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""" |
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Various positional encodings for the transformer. |
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""" |
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import math |
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import torch |
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from torch import nn |
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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=False, 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, mask=None): |
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if mask is None: |
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mask = torch.zeros((x.size(0), x.size(2), x.size(3)), device=x.device, dtype=torch.bool) |
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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(self.num_pos_feats, dtype=torch.float32, device=x.device) |
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dim_t = self.temperature ** (2 * torch.div(dim_t, 2, rounding_mode="floor") / 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()), dim=4 |
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).flatten(3) |
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pos_y = torch.stack( |
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(pos_y[:, :, :, 0::2].sin(), pos_y[:, :, :, 1::2].cos()), dim=4 |
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).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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def __repr__(self, _repr_indent=4): |
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head = "Positional encoding " + self.__class__.__name__ |
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body = [ |
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"num_pos_feats: {}".format(self.num_pos_feats), |
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"temperature: {}".format(self.temperature), |
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"normalize: {}".format(self.normalize), |
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"scale: {}".format(self.scale), |
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] |
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lines = [head] + [" " * _repr_indent + line for line in body] |
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return "\n".join(lines) |
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class PositionEmbeddingSine1D(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=False, 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, mask=None): |
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""" |
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Args: |
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x (Tensor): [T, Q, B, C] |
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Output: temporal positional embedding with the same shape of x. |
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""" |
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if mask is None: |
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mask = torch.zeros((x.size(0), x.size(1), x.size(2)), device=x.device, dtype=torch.bool) |
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not_mask = ~mask |
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z_embed = not_mask.cumsum(0, dtype=torch.float32) |
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if self.normalize: |
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eps = 1e-6 |
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z_embed = z_embed / (z_embed[-1:, :, :] + eps) * self.scale |
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dim_t_z = torch.arange((self.num_pos_feats * 2), dtype=torch.float32, device=x.device) |
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dim_t_z = self.temperature ** (2 * torch.div(dim_t_z, 2, rounding_mode="floor") / (self.num_pos_feats * 2)) |
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pos_z = z_embed[:, :, :, None] / dim_t_z |
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pos_z = torch.stack((pos_z[:, :, :, 0::2].sin(), pos_z[:, :, :, 1::2].cos()), dim=4).flatten(3) |
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pos = pos_z |
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return pos |
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