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import numpy as np
import torch
import torch.nn as nn
class PositionEmbeddingSine1D(nn.Module):
def __init__(self, d_model: int, max_len: int = 500, batch_first: bool = False) -> None:
super().__init__()
self.batch_first = batch_first
pe = torch.zeros(max_len, d_model)
position = torch.arange(0, max_len, dtype=torch.float).unsqueeze(1)
div_term = torch.exp(torch.arange(
0, d_model, 2).float() * (-np.log(10000.0) / d_model))
pe[:, 0::2] = torch.sin(position * div_term)
pe[:, 1::2] = torch.cos(position * div_term)
pe = pe.unsqueeze(0).transpose(0, 1)
self.register_buffer('pe', pe)
def forward(self, x: torch.Tensor) -> torch.Tensor:
if self.batch_first:
x = x + self.pe.permute(1, 0, 2)[:, :x.shape[1], :]
else:
x = x + self.pe[:x.shape[0], :]
return x
class PositionEmbeddingLearned1D(nn.Module):
def __init__(self, d_model: int, max_len: int = 500, batch_first: bool = False) -> None:
super().__init__()
self.batch_first = batch_first
self.pe = nn.Parameter(torch.zeros(max_len, 1, d_model))
self.reset_parameters()
def reset_parameters(self) -> None:
nn.init.uniform_(self.pe)
def forward(self, x: torch.Tensor) -> torch.Tensor:
if self.batch_first:
x = x + self.pe.permute(1, 0, 2)[:, :x.shape[1], :]
else:
x = x + self.pe[:x.shape[0], :]
return x
def build_position_encoding(N_steps: int, position_embedding: str = "sine") -> nn.Module:
if position_embedding == 'sine':
position_embedding = PositionEmbeddingSine1D(N_steps)
elif position_embedding == 'learned':
position_embedding = PositionEmbeddingLearned1D(N_steps)
else:
raise ValueError(f"not supported {position_embedding}")
return position_embedding
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