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import math
import torch
import torch.nn as nn
from mmcv.cnn import uniform_init
from .builder import POSITIONAL_ENCODING
@POSITIONAL_ENCODING.register_module()
class SinePositionalEncoding(nn.Module):
"""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. Default 10000.
normalize (bool, optional): Whether to normalize the position
embedding. Default False.
scale (float, optional): A scale factor that scales the position
embedding. The scale will be used only when `normalize` is True.
Default 2*pi.
eps (float, optional): A value added to the denominator for
numerical stability. Default 1e-6.
"""
def __init__(self,
num_feats,
temperature=10000,
normalize=False,
scale=2 * math.pi,
eps=1e-6):
super(SinePositionalEncoding, self).__init__()
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
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].
"""
not_mask = ~mask
y_embed = not_mask.cumsum(1, dtype=torch.float32)
x_embed = not_mask.cumsum(2, dtype=torch.float32)
if self.normalize:
y_embed = y_embed / (y_embed[:, -1:, :] + self.eps) * self.scale
x_embed = x_embed / (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_x = x_embed[:, :, :, None] / dim_t
pos_y = y_embed[:, :, :, None] / dim_t
pos_x = torch.stack(
(pos_x[:, :, :, 0::2].sin(), pos_x[:, :, :, 1::2].cos()),
dim=4).flatten(3)
pos_y = torch.stack(
(pos_y[:, :, :, 0::2].sin(), pos_y[:, :, :, 1::2].cos()),
dim=4).flatten(3)
pos = torch.cat((pos_y, pos_x), dim=3).permute(0, 3, 1, 2)
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
@POSITIONAL_ENCODING.register_module()
class LearnedPositionalEncoding(nn.Module):
"""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.
Default 50.
col_num_embed (int, optional): The dictionary size of col embeddings.
Default 50.
"""
def __init__(self, num_feats, row_num_embed=50, col_num_embed=50):
super(LearnedPositionalEncoding, self).__init__()
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
self.init_weights()
def init_weights(self):
"""Initialize the learnable weights."""
uniform_init(self.row_embed)
uniform_init(self.col_embed)
def forward(self, mask):
"""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: 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
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