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u2net.py
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# Copyright (c) 2020 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.cvlibs import manager
from paddleseg.models import layers
from paddleseg.utils import utils
__all__ = ['U2Net', 'U2Netp']
@manager.MODELS.add_component
class U2Net(nn.Layer):
"""
The U^2-Net implementation based on PaddlePaddle.
The original article refers to
Xuebin Qin, et, al. "U^2-Net: Going Deeper with Nested U-Structure for Salient Object Detection"
(https://arxiv.org/abs/2005.09007).
Args:
num_classes (int): The unique number of target classes.
in_channels (int, optional): Input channels. Default: 3.
pretrained (str, optional): The path or url of pretrained model for fine tuning. Default: None.
"""
def __init__(self, num_classes, in_channels=3, pretrained=None):
super(U2Net, self).__init__()
self.stage1 = RSU7(in_channels, 32, 64)
self.pool12 = nn.MaxPool2D(2, stride=2, ceil_mode=True)
self.stage2 = RSU6(64, 32, 128)
self.pool23 = nn.MaxPool2D(2, stride=2, ceil_mode=True)
self.stage3 = RSU5(128, 64, 256)
self.pool34 = nn.MaxPool2D(2, stride=2, ceil_mode=True)
self.stage4 = RSU4(256, 128, 512)
self.pool45 = nn.MaxPool2D(2, stride=2, ceil_mode=True)
self.stage5 = RSU4F(512, 256, 512)
self.pool56 = nn.MaxPool2D(2, stride=2, ceil_mode=True)
self.stage6 = RSU4F(512, 256, 512)
# decoder
self.stage5d = RSU4F(1024, 256, 512)
self.stage4d = RSU4(1024, 128, 256)
self.stage3d = RSU5(512, 64, 128)
self.stage2d = RSU6(256, 32, 64)
self.stage1d = RSU7(128, 16, 64)
self.side1 = nn.Conv2D(64, num_classes, 3, padding=1)
self.side2 = nn.Conv2D(64, num_classes, 3, padding=1)
self.side3 = nn.Conv2D(128, num_classes, 3, padding=1)
self.side4 = nn.Conv2D(256, num_classes, 3, padding=1)
self.side5 = nn.Conv2D(512, num_classes, 3, padding=1)
self.side6 = nn.Conv2D(512, num_classes, 3, padding=1)
self.outconv = nn.Conv2D(6 * num_classes, num_classes, 1)
self.pretrained = pretrained
self.init_weight()
def forward(self, x):
hx = x
#stage 1
hx1 = self.stage1(hx)
hx = self.pool12(hx1)
#stage 2
hx2 = self.stage2(hx)
hx = self.pool23(hx2)
#stage 3
hx3 = self.stage3(hx)
hx = self.pool34(hx3)
#stage 4
hx4 = self.stage4(hx)
hx = self.pool45(hx4)
#stage 5
hx5 = self.stage5(hx)
hx = self.pool56(hx5)
#stage 6
hx6 = self.stage6(hx)
hx6up = _upsample_like(hx6, hx5)
#-------------------- decoder --------------------
hx5d = self.stage5d(paddle.concat((hx6up, hx5), 1))
hx5dup = _upsample_like(hx5d, hx4)
hx4d = self.stage4d(paddle.concat((hx5dup, hx4), 1))
hx4dup = _upsample_like(hx4d, hx3)
hx3d = self.stage3d(paddle.concat((hx4dup, hx3), 1))
hx3dup = _upsample_like(hx3d, hx2)
hx2d = self.stage2d(paddle.concat((hx3dup, hx2), 1))
hx2dup = _upsample_like(hx2d, hx1)
hx1d = self.stage1d(paddle.concat((hx2dup, hx1), 1))
#side output
d1 = self.side1(hx1d)
d2 = self.side2(hx2d)
d2 = _upsample_like(d2, d1)
d3 = self.side3(hx3d)
d3 = _upsample_like(d3, d1)
d4 = self.side4(hx4d)
d4 = _upsample_like(d4, d1)
d5 = self.side5(hx5d)
d5 = _upsample_like(d5, d1)
d6 = self.side6(hx6)
d6 = _upsample_like(d6, d1)
d0 = self.outconv(paddle.concat((d1, d2, d3, d4, d5, d6), 1))
return [d0, d1, d2, d3, d4, d5, d6]
def init_weight(self):
if self.pretrained is not None:
utils.load_entire_model(self, self.pretrained)
### U^2-Net small ###
@manager.MODELS.add_component
class U2Netp(nn.Layer):
"""Please Refer to U2Net above."""
def __init__(self, num_classes, in_channels=3, pretrained=None):
super(U2Netp, self).__init__()
self.stage1 = RSU7(in_channels, 16, 64)
self.pool12 = nn.MaxPool2D(2, stride=2, ceil_mode=True)
self.stage2 = RSU6(64, 16, 64)
self.pool23 = nn.MaxPool2D(2, stride=2, ceil_mode=True)
self.stage3 = RSU5(64, 16, 64)
self.pool34 = nn.MaxPool2D(2, stride=2, ceil_mode=True)
self.stage4 = RSU4(64, 16, 64)
self.pool45 = nn.MaxPool2D(2, stride=2, ceil_mode=True)
self.stage5 = RSU4F(64, 16, 64)
self.pool56 = nn.MaxPool2D(2, stride=2, ceil_mode=True)
self.stage6 = RSU4F(64, 16, 64)
# decoder
self.stage5d = RSU4F(128, 16, 64)
self.stage4d = RSU4(128, 16, 64)
self.stage3d = RSU5(128, 16, 64)
self.stage2d = RSU6(128, 16, 64)
self.stage1d = RSU7(128, 16, 64)
self.side1 = nn.Conv2D(64, num_classes, 3, padding=1)
self.side2 = nn.Conv2D(64, num_classes, 3, padding=1)
self.side3 = nn.Conv2D(64, num_classes, 3, padding=1)
self.side4 = nn.Conv2D(64, num_classes, 3, padding=1)
self.side5 = nn.Conv2D(64, num_classes, 3, padding=1)
self.side6 = nn.Conv2D(64, num_classes, 3, padding=1)
self.outconv = nn.Conv2D(6 * num_classes, num_classes, 1)
self.pretrained = pretrained
self.init_weight()
def forward(self, x):
hx = x
#stage 1
hx1 = self.stage1(hx)
hx = self.pool12(hx1)
#stage 2
hx2 = self.stage2(hx)
hx = self.pool23(hx2)
#stage 3
hx3 = self.stage3(hx)
hx = self.pool34(hx3)
#stage 4
hx4 = self.stage4(hx)
hx = self.pool45(hx4)
#stage 5
hx5 = self.stage5(hx)
hx = self.pool56(hx5)
#stage 6
hx6 = self.stage6(hx)
hx6up = _upsample_like(hx6, hx5)
#decoder
hx5d = self.stage5d(paddle.concat((hx6up, hx5), 1))
hx5dup = _upsample_like(hx5d, hx4)
hx4d = self.stage4d(paddle.concat((hx5dup, hx4), 1))
hx4dup = _upsample_like(hx4d, hx3)
hx3d = self.stage3d(paddle.concat((hx4dup, hx3), 1))
hx3dup = _upsample_like(hx3d, hx2)
hx2d = self.stage2d(paddle.concat((hx3dup, hx2), 1))
hx2dup = _upsample_like(hx2d, hx1)
hx1d = self.stage1d(paddle.concat((hx2dup, hx1), 1))
#side output
d1 = self.side1(hx1d)
d2 = self.side2(hx2d)
d2 = _upsample_like(d2, d1)
d3 = self.side3(hx3d)
d3 = _upsample_like(d3, d1)
d4 = self.side4(hx4d)
d4 = _upsample_like(d4, d1)
d5 = self.side5(hx5d)
d5 = _upsample_like(d5, d1)
d6 = self.side6(hx6)
d6 = _upsample_like(d6, d1)
d0 = self.outconv(paddle.concat((d1, d2, d3, d4, d5, d6), 1))
return [d0, d1, d2, d3, d4, d5, d6]
def init_weight(self):
if self.pretrained is not None:
utils.load_entire_model(self, self.pretrained)
class REBNCONV(nn.Layer):
def __init__(self, in_ch=3, out_ch=3, dirate=1):
super(REBNCONV, self).__init__()
self.conv_s1 = nn.Conv2D(in_ch,
out_ch,
3,
padding=1 * dirate,
dilation=1 * dirate)
self.bn_s1 = nn.BatchNorm2D(out_ch)
self.relu_s1 = nn.ReLU()
def forward(self, x):
hx = x
xout = self.relu_s1(self.bn_s1(self.conv_s1(hx)))
return xout
## upsample tensor 'src' to have the same spatial size with tensor 'tar'
def _upsample_like(src, tar):
src = F.upsample(src, size=tar.shape[2:], mode='bilinear')
return src
### RSU-7 ###
class RSU7(nn.Layer): #UNet07DRES(nn.Layer):
def __init__(self, in_ch=3, mid_ch=12, out_ch=3):
super(RSU7, self).__init__()
self.rebnconvin = REBNCONV(in_ch, out_ch, dirate=1)
self.rebnconv1 = REBNCONV(out_ch, mid_ch, dirate=1)
self.pool1 = nn.MaxPool2D(2, stride=2, ceil_mode=True)
self.rebnconv2 = REBNCONV(mid_ch, mid_ch, dirate=1)
self.pool2 = nn.MaxPool2D(2, stride=2, ceil_mode=True)
self.rebnconv3 = REBNCONV(mid_ch, mid_ch, dirate=1)
self.pool3 = nn.MaxPool2D(2, stride=2, ceil_mode=True)
self.rebnconv4 = REBNCONV(mid_ch, mid_ch, dirate=1)
self.pool4 = nn.MaxPool2D(2, stride=2, ceil_mode=True)
self.rebnconv5 = REBNCONV(mid_ch, mid_ch, dirate=1)
self.pool5 = nn.MaxPool2D(2, stride=2, ceil_mode=True)
self.rebnconv6 = REBNCONV(mid_ch, mid_ch, dirate=1)
self.rebnconv7 = REBNCONV(mid_ch, mid_ch, dirate=2)
self.rebnconv6d = REBNCONV(mid_ch * 2, mid_ch, dirate=1)
self.rebnconv5d = REBNCONV(mid_ch * 2, mid_ch, dirate=1)
self.rebnconv4d = REBNCONV(mid_ch * 2, mid_ch, dirate=1)
self.rebnconv3d = REBNCONV(mid_ch * 2, mid_ch, dirate=1)
self.rebnconv2d = REBNCONV(mid_ch * 2, mid_ch, dirate=1)
self.rebnconv1d = REBNCONV(mid_ch * 2, out_ch, dirate=1)
def forward(self, x):
hx = x
hxin = self.rebnconvin(hx)
hx1 = self.rebnconv1(hxin)
hx = self.pool1(hx1)
hx2 = self.rebnconv2(hx)
hx = self.pool2(hx2)
hx3 = self.rebnconv3(hx)
hx = self.pool3(hx3)
hx4 = self.rebnconv4(hx)
hx = self.pool4(hx4)
hx5 = self.rebnconv5(hx)
hx = self.pool5(hx5)
hx6 = self.rebnconv6(hx)
hx7 = self.rebnconv7(hx6)
hx6d = self.rebnconv6d(paddle.concat((hx7, hx6), 1))
hx6dup = _upsample_like(hx6d, hx5)
hx5d = self.rebnconv5d(paddle.concat((hx6dup, hx5), 1))
hx5dup = _upsample_like(hx5d, hx4)
hx4d = self.rebnconv4d(paddle.concat((hx5dup, hx4), 1))
hx4dup = _upsample_like(hx4d, hx3)
hx3d = self.rebnconv3d(paddle.concat((hx4dup, hx3), 1))
hx3dup = _upsample_like(hx3d, hx2)
hx2d = self.rebnconv2d(paddle.concat((hx3dup, hx2), 1))
hx2dup = _upsample_like(hx2d, hx1)
hx1d = self.rebnconv1d(paddle.concat((hx2dup, hx1), 1))
return hx1d + hxin
### RSU-6 ###
class RSU6(nn.Layer): #UNet06DRES(nn.Layer):
def __init__(self, in_ch=3, mid_ch=12, out_ch=3):
super(RSU6, self).__init__()
self.rebnconvin = REBNCONV(in_ch, out_ch, dirate=1)
self.rebnconv1 = REBNCONV(out_ch, mid_ch, dirate=1)
self.pool1 = nn.MaxPool2D(2, stride=2, ceil_mode=True)
self.rebnconv2 = REBNCONV(mid_ch, mid_ch, dirate=1)
self.pool2 = nn.MaxPool2D(2, stride=2, ceil_mode=True)
self.rebnconv3 = REBNCONV(mid_ch, mid_ch, dirate=1)
self.pool3 = nn.MaxPool2D(2, stride=2, ceil_mode=True)
self.rebnconv4 = REBNCONV(mid_ch, mid_ch, dirate=1)
self.pool4 = nn.MaxPool2D(2, stride=2, ceil_mode=True)
self.rebnconv5 = REBNCONV(mid_ch, mid_ch, dirate=1)
self.rebnconv6 = REBNCONV(mid_ch, mid_ch, dirate=2)
self.rebnconv5d = REBNCONV(mid_ch * 2, mid_ch, dirate=1)
self.rebnconv4d = REBNCONV(mid_ch * 2, mid_ch, dirate=1)
self.rebnconv3d = REBNCONV(mid_ch * 2, mid_ch, dirate=1)
self.rebnconv2d = REBNCONV(mid_ch * 2, mid_ch, dirate=1)
self.rebnconv1d = REBNCONV(mid_ch * 2, out_ch, dirate=1)
def forward(self, x):
hx = x
hxin = self.rebnconvin(hx)
hx1 = self.rebnconv1(hxin)
hx = self.pool1(hx1)
hx2 = self.rebnconv2(hx)
hx = self.pool2(hx2)
hx3 = self.rebnconv3(hx)
hx = self.pool3(hx3)
hx4 = self.rebnconv4(hx)
hx = self.pool4(hx4)
hx5 = self.rebnconv5(hx)
hx6 = self.rebnconv6(hx5)
hx5d = self.rebnconv5d(paddle.concat((hx6, hx5), 1))
hx5dup = _upsample_like(hx5d, hx4)
hx4d = self.rebnconv4d(paddle.concat((hx5dup, hx4), 1))
hx4dup = _upsample_like(hx4d, hx3)
hx3d = self.rebnconv3d(paddle.concat((hx4dup, hx3), 1))
hx3dup = _upsample_like(hx3d, hx2)
hx2d = self.rebnconv2d(paddle.concat((hx3dup, hx2), 1))
hx2dup = _upsample_like(hx2d, hx1)
hx1d = self.rebnconv1d(paddle.concat((hx2dup, hx1), 1))
return hx1d + hxin
### RSU-5 ###
class RSU5(nn.Layer): #UNet05DRES(nn.Layer):
def __init__(self, in_ch=3, mid_ch=12, out_ch=3):
super(RSU5, self).__init__()
self.rebnconvin = REBNCONV(in_ch, out_ch, dirate=1)
self.rebnconv1 = REBNCONV(out_ch, mid_ch, dirate=1)
self.pool1 = nn.MaxPool2D(2, stride=2, ceil_mode=True)
self.rebnconv2 = REBNCONV(mid_ch, mid_ch, dirate=1)
self.pool2 = nn.MaxPool2D(2, stride=2, ceil_mode=True)
self.rebnconv3 = REBNCONV(mid_ch, mid_ch, dirate=1)
self.pool3 = nn.MaxPool2D(2, stride=2, ceil_mode=True)
self.rebnconv4 = REBNCONV(mid_ch, mid_ch, dirate=1)
self.rebnconv5 = REBNCONV(mid_ch, mid_ch, dirate=2)
self.rebnconv4d = REBNCONV(mid_ch * 2, mid_ch, dirate=1)
self.rebnconv3d = REBNCONV(mid_ch * 2, mid_ch, dirate=1)
self.rebnconv2d = REBNCONV(mid_ch * 2, mid_ch, dirate=1)
self.rebnconv1d = REBNCONV(mid_ch * 2, out_ch, dirate=1)
def forward(self, x):
hx = x
hxin = self.rebnconvin(hx)
hx1 = self.rebnconv1(hxin)
hx = self.pool1(hx1)
hx2 = self.rebnconv2(hx)
hx = self.pool2(hx2)
hx3 = self.rebnconv3(hx)
hx = self.pool3(hx3)
hx4 = self.rebnconv4(hx)
hx5 = self.rebnconv5(hx4)
hx4d = self.rebnconv4d(paddle.concat((hx5, hx4), 1))
hx4dup = _upsample_like(hx4d, hx3)
hx3d = self.rebnconv3d(paddle.concat((hx4dup, hx3), 1))
hx3dup = _upsample_like(hx3d, hx2)
hx2d = self.rebnconv2d(paddle.concat((hx3dup, hx2), 1))
hx2dup = _upsample_like(hx2d, hx1)
hx1d = self.rebnconv1d(paddle.concat((hx2dup, hx1), 1))
return hx1d + hxin
### RSU-4 ###
class RSU4(nn.Layer): #UNet04DRES(nn.Layer):
def __init__(self, in_ch=3, mid_ch=12, out_ch=3):
super(RSU4, self).__init__()
self.rebnconvin = REBNCONV(in_ch, out_ch, dirate=1)
self.rebnconv1 = REBNCONV(out_ch, mid_ch, dirate=1)
self.pool1 = nn.MaxPool2D(2, stride=2, ceil_mode=True)
self.rebnconv2 = REBNCONV(mid_ch, mid_ch, dirate=1)
self.pool2 = nn.MaxPool2D(2, stride=2, ceil_mode=True)
self.rebnconv3 = REBNCONV(mid_ch, mid_ch, dirate=1)
self.rebnconv4 = REBNCONV(mid_ch, mid_ch, dirate=2)
self.rebnconv3d = REBNCONV(mid_ch * 2, mid_ch, dirate=1)
self.rebnconv2d = REBNCONV(mid_ch * 2, mid_ch, dirate=1)
self.rebnconv1d = REBNCONV(mid_ch * 2, out_ch, dirate=1)
def forward(self, x):
hx = x
hxin = self.rebnconvin(hx)
hx1 = self.rebnconv1(hxin)
hx = self.pool1(hx1)
hx2 = self.rebnconv2(hx)
hx = self.pool2(hx2)
hx3 = self.rebnconv3(hx)
hx4 = self.rebnconv4(hx3)
hx3d = self.rebnconv3d(paddle.concat((hx4, hx3), 1))
hx3dup = _upsample_like(hx3d, hx2)
hx2d = self.rebnconv2d(paddle.concat((hx3dup, hx2), 1))
hx2dup = _upsample_like(hx2d, hx1)
hx1d = self.rebnconv1d(paddle.concat((hx2dup, hx1), 1))
return hx1d + hxin
### RSU-4F ###
class RSU4F(nn.Layer): #UNet04FRES(nn.Layer):
def __init__(self, in_ch=3, mid_ch=12, out_ch=3):
super(RSU4F, self).__init__()
self.rebnconvin = REBNCONV(in_ch, out_ch, dirate=1)
self.rebnconv1 = REBNCONV(out_ch, mid_ch, dirate=1)
self.rebnconv2 = REBNCONV(mid_ch, mid_ch, dirate=2)
self.rebnconv3 = REBNCONV(mid_ch, mid_ch, dirate=4)
self.rebnconv4 = REBNCONV(mid_ch, mid_ch, dirate=8)
self.rebnconv3d = REBNCONV(mid_ch * 2, mid_ch, dirate=4)
self.rebnconv2d = REBNCONV(mid_ch * 2, mid_ch, dirate=2)
self.rebnconv1d = REBNCONV(mid_ch * 2, out_ch, dirate=1)
def forward(self, x):
hx = x
hxin = self.rebnconvin(hx)
hx1 = self.rebnconv1(hxin)
hx2 = self.rebnconv2(hx1)
hx3 = self.rebnconv3(hx2)
hx4 = self.rebnconv4(hx3)
hx3d = self.rebnconv3d(paddle.concat((hx4, hx3), 1))
hx2d = self.rebnconv2d(paddle.concat((hx3d, hx2), 1))
hx1d = self.rebnconv1d(paddle.concat((hx2d, hx1), 1))
return hx1d + hxin