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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 EMANet(nn.Layer):
"""
Expectation Maximization Attention Networks for Semantic Segmentation based on PaddlePaddle.
The original article refers to
Xia Li, et al. "Expectation-Maximization Attention Networks for Semantic Segmentation"
(https://arxiv.org/abs/1907.13426)
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.
ema_channels (int): EMA module channels.
gc_channels (int): The input channels to Global Context Block.
num_bases (int): Number of bases.
stage_num (int): The iteration number for EM.
momentum (float): The parameter for updating bases.
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),
ema_channels=512,
gc_channels=256,
num_bases=64,
stage_num=3,
momentum=0.1,
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 = EMAHead(num_classes, in_channels, ema_channels, gc_channels,
num_bases, stage_num, momentum, 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) 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 EMAHead(nn.Layer):
"""
The EMANet head.
Args:
num_classes (int): The unique number of target classes.
in_channels (tuple): The number of input channels.
ema_channels (int): EMA module channels.
gc_channels (int): The input channels to Global Context Block.
num_bases (int): Number of bases.
stage_num (int): The iteration number for EM.
momentum (float): The parameter for updating bases.
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,
ema_channels,
gc_channels,
num_bases,
stage_num,
momentum,
concat_input=True,
enable_auxiliary_loss=True):
super(EMAHead, self).__init__()
self.in_channels = in_channels[-1]
self.concat_input = concat_input
self.enable_auxiliary_loss = enable_auxiliary_loss
self.emau = EMAU(ema_channels, num_bases, stage_num, momentum=momentum)
self.ema_in_conv = layers.ConvBNReLU(
in_channels=self.in_channels,
out_channels=ema_channels,
kernel_size=3)
self.ema_mid_conv = nn.Conv2D(ema_channels, ema_channels, kernel_size=1)
self.ema_out_conv = layers.ConvBNReLU(
in_channels=ema_channels, out_channels=ema_channels, kernel_size=1)
self.bottleneck = layers.ConvBNReLU(
in_channels=ema_channels, out_channels=gc_channels, kernel_size=3)
self.cls = nn.Sequential(
nn.Dropout2D(p=0.1), nn.Conv2D(gc_channels, num_classes, 1))
self.aux = nn.Sequential(
layers.ConvBNReLU(
in_channels=1024, out_channels=256, kernel_size=3),
nn.Dropout2D(p=0.1),
nn.Conv2D(256, num_classes, 1))
if self.concat_input:
self.conv_cat = layers.ConvBNReLU(
self.in_channels + gc_channels, gc_channels, kernel_size=3)
def forward(self, feat_list):
C3, C4 = feat_list
feats = self.ema_in_conv(C4)
identity = feats
feats = self.ema_mid_conv(feats)
recon = self.emau(feats)
recon = F.relu(recon)
recon = self.ema_out_conv(recon)
output = F.relu(identity + recon)
output = self.bottleneck(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 EMAU(nn.Layer):
'''The Expectation-Maximization Attention Unit (EMAU).
Arguments:
c (int): The input and output channel number.
k (int): The number of the bases.
stage_num (int): The iteration number for EM.
momentum (float): The parameter for updating bases.
'''
def __init__(self, c, k, stage_num=3, momentum=0.1):
super(EMAU, self).__init__()
assert stage_num >= 1
self.stage_num = stage_num
self.momentum = momentum
self.c = c
tmp_mu = self.create_parameter(
shape=[1, c, k],
default_initializer=paddle.nn.initializer.KaimingNormal(k))
mu = F.normalize(paddle.to_tensor(tmp_mu), axis=1, p=2)
self.register_buffer('mu', mu)
def forward(self, x):
x_shape = paddle.shape(x)
x = x.flatten(2)
mu = paddle.tile(self.mu, [x_shape[0], 1, 1])
with paddle.no_grad():
for i in range(self.stage_num):
x_t = paddle.transpose(x, [0, 2, 1])
z = paddle.bmm(x_t, mu)
z = F.softmax(z, axis=2)
z_ = F.normalize(z, axis=1, p=1)
mu = paddle.bmm(x, z_)
mu = F.normalize(mu, axis=1, p=2)
z_t = paddle.transpose(z, [0, 2, 1])
x = paddle.matmul(mu, z_t)
x = paddle.reshape(x, [0, self.c, x_shape[2], x_shape[3]])
if self.training:
mu = paddle.mean(mu, 0, keepdim=True)
mu = F.normalize(mu, axis=1, p=2)
mu = self.mu * (1 - self.momentum) + mu * self.momentum
if paddle.distributed.get_world_size() > 1:
out = paddle.distributed.all_reduce(mu)
if out is not None:
mu = out
mu /= paddle.distributed.get_world_size()
self.mu = mu
return x
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