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# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
"""
Various positional encodings for the transformer.
"""
import math
from typing import List, Optional
import numpy as np
import torch
from torch import Tensor, nn
# from util.misc import NestedTensor
class NestedTensor(object):
def __init__(self, tensors, mask: Optional[Tensor]):
self.tensors = tensors
self.mask = mask
def to(self, device):
# type: (Device) -> NestedTensor # noqa
cast_tensor = self.tensors.to(device)
mask = self.mask
if mask is not None:
assert mask is not None
cast_mask = mask.to(device)
else:
cast_mask = None
return NestedTensor(cast_tensor, cast_mask)
def decompose(self):
return self.tensors, self.mask
def __repr__(self):
return str(self.tensors)
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 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
class PositionEmbeddingSine1D(nn.Module):
def __init__(self, d_model, max_len=500, batch_first=False):
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):
# not used in the final model
if self.batch_first:
pos = self.pe.permute(1, 0, 2)[:, :x.shape[1], :]
else:
pos = self.pe[:x.shape[0], :]
return pos
class PositionEmbeddingLearned1D(nn.Module):
def __init__(self, d_model, max_len=500, batch_first=False):
super().__init__()
self.batch_first = batch_first
# self.dropout = nn.Dropout(p=dropout)
self.pe = nn.Parameter(torch.zeros(max_len, 1, d_model))
# self.pe = pe.unsqueeze(0).transpose(0, 1)
self.reset_parameters()
def reset_parameters(self):
nn.init.uniform_(self.pe)
def forward(self, x):
# not used in the final model
if self.batch_first:
pos = self.pe.permute(1, 0, 2)[:, :x.shape[1], :]
else:
x = x + self.pe[:x.shape[0], :]
return x
# return self.dropout(x)
def build_position_encoding(N_steps,
position_embedding="sine",
embedding_dim="1D"):
# N_steps = hidden_dim // 2
if embedding_dim == "1D":
if position_embedding in ('v2', 'sine'):
position_embedding = PositionEmbeddingSine1D(N_steps)
elif position_embedding in ('v3', 'learned'):
position_embedding = PositionEmbeddingLearned1D(N_steps)
else:
raise ValueError(f"not supported {position_embedding}")
elif embedding_dim == "2D":
if position_embedding in ('v2', 'sine'):
# TODO find a better way of exposing other arguments
position_embedding = PositionEmbeddingSine(N_steps, normalize=True)
elif position_embedding in ('v3', 'learned'):
position_embedding = PositionEmbeddingLearned(N_steps)
else:
raise ValueError(f"not supported {position_embedding}")
else:
raise ValueError(f"not supported {embedding_dim}")
return position_embedding
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