timm/models/eva.py [259:286]:
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
        hidden_features = int(dim * mlp_ratio)
        if swiglu_mlp:
            if scale_mlp:
                # when norm in SwiGLU used, an impl with separate fc for gate & x is used
                self.mlp = SwiGLU(
                    in_features=dim,
                    hidden_features=hidden_features,
                    norm_layer=norm_layer if scale_mlp else None,
                    drop=proj_drop,
                )
            else:
                # w/o any extra norm, an impl with packed weights is used, matches existing GluMLP
                self.mlp = GluMlp(
                    in_features=dim,
                    hidden_features=hidden_features * 2,
                    norm_layer=norm_layer if scale_mlp else None,
                    act_layer=nn.SiLU,
                    gate_last=False,
                    drop=proj_drop,
                )
        else:
            self.mlp = Mlp(
                in_features=dim,
                hidden_features=hidden_features,
                act_layer=act_layer,
                norm_layer=norm_layer if scale_mlp else None,
                drop=proj_drop,
            )
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -



timm/models/eva.py [360:387]:
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
        hidden_features = int(dim * mlp_ratio)
        if swiglu_mlp:
            if scale_mlp:
                # when norm in SwiGLU used, an impl with separate fc for gate & x is used
                self.mlp = SwiGLU(
                    in_features=dim,
                    hidden_features=hidden_features,
                    norm_layer=norm_layer if scale_mlp else None,
                    drop=proj_drop,
                )
            else:
                # w/o any extra norm, an impl with packed fc1 weights is used, matches existing GluMLP
                self.mlp = GluMlp(
                    in_features=dim,
                    hidden_features=hidden_features * 2,
                    norm_layer=norm_layer if scale_mlp else None,
                    act_layer=nn.SiLU,
                    gate_last=False,
                    drop=proj_drop,
                )
        else:
            self.mlp = Mlp(
                in_features=dim,
                hidden_features=hidden_features,
                act_layer=act_layer,
                norm_layer=norm_layer if scale_mlp else None,
                drop=proj_drop,
            )
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -