# 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 paddlex.paddleseg.cvlibs import manager from paddlex.paddleseg.models import layers from paddlex.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_ch (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_ch=3, pretrained=None): super(U2Net, self).__init__() self.stage1 = RSU7(in_ch, 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_ch=3, pretrained=None): super(U2Netp, self).__init__() self.stage1 = RSU7(in_ch, 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=paddle.shape(tar)[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