(self, x)
| 115 | padding=0) |
| 116 | |
| 117 | def forward(self, x): |
| 118 | h_ = x |
| 119 | h_ = self.norm(h_) |
| 120 | q = self.q(h_) |
| 121 | k = self.k(h_) |
| 122 | v = self.v(h_) |
| 123 | |
| 124 | # compute attention |
| 125 | b,c,h,w = q.shape |
| 126 | q = rearrange(q, 'b c h w -> b (h w) c') |
| 127 | k = rearrange(k, 'b c h w -> b c (h w)') |
| 128 | w_ = torch.einsum('bij,bjk->bik', q, k) |
| 129 | |
| 130 | w_ = w_ * (int(c)**(-0.5)) |
| 131 | w_ = torch.nn.functional.softmax(w_, dim=2) |
| 132 | |
| 133 | # attend to values |
| 134 | v = rearrange(v, 'b c h w -> b c (h w)') |
| 135 | w_ = rearrange(w_, 'b i j -> b j i') |
| 136 | h_ = torch.einsum('bij,bjk->bik', v, w_) |
| 137 | h_ = rearrange(h_, 'b c (h w) -> b c h w', h=h) |
| 138 | h_ = self.proj_out(h_) |
| 139 | |
| 140 | return x+h_ |
| 141 | |
| 142 | |
| 143 | class CrossAttention(nn.Module): |
nothing calls this directly
no outgoing calls
no test coverage detected