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Various positional encodings for the transformer.
"""
import math
import torch
from torch import nn
from lib.utils.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 # (b,h,w)
y_embed = not_mask.cumsum(1, dtype=torch.float32) # cumulative sum along axis 1 (h axis) --> (b, h, w)
x_embed = not_mask.cumsum(2, dtype=torch.float32) # cumulative sum along axis 2 (w axis) --> (b, h, w)
if self.normalize:
eps = 1e-6
y_embed = y_embed / (y_embed[:, -1:, :] + eps) * self.scale # 2pi * (y / sigma(y))
x_embed = x_embed / (x_embed[:, :, -1:] + eps) * self.scale # 2pi * (x / sigma(x))
dim_t = torch.arange(self.num_pos_feats, dtype=torch.float32, device=x.device) # (0,1,2,...,d/2)
dim_t = self.temperature ** (2 * (dim_t // 2) / self.num_pos_feats)
pos_x = x_embed[:, :, :, None] / dim_t # (b,h,w,d/2)
pos_y = y_embed[:, :, :, None] / dim_t # (b,h,w,d/2)
pos_x = torch.stack((pos_x[:, :, :, 0::2].sin(), pos_x[:, :, :, 1::2].cos()), dim=4).flatten(3) # (b,h,w,d/2)
pos_y = torch.stack((pos_y[:, :, :, 0::2].sin(), pos_y[:, :, :, 1::2].cos()), dim=4).flatten(3) # (b,h,w,d/2)
pos = torch.cat((pos_y, pos_x), dim=3).permute(0, 3, 1, 2) # (b,h,w,d)
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 # (H,W,C) --> (C,H,W) --> (1,C,H,W) --> (B,C,H,W)
class PositionEmbeddingNone(nn.Module):
"""
No positional encoding.
"""
def __init__(self, num_pos_feats=256):
super().__init__()
self.n_dim = num_pos_feats * 2
def forward(self, tensor_list: NestedTensor):
x = tensor_list.tensors
b, _, h, w = x.size()
return torch.zeros((b, self.n_dim, h, w), device=x.device) # (B, C, H, W)
def build_position_encoding(cfg):
N_steps = cfg.MODEL.HIDDEN_DIM // 2
if cfg.MODEL.POSITION_EMBEDDING in ('v2', 'sine'):
# TODO find a better way of exposing other arguments
position_embedding = PositionEmbeddingSine(N_steps, normalize=True)
elif cfg.MODEL.POSITION_EMBEDDING in ('v3', 'learned'):
position_embedding = PositionEmbeddingLearned(N_steps)
elif cfg.MODEL.POSITION_EMBEDDING in ('None', ):
print("Not using positional encoding.")
position_embedding = PositionEmbeddingNone(N_steps)
else:
raise ValueError(f"not supported {cfg.MODEL.POSITION_EMBEDDING}")
return position_embedding
class PositionEmbeddingLearned_new(nn.Module):
"""
Absolute pos embedding, learned. (allow users to specify the size)
"""
def __init__(self, num_pos_feats=256, sz=20):
super().__init__()
self.sz = sz
self.row_embed = nn.Embedding(sz, num_pos_feats)
self.col_embed = nn.Embedding(sz, 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, bs):
"""bs: batch size"""
h, w = self.sz, self.sz
i = torch.arange(w, device=self.col_embed.weight.device)
j = torch.arange(h, device=self.row_embed.weight.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(bs, 1, 1, 1)
pos = torch.cat([
x_emb.unsqueeze(0).repeat(h, 1, 1),
y_emb.unsqueeze(1).repeat(1, w, 1),
], dim=-1).repeat(1, bs, 1)
# print(bs, pos)
return pos # (H,W,C) --> (C,H,W) --> (1,C,H,W) --> (B,C,H,W)
# def build_position_encoding_new(cfg, sz):
# N_steps = cfg.MODEL.HIDDEN_DIM // 2
# position_embedding = PositionEmbeddingLearned_new(N_steps, sz)
# return position_embedding
def build_position_encoding(embed_dim, sz=256):
N_steps = embed_dim // 2
position_embedding = PositionEmbeddingLearned_new(N_steps, sz)
return position_embedding
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