Swin_transformer解读

1、序言

Swin Transformer一出，谁与争锋。在各个视觉任务领域屠榜，目标检测刷到58.7 AP，实例分割刷到51.1 Mask AP， 语义分割在ADE20K上刷到53.5 mIoU，都是目前第一。本文对Swin Transformer论文做一些解读分析。

2、论文

论文名称：《Swin Transformer: Hierarchical Vision Transformer using Shifted Windows》
下载链接：swin transformer
论文的主要贡献点：
1、通过将自注意力计算限制为不重叠的局部窗口，同时允许跨窗口连接，移动窗口带来了更高的效率。
2、分层体系结构具有在各种尺度上建模的灵活性，并且相对于图像大小具有线性计算复杂性，最重要的是可移植性强。

直观的看和很多CNN模型的结构相似，有不同的下采样倍率，适合多级检测。

之前vit在全局做多头注意力，计算量较大，计算效率不高。swin transformer(简称st)采用local windows(红色的框框)，也就是和cnn的卷积核思想一样，在局部做注意力。不同的是cnn的slide windows会有重叠，而st提出的windows是不重叠的，而是还将特征图向左上角提一下，得到了shift windows，w-msa和sw-msa会交替使用。
先经过patch_embed将输出图像patch化，默认的patch是4x4xc，所以会用4*4卷积，stride是4，channel由3升到96，然后将hw扁平化后加上绝对位置编码(我看代码很多都会选择不加，包括从预训练模型读出来也没有绝对位置编码的参数信息，ape会设为False)，然后经过swin transfomer模块，不同layer设置不同数量的模块数(depths)，sw-msa和w-msa是核心。每个layer后面会用patch merging进行降采样。其他也没有啥了，直接看代码吧。

3、代码

# --------------------------------------------------------
# Swin Transformer
# Copyright (c) 2021 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# Written by Ze Liu
# --------------------------------------------------------import torch
import torch.nn as nn
import torch.utils.checkpoint as checkpoint
from timm.models.layers import DropPath, to_2tuple, trunc_normal_class Mlp(nn.Module):def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.):super().__init__()out_features = out_features or in_featureshidden_features = hidden_features or in_featuresself.fc1 = nn.Linear(in_features, hidden_features)self.act = act_layer()self.fc2 = nn.Linear(hidden_features, out_features)self.drop = nn.Dropout(drop)def forward(self, x):x = self.fc1(x)x = self.act(x)x = self.drop(x)x = self.fc2(x)x = self.drop(x)return xdef window_partition(x, window_size):"""Args:x: (B, H, W, C)window_size (int): window sizeReturns:windows: (num_windows*B, window_size, window_size, C)"""B, H, W, C = x.shapex = 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)return windowsdef window_reverse(windows, window_size, H, W):"""Args:windows: (num_windows*B, window_size, window_size, C)window_size (int): Window sizeH (int): Height of imageW (int): Width of imageReturns:x: (B, H, W, C)"""B = int(windows.shape[0] / (H * W / window_size / window_size))x = windows.view(B, H // window_size, W // window_size, window_size, window_size, -1)x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1)return xclass WindowAttention(nn.Module):r""" 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: Trueqk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if setattn_drop (float, optional): Dropout ratio of attention weight. Default: 0.0proj_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 = dimself.window_size = window_size  # Wh, Wwself.num_heads = num_headshead_dim = dim // num_headsself.scale = qk_scale or head_dim ** -0.5# define a parameter table of relative position biasself.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 windowcoords_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, Wwcoords_flatten = torch.flatten(coords, 1)  # 2, Wh*Wwrelative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :]  # 2, Wh*Ww, Wh*Wwrelative_coords = relative_coords.permute(1, 2, 0).contiguous()  # Wh*Ww, Wh*Ww, 2relative_coords[:, :, 0] += self.window_size[0] - 1  # shift to start from 0relative_coords[:, :, 1] += self.window_size[1] - 1relative_coords[:, :, 0] *= 2 * self.window_size[1] - 1relative_position_index = relative_coords.sum(-1)  # Wh*Ww, Wh*Wwself.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):"""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.shapeqkv = 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.scaleattn = (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,nHrelative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous()  # nH, Wh*Ww, Wh*Wwattn = 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 xdef extra_repr(self) -> str:return f'dim={self.dim}, window_size={self.window_size}, num_heads={self.num_heads}'def flops(self, N):# calculate flops for 1 window with token length of Nflops = 0# qkv = self.qkv(x)flops += N * self.dim * 3 * self.dim# attn = (q @ k.transpose(-2, -1))flops += self.num_heads * N * (self.dim // self.num_heads) * N#  x = (attn @ v)flops += self.num_heads * N * N * (self.dim // self.num_heads)# x = self.proj(x)flops += N * self.dim * self.dimreturn flopsclass SwinTransformerBlock(nn.Module):r""" Swin Transformer Block.Args:dim (int): Number of input channels.input_resolution (tuple[int]): Input resulotion.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: Trueqk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.drop (float, optional): Dropout rate. Default: 0.0attn_drop (float, optional): Attention dropout rate. Default: 0.0drop_path (float, optional): Stochastic depth rate. Default: 0.0act_layer (nn.Module, optional): Activation layer. Default: nn.GELUnorm_layer (nn.Module, optional): Normalization layer.  Default: nn.LayerNorm"""def __init__(self, dim, input_resolution, 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 = dimself.input_resolution = input_resolutionself.num_heads = num_headsself.window_size = window_sizeself.shift_size = shift_sizeself.mlp_ratio = mlp_ratioif min(self.input_resolution) <= self.window_size:# if window size is larger than input resolution, we don't partition windowsself.shift_size = 0self.window_size = min(self.input_resolution)assert 0 <= self.shift_size < self.window_size, "shift_size must in 0-window_size"self.norm1 = norm_layer(dim)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)if self.shift_size > 0:# calculate attention mask for SW-MSAH, W = self.input_resolutionimg_mask = torch.zeros((1, H, W, 1))  # 1 H W 1h_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 = 0for h in h_slices:for w in w_slices:img_mask[:, h, w, :] = cntcnt += 1mask_windows = window_partition(img_mask, self.window_size)  # nW, window_size, window_size, 1mask_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))else:attn_mask = Noneself.register_buffer("attn_mask", attn_mask)def forward(self, x):H, W = self.input_resolutionB, L, C = x.shapeassert L == H * W, "input feature has wrong size"shortcut = xx = self.norm1(x)x = x.view(B, H, W, C)# cyclic shiftif self.shift_size > 0:shifted_x = torch.roll(x, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2))else:shifted_x = x# partition windowsx_windows = window_partition(shifted_x, self.window_size)  # nW*B, window_size, window_size, Cx_windows = x_windows.view(-1, self.window_size * self.window_size, C)  # nW*B, window_size*window_size, C# W-MSA/SW-MSAattn_windows = self.attn(x_windows, mask=self.attn_mask)  # nW*B, window_size*window_size, C# merge windowsattn_windows = attn_windows.view(-1, self.window_size, self.window_size, C)shifted_x = window_reverse(attn_windows, self.window_size, H, W)  # B H' W' C# reverse cyclic shiftif self.shift_size > 0:x = torch.roll(shifted_x, shifts=(self.shift_size, self.shift_size), dims=(1, 2))else:x = shifted_xx = x.view(B, H * W, C)# FFNx = shortcut + self.drop_path(x)x = x + self.drop_path(self.mlp(self.norm2(x)))return xdef extra_repr(self) -> str:return f"dim={self.dim}, input_resolution={self.input_resolution}, num_heads={self.num_heads}, " \f"window_size={self.window_size}, shift_size={self.shift_size}, mlp_ratio={self.mlp_ratio}"def flops(self):flops = 0H, W = self.input_resolution# norm1flops += self.dim * H * W# W-MSA/SW-MSAnW = H * W / self.window_size / self.window_sizeflops += nW * self.attn.flops(self.window_size * self.window_size)# mlpflops += 2 * H * W * self.dim * self.dim * self.mlp_ratio# norm2flops += self.dim * H * Wreturn flopsclass PatchMerging(nn.Module):r""" Patch Merging Layer.Args:input_resolution (tuple[int]): Resolution of input feature.dim (int): Number of input channels.norm_layer (nn.Module, optional): Normalization layer.  Default: nn.LayerNorm"""def __init__(self, input_resolution, dim, norm_layer=nn.LayerNorm):super().__init__()self.input_resolution = input_resolutionself.dim = dimself.reduction = nn.Linear(4 * dim, 2 * dim, bias=False)self.norm = norm_layer(4 * dim)def forward(self, x):"""x: B, H*W, C"""H, W = self.input_resolutionB, L, C = x.shapeassert L == H * W, "input feature has wrong size"assert H % 2 == 0 and W % 2 == 0, f"x size ({H}*{W}) are not even."x = x.view(B, H, W, C)x0 = x[:, 0::2, 0::2, :]  # B H/2 W/2 Cx1 = x[:, 1::2, 0::2, :]  # B H/2 W/2 Cx2 = x[:, 0::2, 1::2, :]  # B H/2 W/2 Cx3 = x[:, 1::2, 1::2, :]  # B H/2 W/2 Cx = torch.cat([x0, x1, x2, x3], -1)  # B H/2 W/2 4*Cx = x.view(B, -1, 4 * C)  # B H/2*W/2 4*Cx = self.norm(x)x = self.reduction(x)return xdef extra_repr(self) -> str:return f"input_resolution={self.input_resolution}, dim={self.dim}"def flops(self):H, W = self.input_resolutionflops = H * W * self.dimflops += (H // 2) * (W // 2) * 4 * self.dim * 2 * self.dimreturn flopsclass BasicLayer(nn.Module):""" A basic Swin Transformer layer for one stage.Args:dim (int): Number of input channels.input_resolution (tuple[int]): Input resolution.depth (int): Number of blocks.num_heads (int): Number of attention heads.window_size (int): Local window size.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: Trueqk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.drop (float, optional): Dropout rate. Default: 0.0attn_drop (float, optional): Attention dropout rate. Default: 0.0drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNormdownsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: Noneuse_checkpoint (bool): Whether to use checkpointing to save memory. Default: False."""def __init__(self, dim, input_resolution, depth, num_heads, window_size,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.dim = dimself.input_resolution = input_resolutionself.depth = depthself.use_checkpoint = use_checkpoint# build blocksself.blocks = nn.ModuleList([SwinTransformerBlock(dim=dim, input_resolution=input_resolution,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 layerif downsample is not None:self.downsample = downsample(input_resolution, dim=dim, norm_layer=norm_layer)else:self.downsample = Nonedef forward(self, x):for blk in self.blocks:if self.use_checkpoint:x = checkpoint.checkpoint(blk, x)else:x = blk(x)if self.downsample is not None:x = self.downsample(x)return xdef extra_repr(self) -> str:return f"dim={self.dim}, input_resolution={self.input_resolution}, depth={self.depth}"def flops(self):flops = 0for blk in self.blocks:flops += blk.flops()if self.downsample is not None:flops += self.downsample.flops()return flopsclass PatchEmbed(nn.Module):r""" Image to Patch EmbeddingArgs:img_size (int): Image size.  Default: 224.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, img_size=224, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None):super().__init__()img_size = to_2tuple(img_size)patch_size = to_2tuple(patch_size)patches_resolution = [img_size[0] // patch_size[0], img_size[1] // patch_size[1]]self.img_size = img_sizeself.patch_size = patch_sizeself.patches_resolution = patches_resolutionself.num_patches = patches_resolution[0] * patches_resolution[1]self.in_chans = in_chansself.embed_dim = embed_dimself.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 = Nonedef forward(self, x):B, C, H, W = x.shape# FIXME look at relaxing size constraintsassert H == self.img_size[0] and W == self.img_size[1], \f"Input image size ({H}*{W}) doesn't match model ({self.img_size[0]}*{self.img_size[1]})."x = self.proj(x).flatten(2).transpose(1, 2)  # B Ph*Pw Cif self.norm is not None:x = self.norm(x)return xdef flops(self):Ho, Wo = self.patches_resolutionflops = Ho * Wo * self.embed_dim * self.in_chans * (self.patch_size[0] * self.patch_size[1])if self.norm is not None:flops += Ho * Wo * self.embed_dimreturn flopsclass SwinTransformer(nn.Module):r""" Swin TransformerA PyTorch impl of : `Swin Transformer: Hierarchical Vision Transformer using Shifted Windows`  -https://arxiv.org/pdf/2103.14030Args:img_size (int | tuple(int)): Input image size. Default 224patch_size (int | tuple(int)): Patch size. Default: 4in_chans (int): Number of input image channels. Default: 3num_classes (int): Number of classes for classification head. Default: 1000embed_dim (int): Patch embedding dimension. Default: 96depths (tuple(int)): Depth of each Swin Transformer layer.num_heads (tuple(int)): Number of attention heads in different layers.window_size (int): Window size. Default: 7mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: Trueqk_scale (float): Override default qk scale of head_dim ** -0.5 if set. Default: Nonedrop_rate (float): Dropout rate. Default: 0attn_drop_rate (float): Attention dropout rate. Default: 0drop_path_rate (float): Stochastic depth rate. Default: 0.1norm_layer (nn.Module): Normalization layer. Default: nn.LayerNorm.ape (bool): If True, add absolute position embedding to the patch embedding. Default: Falsepatch_norm (bool): If True, add normalization after patch embedding. Default: Trueuse_checkpoint (bool): Whether to use checkpointing to save memory. Default: False"""def __init__(self, img_size=224, patch_size=4, in_chans=3, num_classes=1000,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=0.1,norm_layer=nn.LayerNorm, ape=False, patch_norm=True,use_checkpoint=False, **kwargs):super().__init__()self.num_classes = num_classesself.num_layers = len(depths)self.embed_dim = embed_dimself.ape = apeself.patch_norm = patch_normself.num_features = int(embed_dim * 2 ** (self.num_layers - 1))self.mlp_ratio = mlp_ratio# split image into non-overlapping patchesself.patch_embed = PatchEmbed(img_size=img_size, patch_size=patch_size, in_chans=in_chans, embed_dim=embed_dim,norm_layer=norm_layer if self.patch_norm else None)num_patches = self.patch_embed.num_patchespatches_resolution = self.patch_embed.patches_resolutionself.patches_resolution = patches_resolution# absolute position embeddingif self.ape:self.absolute_pos_embed = nn.Parameter(torch.zeros(1, num_patches, embed_dim))trunc_normal_(self.absolute_pos_embed, std=.02)self.pos_drop = nn.Dropout(p=drop_rate)# stochastic depthdpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))]  # stochastic depth decay rule# build layersself.layers = nn.ModuleList()for i_layer in range(self.num_layers):layer = BasicLayer(dim=int(embed_dim * 2 ** i_layer),input_resolution=(patches_resolution[0] // (2 ** i_layer),patches_resolution[1] // (2 ** i_layer)),depth=depths[i_layer],num_heads=num_heads[i_layer],window_size=window_size,mlp_ratio=self.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)self.norm = norm_layer(self.num_features)self.avgpool = nn.AdaptiveAvgPool1d(1)self.head = nn.Linear(self.num_features, num_classes) if num_classes > 0 else nn.Identity()self.apply(self._init_weights)def _init_weights(self, 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)@torch.jit.ignoredef no_weight_decay(self):return {'absolute_pos_embed'}@torch.jit.ignoredef no_weight_decay_keywords(self):return {'relative_position_bias_table'}def forward_features(self, x):x = self.patch_embed(x)if self.ape:x = x + self.absolute_pos_embedx = self.pos_drop(x)for layer in self.layers:x = layer(x)x = self.norm(x)  # B L Cx = self.avgpool(x.transpose(1, 2))  # B C 1x = torch.flatten(x, 1)return xdef forward(self, x):x = self.forward_features(x)x = self.head(x)return xdef flops(self):flops = 0flops += self.patch_embed.flops()for i, layer in enumerate(self.layers):flops += layer.flops()flops += self.num_features * self.patches_resolution[0] * self.patches_resolution[1] // (2 ** self.num_layers)flops += self.num_features * self.num_classesreturn flops

4、总结

模型很牛逼，美中不足的是距离实际落地还有很大的距离，因为转tensort后速度很慢，tiny版本比resnet50还慢。我的实验还在继续，实在不行用做检测蒸馏的teacher。

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