|
|
| """
|
| Various positional encodings for the transformer.
|
| """
|
| import math
|
| import torch
|
| from torch import nn
|
|
|
| from util.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
|
|
|
| def forward(self, tensor_list: NestedTensor):
|
| x = tensor_list.tensors
|
| mask = tensor_list.mask
|
| 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=x.device)
|
| dim_t = self.temperature ** (2 * (dim_t // 2) / 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
|
|
|
|
|
| class PositionEmbeddingLearned(nn.Module):
|
| """
|
| Absolute pos embedding, learned.
|
| """
|
| def __init__(self, num_pos_feats=256):
|
| super().__init__()
|
| self.row_embed = nn.Embedding(50, num_pos_feats)
|
| self.col_embed = nn.Embedding(50, num_pos_feats)
|
| self.reset_parameters()
|
|
|
| def reset_parameters(self):
|
| nn.init.uniform_(self.row_embed.weight)
|
| nn.init.uniform_(self.col_embed.weight)
|
|
|
| def forward(self, tensor_list: NestedTensor):
|
| x = tensor_list.tensors
|
| h, w = x.shape[-2:]
|
| i = torch.arange(w, device=x.device)
|
| j = torch.arange(h, device=x.device)
|
| x_emb = self.col_embed(i)
|
| y_emb = self.row_embed(j)
|
| pos = torch.cat([
|
| x_emb.unsqueeze(0).repeat(h, 1, 1),
|
| y_emb.unsqueeze(1).repeat(1, w, 1),
|
| ], dim=-1).permute(2, 0, 1).unsqueeze(0).repeat(x.shape[0], 1, 1, 1)
|
| return pos
|
|
|
|
|
| def build_position_encoding(args):
|
| N_steps = args.hidden_dim // 2
|
| if args.position_embedding in ('v2', 'sine'):
|
|
|
| position_embedding = PositionEmbeddingSine(N_steps, normalize=True)
|
| elif args.position_embedding in ('v3', 'learned'):
|
| position_embedding = PositionEmbeddingLearned(N_steps)
|
| else:
|
| raise ValueError(f"not supported {args.position_embedding}")
|
|
|
| return position_embedding
|
|
|
