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| import torch
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| import torch.nn as nn
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| import math
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| class PositionEmbeddingSine(nn.Module):
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| """
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| This is a more standard version of the position embedding, very similar to the one
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| used by the Attention is all you need paper, generalized to work on images.
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| """
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| def __init__(self, num_pos_feats=64, temperature=10000, normalize=True, scale=None):
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| super().__init__()
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| self.num_pos_feats = num_pos_feats
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| self.temperature = temperature
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| self.normalize = normalize
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| if scale is not None and normalize is False:
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| raise ValueError("normalize should be True if scale is passed")
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| if scale is None:
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| scale = 2 * math.pi
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| self.scale = scale
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| def forward(self, x):
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| b, c, h, w = x.size()
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| mask = torch.ones((b, h, w), device=x.device)
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| y_embed = mask.cumsum(1, dtype=torch.float32)
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| x_embed = mask.cumsum(2, dtype=torch.float32)
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| if self.normalize:
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| eps = 1e-6
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| y_embed = y_embed / (y_embed[:, -1:, :] + eps) * self.scale
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| x_embed = x_embed / (x_embed[:, :, -1:] + eps) * self.scale
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| dim_t = torch.arange(self.num_pos_feats, dtype=torch.float32, device=x.device)
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| dim_t = self.temperature ** (2 * (dim_t // 2) / self.num_pos_feats)
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| pos_x = x_embed[:, :, :, None] / dim_t
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| pos_y = y_embed[:, :, :, None] / dim_t
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| pos_x = torch.stack((pos_x[:, :, :, 0::2].sin(), pos_x[:, :, :, 1::2].cos()), dim=4).flatten(3)
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| pos_y = torch.stack((pos_y[:, :, :, 0::2].sin(), pos_y[:, :, :, 1::2].cos()), dim=4).flatten(3)
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| pos = torch.cat((pos_y, pos_x), dim=3).permute(0, 3, 1, 2)
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| return pos
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|
