| import math |
| import torch |
| from torch import nn |
|
|
| from uniperceiver.utils.registry import Registry |
|
|
| POSITION_ENC_REGISTRY = Registry("POSITION_ENC") |
| POSITION_ENC_REGISTRY.__doc__ = """ |
| Registry for positional encoding |
| """ |
|
|
| __all__ = ["SinusoidEncoding", "NNEmbeddingEncoding"] |
|
|
| def build_position_encoding(cfg, dim, max_len): |
| name = cfg.MODEL.TOKEN_EMBED.POSITION |
| return POSITION_ENC_REGISTRY.get(name)(dim, max_len) |
|
|
| @POSITION_ENC_REGISTRY.register() |
| class SinusoidEncoding(nn.Module): |
| def __init__(self, dim, max_len): |
| super(SinusoidEncoding, self).__init__() |
| pe = torch.zeros(max_len, dim) |
| position = torch.arange(0, max_len).unsqueeze(1).float() |
| div_term = torch.exp(torch.arange(0, dim, 2).float() * |
| -(math.log(max_len * 2.0) / dim)) |
| pe[:, 0::2] = torch.sin(position * div_term) |
| pe[:, 1::2] = torch.cos(position * div_term) |
| pe = pe.unsqueeze(0) |
| self.register_buffer('pe', pe) |
|
|
| def forward(self, x): |
| if isinstance(x, int): |
| return self.pe[:, x] |
| else: |
| x_size = x.size(1) |
| return self.pe[:, :x_size] |
|
|
| @POSITION_ENC_REGISTRY.register() |
| class NNEmbeddingEncoding(nn.Module): |
| def __init__(self, dim, max_len): |
| super(NNEmbeddingEncoding, self).__init__() |
| self.position_embeddings = nn.Embedding(max_len, dim) |
|
|
| def forward(self, x, start_time=0): |
| if isinstance(x, int): |
| position_embeddings = self.position_embeddings(torch.tensor([x], dtype=torch.long).cuda()) |
| elif isinstance(x, torch.Tensor) and x.dim()==1: |
| position_embeddings = self.position_embeddings(x) |
| else: |
| x_size = x.size(1) |
| position_ids = torch.arange(x_size, dtype=torch.long, device=x.device) + start_time |
| position_embeddings = self.position_embeddings(position_ids) |
| return position_embeddings |
