head_extractor/src/mmdet/models/layers/positional_encoding.py

# Copyright (c) OpenMMLab. All rights reserved.
import math
from typing import Optional

import torch
import torch.nn as nn
from mmengine.model import BaseModule
from torch import Tensor

from mmdet.registry import MODELS
from mmdet.utils import MultiConfig, OptMultiConfig


@MODELS.register_module()
class SinePositionalEncoding(BaseModule):
    """Position encoding with sine and cosine functions.

    See `End-to-End Object Detection with Transformers
    <https://arxiv.org/pdf/2005.12872>`_ for details.

    Args:
        num_feats (int): The feature dimension for each position
            along x-axis or y-axis. Note the final returned dimension
            for each position is 2 times of this value.
        temperature (int, optional): The temperature used for scaling
            the position embedding. Defaults to 10000.
        normalize (bool, optional): Whether to normalize the position
            embedding. Defaults to False.
        scale (float, optional): A scale factor that scales the position
            embedding. The scale will be used only when `normalize` is True.
            Defaults to 2*pi.
        eps (float, optional): A value added to the denominator for
            numerical stability. Defaults to 1e-6.
        offset (float): offset add to embed when do the normalization.
            Defaults to 0.
        init_cfg (dict or list[dict], optional): Initialization config dict.
            Defaults to None
    """

    def __init__(self,
                 num_feats: int,
                 temperature: int = 10000,
                 normalize: bool = False,
                 scale: float = 2 * math.pi,
                 eps: float = 1e-6,
                 offset: float = 0.,
                 init_cfg: OptMultiConfig = None) -> None:
        super().__init__(init_cfg=init_cfg)
        if normalize:
            assert isinstance(scale, (float, int)), 'when normalize is set,' \
                'scale should be provided and in float or int type, ' \
                f'found {type(scale)}'
        self.num_feats = num_feats
        self.temperature = temperature
        self.normalize = normalize
        self.scale = scale
        self.eps = eps
        self.offset = offset

    def forward(self, mask: Tensor, input: Optional[Tensor] = None) -> Tensor:
        """Forward function for `SinePositionalEncoding`.

        Args:
            mask (Tensor): ByteTensor mask. Non-zero values representing
                ignored positions, while zero values means valid positions
                for this image. Shape [bs, h, w].
            input (Tensor, optional): Input image/feature Tensor.
                Shape [bs, c, h, w]

        Returns:
            pos (Tensor): Returned position embedding with shape
                [bs, num_feats*2, h, w].
        """
        assert not (mask is None and input is None)

        if mask is not None:
            B, H, W = mask.size()
            device = mask.device
            # For convenience of exporting to ONNX,
            # it's required to convert
            # `masks` from bool to int.
            mask = mask.to(torch.int)
            not_mask = 1 - mask  # logical_not
            y_embed = not_mask.cumsum(1, dtype=torch.float32)
            x_embed = not_mask.cumsum(2, dtype=torch.float32)
        else:
            # single image or batch image with no padding
            B, _, H, W = input.shape
            device = input.device
            x_embed = torch.arange(
                1, W + 1, dtype=torch.float32, device=device)
            x_embed = x_embed.view(1, 1, -1).repeat(B, H, 1)
            y_embed = torch.arange(
                1, H + 1, dtype=torch.float32, device=device)
            y_embed = y_embed.view(1, -1, 1).repeat(B, 1, W)
        if self.normalize:
            y_embed = (y_embed + self.offset) / \
                      (y_embed[:, -1:, :] + self.eps) * self.scale
            x_embed = (x_embed + self.offset) / \
                      (x_embed[:, :, -1:] + self.eps) * self.scale
        dim_t = torch.arange(
            self.num_feats, dtype=torch.float32, device=device)
        dim_t = self.temperature**(2 * (dim_t // 2) / self.num_feats)
        pos_x = x_embed[:, :, :, None] / dim_t
        pos_y = y_embed[:, :, :, None] / dim_t
        # use `view` instead of `flatten` for dynamically exporting to ONNX

        pos_x = torch.stack(
            (pos_x[:, :, :, 0::2].sin(), pos_x[:, :, :, 1::2].cos()),
            dim=4).view(B, H, W, -1)
        pos_y = torch.stack(
            (pos_y[:, :, :, 0::2].sin(), pos_y[:, :, :, 1::2].cos()),
            dim=4).view(B, H, W, -1)
        pos = torch.cat((pos_y, pos_x), dim=3).permute(0, 3, 1, 2)
        return pos

    def __repr__(self) -> str:
        """str: a string that describes the module"""
        repr_str = self.__class__.__name__
        repr_str += f'(num_feats={self.num_feats}, '
        repr_str += f'temperature={self.temperature}, '
        repr_str += f'normalize={self.normalize}, '
        repr_str += f'scale={self.scale}, '
        repr_str += f'eps={self.eps})'
        return repr_str


@MODELS.register_module()
class LearnedPositionalEncoding(BaseModule):
    """Position embedding with learnable embedding weights.

    Args:
        num_feats (int): The feature dimension for each position
            along x-axis or y-axis. The final returned dimension for
            each position is 2 times of this value.
        row_num_embed (int, optional): The dictionary size of row embeddings.
            Defaults to 50.
        col_num_embed (int, optional): The dictionary size of col embeddings.
            Defaults to 50.
        init_cfg (dict or list[dict], optional): Initialization config dict.
    """

    def __init__(
        self,
        num_feats: int,
        row_num_embed: int = 50,
        col_num_embed: int = 50,
        init_cfg: MultiConfig = dict(type='Uniform', layer='Embedding')
    ) -> None:
        super().__init__(init_cfg=init_cfg)
        self.row_embed = nn.Embedding(row_num_embed, num_feats)
        self.col_embed = nn.Embedding(col_num_embed, num_feats)
        self.num_feats = num_feats
        self.row_num_embed = row_num_embed
        self.col_num_embed = col_num_embed

    def forward(self, mask: Tensor) -> Tensor:
        """Forward function for `LearnedPositionalEncoding`.

        Args:
            mask (Tensor): ByteTensor mask. Non-zero values representing
                ignored positions, while zero values means valid positions
                for this image. Shape [bs, h, w].

        Returns:
            pos (Tensor): Returned position embedding with shape
                [bs, num_feats*2, h, w].
        """
        h, w = mask.shape[-2:]
        x = torch.arange(w, device=mask.device)
        y = torch.arange(h, device=mask.device)
        x_embed = self.col_embed(x)
        y_embed = self.row_embed(y)
        pos = torch.cat(
            (x_embed.unsqueeze(0).repeat(h, 1, 1), y_embed.unsqueeze(1).repeat(
                1, w, 1)),
            dim=-1).permute(2, 0,
                            1).unsqueeze(0).repeat(mask.shape[0], 1, 1, 1)
        return pos

    def __repr__(self) -> str:
        """str: a string that describes the module"""
        repr_str = self.__class__.__name__
        repr_str += f'(num_feats={self.num_feats}, '
        repr_str += f'row_num_embed={self.row_num_embed}, '
        repr_str += f'col_num_embed={self.col_num_embed})'
        return repr_str


@MODELS.register_module()
class SinePositionalEncoding3D(SinePositionalEncoding):
    """Position encoding with sine and cosine functions.

    See `End-to-End Object Detection with Transformers
    <https://arxiv.org/pdf/2005.12872>`_ for details.

    Args:
        num_feats (int): The feature dimension for each position
            along x-axis or y-axis. Note the final returned dimension
            for each position is 2 times of this value.
        temperature (int, optional): The temperature used for scaling
            the position embedding. Defaults to 10000.
        normalize (bool, optional): Whether to normalize the position
            embedding. Defaults to False.
        scale (float, optional): A scale factor that scales the position
            embedding. The scale will be used only when `normalize` is True.
            Defaults to 2*pi.
        eps (float, optional): A value added to the denominator for
            numerical stability. Defaults to 1e-6.
        offset (float): offset add to embed when do the normalization.
            Defaults to 0.
        init_cfg (dict or list[dict], optional): Initialization config dict.
            Defaults to None.
    """

    def forward(self, mask: Tensor) -> Tensor:
        """Forward function for `SinePositionalEncoding3D`.

        Args:
            mask (Tensor): ByteTensor mask. Non-zero values representing
                ignored positions, while zero values means valid positions
                for this image. Shape [bs, t, h, w].

        Returns:
            pos (Tensor): Returned position embedding with shape
                [bs, num_feats*2, h, w].
        """
        assert mask.dim() == 4,\
            f'{mask.shape} should be a 4-dimensional Tensor,' \
            f' got {mask.dim()}-dimensional Tensor instead '
        # For convenience of exporting to ONNX, it's required to convert
        # `masks` from bool to int.
        mask = mask.to(torch.int)
        not_mask = 1 - mask  # logical_not
        z_embed = not_mask.cumsum(1, dtype=torch.float32)
        y_embed = not_mask.cumsum(2, dtype=torch.float32)
        x_embed = not_mask.cumsum(3, dtype=torch.float32)
        if self.normalize:
            z_embed = (z_embed + self.offset) / \
                      (z_embed[:, -1:, :, :] + self.eps) * self.scale
            y_embed = (y_embed + self.offset) / \
                      (y_embed[:, :, -1:, :] + self.eps) * self.scale
            x_embed = (x_embed + self.offset) / \
                      (x_embed[:, :, :, -1:] + self.eps) * self.scale
        dim_t = torch.arange(
            self.num_feats, dtype=torch.float32, device=mask.device)
        dim_t = self.temperature**(2 * (dim_t // 2) / self.num_feats)

        dim_t_z = torch.arange((self.num_feats * 2),
                               dtype=torch.float32,
                               device=mask.device)
        dim_t_z = self.temperature**(2 * (dim_t_z // 2) / (self.num_feats * 2))

        pos_x = x_embed[:, :, :, :, None] / dim_t
        pos_y = y_embed[:, :, :, :, None] / dim_t
        pos_z = z_embed[:, :, :, :, None] / dim_t_z
        # use `view` instead of `flatten` for dynamically exporting to ONNX
        B, T, H, W = mask.size()
        pos_x = torch.stack(
            (pos_x[:, :, :, :, 0::2].sin(), pos_x[:, :, :, :, 1::2].cos()),
            dim=5).view(B, T, H, W, -1)
        pos_y = torch.stack(
            (pos_y[:, :, :, :, 0::2].sin(), pos_y[:, :, :, :, 1::2].cos()),
            dim=5).view(B, T, H, W, -1)
        pos_z = torch.stack(
            (pos_z[:, :, :, :, 0::2].sin(), pos_z[:, :, :, :, 1::2].cos()),
            dim=5).view(B, T, H, W, -1)
        pos = (torch.cat((pos_y, pos_x), dim=4) + pos_z).permute(0, 1, 4, 2, 3)
        return pos