mirror of
https://github.com/open-mmlab/mmocr.git
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56 lines
1.9 KiB
Python
56 lines
1.9 KiB
Python
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import math
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import torch
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from torch import Tensor, nn
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class PositionEmbeddingSine(nn.Module):
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"""This is a more standard version of the position embedding, very similar
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to the one used by the Attention is all you need paper, generalized to work
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on images.
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Adapted from https://github.com/shannanyinxiang/SPTS.
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"""
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def __init__(self,
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num_pos_feats=64,
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temperature=10000,
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normalize=True,
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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, mask: Tensor):
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assert mask is not None
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not_mask = ~mask
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y_embed = not_mask.cumsum(1, dtype=torch.float32)
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x_embed = not_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(
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self.num_pos_feats, dtype=torch.float32, device=mask.device)
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dim_t = self.temperature**(2 *
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torch.div(dim_t, 2, rounding_mode='floor') /
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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(
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(pos_x[:, :, :, 0::2].sin(), pos_x[:, :, :, 1::2].cos()),
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dim=4).flatten(3)
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pos_y = torch.stack(
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(pos_y[:, :, :, 0::2].sin(), pos_y[:, :, :, 1::2].cos()),
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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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