class PatchEmbed3D(nn.Module): """ Video to Patch Embedding.Args: patch_size (int): Patch token size. Default: (2,4,4). in_chans (int): Number of input video 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=(2,4,4), in_chans=3, embed_dim=96, norm_layer=None): super().__init__() self.patch_size = patch_sizeself.in_chans = in_chans self.embed_dim = embed_dimself.proj = nn.Conv3d(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): """Forward function.""" # padding _, _, D, H, W = x.size() #BCDHW #DHW正好对应patch_size[0],patch_size[1],patch_size[2],防止除不开先pad if W % self.patch_size[2] != 0: x = F.pad(x, (0, self.patch_size[2] - W % self.patch_size[2])) if H % self.patch_size[1] != 0: x = F.pad(x, (0, 0, 0, self.patch_size[1] - H % self.patch_size[1])) if D % self.patch_size[0] != 0: x = F.pad(x, (0, 0, 0, 0, 0, self.patch_size[0] - D % self.patch_size[0]))x = self.proj(x)# B C D Wh Ww, 其中D Wh Ww表示经过3d卷积后特征的大小 if self.norm is not None: #默认会使用nn.LayerNorm,所以下面程序必运行 D, Wh, Ww = x.size(2), x.size(3), x.size(4) x = x.flatten(2).transpose(1, 2) #B, C, D, Wh, Ww -> B, C, D*Wh*Ww ->B,D*Wh*Ww, C #因为要层归一化，所以要拉成上面的形状，把C放在最后 x = self.norm(x) x = x.transpose(1, 2).view(-1, self.embed_dim, D, Wh, Ww) #又拉回 B, C, D, Wh, Wwreturn x

class BasicLayer(nn.Module): """ A basic Swin Transformer layer for one stage. """def __init__(self, dim, # 以第一层为例 为96 depth, #以第一层为例 为2 num_heads, #以第一层为例 为3 window_size=(1,7,7), # (8,7,7) mlp_ratio=4., qkv_bias=False, #true qk_scale=None, drop=0., attn_drop=0., drop_path=0.,#以第一层为例 为[0, 0.01818182] norm_layer=nn.LayerNorm, downsample=None, #PatchMerging use_checkpoint=False): super().__init__() self.window_size = window_size # (8,7,7) self.shift_size = tuple(i // 2 for i in window_size) #(4,3,3) self.depth = depth # 2 self.use_checkpoint = use_checkpoint# build blocks self.blocks = nn.ModuleList([ SwinTransformerBlock3D( dim=dim, #96 num_heads=num_heads, # 3 window_size=window_size, # 第一个block的shiftsize=(0,0,0)，也就是W-MSA不进行shift，第2个shiftsize=(4,3,3) shift_size=(0,0,0) if (i % 2 == 0) else self.shift_size, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, # true qk_scale=qk_scale, # None drop=drop, attn_drop=attn_drop, drop_path=drop_path[i] if isinstance(drop_path, list) else drop_path, norm_layer=norm_layer, use_checkpoint=use_checkpoint, ) for i in range(depth)]) # depth = 2self.downsample = downsample if self.downsample is not None: self.downsample = downsample(dim=dim, norm_layer=norm_layer)def forward(self, x): """ Forward function. """ # calculate attention mask for SW-MSA B, C, D, H, W = x.shape window_size, shift_size = get_window_size((D,H,W), self.window_size, self.shift_size) x = rearrange(x, 'b c d h w -> b d h w c') Dp = int(np.ceil(D / window_size[0])) * window_size[0] # 1*8 Hp = int(np.ceil(H / window_size[1])) * window_size[1] # 56/7 *7 Wp = int(np.ceil(W / window_size[2])) * window_size[2] # 56/7 *7 # 计算一个attention_mask用于SW-MSA，怎么shitfed以及mask如何推导见后文 attn_mask = compute_mask(Dp, Hp, Wp, window_size, shift_size, x.device) # (8,7,7) (0,3,3)# 以第一个stage为例，里面有2个block，第一个block进行W-MSA，第二个block进行SW-MSA # 如何W-MSA SW-MSA 见下述 for blk in self.blocks: x = blk(x, attn_mask) #改变形状，把C放到最后一维度（因为PatchMerging里有layernom和全连接层） x = x.view(B, D, H, W, -1) # 用PatchMerging 进行patch的拼接和全连接层 实现下采样 if self.downsample is not None: x = self.downsample(x) x = rearrange(x, 'b d h w c -> b c d h w') return x

class PatchMerging(nn.Module): """ Patch Merging Layer """ def __init__(self, dim, norm_layer=nn.LayerNorm): super().__init__() self.dim = dim #用全连接层把C由4C->2C，因为是4个cat一起所以是4C self.reduction = nn.Linear(4 * dim, 2 * dim, bias=False) self.norm = norm_layer(4 * dim)def forward(self, x): """ Forward function. """ B, D, H, W, C = x.shape# 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))x0 = x[:, :, 0::2, 0::2, :]# B D H/2 W/2 C x1 = x[:, :, 1::2, 0::2, :]# B D H/2 W/2 C x2 = x[:, :, 0::2, 1::2, :]# B D H/2 W/2 C x3 = x[:, :, 1::2, 1::2, :]# B D H/2 W/2 C # 每2X2个patch cat到一起 x = torch.cat([x0, x1, x2, x3], -1)# B D H/2 W/2 4*Cx = self.norm(x) # 层归一化 x = self.reduction(x) # 全连接层 降维return x

class SwinTransformerBlock3D(nn.Module): """ Swin Transformer Block. """def __init__(self, dim, num_heads, window_size=(2,7,7), shift_size=(0,0,0), 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, use_checkpoint=False): super().__init__() self.dim = dim self.num_heads = num_heads self.window_size = window_size self.shift_size = shift_size self.mlp_ratio = mlp_ratio self.use_checkpoint=use_checkpointassert 0 <= self.shift_size[0] < self.window_size[0], "shift_size must in 0-window_size" assert 0 <= self.shift_size[1] < self.window_size[1], "shift_size must in 0-window_size" assert 0 <= self.shift_size[2] < self.window_size[2], "shift_size must in 0-window_size"self.norm1 = norm_layer(dim) self.attn = WindowAttention3D( dim, window_size=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)def forward_part1(self, x, mask_matrix): B, D, H, W, C = x.shape # 1 先计算出当前block的window_size, 和shift_size window_size, shift_size = get_window_size((D, H, W), self.window_size, self.shift_size)# 2 经过一个layer_norm x = self.norm1(x)# pad一下特征图避免除不开 # pad feature maps to multiples of window size pad_l = pad_t = pad_d0 = 0 pad_d1 = (window_size[0] - D % window_size[0]) % window_size[0] pad_b = (window_size[1] - H % window_size[1]) % window_size[1] pad_r = (window_size[2] - W % window_size[2]) % window_size[2] x = F.pad(x, (0, 0, pad_l, pad_r, pad_t, pad_b, pad_d0, pad_d1)) _, Dp, Hp, Wp, _ = x.shape# 3 判断是否需要对特征图进行shift # cyclic shift if any(i > 0 for i in shift_size): shifted_x = torch.roll(x, shifts=(-shift_size[0], -shift_size[1], -shift_size[2]), dims=(1, 2, 3)) attn_mask = mask_matrix else: shifted_x = x attn_mask = None# 4 将特征图切成一个个的窗口（都是reshape操作） # partition windows x_windows = window_partition(shifted_x, window_size)# B*nW, Wd*Wh*Ww, C# 5 通过attn_mask是否为None判断进行W-MSA还是SW-MSA # W-MSA/SW-MSA attn_windows = self.attn(x_windows, mask=attn_mask)# B*nW, Wd*Wh*Ww, C# 6 把窗口在合并回来，看成4的逆操作，同样都是reshape操作 # merge windows attn_windows = attn_windows.view(-1, *(window_size+(C,))) #(B*num_windows, window_size, window_size, C) shifted_x = window_reverse(attn_windows, window_size, B, Dp, Hp, Wp)# B D' H' W' C# 7 如果之前shitf过，也要还原回去 # reverse cyclic shift if any(i > 0 for i in shift_size): x = torch.roll(shifted_x, shifts=(shift_size[0], shift_size[1], shift_size[2]), dims=(1, 2, 3)) else: x = shifted_x# 去掉pad if pad_d1 >0 or pad_r > 0 or pad_b > 0: x = x[:, :D, :H, :W, :].contiguous() return xdef forward_part2(self, x): # 经过FFN return self.drop_path(self.mlp(self.norm2(x)))def forward(self, x, mask_matrix): """ Forward function.Args: x: Input feature, tensor size (B, D, H, W, C). mask_matrix: Attention mask for cyclic shift. """ # tranformer的常规操作，包含MSA、残差连接、dropout、FFN，只不过MSA变成W-MSA或者SW-MSA shortcut = x if self.use_checkpoint: x = checkpoint.checkpoint(self.forward_part1, x, mask_matrix) else: x = self.forward_part1(x, mask_matrix) x = shortcut + self.drop_path(x)if self.use_checkpoint: x = x + checkpoint.checkpoint(self.forward_part2, x) else: x = x + self.forward_part2(x)return x

class WindowAttention3D(nn.Module): """ Window based multi-head self attention (W-MSA) module with relative position """def __init__(self, dim, window_size, num_heads, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0.):super().__init__() self.dim = dim self.window_size = window_size# Wd, Wh, Ww self.num_heads = num_heads head_dim = dim // num_heads# 每个注意力头对应的通道数 self.scale = qk_scale or head_dim ** -0.5# define a parameter table of relative position bias # 设置一个形状为(2*Wd-1*2*(Wh-1) * 2*(Ww-1), nH)的可学习变量 ,用于后续的位置编码 self.relative_position_bias_table = nn.Parameter( torch.zeros((2 * window_size[0] - 1) * (2 * window_size[1] - 1) * (2 * window_size[2] - 1), num_heads))# 2*Wd-1 * 2*Wh-1 * 2*Ww-1, nH# 获取窗口内每对token的相对位置索引 # get pair-wise relative position index for each token inside the window coords_d = torch.arange(self.window_size[0]) coords_h = torch.arange(self.window_size[1]) coords_w = torch.arange(self.window_size[2]) coords = torch.stack(torch.meshgrid(coords_d, coords_h, coords_w))# 3, Wd, Wh, Ww coords_flatten = torch.flatten(coords, 1)# 3, Wd*Wh*Ww #利用广播机制 ,分别在第二维 ,第一维 ,插入一个维度 ,进行广播相减 ,得到 3, Wd*Wh*Ww, Wd*Wh*Ww的张量 relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :]# 3, Wd*Wh*Ww, Wd*Wh*Ww relative_coords = relative_coords.permute(1, 2, 0).contiguous()# Wd*Wh*Ww, Wd*Wh*Ww, 3 #因为采取的是相减 ,所以得到的索引是从负数开始的 ,所以加上偏移量 ,让其从0开始 relative_coords[:, :, 0] += self.window_size[0] - 1# shift to start from 0 relative_coords[:, :, 1] += self.window_size[1] - 1 relative_coords[:, :, 2] += self.window_size[2] - 1 # 后续我们需要将其展开成一维偏移量 而对于(1 ,2)和(2 ,1)这两个坐标 在二维上是不同的, # 但是通过将x,y坐标相加转换为一维偏移的时候,他的偏移量是相等的,所以对其做乘法以进行区分 relative_coords[:, :, 0] *= (2 * self.window_size[1] - 1) * (2 * self.window_size[2] - 1) relative_coords[:, :, 1] *= (2 * self.window_size[2] - 1) #在最后一维上进行求和 ,展开成一个一维坐标 ,并注册为一个不参与网络学习的常量 relative_position_index = relative_coords.sum(-1)# Wd*Wh*Ww, Wd*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)# 截断正态分布初始化 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, N, N) or None """ # numWindows*B, N, C ,其中N=window_size_d * window_size_h * window_size_w B_, N, C = x.shape # 然后经过self.qkv这个全连接层后进行reshape到(3, numWindows*B, num_heads,N, c//num_heads) # 3表示3个向量,刚好分配给q,k,v, 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]# B_, nH, N, C# 根据公式,对q乘以一个scale缩放系数, # 然后与k（为了满足矩阵乘要求，需要将最后两个维度调换）进行相乘. # 得(numWindows*B, num_heads, N, N)的attn张量 q = q * self.scale # selfattention公式里的根号下dk attn = q @ k.transpose(-2, -1)# 之前我们针对位置编码设置了个形状为(2*Wd-1*2*(Wh-1) * 2*(Ww-1), numHeads)的可学习变量. # 我们用计算得到的相对编码位置索引self.relative_position_index选取, # 得到形状为(nH, Wd*Wh*Ww, Wd*Wh*Ww)的编码,加到attn张量上 relative_position_bias = self.relative_position_bias_table[self.relative_position_index[:N, :N].reshape(-1)].reshape( N, N, -1)# Wd*Wh*Ww,Wd*Wh*Ww,nH relative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous()# nH, Wd*Wh*Ww, Wd*Wh*Ww attn = attn + relative_position_bias.unsqueeze(0) # B_, nH, N, N# 剩下就是跟transformer一样的softmax，dropout,与V矩阵乘,再经过一层全连接层和dropout if mask is not None: # mask.shape =nW, N, N,其中N = Wd*Wh*Ww nW = mask.shape[0] # 将mask加到attention的计算结果再进行softmax, # 由于mask的值设置为-100,softmax后就会忽略掉对应的值,从而达到mask的效果 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

def compute_mask(D, H, W, window_size, shift_size, device): img_mask = torch.zeros((1, D, H, W, 1), device=device)# 1 Dp Hp Wp 1 cnt = 0 # 切片操作,假设不看d维度,见详解图 for d in slice(-window_size[0]), slice(-window_size[0], -shift_size[0]), slice(-shift_size[0],None): for h in slice(-window_size[1]), slice(-window_size[1], -shift_size[1]), slice(-shift_size[1],None): for w in slice(-window_size[2]), slice(-window_size[2], -shift_size[2]), slice(-shift_size[2],None): img_mask[:, d, h, w, :] = cnt cnt += 1 mask_windows = window_partition(img_mask, window_size)# nW, ws[0]*ws[1]*ws[2], 1 mask_windows = mask_windows.squeeze(-1)# nW, ws[0]*ws[1]*ws[2] # nW, 1, ws[0]*ws[1]*ws[2] - nW, ws[0]*ws[1]*ws[2],1会触发广播机制,将维度不匹配维度中维度为1的复制然后匹配上 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)) return attn_mask

if mask is not None: # mask.shape =nW, N, N,其中N = Wd*Wh*Ww nW = mask.shape[0] # 将mask加到attention的计算结果再进行softmax, # 由于mask的值设置为-100,softmax后就会忽略掉对应的值,从而达到mask的效果 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)

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