里就不多说了，要是不知道的话可以参考我的另一篇博文：ref

        计算自注意力的代码如下：

class WindowAttention(nn.Module):
    """ Window based multi-head self attention (W-MSA) module with relative position bias.
    It supports both of shifted and non-shifted window.

    Args:
        dim (int): Number of input channels.
        window_size (tuple[int]): The height and width of the window.
        num_heads (int): Number of attention heads.
        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
        attn_drop (float, optional): Dropout ratio of attention weight. Default: 0.0
        proj_drop (float, optional): Dropout ratio of output. Default: 0.0
    """

    def __init__(self, dim, window_size, num_heads, qkv_bias=True, qk_scale=None, attn_drop=0., proj_drop=0.):

        super().__init__()
        self.dim = dim
        self.window_size = window_size  # Wh, Ww
        self.num_heads = num_heads
        head_dim = dim // num_heads
        self.scale = qk_scale or head_dim ** -0.5

        # define a parameter table of relative position bias
        self.relative_position_bias_table = nn.Parameter(
            torch.zeros((2 * window_size[0] - 1) * (2 * window_size[1] - 1), num_heads))  # 2*Wh-1 * 2*Ww-1, nH

        # get pair-wise relative position index for each token inside the window
        coords_h = torch.arange(self.window_size[0])
        coords_w = torch.arange(self.window_size[1])
        coords = torch.stack(torch.meshgrid([coords_h, coords_w]))  # 2, Wh, Ww
        coords_flatten = torch.flatten(coords, 1)  # 2, Wh*Ww
        relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :]  # 2, Wh*Ww, Wh*Ww
        relative_coords = relative_coords.permute(1, 2, 0).contiguous()  # Wh*Ww, Wh*Ww, 2
        relative_coords[:, :, 0] += self.window_size[0] - 1  # shift to start from 0
        relative_coords[:, :, 1] += self.window_size[1] - 1
        relative_coords[:, :, 0] *= 2 * self.window_size[1] - 1
        relative_position_index = relative_coords.sum(-1)  # Wh*Ww, Wh*Ww
        self.register_buffer("relative_position_index", relative_position_index)

        self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
        self.attn_drop = nn.Dropout(attn_drop)
        self.proj = nn.Linear(dim, dim)
        self.proj_drop = nn.Dropout(proj_drop)

        trunc_normal_(self.relative_position_bias_table, std=.02)
        self.softmax = nn.Softmax(dim=-1)

    def forward(self, x, mask=None):
        """ Forward function.

        Args:
            x: input features with shape of (num_windows*B, N, C)
            mask: (0/-inf) mask with shape of (num_windows, Wh*Ww, Wh*Ww) or None
        """
        B_, N, C = x.shape
        qkv = self.qkv(x).reshape(B_, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
        q, k, v = qkv[0], qkv[1], qkv[2]  # make torchscript happy (cannot use tensor as tuple)

        q = q * self.scale
        attn = (q @ k.transpose(-2, -1))

        relative_position_bias = self.relative_position_bias_table[self.relative_position_index.view(-1)].view(
            self.window_size[0] * self.window_size[1], self.window_size[0] * self.window_size[1], -1)  # Wh*Ww,Wh*Ww,nH
        relative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous()  # nH, Wh*Ww, Wh*Ww
        attn = attn + relative_position_bias.unsqueeze(0)

        if mask is not None:
            nW = mask.shape[0]
            attn = attn.view(B_ // nW, nW, self.num_heads, N, N) + mask.unsqueeze(1).unsqueeze(0)
            attn = attn.view(-1, self.num_heads, N, N)
            attn = self.softmax(attn)
        else:
            attn = self.softmax(attn)

        attn = self.attn_drop(attn)

        x = (attn @ v).transpose(1, 2).reshape(B_, N, C)
        x = self.proj(x)
        x = self.proj_drop(x)
        return x

        这里有个细节就是在计算自注意力后，加了个截断正太分布的relative_position_bias

        回归transformer block，算法为了使每个窗之间有相互联系，设计了shift window，这一设计能够大幅降低计算量。在计算SW-MSA时，先要使用torch.roll将输入[2,49,56,96]在1，2维上（也就是高宽上）做滚动。

        下面有个小例子，还是将维度降低，方便看数据形式，容易理解

import torch

a = torch.arange(0, 49).reshape((7, 7))
print(a)
b = torch.roll(a, shifts=(-3, -3), dims=(0, 1))
print(b)

# tensor([[ 0,  1,  2,  3,  4,  5,  6],
#         [ 7,  8,  9, 10, 11, 12, 13],
#         [14, 15, 16, 17, 18, 19, 20],
#         [21, 22, 23, 24, 25, 26, 27],
#         [28, 29, 30, 31, 32, 33, 34],
#         [35, 36, 37, 38, 39, 40, 41],
#         [42, 43, 44, 45, 46, 47, 48]])

# tensor([[24, 25, 26, 27, |21, 22, 23],
#         [31, 32, 33, 34, |28, 29, 30],
#         [38, 39, 40, 41, |35, 36, 37],
#         [45, 46, 47, 48, |42, 43, 44],
#         — — - - - - - - - - - - --  - -
#         [ 3,  4,  5,  6, | 0,  1,  2],
#         [10, 11, 12, 13, | 7,  8,  9],
#         [17, 18, 19, 20, |14, 15, 16]])

        也是就论文中的这张图中间部分：

         进入自注意力层后，基本操作和W-MSA一致，就是多加了个之前生成的mask

        最后就是PatchMerging

class PatchMerging(nn.Module):
    """ Patch Merging Layer

    Args:
        dim (int): Number of input channels.
        norm_layer (nn.Module, optional): Normalization layer.  Default: nn.LayerNorm
    """
    def __init__(self, dim, norm_layer=nn.LayerNorm):
        super().__init__()
        self.dim = dim
        self.reduction = nn.Linear(4 * dim, 2 * dim, bias=False)
        self.norm = norm_layer(4 * dim)

    def forward(self, x, H, W):
        """ Forward function.

        Args:
            x: Input feature, tensor size (B, H*W, C).
            H, W: Spatial resolution of the input feature.
        """
        B, L, C = x.shape
        assert L == H * W, "input feature has wrong size"

        x = x.view(B, H, W, C)

        # padding
        pad_input = (H % 2 == 1) or (W % 2 == 1)
        if pad_input:
            x = F.pad(x, (0, 0, 0, W % 2, 0, H % 2))

        # 这里实现path merging  图片缩小一半
        # 0::2 从 0 开始 隔一个点取一个值
        # 1::2 从 1 开始 隔一个点取一个值
        x0 = x[:, 0::2, 0::2, :]  # B H/2 W/2 C
        x1 = x[:, 1::2, 0::2, :]  # B H/2 W/2 C
        x2 = x[:, 0::2, 1::2, :]  # B H/2 W/2 C
        x3 = x[:, 1::2, 1::2, :]  # B H/2 W/2 C
        x = torch.cat([x0, x1, x2, x3], -1)  # B H/2 W/2 4*C
        x = x.view(B, -1, 4 * C)  # B H/2*W/2 4*C

        x = self.norm(x)

        # 降维到2 * dim 图片缩小一半 通道维度增加一倍
        x = self.reduction(x)

        return x

        PatchMerging实现的功能有点像yolov5中的focus操作，将图像缩小一半，再将通道数增加一倍。

        到这里基本将swin transformer的主体结构讲完了。