patch_embed.py

# MIT License
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# Copyright (c) 2026 audio-embeddings contributors
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from collections.abc import Sequence
from math import prod

import torch
import torch.nn as nn
from timm.layers import PatchEmbed as TimmPatchEmbed


def _is_power_of_two(value: int) -> bool:
    return value > 0 and value & (value - 1) == 0


def _build_hmlp_kernel_schedule(
    patch_size: tuple[int, int],
) -> tuple[tuple[int, int], ...]:
    """Build non-overlapping aggregation steps for one target patch.

    The paper starts from 4x4 subpatches and doubles both axes until reaching
    16x16. For rectangular patches, an axis stops growing once it reaches its
    target while the other axis keeps doubling.
    """
    patch_height, patch_width = patch_size
    if not _is_power_of_two(patch_height) or not _is_power_of_two(patch_width):
        raise ValueError(
            "hMLP patch dimensions must be powers of two so each axis can be "
            f"doubled exactly, got {patch_size}"
        )

    first_kernel = (min(4, patch_height), min(4, patch_width))
    schedule = [first_kernel]
    current_height, current_width = first_kernel

    while (current_height, current_width) != patch_size:
        kernel_height = 2 if current_height < patch_height else 1
        kernel_width = 2 if current_width < patch_width else 1
        schedule.append((kernel_height, kernel_width))
        current_height *= kernel_height
        current_width *= kernel_width

    return tuple(schedule)


def _resolve_hmlp_kernel_schedule(
    patch_size: tuple[int, int],
    kernel_schedule: Sequence[Sequence[int]] | None,
) -> tuple[tuple[int, int], ...]:
    if kernel_schedule is None:
        return _build_hmlp_kernel_schedule(patch_size)

    schedule: list[tuple[int, int]] = []
    for stage_index, stage in enumerate(kernel_schedule):
        try:
            stage_values = tuple(stage)
        except TypeError as error:
            raise ValueError(
                "Each hMLP stage must contain [kernel_height, kernel_width], "
                f"stage {stage_index} has {stage}"
            ) from error
        if len(stage_values) != 2:
            raise ValueError(
                "Each hMLP stage must contain [kernel_height, kernel_width], "
                f"stage {stage_index} has {stage}"
            )
        try:
            kernel_size = tuple(int(value) for value in stage_values)
        except (TypeError, ValueError) as error:
            raise ValueError(
                f"hMLP stage kernels must be integers, got {stage_values}"
            ) from error
        if any(
            isinstance(original, bool) or normalized != original
            for normalized, original in zip(kernel_size, stage_values)
        ):
            raise ValueError(f"hMLP stage kernels must be integers, got {stage_values}")
        if any(value <= 0 for value in kernel_size):
            raise ValueError(f"hMLP stage kernels must be positive, got {kernel_size}")
        schedule.append(kernel_size)

    if not schedule:
        raise ValueError("hMLP kernel_schedule must contain at least one stage")

    aggregated_patch_size = tuple(
        prod(kernel_size[axis] for kernel_size in schedule) for axis in range(2)
    )
    if aggregated_patch_size != patch_size:
        raise ValueError(
            "hMLP kernel_schedule stages must multiply to patch_size; "
            f"got {aggregated_patch_size} from {tuple(schedule)}, expected {patch_size}"
        )

    return tuple(schedule)


class HierarchicalMLPPatchEmbed(nn.Module):
    """hMLP patch stem with independent, hierarchical patch aggregation."""

    def __init__(
        self,
        img_size: tuple[int, int] = (128, 256),
        patch_size: tuple[int, int] = (16, 16),
        in_chans: int = 1,
        embed_dim: int = 768,
        bias: bool = True,
        kernel_schedule: Sequence[Sequence[int]] | None = None,
    ) -> None:
        super().__init__()
        self.img_size = tuple(img_size)
        self.patch_size = tuple(patch_size)
        self.in_chans = in_chans
        self.embed_dim = embed_dim
        self.bias = bias
        self.kernel_schedule = _resolve_hmlp_kernel_schedule(
            self.patch_size,
            kernel_schedule,
        )
        self.num_patches = (self.img_size[0] // self.patch_size[0]) * (
            self.img_size[1] // self.patch_size[1]
        )

        hidden_dim = max(1, embed_dim // 4)
        layers: list[nn.Module] = []
        input_dim = in_chans
        for stage_index, kernel_size in enumerate(self.kernel_schedule):
            is_last = stage_index == len(self.kernel_schedule) - 1
            output_dim = embed_dim if is_last else hidden_dim
            layers.extend(
                [
                    nn.Conv2d(
                        input_dim,
                        output_dim,
                        kernel_size=kernel_size,
                        stride=kernel_size,
                        bias=bias,
                    ),
                    nn.SyncBatchNorm(output_dim),
                ]
            )
            if not is_last:
                layers.append(nn.GELU())
            input_dim = output_dim

        self.proj = nn.Sequential(*layers)

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        if x.ndim != 4:
            raise ValueError(f"Expected input with shape [B, C, H, W], got {x.shape}")
        if x.shape[1] != self.in_chans:
            raise ValueError(
                f"Expected {self.in_chans} input channels, got {x.shape[1]}"
            )
        if x.shape[2] % self.patch_size[0] != 0 or x.shape[3] % self.patch_size[1] != 0:
            raise ValueError(
                "Input spatial dimensions must be divisible by the hMLP patch "
                f"size {self.patch_size}, got {tuple(x.shape[2:])}"
            )

        return self.proj(x).flatten(2).transpose(1, 2)


class PatchEmbed(nn.Module):
    """
    2D Image to Patch Embedding.

    Args:
        img_size (tuple[int, int]): Input image size (H, W).
        patch_size (tuple[int, int]): Patch size (H, W).
        in_chans (int): Number of input channels.
        embed_dim (int): Embedding dimension.
    """

    def __init__(
        self,
        img_size: tuple[int, int] = (128, 256),
        patch_size: tuple[int, int] = (16, 16),
        in_chans: int = 1,
        embed_dim: int = 768,
        bias: bool = True,
        stem_type: str = "linear",
        hmlp_kernel_schedule: Sequence[Sequence[int]] | None = None,
    ):
        super().__init__()
        self.img_size = tuple(img_size)
        self.patch_size = tuple(patch_size)
        self.in_chans = in_chans
        self.embed_dim = embed_dim
        self.bias = bias
        self.stem_type = stem_type.strip().lower().replace("-", "_")

        if self.stem_type == "linear":
            self.patch_embed = TimmPatchEmbed(
                img_size=img_size,
                patch_size=patch_size,
                in_chans=in_chans,
                embed_dim=embed_dim,
                flatten=True,
                bias=bias,
                strict_img_size=False,
            )
        elif self.stem_type == "hmlp":
            self.patch_embed = HierarchicalMLPPatchEmbed(
                img_size=img_size,
                patch_size=patch_size,
                in_chans=in_chans,
                embed_dim=embed_dim,
                bias=bias,
                kernel_schedule=hmlp_kernel_schedule,
            )
        else:
            raise ValueError(
                f"Unknown stem_type={stem_type!r}; expected 'linear' or 'hmlp'"
            )
        self.num_patches = self.patch_embed.num_patches

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        """
        Forward pass.

        Args:
            x (torch.Tensor): Input tensor [B, C, H, W].

        Returns:
            torch.Tensor: Patch embeddings [B, N, D].
        """
        return self.patch_embed(x)