Method __repr__

skills/paper2code/worked/ddpm/src/model.py:372–382

(self)

        return self.output_conv(h)  # (batch, C, H, W) — predicted noise

    def __repr__(self) -> str:
        total_params = sum(p.numel() for p in self.parameters())
        trainable_params = sum(p.numel() for p in self.parameters() if p.requires_grad)
        return (
            f"UNet(\n"
            f"  image_size={self.config.image_size}, base_channels={self.config.base_channels},\n"
            f"  channel_mults={self.config.channel_mults}, num_res_blocks={self.config.num_res_blocks},\n"
            f"  total_params={total_params:,},\n"
            f"  trainable_params={trainable_params:,}\n"
            f")"
        )