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# Copyright (c) OpenMMLab. All rights reserved.
import math
from typing import Optional
import torch
import torch.nn as nn
from mmengine.model import BaseModule
from torch import Tensor
from mmdet.registry import MODELS
from mmdet.utils import MultiConfig, OptMultiConfig
@MODELS.register_module()
class SinePositionalEncoding(BaseModule):
"""Position encoding with sine and cosine functions.
See `End-to-End Object Detection with Transformers
<https://arxiv.org/pdf/2005.12872>`_ for details.
Args:
num_feats (int): The feature dimension for each position
along x-axis or y-axis. Note the final returned dimension
for each position is 2 times of this value.
temperature (int, optional): The temperature used for scaling
the position embedding. Defaults to 10000.
normalize (bool, optional): Whether to normalize the position
embedding. Defaults to False.
scale (float, optional): A scale factor that scales the position
embedding. The scale will be used only when `normalize` is True.
Defaults to 2*pi.
eps (float, optional): A value added to the denominator for
numerical stability. Defaults to 1e-6.
offset (float): offset add to embed when do the normalization.
Defaults to 0.
init_cfg (dict or list[dict], optional): Initialization config dict.
Defaults to None
"""
def __init__(self,
num_feats: int,
temperature: int = 10000,
normalize: bool = False,
scale: float = 2 * math.pi,
eps: float = 1e-6,
offset: float = 0.,
init_cfg: OptMultiConfig = None) -> None:
super().__init__(init_cfg=init_cfg)
if normalize:
assert isinstance(scale, (float, int)), 'when normalize is set,' \
'scale should be provided and in float or int type, ' \
f'found {type(scale)}'
self.num_feats = num_feats
self.temperature = temperature
self.normalize = normalize
self.scale = scale
self.eps = eps
self.offset = offset
def forward(self, mask: Tensor, input: Optional[Tensor] = None) -> Tensor:
"""Forward function for `SinePositionalEncoding`.
Args:
mask (Tensor): ByteTensor mask. Non-zero values representing
ignored positions, while zero values means valid positions
for this image. Shape [bs, h, w].
input (Tensor, optional): Input image/feature Tensor.
Shape [bs, c, h, w]
Returns:
pos (Tensor): Returned position embedding with shape
[bs, num_feats*2, h, w].
"""
assert not (mask is None and input is None)
if mask is not None:
B, H, W = mask.size()
device = mask.device
# For convenience of exporting to ONNX,
# it's required to convert
# `masks` from bool to int.
mask = mask.to(torch.int)
not_mask = 1 - mask # logical_not
y_embed = not_mask.cumsum(1, dtype=torch.float32)
x_embed = not_mask.cumsum(2, dtype=torch.float32)
else:
# single image or batch image with no padding
B, _, H, W = input.shape
device = input.device
x_embed = torch.arange(
1, W + 1, dtype=torch.float32, device=device)
x_embed = x_embed.view(1, 1, -1).repeat(B, H, 1)
y_embed = torch.arange(
1, H + 1, dtype=torch.float32, device=device)
y_embed = y_embed.view(1, -1, 1).repeat(B, 1, W)
if self.normalize:
y_embed = (y_embed + self.offset) / \
(y_embed[:, -1:, :] + self.eps) * self.scale
x_embed = (x_embed + self.offset) / \
(x_embed[:, :, -1:] + self.eps) * self.scale
dim_t = torch.arange(
self.num_feats, dtype=torch.float32, device=device)
dim_t = self.temperature**(2 * (dim_t // 2) / self.num_feats)
pos_x = x_embed[:, :, :, None] / dim_t
pos_y = y_embed[:, :, :, None] / dim_t
# use `view` instead of `flatten` for dynamically exporting to ONNX
pos_x = torch.stack(
(pos_x[:, :, :, 0::2].sin(), pos_x[:, :, :, 1::2].cos()),
dim=4).view(B, H, W, -1)
pos_y = torch.stack(
(pos_y[:, :, :, 0::2].sin(), pos_y[:, :, :, 1::2].cos()),
dim=4).view(B, H, W, -1)
pos = torch.cat((pos_y, pos_x), dim=3).permute(0, 3, 1, 2)
return pos
def __repr__(self) -> str:
"""str: a string that describes the module"""
repr_str = self.__class__.__name__
repr_str += f'(num_feats={self.num_feats}, '
repr_str += f'temperature={self.temperature}, '
repr_str += f'normalize={self.normalize}, '
repr_str += f'scale={self.scale}, '
repr_str += f'eps={self.eps})'
return repr_str
@MODELS.register_module()
class LearnedPositionalEncoding(BaseModule):
"""Position embedding with learnable embedding weights.
Args:
num_feats (int): The feature dimension for each position
along x-axis or y-axis. The final returned dimension for
each position is 2 times of this value.
row_num_embed (int, optional): The dictionary size of row embeddings.
Defaults to 50.
col_num_embed (int, optional): The dictionary size of col embeddings.
Defaults to 50.
init_cfg (dict or list[dict], optional): Initialization config dict.
"""
def __init__(
self,
num_feats: int,
row_num_embed: int = 50,
col_num_embed: int = 50,
init_cfg: MultiConfig = dict(type='Uniform', layer='Embedding')
) -> None:
super().__init__(init_cfg=init_cfg)
self.row_embed = nn.Embedding(row_num_embed, num_feats)
self.col_embed = nn.Embedding(col_num_embed, num_feats)
self.num_feats = num_feats
self.row_num_embed = row_num_embed
self.col_num_embed = col_num_embed
def forward(self, mask: Tensor) -> Tensor:
"""Forward function for `LearnedPositionalEncoding`.
Args:
mask (Tensor): ByteTensor mask. Non-zero values representing
ignored positions, while zero values means valid positions
for this image. Shape [bs, h, w].
Returns:
pos (Tensor): Returned position embedding with shape
[bs, num_feats*2, h, w].
"""
h, w = mask.shape[-2:]
x = torch.arange(w, device=mask.device)
y = torch.arange(h, device=mask.device)
x_embed = self.col_embed(x)
y_embed = self.row_embed(y)
pos = torch.cat(
(x_embed.unsqueeze(0).repeat(h, 1, 1), y_embed.unsqueeze(1).repeat(
1, w, 1)),
dim=-1).permute(2, 0,
1).unsqueeze(0).repeat(mask.shape[0], 1, 1, 1)
return pos
def __repr__(self) -> str:
"""str: a string that describes the module"""
repr_str = self.__class__.__name__
repr_str += f'(num_feats={self.num_feats}, '
repr_str += f'row_num_embed={self.row_num_embed}, '
repr_str += f'col_num_embed={self.col_num_embed})'
return repr_str
@MODELS.register_module()
class SinePositionalEncoding3D(SinePositionalEncoding):
"""Position encoding with sine and cosine functions.
See `End-to-End Object Detection with Transformers
<https://arxiv.org/pdf/2005.12872>`_ for details.
Args:
num_feats (int): The feature dimension for each position
along x-axis or y-axis. Note the final returned dimension
for each position is 2 times of this value.
temperature (int, optional): The temperature used for scaling
the position embedding. Defaults to 10000.
normalize (bool, optional): Whether to normalize the position
embedding. Defaults to False.
scale (float, optional): A scale factor that scales the position
embedding. The scale will be used only when `normalize` is True.
Defaults to 2*pi.
eps (float, optional): A value added to the denominator for
numerical stability. Defaults to 1e-6.
offset (float): offset add to embed when do the normalization.
Defaults to 0.
init_cfg (dict or list[dict], optional): Initialization config dict.
Defaults to None.
"""
def forward(self, mask: Tensor) -> Tensor:
"""Forward function for `SinePositionalEncoding3D`.
Args:
mask (Tensor): ByteTensor mask. Non-zero values representing
ignored positions, while zero values means valid positions
for this image. Shape [bs, t, h, w].
Returns:
pos (Tensor): Returned position embedding with shape
[bs, num_feats*2, h, w].
"""
assert mask.dim() == 4,\
f'{mask.shape} should be a 4-dimensional Tensor,' \
f' got {mask.dim()}-dimensional Tensor instead '
# For convenience of exporting to ONNX, it's required to convert
# `masks` from bool to int.
mask = mask.to(torch.int)
not_mask = 1 - mask # logical_not
z_embed = not_mask.cumsum(1, dtype=torch.float32)
y_embed = not_mask.cumsum(2, dtype=torch.float32)
x_embed = not_mask.cumsum(3, dtype=torch.float32)
if self.normalize:
z_embed = (z_embed + self.offset) / \
(z_embed[:, -1:, :, :] + self.eps) * self.scale
y_embed = (y_embed + self.offset) / \
(y_embed[:, :, -1:, :] + self.eps) * self.scale
x_embed = (x_embed + self.offset) / \
(x_embed[:, :, :, -1:] + self.eps) * self.scale
dim_t = torch.arange(
self.num_feats, dtype=torch.float32, device=mask.device)
dim_t = self.temperature**(2 * (dim_t // 2) / self.num_feats)
dim_t_z = torch.arange((self.num_feats * 2),
dtype=torch.float32,
device=mask.device)
dim_t_z = self.temperature**(2 * (dim_t_z // 2) / (self.num_feats * 2))
pos_x = x_embed[:, :, :, :, None] / dim_t
pos_y = y_embed[:, :, :, :, None] / dim_t
pos_z = z_embed[:, :, :, :, None] / dim_t_z
# use `view` instead of `flatten` for dynamically exporting to ONNX
B, T, H, W = mask.size()
pos_x = torch.stack(
(pos_x[:, :, :, :, 0::2].sin(), pos_x[:, :, :, :, 1::2].cos()),
dim=5).view(B, T, H, W, -1)
pos_y = torch.stack(
(pos_y[:, :, :, :, 0::2].sin(), pos_y[:, :, :, :, 1::2].cos()),
dim=5).view(B, T, H, W, -1)
pos_z = torch.stack(
(pos_z[:, :, :, :, 0::2].sin(), pos_z[:, :, :, :, 1::2].cos()),
dim=5).view(B, T, H, W, -1)
pos = (torch.cat((pos_y, pos_x), dim=4) + pos_z).permute(0, 1, 4, 2, 3)
return pos
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