| |
|
|
| """ |
| |
| Various positional encodings for the transformer. |
| |
| """ |
|
|
| import math |
| import torch |
| from torch import nn |
|
|
|
|
| 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=False, scale=None): |
| super().__init__() |
| self.num_pos_feats = num_pos_feats // 2 |
| 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, f_s): |
| not_mask = torch.ones_like(f_s[:, :, 0].reshape(-1, 26, 26)) |
| y_embed = not_mask.cumsum(1, dtype=torch.float32) |
| x_embed = not_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=f_s.device) |
| dim_t = self.temperature ** (2 * torch.div(dim_t, 2,rounding_mode = 'floor')/ 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).reshape_as(f_s) |
| return pos |
|
|
