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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 PositionEmbeddingSine1D(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=256, 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 # [B, L, C]
mask = tensor_list.mask # [B, L]
assert mask is not None
not_mask = ~mask
x_embed = not_mask.cumsum(1, dtype=torch.float32) # [B, L]
if self.normalize:
eps = 1e-6
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 # [B, L, C]
pos_x = torch.stack((pos_x[:, :, 0::2].sin(), pos_x[:, :, 1::2].cos()), dim=3).flatten(2) # [B, L, C]
# pos = pos_x.permute(0, 2, 1) # [B, C, L]
return pos_x
class PositionEmbeddingSine2D(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, mask):
# mask: (B, H, W)
not_mask = ~mask # (batch,h,w) ~mask:按位取反
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 # 2π * (y / sigma(y)) (16,8,8)
x_embed = x_embed / (x_embed[:, :, -1:] + eps) * self.scale # 2π * (x / sigma(x)) (16,8,8)
dim_t = torch.arange(self.num_pos_feats, dtype=torch.float32, device=mask.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) (16,8,8,128)
pos_y = torch.stack((pos_y[:, :, :, 0::2].sin(), pos_y[:, :, :, 1::2].cos()), dim=4).flatten(3) # (b,h,w,d/2) (16,8,8,128)
pos = torch.cat((pos_y, pos_x), dim=3).permute(0, 3, 1, 2) # (b,h,w,d) (16,8,8,256) to (16,256,8,8)
return pos
class PositionEmbeddingLearned(nn.Module):
"""
Absolute pos embedding, learned.
"""
def __init__(self, in_dim, out_dim):
super().__init__()
self.embed = nn.Embedding(in_dim, out_dim)
self.reset_parameters()
def reset_parameters(self):
nn.init.uniform_(self.embed.weight)
def forward(self, x):
# x: (B, 5, C) or (B, mask, C) or (B, bbox+mask, C)
n = x.size(1)
i = torch.arange(n, device=x.device)
pos = self.embed(i).unsqueeze(0).repeat(x.size(0), 1, 1) # (N,C) --> (1,N,C) --> (B,N,C)
return pos
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_memory_position_encoding(cfg):
N_steps = cfg.MODEL.DECODER.HIDDEN_DIM // 2 # N_steps = 128
if cfg.MODEL.DECODER.MEMORY_POSITION_EMBEDDING in ('v2', 'sine'):
# TODO find a better way of exposing other arguments
position_embedding = PositionEmbeddingSine2D(N_steps, normalize=True)
elif cfg.MODEL.DECODER.MEMORY_POSITION_EMBEDDING in ('v3', 'learned'):
position_embedding = PositionEmbeddingLearned(N_steps)
elif cfg.MODEL.DECODER.MEMORY_POSITION_EMBEDDING in ('None', ):
print("Not using positional encoding.")
position_embedding = PositionEmbeddingNone(N_steps)
else:
raise ValueError(f"not supported {cfg.MODEL.DECODER.MEMORY_POSITION_EMBEDDING}")
return position_embedding
def build_query_position_encoding(cfg):
bbox_task = cfg.TRAIN.BBOX_TASK
language_task = getattr(cfg.TRAIN, "LANGUAGE_TASK", False)
if bbox_task and language_task: # bbox and language tasks
seq_in_dim = (4+1) + (1+1)
elif bbox_task and not language_task: # bbox level
seq_in_dim = 4+1
N_steps = cfg.MODEL.DECODER.HIDDEN_DIM
if cfg.MODEL.DECODER.QUERY_POSITION_EMBEDDING in ('v2', 'sine'):
# TODO find a better way of exposing other arguments
position_embedding = PositionEmbeddingSine2D(N_steps, normalize=True)
elif cfg.MODEL.DECODER.QUERY_POSITION_EMBEDDING in ('v3', 'learned'):
position_embedding = PositionEmbeddingLearned(in_dim=seq_in_dim, out_dim=N_steps)
elif cfg.MODEL.DECODER.QUERY_POSITION_EMBEDDING in ('None', ):
print("Not using positional encoding.")
position_embedding = PositionEmbeddingNone(N_steps)
else:
raise ValueError(f"not supported {cfg.MODEL.DECODER.QUERY_POSITION_EMBEDDING}")
return position_embedding |