def encode_to_vector()

in shap_e/models/transmitter/multiview_encoder.py [0:0]

• 25 lines of code
• 2 McCabe index (conditional complexity)

    def encode_to_vector(self, batch: AttrDict, options: Optional[AttrDict] = None) -> torch.Tensor:
        _ = options

        all_views = self.views_to_tensor(batch.views).to(self.device)
        if self.use_depth:
            all_views = torch.cat([all_views, self.depths_to_tensor(batch.depths)], dim=2)
        all_cameras = self.cameras_to_tensor(batch.cameras).to(self.device)

        batch_size, num_views, _, _, _ = all_views.shape

        views_proj = self.patch_emb(
            all_views.reshape([batch_size * num_views, *all_views.shape[2:]])
        )
        views_proj = (
            views_proj.reshape([batch_size, num_views, self.width, -1])
            .permute(0, 1, 3, 2)
            .contiguous()
        )  # [batch_size x num_views x n_patches x width]

        cameras_proj = self.camera_emb(all_cameras).reshape([batch_size, num_views, 1, self.width])

        h = torch.cat([views_proj, cameras_proj], dim=2).reshape([batch_size, -1, self.width])
        h = h + self.pos_emb
        h = torch.cat([h, self.output_tokens[None].repeat(len(h), 1, 1)], dim=1)
        h = self.ln_pre(h)
        h = self.backbone(h)
        h = self.ln_post(h)
        h = h[:, self.n_ctx :]
        h = self.output_proj(h).flatten(1)

        return h