x = torch.roll(shifted_x, shifts=(self.shift_size, self.shift_size), dims=(1, 2)), 实现这个图像的移动,简单的小例子: class SwinTransformerBlock(nn.Module): r""" Swin Transformer Block. Args: dim (int): Number of input channels. input_resolution (tuple[int]): Input resulotion.num_...
self.shift_size = 0 self.window_size = min(self.input_resolution) assert 0 <= self.shift_size < self.window_size, "shift_size must in 0-window_size" self.padding = [self.window_size - self.shift_size, self.shift_size, self.window_size - self.shift_size, self.shift_size] # P_...
# cyclic shiftifself.shift_size >0:#做不做窗口滑动,刚开始shift_size为0,不做偏移ifnotself.fused_window_process: shifted_x = torch.roll(x, shifts=(-self.shift_size, -self.shift_size), dims=(1,2))#进行偏移# partition windowsx_windows = window_partition(shifted_x, self.window_size)# ...
之前我们针对位置编码设置了个形状为(2*window_size-1*2*window_size-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(window_size*window_size, window_size*window_size, numHeads)的编码,加到attn张量上 暂不考虑mask的情况,剩下就是跟transformer一样...
Swin Transformer整体外部变换过程 def forward_raw(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=(...
首先,最大的一个类就是SwinTransformer,它定义了整个Swin Transformer的框架。接着是BasicLayer类,它是Swin Transformer Block和Patch Merging的组合。【注意,代码中是Swin Transformer Block+patch merging组合在一起,而不是理论部分的Patch merging+Swin Transformer Block】 ...
仅仅对窗口(window)单独施加注意力,如何解决窗口(window)之间的信息流动?交替使用W-MSA和SW-MSA模块,因此SwinTransformerBlock必须是偶数。如下图所示: image.png 整体流程如下: 先对特征图进行LayerNorm 通过self.shift_size决定是否需要对特征图进行shift
class SwinTransformerBlock(nn.Module):def __init__(self,dim,n_heads,window_size=7,shift_size=0,mlp_ratio=4.,qkv_bias=True,proj_dropout=0.,attn_dropout=0.,dropout=0.,norm_layer=nn.LayerNorm):super(SwinTransformerBlock, self).__init__()self.dim = dimself.n_heads = n_headsself....
class SwinTransformerBlock(nn.Layer): """ 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_ra...
shift_size>0:attn_mask=generate_mask(window_size=self.window_size,shift_size=self.shift_size,...