SinePositionalEncoding

Transformer Positional Encoding

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

    See `End-to-End Object Detection with Transformers
    `_ 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.
            Default: None
    """

    def __init__(self,
                 num_feats,
                 temperature=10000,
                 normalize=False,
                 scale=2 * math.pi,
                 eps=1e-6,
                 offset=0.,
                 init_cfg=None):
        super(SinePositionalEncoding, self).__init__(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):
        """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].

        Returns:
            pos (Tensor): Returned position embedding with shape
                [bs, num_feats*2, h, w].
        """
        # For convenience of exporting to ONNX, it's required to convert
        # `masks` from bool to int.
        mask = mask.to(torch.int) # mask 值为bool类型，将bool类型转换为int类型
        not_mask = 1 - mask  # logical_not # 将mask中值取反， 没有padding得部分为1，padding得部分为0
        y_embed = not_mask.cumsum(1, dtype=torch.float32) # 按照h 方向累加， 求出h每一个位置的坐标
        x_embed = not_mask.cumsum(2, dtype=torch.float32) # 按照w 方向累加， 求出w每一个位置的坐标
        if self.normalize: # 是否对数据进行标准化处理， y_embed[:,-1：， ：]代表h方向的最大值
            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) # 生成[0, 128]的数组
        dim_t = self.temperature**(2 * (dim_t // 2) / self.num_feats) # 归一化
        pos_x = x_embed[:, :, :, None] / dim_t # [b, h, w, 1] -> [b, h, w, 128]
        pos_y = y_embed[:, :, :, None] / dim_t # [b, h, w, 1] -> [b, h, w, 128]
        # use `view` instead of `flatten` for dynamically exporting to ONNX
        B, H, W = mask.size()
        pos_x = torch.stack(
            (pos_x[:, :, :, 0::2].sin(), pos_x[:, :, :, 1::2].cos()),
            dim=4).view(B, H, W, -1) # 对偶数位置进行sin处理， 对奇数位置进行cos处理. [b, h, w, 64, 2] -> [b, h, w, 128]
        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) # [b, h, w, 128] -> [b, h, w, 256] -> [b, 256, h, w]
        return pos