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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 |