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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 CustomLearnedPositionalEncoding(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.
Default 50.
col_num_embed (int, optional): The dictionary size of col embeddings.
Default 50.
init_cfg (dict or list[dict], optional): Initialization config dict.
"""
def __init__(self,
num_feats,
row_num_embed=50,
col_num_embed=50,
init_cfg=dict(type='Uniform', layer='Embedding')):
super(CustomLearnedPositionalEncoding, self).__init__(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, bs, h, w, device):
"""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=device)
y = torch.arange(h, device=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(bs, 1, 1, 1)
return pos
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