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import math
import torch
import torch.nn as nn
from mmcv.cnn.bricks.transformer import POSITIONAL_ENCODING
from mmcv.runner import BaseModule
@POSITIONAL_ENCODING.register_module()
class SinePositionalEncoding3D(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.
Default: None
"""
def __init__(self,
num_feats,
temperature=10000,
normalize=False,
scale=2 * math.pi,
eps=1e-6,
offset=0.,
init_cfg=None):
super(SinePositionalEncoding3D, 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)
not_mask = 1 - mask # logical_not
n_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:
n_embed = (n_embed + self.offset) / \
(n_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)
pos_n = n_embed[:, :, :, :, None] / dim_t
pos_x = x_embed[:, :, :, :, None] / dim_t
pos_y = y_embed[:, :, :, :, None] / dim_t
# use `view` instead of `flatten` for dynamically exporting to ONNX
B, N, H, W = mask.size()
pos_n = torch.stack(
(pos_n[:, :, :, :, 0::2].sin(), pos_n[:, :, :, :, 1::2].cos()),
dim=4).view(B, N, H, W, -1)
pos_x = torch.stack(
(pos_x[:, :, :, :, 0::2].sin(), pos_x[:, :, :, :, 1::2].cos()),
dim=4).view(B, N, H, W, -1)
pos_y = torch.stack(
(pos_y[:, :, :, :, 0::2].sin(), pos_y[:, :, :, :, 1::2].cos()),
dim=4).view(B, N, H, W, -1)
pos = torch.cat((pos_n, pos_y, pos_x), dim=4).permute(0, 1, 4, 2, 3)
return pos
def __repr__(self):
"""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 |