File size: 7,122 Bytes
8aa674c |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 |
# coding=utf-8
# Copyright 2022 The IDEA Authors. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ------------------------------------------------------------------------------------------------
# Various positional encodings for the transformer.
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
# Copyright (c) OpenMMLab. All rights reserved.
# ------------------------------------------------------------------------------------------------
# Modified from:
# https://github.com/facebookresearch/detr/blob/main/models/position_encoding.py
# https://github.com/open-mmlab/mmdetection/blob/master/mmdet/models/utils/positional_encoding.py
# ------------------------------------------------------------------------------------------------
import math
import torch
import torch.nn as nn
from ...builder import TRANSFORMERS
@TRANSFORMERS.register_module()
class PositionEmbeddingSine(nn.Module):
"""Sinusoidal position embedding used in DETR model.
Please see `End-to-End Object Detection with Transformers
<https://arxiv.org/pdf/2005.12872>`_ for more details.
Args:
num_pos_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 the input value.
temperature (int, optional): The temperature used for scaling
the position embedding. Default: 10000.
scale (float, optional): A scale factor that scales the position
embedding. The scale will be used only when `normalize` is True.
Default: 2*pi.
eps (float, optional): A value added to the denominator for numerical
stability. Default: 1e-6.
offset (float): An offset added to embed when doing normalization.
normalize (bool, optional): Whether to normalize the position embedding.
Default: False.
"""
def __init__(
self,
num_pos_feats: int = 64,
temperature: int = 10000,
scale: float = 2 * math.pi,
eps: float = 1e-6,
offset: float = 0.0,
normalize: bool = False,
):
super().__init__()
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_pos_feats = num_pos_feats
self.temperature = temperature
self.normalize = normalize
self.scale = scale
self.eps = eps
self.offset = offset
def forward(self, mask: torch.Tensor, **kwargs) -> torch.Tensor:
"""Forward function for `PositionEmbeddingSine`.
Args:
mask (torch.Tensor): ByteTensor mask. Non-zero values representing
ignored positions, while zero values means valid positions
for the input tensor. Shape as `(bs, t)`.
Returns:
torch.Tensor: Returned position embedding with
shape `(bs, num_pos_feats * 2, t)`
"""
assert mask is not None
not_mask = ~mask
embed = not_mask.cumsum(1, dtype=torch.float32)
if self.normalize:
embed = (embed + self.offset) / (embed[:, -1:] + self.eps) * self.scale
dim_t = torch.arange(self.num_pos_feats, dtype=torch.float32, device=mask.device)
dim_t = self.temperature ** (2 * torch.div(dim_t, 2, rounding_mode="floor") / self.num_pos_feats)
pos = embed[:, :, None] / dim_t
pos = torch.stack((pos[:, :, 0::2].sin(), pos[:, :, 1::2].cos()), dim=3).flatten(2)
return pos # [bs,t,c]
def __repr__(self):
rep_str = self.__class__.__name__ + "("
rep_str += f"num_pos_feats={str(self.num_pos_feats)}, "
rep_str += f"temperature={str(self.temperature)}, "
rep_str += f"normalize={str(self.normalize)}, "
rep_str += f"offset={str(self.offset)})"
return rep_str
@TRANSFORMERS.register_module()
class PositionEmbeddingLearned(nn.Module):
"""
Position embedding with learnable embedding weights.
Args:
num_pos_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 the input value.
row_num_embed (int, optional): The dictionary size of row embeddings.
Default: 50.
col_num_embed (int, optional): The dictionary size of column embeddings.
Default: 50.
"""
def __init__(self, num_pos_feats: int = 256, num_embed: int = 100):
super().__init__()
self.num_embed = num_embed
self.num_pos_feats = num_pos_feats
self.embed = nn.Embedding(num_embed, num_pos_feats)
self.reset_parameters()
def reset_parameters(self):
nn.init.uniform_(self.embed.weight)
def forward(self, mask):
"""Forward function for `PositionEmbeddingLearned`.
Args:
mask (torch.Tensor): ByteTensor mask. Non-zero values representing
ignored positions, while zero values means valid positions
for the input tensor. Shape as `(bs, t)`.
Returns:
torch.Tensor: Returned position embedding with
shape `(bs, num_pos_feats * 2, t)`
"""
bs, t = mask.shape
emb = self.embed(torch.arange(t, device=mask.device))
pos = emb.unsqueeze(0).repeat(bs, 1, 1)
return pos
def get_sine_pos_embed(pos_tensor: torch.Tensor, num_pos_feats: int = 128, temperature: int = 10000):
"""generate sine position embedding from a position tensor
Args:
pos_tensor (torch.Tensor): Shape as `(None, n)`.
num_pos_feats (int): projected shape for each float in the tensor. Default: 128
temperature (int): The temperature used for scaling
the position embedding. Default: 10000.
Returns:
torch.Tensor: Returned position embedding # noqa
with shape `(None, n * num_pos_feats)`.
"""
scale = 2 * math.pi
dim_t = torch.arange(num_pos_feats, dtype=torch.float32, device=pos_tensor.device)
dim_t = temperature ** (2 * torch.div(dim_t, 2, rounding_mode="floor") / num_pos_feats)
def sine_func(x: torch.Tensor):
sin_x = x * scale / dim_t
sin_x = torch.stack((sin_x[:, :, 0::2].sin(), sin_x[:, :, 1::2].cos()), dim=3).flatten(2)
return sin_x
pos_res = [sine_func(x) for x in pos_tensor.split([1] * pos_tensor.shape[-1], dim=-1)]
pos_res = torch.cat(pos_res, dim=2)
return pos_res
|