Spaces:
Runtime error
Runtime error
File size: 11,012 Bytes
f7ac35e |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 |
# Copyright (c) OpenMMLab. All rights reserved.
from abc import ABCMeta
import torch
import torch.nn as nn
from ..utils import constant_init, normal_init
from .conv_module import ConvModule
from .registry import PLUGIN_LAYERS
class _NonLocalNd(nn.Module, metaclass=ABCMeta):
"""Basic Non-local module.
This module is proposed in
"Non-local Neural Networks"
Paper reference: https://arxiv.org/abs/1711.07971
Code reference: https://github.com/AlexHex7/Non-local_pytorch
Args:
in_channels (int): Channels of the input feature map.
reduction (int): Channel reduction ratio. Default: 2.
use_scale (bool): Whether to scale pairwise_weight by
`1/sqrt(inter_channels)` when the mode is `embedded_gaussian`.
Default: True.
conv_cfg (None | dict): The config dict for convolution layers.
If not specified, it will use `nn.Conv2d` for convolution layers.
Default: None.
norm_cfg (None | dict): The config dict for normalization layers.
Default: None. (This parameter is only applicable to conv_out.)
mode (str): Options are `gaussian`, `concatenation`,
`embedded_gaussian` and `dot_product`. Default: embedded_gaussian.
"""
def __init__(self,
in_channels,
reduction=2,
use_scale=True,
conv_cfg=None,
norm_cfg=None,
mode='embedded_gaussian',
**kwargs):
super(_NonLocalNd, self).__init__()
self.in_channels = in_channels
self.reduction = reduction
self.use_scale = use_scale
self.inter_channels = max(in_channels // reduction, 1)
self.mode = mode
if mode not in [
'gaussian', 'embedded_gaussian', 'dot_product', 'concatenation'
]:
raise ValueError("Mode should be in 'gaussian', 'concatenation', "
f"'embedded_gaussian' or 'dot_product', but got "
f'{mode} instead.')
# g, theta, phi are defaulted as `nn.ConvNd`.
# Here we use ConvModule for potential usage.
self.g = ConvModule(
self.in_channels,
self.inter_channels,
kernel_size=1,
conv_cfg=conv_cfg,
act_cfg=None)
self.conv_out = ConvModule(
self.inter_channels,
self.in_channels,
kernel_size=1,
conv_cfg=conv_cfg,
norm_cfg=norm_cfg,
act_cfg=None)
if self.mode != 'gaussian':
self.theta = ConvModule(
self.in_channels,
self.inter_channels,
kernel_size=1,
conv_cfg=conv_cfg,
act_cfg=None)
self.phi = ConvModule(
self.in_channels,
self.inter_channels,
kernel_size=1,
conv_cfg=conv_cfg,
act_cfg=None)
if self.mode == 'concatenation':
self.concat_project = ConvModule(
self.inter_channels * 2,
1,
kernel_size=1,
stride=1,
padding=0,
bias=False,
act_cfg=dict(type='ReLU'))
self.init_weights(**kwargs)
def init_weights(self, std=0.01, zeros_init=True):
if self.mode != 'gaussian':
for m in [self.g, self.theta, self.phi]:
normal_init(m.conv, std=std)
else:
normal_init(self.g.conv, std=std)
if zeros_init:
if self.conv_out.norm_cfg is None:
constant_init(self.conv_out.conv, 0)
else:
constant_init(self.conv_out.norm, 0)
else:
if self.conv_out.norm_cfg is None:
normal_init(self.conv_out.conv, std=std)
else:
normal_init(self.conv_out.norm, std=std)
def gaussian(self, theta_x, phi_x):
# NonLocal1d pairwise_weight: [N, H, H]
# NonLocal2d pairwise_weight: [N, HxW, HxW]
# NonLocal3d pairwise_weight: [N, TxHxW, TxHxW]
pairwise_weight = torch.matmul(theta_x, phi_x)
pairwise_weight = pairwise_weight.softmax(dim=-1)
return pairwise_weight
def embedded_gaussian(self, theta_x, phi_x):
# NonLocal1d pairwise_weight: [N, H, H]
# NonLocal2d pairwise_weight: [N, HxW, HxW]
# NonLocal3d pairwise_weight: [N, TxHxW, TxHxW]
pairwise_weight = torch.matmul(theta_x, phi_x)
if self.use_scale:
# theta_x.shape[-1] is `self.inter_channels`
pairwise_weight /= theta_x.shape[-1]**0.5
pairwise_weight = pairwise_weight.softmax(dim=-1)
return pairwise_weight
def dot_product(self, theta_x, phi_x):
# NonLocal1d pairwise_weight: [N, H, H]
# NonLocal2d pairwise_weight: [N, HxW, HxW]
# NonLocal3d pairwise_weight: [N, TxHxW, TxHxW]
pairwise_weight = torch.matmul(theta_x, phi_x)
pairwise_weight /= pairwise_weight.shape[-1]
return pairwise_weight
def concatenation(self, theta_x, phi_x):
# NonLocal1d pairwise_weight: [N, H, H]
# NonLocal2d pairwise_weight: [N, HxW, HxW]
# NonLocal3d pairwise_weight: [N, TxHxW, TxHxW]
h = theta_x.size(2)
w = phi_x.size(3)
theta_x = theta_x.repeat(1, 1, 1, w)
phi_x = phi_x.repeat(1, 1, h, 1)
concat_feature = torch.cat([theta_x, phi_x], dim=1)
pairwise_weight = self.concat_project(concat_feature)
n, _, h, w = pairwise_weight.size()
pairwise_weight = pairwise_weight.view(n, h, w)
pairwise_weight /= pairwise_weight.shape[-1]
return pairwise_weight
def forward(self, x):
# Assume `reduction = 1`, then `inter_channels = C`
# or `inter_channels = C` when `mode="gaussian"`
# NonLocal1d x: [N, C, H]
# NonLocal2d x: [N, C, H, W]
# NonLocal3d x: [N, C, T, H, W]
n = x.size(0)
# NonLocal1d g_x: [N, H, C]
# NonLocal2d g_x: [N, HxW, C]
# NonLocal3d g_x: [N, TxHxW, C]
g_x = self.g(x).view(n, self.inter_channels, -1)
g_x = g_x.permute(0, 2, 1)
# NonLocal1d theta_x: [N, H, C], phi_x: [N, C, H]
# NonLocal2d theta_x: [N, HxW, C], phi_x: [N, C, HxW]
# NonLocal3d theta_x: [N, TxHxW, C], phi_x: [N, C, TxHxW]
if self.mode == 'gaussian':
theta_x = x.view(n, self.in_channels, -1)
theta_x = theta_x.permute(0, 2, 1)
if self.sub_sample:
phi_x = self.phi(x).view(n, self.in_channels, -1)
else:
phi_x = x.view(n, self.in_channels, -1)
elif self.mode == 'concatenation':
theta_x = self.theta(x).view(n, self.inter_channels, -1, 1)
phi_x = self.phi(x).view(n, self.inter_channels, 1, -1)
else:
theta_x = self.theta(x).view(n, self.inter_channels, -1)
theta_x = theta_x.permute(0, 2, 1)
phi_x = self.phi(x).view(n, self.inter_channels, -1)
pairwise_func = getattr(self, self.mode)
# NonLocal1d pairwise_weight: [N, H, H]
# NonLocal2d pairwise_weight: [N, HxW, HxW]
# NonLocal3d pairwise_weight: [N, TxHxW, TxHxW]
pairwise_weight = pairwise_func(theta_x, phi_x)
# NonLocal1d y: [N, H, C]
# NonLocal2d y: [N, HxW, C]
# NonLocal3d y: [N, TxHxW, C]
y = torch.matmul(pairwise_weight, g_x)
# NonLocal1d y: [N, C, H]
# NonLocal2d y: [N, C, H, W]
# NonLocal3d y: [N, C, T, H, W]
y = y.permute(0, 2, 1).contiguous().reshape(n, self.inter_channels,
*x.size()[2:])
output = x + self.conv_out(y)
return output
class NonLocal1d(_NonLocalNd):
"""1D Non-local module.
Args:
in_channels (int): Same as `NonLocalND`.
sub_sample (bool): Whether to apply max pooling after pairwise
function (Note that the `sub_sample` is applied on spatial only).
Default: False.
conv_cfg (None | dict): Same as `NonLocalND`.
Default: dict(type='Conv1d').
"""
def __init__(self,
in_channels,
sub_sample=False,
conv_cfg=dict(type='Conv1d'),
**kwargs):
super(NonLocal1d, self).__init__(
in_channels, conv_cfg=conv_cfg, **kwargs)
self.sub_sample = sub_sample
if sub_sample:
max_pool_layer = nn.MaxPool1d(kernel_size=2)
self.g = nn.Sequential(self.g, max_pool_layer)
if self.mode != 'gaussian':
self.phi = nn.Sequential(self.phi, max_pool_layer)
else:
self.phi = max_pool_layer
@PLUGIN_LAYERS.register_module()
class NonLocal2d(_NonLocalNd):
"""2D Non-local module.
Args:
in_channels (int): Same as `NonLocalND`.
sub_sample (bool): Whether to apply max pooling after pairwise
function (Note that the `sub_sample` is applied on spatial only).
Default: False.
conv_cfg (None | dict): Same as `NonLocalND`.
Default: dict(type='Conv2d').
"""
_abbr_ = 'nonlocal_block'
def __init__(self,
in_channels,
sub_sample=False,
conv_cfg=dict(type='Conv2d'),
**kwargs):
super(NonLocal2d, self).__init__(
in_channels, conv_cfg=conv_cfg, **kwargs)
self.sub_sample = sub_sample
if sub_sample:
max_pool_layer = nn.MaxPool2d(kernel_size=(2, 2))
self.g = nn.Sequential(self.g, max_pool_layer)
if self.mode != 'gaussian':
self.phi = nn.Sequential(self.phi, max_pool_layer)
else:
self.phi = max_pool_layer
class NonLocal3d(_NonLocalNd):
"""3D Non-local module.
Args:
in_channels (int): Same as `NonLocalND`.
sub_sample (bool): Whether to apply max pooling after pairwise
function (Note that the `sub_sample` is applied on spatial only).
Default: False.
conv_cfg (None | dict): Same as `NonLocalND`.
Default: dict(type='Conv3d').
"""
def __init__(self,
in_channels,
sub_sample=False,
conv_cfg=dict(type='Conv3d'),
**kwargs):
super(NonLocal3d, self).__init__(
in_channels, conv_cfg=conv_cfg, **kwargs)
self.sub_sample = sub_sample
if sub_sample:
max_pool_layer = nn.MaxPool3d(kernel_size=(1, 2, 2))
self.g = nn.Sequential(self.g, max_pool_layer)
if self.mode != 'gaussian':
self.phi = nn.Sequential(self.phi, max_pool_layer)
else:
self.phi = max_pool_layer
|