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# Copyright (c) 2021 PaddlePaddle 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.
import paddle
import paddle.nn as nn
import paddle.nn.functional as F
from paddleseg.models import layers
from paddleseg.cvlibs import manager
from paddleseg.utils import utils
@manager.MODELS.add_component
class DNLNet(nn.Layer):
"""Disentangled Non-Local Neural Networks.
The original article refers to
Minghao Yin, et al. "Disentangled Non-Local Neural Networks"
(https://arxiv.org/abs/2006.06668)
Args:
num_classes (int): The unique number of target classes.
backbone (Paddle.nn.Layer): A backbone network.
backbone_indices (tuple): The values in the tuple indicate the indices of output of backbone.
reduction (int): Reduction factor of projection transform. Default: 2.
use_scale (bool): Whether to scale pairwise_weight by
sqrt(1/inter_channels). Default: False.
mode (str): The nonlocal mode. Options are 'embedded_gaussian',
'dot_product'. Default: 'embedded_gaussian'.
temperature (float): Temperature to adjust attention. Default: 0.05.
concat_input (bool): Whether concat the input and output of convs before classification layer. Default: True
enable_auxiliary_loss (bool, optional): A bool value indicates whether adding auxiliary loss. Default: True.
align_corners (bool): An argument of F.interpolate. It should be set to False when the output size of feature
is even, e.g. 1024x512, otherwise it is True, e.g. 769x769. Default: False.
pretrained (str, optional): The path or url of pretrained model. Default: None.
"""
def __init__(self,
num_classes,
backbone,
backbone_indices=(2, 3),
reduction=2,
use_scale=True,
mode='embedded_gaussian',
temperature=0.05,
concat_input=True,
enable_auxiliary_loss=True,
align_corners=False,
pretrained=None):
super().__init__()
self.backbone = backbone
self.backbone_indices = backbone_indices
in_channels = [self.backbone.feat_channels[i] for i in backbone_indices]
self.head = DNLHead(num_classes, in_channels, reduction, use_scale,
mode, temperature, concat_input,
enable_auxiliary_loss)
self.align_corners = align_corners
self.pretrained = pretrained
self.init_weight()
def forward(self, x):
feats = self.backbone(x)
feats = [feats[i] for i in self.backbone_indices]
logit_list = self.head(feats)
logit_list = [
F.interpolate(
logit,
paddle.shape(x)[2:],
mode='bilinear',
align_corners=self.align_corners,
align_mode=1) for logit in logit_list
]
return logit_list
def init_weight(self):
if self.pretrained is not None:
utils.load_entire_model(self, self.pretrained)
class DNLHead(nn.Layer):
"""
The DNLNet head.
Args:
num_classes (int): The unique number of target classes.
in_channels (tuple): The number of input channels.
reduction (int): Reduction factor of projection transform. Default: 2.
use_scale (bool): Whether to scale pairwise_weight by
sqrt(1/inter_channels). Default: False.
mode (str): The nonlocal mode. Options are 'embedded_gaussian',
'dot_product'. Default: 'embedded_gaussian.'.
temperature (float): Temperature to adjust attention. Default: 0.05
concat_input (bool): Whether concat the input and output of convs before classification layer. Default: True
enable_auxiliary_loss (bool, optional): A bool value indicates whether adding auxiliary loss. Default: True.
"""
def __init__(self,
num_classes,
in_channels,
reduction,
use_scale,
mode,
temperature,
concat_input=True,
enable_auxiliary_loss=True,
**kwargs):
super(DNLHead, self).__init__()
self.in_channels = in_channels[-1]
self.concat_input = concat_input
self.enable_auxiliary_loss = enable_auxiliary_loss
inter_channels = self.in_channels // 4
self.dnl_block = DisentangledNonLocal2D(
in_channels=inter_channels,
reduction=reduction,
use_scale=use_scale,
temperature=temperature,
mode=mode)
self.conv0 = layers.ConvBNReLU(
in_channels=self.in_channels,
out_channels=inter_channels,
kernel_size=3,
bias_attr=False)
self.conv1 = layers.ConvBNReLU(
in_channels=inter_channels,
out_channels=inter_channels,
kernel_size=3,
bias_attr=False)
self.cls = nn.Sequential(
nn.Dropout2D(p=0.1), nn.Conv2D(inter_channels, num_classes, 1))
self.aux = nn.Sequential(
layers.ConvBNReLU(
in_channels=1024,
out_channels=256,
kernel_size=3,
bias_attr=False),
nn.Dropout2D(p=0.1),
nn.Conv2D(256, num_classes, 1))
if self.concat_input:
self.conv_cat = layers.ConvBNReLU(
self.in_channels + inter_channels,
inter_channels,
kernel_size=3,
bias_attr=False)
def forward(self, feat_list):
C3, C4 = feat_list
output = self.conv0(C4)
output = self.dnl_block(output)
output = self.conv1(output)
if self.concat_input:
output = self.conv_cat(paddle.concat([C4, output], axis=1))
output = self.cls(output)
if self.enable_auxiliary_loss:
auxout = self.aux(C3)
return [output, auxout]
else:
return [output]
class DisentangledNonLocal2D(layers.NonLocal2D):
"""Disentangled Non-Local Blocks.
Args:
temperature (float): Temperature to adjust attention.
"""
def __init__(self, temperature, *arg, **kwargs):
super().__init__(*arg, **kwargs)
self.temperature = temperature
self.conv_mask = nn.Conv2D(self.in_channels, 1, kernel_size=1)
def embedded_gaussian(self, theta_x, phi_x):
pairwise_weight = paddle.matmul(theta_x, phi_x)
if self.use_scale:
pairwise_weight /= theta_x.shape[-1]**0.5
pairwise_weight /= self.temperature
pairwise_weight = F.softmax(pairwise_weight, -1)
return pairwise_weight
def forward(self, x):
x_shape = paddle.shape(x)
g_x = self.g(x).reshape([0, self.inter_channels,
-1]).transpose([0, 2, 1])
if self.mode == "gaussian":
theta_x = paddle.transpose(
x.reshape([0, self.in_channels, -1]), [0, 2, 1])
if self.sub_sample:
phi_x = paddle.transpose(self.phi(x), [0, self.in_channels, -1])
else:
phi_x = paddle.transpose(x, [0, self.in_channels, -1])
elif self.mode == "concatenation":
theta_x = paddle.reshape(
self.theta(x), [0, self.inter_channels, -1, 1])
phi_x = paddle.reshape(self.phi(x), [0, self.inter_channels, 1, -1])
else:
theta_x = self.theta(x).reshape([0, self.inter_channels,
-1]).transpose([0, 2, 1])
phi_x = paddle.reshape(self.phi(x), [0, self.inter_channels, -1])
theta_x -= paddle.mean(theta_x, axis=-2, keepdim=True)
phi_x -= paddle.mean(phi_x, axis=-1, keepdim=True)
pairwise_func = getattr(self, self.mode)
pairwise_weight = pairwise_func(theta_x, phi_x)
y = paddle.matmul(pairwise_weight, g_x).transpose([0, 2, 1]).reshape(
[0, self.inter_channels, x_shape[2], x_shape[3]])
unary_mask = F.softmax(
paddle.reshape(self.conv_mask(x), [0, 1, -1]), -1)
unary_x = paddle.matmul(unary_mask, g_x).transpose([0, 2, 1]).reshape(
[0, self.inter_channels, 1, 1])
output = x + self.conv_out(y + unary_x)
return output
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