# 初始化图中的相对位置偏置表,长度为(2M-1)*(2M-1)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# 每个像素点的横纵坐标coords_h=torch.arange(self.window_size[0])coords_w=torch.arange(se...
Swin Transformer是通过把Transformer block中标准的多头自注意力(multi-head self attention,MSA)模块替换为基于shifted windows的模块 而构建的,其他层保持不变。如fig3(b)所示,一个Swin Transformer block包含a shifted window based MSA module,followed by a 2-layer MLP with GELU nonlinearity in between. layer...
如果说Shifted Window是Swin Transformer的精华，那么Attention Mask则可以算作是Shifted Window的精华。Attention Mask主要干的事就是设置合理的mask，使得Shifted Window Attention在与Window Attention相同的窗口个数下，得到等价的计算结果。如下图所示，分别给SWA和WA加上index后，再计算window attention的时候，希望有相...
一个 Swin-transformer block 包含一个 shifted window based MSA module,2-layer MLP with GELU non-linearity。 2.1. Shifted Window based Self-Attention: Self-attention with non-overlapped windows. 为了更加有效地建模,作者在 local-windows 内部提出利用 self-attention 进行计算。假设每一个窗口包含 M*M ...
Shifted Window based Self-Attention 普通は全パッチ間とのself-attention(global self-attention)をするので、画像だと計算量が2次で増えていく ローカルウィンドウ内だけで、self-attentionすることにする でもウィンドウ間の関係がとれない→ウィンドウの境界をまたぐように、別のウィンドウでも...
Window-based attention: Dividing the image into fixed-size windows, with self-attention restricted within each non-overlapping region, and allowing cross-window connections.Hierarchical structure: The model can adapt to various image sizes with linear computational complexity, providing ...
Global self-attention computation is generally unaffordable for a large hw, while the window based self-attention is scalable. Shifted window partitioning in successive blocks The window-based self-attention module lacks connections across windows, which limits its modeling power. To introduce cross...
Window-based patch self-attention can use the local connectivity of the image features, and the shifted window-based patch self-attention enables the communication of information between different patches in the entire image scope. Through in-depth research on the effects of different sizes of ...
Hey authors, great repo for boosting training of Attention-based models. I wonder how the code can be ported to support (shifted)WindowAttention? To my knowledge, the (S)WindowAttention differs from traditional Attention on: SWAttention has a relative position bias term inside softmax:Softmax(...
Window-based patch self-attention can use the local connectivity of the image features, and the shifted window-based patch self-attention enables the communication of information between different patches in the entire image scope. Through in-depth research on the effects of different sizes of ...