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|
| | """ |
| | Various positional encodings for the transformer. |
| | """ |
| | import math |
| | import torch |
| | from torch import nn |
| |
|
| | from misc.detr_utils.misc import NestedTensor |
| |
|
| |
|
| | 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 |
| | 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 |
| | self.max_duration = 256 |
| | self.duration_embed_layer = nn.Linear(self.max_duration, self.max_duration) |
| |
|
| | def forward(self, tensor_list: NestedTensor): |
| | x = tensor_list.tensors |
| | mask = tensor_list.mask |
| | duration = tensor_list.duration |
| | assert mask is not None |
| | not_mask = ~mask |
| | x_embed = not_mask.cumsum(1, dtype=torch.float32) |
| | if self.normalize: |
| | eps = 1e-6 |
| | x_embed = (x_embed - 0.5) / (x_embed[:, -1:] + eps) * self.scale |
| |
|
| | dim_t = torch.arange(self.num_pos_feats, dtype=torch.float32, device=x.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_x = torch.stack((pos_x[:, :, 0::2].sin(), pos_x[:, :, 1::2].cos()), dim=3).flatten(2) |
| |
|
| | dur_embed = self.duration_embedding(duration).reshape(-1,1,self.max_duration).expand_as(pos_x) |
| | pos = torch.cat((pos_x, dur_embed), dim=2).permute(0, 2, 1) |
| | return pos |
| |
|
| | def duration_embedding(self, durations): |
| | out = torch.zeros(len(durations), self.max_duration, device=durations.device) |
| | durations = durations.int() |
| | for ii in range(len(durations)): |
| | out[ii, :durations[ii]] = 1 |
| | out = self.duration_embed_layer(out) |
| | return out |
| |
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| |
|
| | def build_position_encoding(position_embedding, N_steps): |
| | if position_embedding in ('v2', 'sine'): |
| | |
| | position_embedding = PositionEmbeddingSine(N_steps, normalize=True) |
| | else: |
| | raise ValueError(f"not supported {position_embedding}") |
| |
|
| | return position_embedding |
| |
|
