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# copyright (c) 2021 PaddlePaddle Authors. All Rights Reserve.
#
# 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.nn as nn
from paddleseg import utils
from paddleseg.cvlibs import manager
@manager.MODELS.add_component
class PortraitNet(nn.Layer):
"""
The PortraitNet implementation based on PaddlePaddle.
The original article refers to
Song-Hai Zhanga, Xin Donga, Jia Lib, Ruilong Lia, Yong-Liang Yangc
"PortraitNet: Real-time Portrait Segmentation Network for Mobile Device"
(https://www.yongliangyang.net/docs/mobilePotrait_c&g19.pdf).
Args:
num_classes (int, optional): The unique number of target classes. Default: 2.
backbone (Paddle.nn.Layer): Backbone network, currently support MobileNetV2.
add_edge (bool, optional): Whether output to edge. Default: False
pretrained (str, optional): The path or url of pretrained model. Default: None
"""
def __init__(self,
num_classes,
backbone,
min_channel=16,
channel_ratio=1.0,
add_edge=False,
pretrained=None):
super(PortraitNet, self).__init__()
self.backbone = backbone
self.head = PortraitNetHead(num_classes, min_channel, channel_ratio,
add_edge)
self.pretrained = pretrained
self.init_weight()
def forward(self, x):
feat_list = self.backbone(x)
logits_list = self.head(feat_list)
return [logits_list]
def init_weight(self):
if self.pretrained is not None:
utils.load_entire_model(self, self.pretrained)
class PortraitNetHead(nn.Layer):
def __init__(self,
num_classes,
min_channel=16,
channel_ratio=1.0,
add_edge=False):
super().__init__()
self.min_channel = min_channel
self.channel_ratio = channel_ratio
self.add_edge = add_edge
self.deconv1 = nn.Conv2DTranspose(
self.depth(96),
self.depth(96),
groups=1,
kernel_size=4,
stride=2,
padding=1,
bias_attr=False)
self.deconv2 = nn.Conv2DTranspose(
self.depth(32),
self.depth(32),
groups=1,
kernel_size=4,
stride=2,
padding=1,
bias_attr=False)
self.deconv3 = nn.Conv2DTranspose(
self.depth(24),
self.depth(24),
groups=1,
kernel_size=4,
stride=2,
padding=1,
bias_attr=False)
self.deconv4 = nn.Conv2DTranspose(
self.depth(16),
self.depth(16),
groups=1,
kernel_size=4,
stride=2,
padding=1,
bias_attr=False)
self.deconv5 = nn.Conv2DTranspose(
self.depth(8),
self.depth(8),
groups=1,
kernel_size=4,
stride=2,
padding=1,
bias_attr=False)
self.transit1 = ResidualBlock(self.depth(320), self.depth(96))
self.transit2 = ResidualBlock(self.depth(96), self.depth(32))
self.transit3 = ResidualBlock(self.depth(32), self.depth(24))
self.transit4 = ResidualBlock(self.depth(24), self.depth(16))
self.transit5 = ResidualBlock(self.depth(16), self.depth(8))
self.pred = nn.Conv2D(
self.depth(8), num_classes, 3, 1, 1, bias_attr=False)
if self.add_edge:
self.edge = nn.Conv2D(
self.depth(8), num_classes, 3, 1, 1, bias_attr=False)
def depth(self, channels):
min_channel = min(channels, self.min_channel)
return max(min_channel, int(channels * self.channel_ratio))
def forward(self, feat_list):
feature_1_4, feature_1_8, feature_1_16, feature_1_32 = feat_list
up_1_16 = self.deconv1(self.transit1(feature_1_32))
up_1_8 = self.deconv2(self.transit2(feature_1_16 + up_1_16))
up_1_4 = self.deconv3(self.transit3(feature_1_8 + up_1_8))
up_1_2 = self.deconv4(self.transit4(feature_1_4 + up_1_4))
up_1_1 = self.deconv5(self.transit5(up_1_2))
pred = self.pred(up_1_1)
if self.add_edge:
edge = self.edge(up_1_1)
return pred, edge
else:
return pred
class ConvDw(nn.Layer):
def __init__(self, inp, oup, kernel, stride):
super(ConvDw, self).__init__()
self.conv = nn.Sequential(
nn.Conv2D(
inp,
inp,
kernel,
stride, (kernel - 1) // 2,
groups=inp,
bias_attr=False),
nn.BatchNorm2D(
num_features=inp, epsilon=1e-05, momentum=0.1),
nn.ReLU(),
nn.Conv2D(
inp, oup, 1, 1, 0, bias_attr=False),
nn.BatchNorm2D(
num_features=oup, epsilon=1e-05, momentum=0.1),
nn.ReLU(), )
def forward(self, x):
return self.conv(x)
class ResidualBlock(nn.Layer):
def __init__(self, inp, oup, stride=1):
super(ResidualBlock, self).__init__()
self.block = nn.Sequential(
ConvDw(
inp, oup, 3, stride=stride),
nn.Conv2D(
in_channels=oup,
out_channels=oup,
kernel_size=3,
stride=1,
padding=1,
groups=oup,
bias_attr=False),
nn.BatchNorm2D(
num_features=oup, epsilon=1e-05, momentum=0.1),
nn.ReLU(),
nn.Conv2D(
in_channels=oup,
out_channels=oup,
kernel_size=1,
stride=1,
padding=0,
bias_attr=False),
nn.BatchNorm2D(
num_features=oup, epsilon=1e-05, momentum=0.1), )
if inp == oup:
self.residual = None
else:
self.residual = nn.Sequential(
nn.Conv2D(
in_channels=inp,
out_channels=oup,
kernel_size=1,
stride=1,
padding=0,
bias_attr=False),
nn.BatchNorm2D(
num_features=oup, epsilon=1e-05, momentum=0.1), )
self.relu = nn.ReLU()
def forward(self, x):
residual = x
out = self.block(x)
if self.residual is not None:
residual = self.residual(x)
out += residual
out = self.relu(out)
return out
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