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| import math | |
| import torch | |
| from torch import Tensor, 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. | |
| Adapted from https://github.com/shannanyinxiang/SPTS. | |
| """ | |
| 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, mask: Tensor): | |
| assert mask is not None | |
| not_mask = ~mask | |
| 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=mask.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).permute(0, 3, 1, 2) | |
| return pos | |