Method forward

plot-adapter/main.py:141–209

(self)

    def forward(self):
       
        ctx = self.ctx
        if ctx.dim() == 3:
            ctx = ctx.unsqueeze(0).expand(self.n_cls, -1, -1,-1) 
        
        ctx = ctx.permute(1, 0, 2, 3) 
        ctx = ctx.contiguous().view(self.N*self.n_cls,self.n_ctx,ctx.shape[3])

        prefix = self.token_prefix
        suffix = self.token_suffix

        if self.class_token_position == "end":
            prompts = torch.cat(
                [
                    prefix,  # (n_cls, 1, dim)
                    ctx,     # (n_cls, n_ctx, dim)
                    suffix,  # (n_cls, *, dim)
                ],
                dim=1,
            )

        elif self.class_token_position == "middle":
            half_n_ctx = self.n_ctx // 2
            prompts = []
            for i in range(self.n_cls):
                name_len = self.name_lens[i]
                prefix_i = prefix[i : i + 1, :, :]
                class_i = suffix[i : i + 1, :name_len, :]
                suffix_i = suffix[i : i + 1, name_len:, :]
                ctx_i_half1 = ctx[i : i + 1, :half_n_ctx, :]
                ctx_i_half2 = ctx[i : i + 1, half_n_ctx:, :]
                prompt = torch.cat(
                    [
                        prefix_i,     # (1, 1, dim)
                        ctx_i_half1,  # (1, n_ctx//2, dim)
                        class_i,      # (1, name_len, dim)
                        ctx_i_half2,  # (1, n_ctx//2, dim)
                        suffix_i,     # (1, *, dim)
                    ],
                    dim=1,
                )
                prompts.append(prompt)
            prompts = torch.cat(prompts, dim=0)

        elif self.class_token_position == "front":
            prompts = []
            for i in range(self.n_cls):
                name_len = self.name_lens[i]
                prefix_i = prefix[i : i + 1, :, :]
                class_i = suffix[i : i + 1, :name_len, :]
                suffix_i = suffix[i : i + 1, name_len:, :]
                ctx_i = ctx[i : i + 1, :, :]
                prompt = torch.cat(
                    [
                        prefix_i,  # (1, 1, dim)
                        class_i,   # (1, name_len, dim)
                        ctx_i,     # (1, n_ctx, dim)