Method forward

semantic_sam/backbone/focal.py:166–195

Forward function. Args: x: Input feature, tensor size (B, H*W, C). H, W: Spatial resolution of the input feature.

(self, x)

            self.gamma_2 = nn.Parameter(layerscale_value * torch.ones((dim)), requires_grad=True)

    def forward(self, x):
        """ Forward function.

        Args:
            x: Input feature, tensor size (B, H*W, C).
            H, W: Spatial resolution of the input feature.
        """
        B, L, C = x.shape
        H, W = self.H, self.W
        assert L == H * W, "input feature has wrong size"

        shortcut = x
        if not self.use_postln:
            x = self.norm1(x)
        x = x.view(B, H, W, C)
        
        # FM
        x = self.modulation(x).view(B, H * W, C)
        if self.use_postln:
            x = self.norm1(x)

        # FFN
        x = shortcut + self.drop_path(self.gamma_1 * x)

        if self.use_postln:
            x = x + self.drop_path(self.gamma_2 * self.norm2(self.mlp(x)))
        else:
            x = x + self.drop_path(self.gamma_2 * self.mlp(self.norm2(x)))

        return x

class BasicLayer(nn.Module):
    """ A basic focal modulation layer for one stage.