ultralytics/models/sam/modules/encoders.py

# Ultralytics YOLO 🚀, AGPL-3.0 license

from typing import Any, Optional, Tuple, Type

import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F

from ultralytics.nn.modules import LayerNorm2d, MLPBlock


# This class and its supporting functions below lightly adapted from the ViTDet backbone available at: https://github.com/facebookresearch/detectron2/blob/main/detectron2/modeling/backbone/vit.py # noqa
class ImageEncoderViT(nn.Module):

    def __init__(

            self,

            img_size: int = 1024,

            patch_size: int = 16,

            in_chans: int = 3,

            embed_dim: int = 768,

            depth: int = 12,

            num_heads: int = 12,

            mlp_ratio: float = 4.0,

            out_chans: int = 256,

            qkv_bias: bool = True,

            norm_layer: Type[nn.Module] = nn.LayerNorm,

            act_layer: Type[nn.Module] = nn.GELU,

            use_abs_pos: bool = True,

            use_rel_pos: bool = False,

            rel_pos_zero_init: bool = True,

            window_size: int = 0,

            global_attn_indexes: Tuple[int, ...] = (),

    ) -> None:
        """

        Args:

            img_size (int): Input image size.

            patch_size (int): Patch size.

            in_chans (int): Number of input image channels.

            embed_dim (int): Patch embedding dimension.

            depth (int): Depth of ViT.

            num_heads (int): Number of attention heads in each ViT block.

            mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.

            qkv_bias (bool): If True, add a learnable bias to query, key, value.

            norm_layer (nn.Module): Normalization layer.

            act_layer (nn.Module): Activation layer.

            use_abs_pos (bool): If True, use absolute positional embeddings.

            use_rel_pos (bool): If True, add relative positional embeddings to the attention map.

            rel_pos_zero_init (bool): If True, zero initialize relative positional parameters.

            window_size (int): Window size for window attention blocks.

            global_attn_indexes (list): Indexes for blocks using global attention.

        """
        super().__init__()
        self.img_size = img_size

        self.patch_embed = PatchEmbed(
            kernel_size=(patch_size, patch_size),
            stride=(patch_size, patch_size),
            in_chans=in_chans,
            embed_dim=embed_dim,
        )

        self.pos_embed: Optional[nn.Parameter] = None
        if use_abs_pos:
            # Initialize absolute positional embedding with pretrain image size.
            self.pos_embed = nn.Parameter(torch.zeros(1, img_size // patch_size, img_size // patch_size, embed_dim))

        self.blocks = nn.ModuleList()
        for i in range(depth):
            block = Block(
                dim=embed_dim,
                num_heads=num_heads,
                mlp_ratio=mlp_ratio,
                qkv_bias=qkv_bias,
                norm_layer=norm_layer,
                act_layer=act_layer,
                use_rel_pos=use_rel_pos,
                rel_pos_zero_init=rel_pos_zero_init,
                window_size=window_size if i not in global_attn_indexes else 0,
                input_size=(img_size // patch_size, img_size // patch_size),
            )
            self.blocks.append(block)

        self.neck = nn.Sequential(
            nn.Conv2d(
                embed_dim,
                out_chans,
                kernel_size=1,
                bias=False,
            ),
            LayerNorm2d(out_chans),
            nn.Conv2d(
                out_chans,
                out_chans,
                kernel_size=3,
                padding=1,
                bias=False,
            ),
            LayerNorm2d(out_chans),
        )

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        x = self.patch_embed(x)
        if self.pos_embed is not None:
            x = x + self.pos_embed

        for blk in self.blocks:
            x = blk(x)

        x = self.neck(x.permute(0, 3, 1, 2))

        return x


class PromptEncoder(nn.Module):

    def __init__(

        self,

        embed_dim: int,

        image_embedding_size: Tuple[int, int],

        input_image_size: Tuple[int, int],

        mask_in_chans: int,

        activation: Type[nn.Module] = nn.GELU,

    ) -> None:
        """

        Encodes prompts for input to SAM's mask decoder.



        Arguments:

          embed_dim (int): The prompts' embedding dimension

          image_embedding_size (tuple(int, int)): The spatial size of the

            image embedding, as (H, W).

          input_image_size (int): The padded size of the image as input

            to the image encoder, as (H, W).

          mask_in_chans (int): The number of hidden channels used for

            encoding input masks.

          activation (nn.Module): The activation to use when encoding

            input masks.

        """
        super().__init__()
        self.embed_dim = embed_dim
        self.input_image_size = input_image_size
        self.image_embedding_size = image_embedding_size
        self.pe_layer = PositionEmbeddingRandom(embed_dim // 2)

        self.num_point_embeddings: int = 4  # pos/neg point + 2 box corners
        point_embeddings = [nn.Embedding(1, embed_dim) for _ in range(self.num_point_embeddings)]
        self.point_embeddings = nn.ModuleList(point_embeddings)
        self.not_a_point_embed = nn.Embedding(1, embed_dim)

        self.mask_input_size = (4 * image_embedding_size[0], 4 * image_embedding_size[1])
        self.mask_downscaling = nn.Sequential(
            nn.Conv2d(1, mask_in_chans // 4, kernel_size=2, stride=2),
            LayerNorm2d(mask_in_chans // 4),
            activation(),
            nn.Conv2d(mask_in_chans // 4, mask_in_chans, kernel_size=2, stride=2),
            LayerNorm2d(mask_in_chans),
            activation(),
            nn.Conv2d(mask_in_chans, embed_dim, kernel_size=1),
        )
        self.no_mask_embed = nn.Embedding(1, embed_dim)

    def get_dense_pe(self) -> torch.Tensor:
        """

        Returns the positional encoding used to encode point prompts,

        applied to a dense set of points the shape of the image encoding.



        Returns:

          torch.Tensor: Positional encoding with shape

            1x(embed_dim)x(embedding_h)x(embedding_w)

        """
        return self.pe_layer(self.image_embedding_size).unsqueeze(0)

    def _embed_points(

        self,

        points: torch.Tensor,

        labels: torch.Tensor,

        pad: bool,

    ) -> torch.Tensor:
        """Embeds point prompts."""
        points = points + 0.5  # Shift to center of pixel
        if pad:
            padding_point = torch.zeros((points.shape[0], 1, 2), device=points.device)
            padding_label = -torch.ones((labels.shape[0], 1), device=labels.device)
            points = torch.cat([points, padding_point], dim=1)
            labels = torch.cat([labels, padding_label], dim=1)
        point_embedding = self.pe_layer.forward_with_coords(points, self.input_image_size)
        point_embedding[labels == -1] = 0.0
        point_embedding[labels == -1] += self.not_a_point_embed.weight
        point_embedding[labels == 0] += self.point_embeddings[0].weight
        point_embedding[labels == 1] += self.point_embeddings[1].weight
        return point_embedding

    def _embed_boxes(self, boxes: torch.Tensor) -> torch.Tensor:
        """Embeds box prompts."""
        boxes = boxes + 0.5  # Shift to center of pixel
        coords = boxes.reshape(-1, 2, 2)
        corner_embedding = self.pe_layer.forward_with_coords(coords, self.input_image_size)
        corner_embedding[:, 0, :] += self.point_embeddings[2].weight
        corner_embedding[:, 1, :] += self.point_embeddings[3].weight
        return corner_embedding

    def _embed_masks(self, masks: torch.Tensor) -> torch.Tensor:
        """Embeds mask inputs."""
        return self.mask_downscaling(masks)

    def _get_batch_size(

        self,

        points: Optional[Tuple[torch.Tensor, torch.Tensor]],

        boxes: Optional[torch.Tensor],

        masks: Optional[torch.Tensor],

    ) -> int:
        """

        Gets the batch size of the output given the batch size of the input prompts.

        """
        if points is not None:
            return points[0].shape[0]
        elif boxes is not None:
            return boxes.shape[0]
        elif masks is not None:
            return masks.shape[0]
        else:
            return 1

    def _get_device(self) -> torch.device:
        return self.point_embeddings[0].weight.device

    def forward(

        self,

        points: Optional[Tuple[torch.Tensor, torch.Tensor]],

        boxes: Optional[torch.Tensor],

        masks: Optional[torch.Tensor],

    ) -> Tuple[torch.Tensor, torch.Tensor]:
        """

        Embeds different types of prompts, returning both sparse and dense

        embeddings.



        Arguments:

          points (tuple(torch.Tensor, torch.Tensor), None): point coordinates

            and labels to embed.

          boxes (torch.Tensor, None): boxes to embed

          masks (torch.Tensor, None): masks to embed



        Returns:

          torch.Tensor: sparse embeddings for the points and boxes, with shape

            BxNx(embed_dim), where N is determined by the number of input points

            and boxes.

          torch.Tensor: dense embeddings for the masks, in the shape

            Bx(embed_dim)x(embed_H)x(embed_W)

        """
        bs = self._get_batch_size(points, boxes, masks)
        sparse_embeddings = torch.empty((bs, 0, self.embed_dim), device=self._get_device())
        if points is not None:
            coords, labels = points
            point_embeddings = self._embed_points(coords, labels, pad=(boxes is None))
            sparse_embeddings = torch.cat([sparse_embeddings, point_embeddings], dim=1)
        if boxes is not None:
            box_embeddings = self._embed_boxes(boxes)
            sparse_embeddings = torch.cat([sparse_embeddings, box_embeddings], dim=1)

        if masks is not None:
            dense_embeddings = self._embed_masks(masks)
        else:
            dense_embeddings = self.no_mask_embed.weight.reshape(1, -1, 1,
                                                                 1).expand(bs, -1, self.image_embedding_size[0],
                                                                           self.image_embedding_size[1])

        return sparse_embeddings, dense_embeddings


class PositionEmbeddingRandom(nn.Module):
    """

    Positional encoding using random spatial frequencies.

    """

    def __init__(self, num_pos_feats: int = 64, scale: Optional[float] = None) -> None:
        super().__init__()
        if scale is None or scale <= 0.0:
            scale = 1.0
        self.register_buffer(
            'positional_encoding_gaussian_matrix',
            scale * torch.randn((2, num_pos_feats)),
        )

    def _pe_encoding(self, coords: torch.Tensor) -> torch.Tensor:
        """Positionally encode points that are normalized to [0,1]."""
        # assuming coords are in [0, 1]^2 square and have d_1 x ... x d_n x 2 shape
        coords = 2 * coords - 1
        coords = coords @ self.positional_encoding_gaussian_matrix
        coords = 2 * np.pi * coords
        # outputs d_1 x ... x d_n x C shape
        return torch.cat([torch.sin(coords), torch.cos(coords)], dim=-1)

    def forward(self, size: Tuple[int, int]) -> torch.Tensor:
        """Generate positional encoding for a grid of the specified size."""
        h, w = size
        device: Any = self.positional_encoding_gaussian_matrix.device
        grid = torch.ones((h, w), device=device, dtype=torch.float32)
        y_embed = grid.cumsum(dim=0) - 0.5
        x_embed = grid.cumsum(dim=1) - 0.5
        y_embed = y_embed / h
        x_embed = x_embed / w

        pe = self._pe_encoding(torch.stack([x_embed, y_embed], dim=-1))
        return pe.permute(2, 0, 1)  # C x H x W

    def forward_with_coords(self, coords_input: torch.Tensor, image_size: Tuple[int, int]) -> torch.Tensor:
        """Positionally encode points that are not normalized to [0,1]."""
        coords = coords_input.clone()
        coords[:, :, 0] = coords[:, :, 0] / image_size[1]
        coords[:, :, 1] = coords[:, :, 1] / image_size[0]
        return self._pe_encoding(coords.to(torch.float))  # B x N x C


class Block(nn.Module):
    """Transformer blocks with support of window attention and residual propagation blocks"""

    def __init__(

        self,

        dim: int,

        num_heads: int,

        mlp_ratio: float = 4.0,

        qkv_bias: bool = True,

        norm_layer: Type[nn.Module] = nn.LayerNorm,

        act_layer: Type[nn.Module] = nn.GELU,

        use_rel_pos: bool = False,

        rel_pos_zero_init: bool = True,

        window_size: int = 0,

        input_size: Optional[Tuple[int, int]] = None,

    ) -> None:
        """

        Args:

            dim (int): Number of input channels.

            num_heads (int): Number of attention heads in each ViT block.

            mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.

            qkv_bias (bool): If True, add a learnable bias to query, key, value.

            norm_layer (nn.Module): Normalization layer.

            act_layer (nn.Module): Activation layer.

            use_rel_pos (bool): If True, add relative positional embeddings to the attention map.

            rel_pos_zero_init (bool): If True, zero initialize relative positional parameters.

            window_size (int): Window size for window attention blocks. If it equals 0, then

                use global attention.

            input_size (tuple(int, int), None): Input resolution for calculating the relative

                positional parameter size.

        """
        super().__init__()
        self.norm1 = norm_layer(dim)
        self.attn = Attention(
            dim,
            num_heads=num_heads,
            qkv_bias=qkv_bias,
            use_rel_pos=use_rel_pos,
            rel_pos_zero_init=rel_pos_zero_init,
            input_size=input_size if window_size == 0 else (window_size, window_size),
        )

        self.norm2 = norm_layer(dim)
        self.mlp = MLPBlock(embedding_dim=dim, mlp_dim=int(dim * mlp_ratio), act=act_layer)

        self.window_size = window_size

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        shortcut = x
        x = self.norm1(x)
        # Window partition
        if self.window_size > 0:
            H, W = x.shape[1], x.shape[2]
            x, pad_hw = window_partition(x, self.window_size)

        x = self.attn(x)
        # Reverse window partition
        if self.window_size > 0:
            x = window_unpartition(x, self.window_size, pad_hw, (H, W))

        x = shortcut + x
        x = x + self.mlp(self.norm2(x))

        return x


class Attention(nn.Module):
    """Multi-head Attention block with relative position embeddings."""

    def __init__(

        self,

        dim: int,

        num_heads: int = 8,

        qkv_bias: bool = True,

        use_rel_pos: bool = False,

        rel_pos_zero_init: bool = True,

        input_size: Optional[Tuple[int, int]] = None,

    ) -> None:
        """

        Args:

            dim (int): Number of input channels.

            num_heads (int): Number of attention heads.

            qkv_bias (bool):  If True, add a learnable bias to query, key, value.

            rel_pos_zero_init (bool): If True, zero initialize relative positional parameters.

            input_size (tuple(int, int), None): Input resolution for calculating the relative

                positional parameter size.

        """
        super().__init__()
        self.num_heads = num_heads
        head_dim = dim // num_heads
        self.scale = head_dim ** -0.5

        self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
        self.proj = nn.Linear(dim, dim)

        self.use_rel_pos = use_rel_pos
        if self.use_rel_pos:
            assert (input_size is not None), 'Input size must be provided if using relative positional encoding.'
            # initialize relative positional embeddings
            self.rel_pos_h = nn.Parameter(torch.zeros(2 * input_size[0] - 1, head_dim))
            self.rel_pos_w = nn.Parameter(torch.zeros(2 * input_size[1] - 1, head_dim))

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        B, H, W, _ = x.shape
        # qkv with shape (3, B, nHead, H * W, C)
        qkv = self.qkv(x).reshape(B, H * W, 3, self.num_heads, -1).permute(2, 0, 3, 1, 4)
        # q, k, v with shape (B * nHead, H * W, C)
        q, k, v = qkv.reshape(3, B * self.num_heads, H * W, -1).unbind(0)

        attn = (q * self.scale) @ k.transpose(-2, -1)

        if self.use_rel_pos:
            attn = add_decomposed_rel_pos(attn, q, self.rel_pos_h, self.rel_pos_w, (H, W), (H, W))

        attn = attn.softmax(dim=-1)
        x = (attn @ v).view(B, self.num_heads, H, W, -1).permute(0, 2, 3, 1, 4).reshape(B, H, W, -1)
        x = self.proj(x)

        return x


def window_partition(x: torch.Tensor, window_size: int) -> Tuple[torch.Tensor, Tuple[int, int]]:
    """

    Partition into non-overlapping windows with padding if needed.

    Args:

        x (tensor): input tokens with [B, H, W, C].

        window_size (int): window size.



    Returns:

        windows: windows after partition with [B * num_windows, window_size, window_size, C].

        (Hp, Wp): padded height and width before partition

    """
    B, H, W, C = x.shape

    pad_h = (window_size - H % window_size) % window_size
    pad_w = (window_size - W % window_size) % window_size
    if pad_h > 0 or pad_w > 0:
        x = F.pad(x, (0, 0, 0, pad_w, 0, pad_h))
    Hp, Wp = H + pad_h, W + pad_w

    x = x.view(B, Hp // window_size, window_size, Wp // window_size, window_size, C)
    windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C)
    return windows, (Hp, Wp)


def window_unpartition(windows: torch.Tensor, window_size: int, pad_hw: Tuple[int, int],

                       hw: Tuple[int, int]) -> torch.Tensor:
    """

    Window unpartition into original sequences and removing padding.

    Args:

        windows (tensor): input tokens with [B * num_windows, window_size, window_size, C].

        window_size (int): window size.

        pad_hw (Tuple): padded height and width (Hp, Wp).

        hw (Tuple): original height and width (H, W) before padding.



    Returns:

        x: unpartitioned sequences with [B, H, W, C].

    """
    Hp, Wp = pad_hw
    H, W = hw
    B = windows.shape[0] // (Hp * Wp // window_size // window_size)
    x = windows.view(B, Hp // window_size, Wp // window_size, window_size, window_size, -1)
    x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, Hp, Wp, -1)

    if Hp > H or Wp > W:
        x = x[:, :H, :W, :].contiguous()
    return x


def get_rel_pos(q_size: int, k_size: int, rel_pos: torch.Tensor) -> torch.Tensor:
    """

    Get relative positional embeddings according to the relative positions of

        query and key sizes.

    Args:

        q_size (int): size of query q.

        k_size (int): size of key k.

        rel_pos (Tensor): relative position embeddings (L, C).



    Returns:

        Extracted positional embeddings according to relative positions.

    """
    max_rel_dist = int(2 * max(q_size, k_size) - 1)
    # Interpolate rel pos if needed.
    if rel_pos.shape[0] != max_rel_dist:
        # Interpolate rel pos.
        rel_pos_resized = F.interpolate(
            rel_pos.reshape(1, rel_pos.shape[0], -1).permute(0, 2, 1),
            size=max_rel_dist,
            mode='linear',
        )
        rel_pos_resized = rel_pos_resized.reshape(-1, max_rel_dist).permute(1, 0)
    else:
        rel_pos_resized = rel_pos

    # Scale the coords with short length if shapes for q and k are different.
    q_coords = torch.arange(q_size)[:, None] * max(k_size / q_size, 1.0)
    k_coords = torch.arange(k_size)[None, :] * max(q_size / k_size, 1.0)
    relative_coords = (q_coords - k_coords) + (k_size - 1) * max(q_size / k_size, 1.0)

    return rel_pos_resized[relative_coords.long()]


def add_decomposed_rel_pos(

    attn: torch.Tensor,

    q: torch.Tensor,

    rel_pos_h: torch.Tensor,

    rel_pos_w: torch.Tensor,

    q_size: Tuple[int, int],

    k_size: Tuple[int, int],

) -> torch.Tensor:
    """

    Calculate decomposed Relative Positional Embeddings from :paper:`mvitv2`.

    https://github.com/facebookresearch/mvit/blob/19786631e330df9f3622e5402b4a419a263a2c80/mvit/models/attention.py   # noqa B950

    Args:

        attn (Tensor): attention map.

        q (Tensor): query q in the attention layer with shape (B, q_h * q_w, C).

        rel_pos_h (Tensor): relative position embeddings (Lh, C) for height axis.

        rel_pos_w (Tensor): relative position embeddings (Lw, C) for width axis.

        q_size (Tuple): spatial sequence size of query q with (q_h, q_w).

        k_size (Tuple): spatial sequence size of key k with (k_h, k_w).



    Returns:

        attn (Tensor): attention map with added relative positional embeddings.

    """
    q_h, q_w = q_size
    k_h, k_w = k_size
    Rh = get_rel_pos(q_h, k_h, rel_pos_h)
    Rw = get_rel_pos(q_w, k_w, rel_pos_w)

    B, _, dim = q.shape
    r_q = q.reshape(B, q_h, q_w, dim)
    rel_h = torch.einsum('bhwc,hkc->bhwk', r_q, Rh)
    rel_w = torch.einsum('bhwc,wkc->bhwk', r_q, Rw)

    attn = (attn.view(B, q_h, q_w, k_h, k_w) + rel_h[:, :, :, :, None] + rel_w[:, :, :, None, :]).view(
        B, q_h * q_w, k_h * k_w)

    return attn


class PatchEmbed(nn.Module):
    """

    Image to Patch Embedding.

    """

    def __init__(

            self,

            kernel_size: Tuple[int, int] = (16, 16),

            stride: Tuple[int, int] = (16, 16),

            padding: Tuple[int, int] = (0, 0),

            in_chans: int = 3,

            embed_dim: int = 768,

    ) -> None:
        """

        Args:

            kernel_size (Tuple): kernel size of the projection layer.

            stride (Tuple): stride of the projection layer.

            padding (Tuple): padding size of the projection layer.

            in_chans (int): Number of input image channels.

            embed_dim (int): Patch embedding dimension.

        """
        super().__init__()

        self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=kernel_size, stride=stride, padding=padding)

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        x = self.proj(x)
        # B C H W -> B H W C
        x = x.permute(0, 2, 3, 1)
        return x