| |
|
| |
|
| |
|
| | import torch
|
| | import torch.nn as nn
|
| | import math
|
| |
|
| |
|
| | class PositionEmbeddingSine(nn.Module):
|
| | """
|
| | This is a more standard version of the position embedding, very similar to the one
|
| | used by the Attention is all you need paper, generalized to work on images.
|
| | """
|
| |
|
| | def __init__(self, num_pos_feats=64, temperature=10000, normalize=True, scale=None):
|
| | super().__init__()
|
| | self.num_pos_feats = num_pos_feats
|
| | self.temperature = temperature
|
| | self.normalize = normalize
|
| | if scale is not None and normalize is False:
|
| | raise ValueError("normalize should be True if scale is passed")
|
| | if scale is None:
|
| | scale = 2 * math.pi
|
| | self.scale = scale
|
| |
|
| | def forward(self, x):
|
| |
|
| |
|
| | b, c, h, w = x.size()
|
| | mask = torch.ones((b, h, w), device=x.device)
|
| | y_embed = mask.cumsum(1, dtype=torch.float32)
|
| | x_embed = mask.cumsum(2, dtype=torch.float32)
|
| | if self.normalize:
|
| | eps = 1e-6
|
| | y_embed = y_embed / (y_embed[:, -1:, :] + eps) * self.scale
|
| | x_embed = x_embed / (x_embed[:, :, -1:] + eps) * self.scale
|
| |
|
| | dim_t = torch.arange(self.num_pos_feats, dtype=torch.float32, device=x.device)
|
| | dim_t = self.temperature ** (2 * (dim_t // 2) / self.num_pos_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
|
| |
|
