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"""Various positional encodings for the transformer."""
import math
import torch
from torch import nn
from util.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
y_embed = not_mask.cumsum(1, dtype=torch.float32)
x_embed = not_mask.cumsum(2, 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=4).flatten(3)
pos_y = torch.stack(
(pos_y[:, :, :, 0::2].sin(), pos_y[:, :, :, 1::2].cos()),
dim=4).flatten(3)
pos = torch.cat((pos_y, pos_x), dim=3).permute(0, 3, 1, 2)
return pos
class PositionEmbeddingSineHW(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,
temperatureH=10000,
temperatureW=10000,
normalize=False,
scale=None):
super().__init__()
self.num_pos_feats = num_pos_feats # 128
self.temperatureH = temperatureH # 20
self.temperatureW = temperatureW
self.normalize = normalize # true
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
y_embed = not_mask.cumsum(1, dtype=torch.float32)
x_embed = not_mask.cumsum(2, dtype=torch.float32)
# import pdb; pdb.set_trace()
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_tx = torch.arange(self.num_pos_feats,
dtype=torch.float32,
device=x.device)
dim_tx = self.temperatureW**(2 * (dim_tx // 2) / self.num_pos_feats)
pos_x = x_embed[:, :, :, None] / dim_tx
dim_ty = torch.arange(self.num_pos_feats,
dtype=torch.float32,
device=x.device)
dim_ty = self.temperatureH**(2 * (dim_ty // 2) / self.num_pos_feats)
pos_y = y_embed[:, :, :, None] / dim_ty
pos_x = torch.stack(
(pos_x[:, :, :, 0::2].sin(), pos_x[:, :, :, 1::2].cos()),
dim=4).flatten(3)
pos_y = torch.stack(
(pos_y[:, :, :, 0::2].sin(), pos_y[:, :, :, 1::2].cos()),
dim=4).flatten(3)
pos = torch.cat((pos_y, pos_x), dim=3).permute(0, 3, 1, 2)
# import pdb; pdb.set_trace()
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
def build_position_encoding(args):
N_steps = args.hidden_dim // 2 # 256//2
if args.position_embedding in ('v2', 'sine'): # sine
# TODO find a better way of exposing other arguments
position_embedding = PositionEmbeddingSineHW(
N_steps,
temperatureH=args.pe_temperatureH,
temperatureW=args.pe_temperatureW,
normalize=True)
elif args.position_embedding in ('v3', 'learned'):
position_embedding = PositionEmbeddingLearned(N_steps)
else:
raise ValueError(f'not supported {args.position_embedding}')
return position_embedding
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