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# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
"""
Various positional encodings for the transformer.
"""
import math
import torch
from torch import nn
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, token_tensors):
## input: (B,C,H,W)
x = token_tensors
h, w = x.shape[-2:]
identity_map= torch.ones((h,w), device=x.device)
y_embed = identity_map.cumsum(0, dtype=torch.float32)
x_embed = identity_map.cumsum(1, 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=3).flatten(2)
pos_y = torch.stack((pos_y[:, :, 0::2].sin(), pos_y[:, :, 1::2].cos()), dim=3).flatten(2)
pos = torch.cat((pos_y, pos_x), dim=2).permute(2, 0, 1)
batch_pos = pos.unsqueeze(0).repeat(x.shape[0], 1, 1, 1)
return batch_pos
class PositionEmbeddingLearned(nn.Module):
"""
Absolute pos embedding, learned.
"""
def __init__(self, n_pos_x=16, n_pos_y=16, num_pos_feats=64):
super().__init__()
self.row_embed = nn.Embedding(n_pos_y, num_pos_feats)
self.col_embed = nn.Embedding(n_pos_x, 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, token_tensors):
## input: (B,C,H,W)
x = token_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)
batch_pos = pos.unsqueeze(0).repeat(x.shape[0], 1, 1, 1)
return batch_pos
def build_position_encoding(num_pos_feats=64, n_pos_x=16, n_pos_y=16, is_learned=False):
if is_learned:
position_embedding = PositionEmbeddingLearned(n_pos_x, n_pos_y, num_pos_feats)
else:
position_embedding = PositionEmbeddingSine(num_pos_feats, normalize=True)
return position_embedding |