unet_3plus
1060 320*320 8ms，cpu800 ms
# -*- coding: utf-8 -*-
import os
import time
import numpy as np
import cv2
# -*- coding: utf-8 -*-
import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
from numpy import *
from torch.nn import init
def weights_init_normal(m):
classname = m.__class__.__name__
#print(classname)
if classname.find('Conv') != -1:
init.normal_(m.weight.data, 0.0, 0.02)
elif classname.find('Linear') != -1:
init.normal_(m.weight.data, 0.0, 0.02)
elif classname.find('BatchNorm') != -1:
init.normal_(m.weight.data, 1.0, 0.02)
init.constant_(m.bias.data, 0.0)
def weights_init_xavier(m):
classname = m.__class__.__name__
#print(classname)
if classname.find('Conv') != -1:
init.xavier_normal_(m.weight.data, gain=1)
elif classname.find('Linear') != -1:
init.xavier_normal_(m.weight.data, gain=1)
elif classname.find('BatchNorm') != -1:
init.normal_(m.weight.data, 1.0, 0.02)
init.constant_(m.bias.data, 0.0)
def weights_init_orthogonal(m):
classname = m.__class__.__name__
#print(classname)
if classname.find('Conv') != -1:
init.orthogonal_(m.weight.data, gain=1)
elif classname.find('Linear') != -1:
init.orthogonal_(m.weight.data, gain=1)
elif classname.find('BatchNorm') != -1:
init.normal_(m.weight.data, 1.0, 0.02)
init.constant_(m.bias.data, 0.0)
def weights_init_kaiming(m):
classname = m.__class__.__name__
#print(classname)
if classname.find('Conv') != -1:
init.kaiming_normal_(m.weight.data, a=0, mode='fan_in')
elif classname.find('Linear') != -1:
init.kaiming_normal_(m.weight.data, a=0, mode='fan_in')
elif classname.find('BatchNorm') != -1:
init.normal_(m.weight.data, 1.0, 0.02)
init.constant_(m.bias.data, 0.0)
def init_weights(net, init_type='normal'):
#print('initialization method [%s]' % init_type)
if init_type == 'normal':
net.apply(weights_init_normal)
elif init_type == 'xavier':
net.apply(weights_init_xavier)
elif init_type == 'kaiming':
net.apply(weights_init_kaiming)
elif init_type == 'orthogonal':
net.apply(weights_init_orthogonal)
else:
raise NotImplementedError('initialization method [%s] is not implemented' % init_type)
class unetConv2(nn.Module):
def __init__(self, in_size, out_size, is_batchnorm, n=2, ks=3, stride=1, padding=1):
super(unetConv2, self).__init__()
self.n = n
self.ks = ks
self.stride = stride
self.padding = padding
s = stride
p = padding
if is_batchnorm:
for i in range(1, n + 1):
conv = nn.Sequential(nn.Conv2d(in_size, out_size, ks, s, p),
nn.BatchNorm2d(out_size),
nn.ReLU(inplace=True), )
setattr(self, 'conv%d' % i, conv)
in_size = out_size
else:
for i in range(1, n + 1):
conv = nn.Sequential(nn.Conv2d(in_size, out_size, ks, s, p),
nn.ReLU(inplace=True), )
setattr(self, 'conv%d' % i, conv)
in_size = out_size
# initialise the blocks
for m in self.children():
init_weights(m, init_type='kaiming')
def forward(self, inputs):
x = inputs
for i in range(1, self.n + 1):
conv = getattr(self, 'conv%d' % i)
x = conv(x)
return x
class UNet_3Plus(nn.Module):
def __init__(self, args):
super(UNet_3Plus, self).__init__()
self.args = args
in_channels = 3
n_classes = 1
feature_scale = 4
is_deconv = True
is_batchnorm = True
self.is_deconv = is_deconv
self.in_channels = in_channels
self.is_batchnorm = is_batchnorm
self.feature_scale = feature_scale
filters = [16, 32, 64, 128, 256]
## -------------Encoder--------------
self.conv1 = unetConv2(self.in_channels, filters[0], self.is_batchnorm)
self.maxpool1 = nn.MaxPool2d(kernel_size=2)
self.conv2 = unetConv2(filters[0], filters[1], self.is_batchnorm)
self.maxpool2 = nn.MaxPool2d(kernel_size=2)
self.conv3 = unetConv2(filters[1], filters[2], self.is_batchnorm)
self.maxpool3 = nn.MaxPool2d(kernel_size=2)
self.conv4 = unetConv2(filters[2], filters[3], self.is_batchnorm)
self.maxpool4 = nn.MaxPool2d(kernel_size=2)
self.conv5 = unetConv2(filters[3], filters[4], self.is_batchnorm)
## -------------Decoder--------------
self.CatChannels = filters[0]
self.CatBlocks = 5
self.UpChannels = self.CatChannels * self.CatBlocks
'''stage 4d'''
# h1->320*320, hd4->40*40, Pooling 8 times
self.h1_PT_hd4 = nn.MaxPool2d(8, 8, ceil_mode=True)
self.h1_PT_hd4_conv = nn.Conv2d(filters[0], self.CatChannels, 3, padding=1)
self.h1_PT_hd4_bn = nn.BatchNorm2d(self.CatChannels)
self.h1_PT_hd4_relu = nn.ReLU(inplace=True)
# h2->160*160, hd4->40*40, Pooling 4 times
self.h2_PT_hd4 = nn.MaxPool2d(4, 4, ceil_mode=True)
self.h2_PT_hd4_conv = nn.Conv2d(filters[1], self.CatChannels, 3, padding=1)
self.h2_PT_hd4_bn = nn.BatchNorm2d(self.CatChannels)
self.h2_PT_hd4_relu = nn.ReLU(inplace=True)
# h3->80*80, hd4->40*40, Pooling 2 times
self.h3_PT_hd4 = nn.MaxPool2d(2, 2, ceil_mode=True)
self.h3_PT_hd4_conv = nn.Conv2d(filters[2], self.CatChannels, 3, padding=1)
self.h3_PT_hd4_bn = nn.BatchNorm2d(self.CatChannels)
self.h3_PT_hd4_relu = nn.ReLU(inplace=True)
# h4->40*40, hd4->40*40, Concatenation
self.h4_Cat_hd4_conv = nn.Conv2d(filters[3], self.CatChannels, 3, padding=1)
self.h4_Cat_hd4_bn = nn.BatchNorm2d(self.CatChannels)
self.h4_Cat_hd4_relu = nn.ReLU(inplace=True)
# hd5->20*20, hd4->40*40, Upsample 2 times
self.hd5_UT_hd4 = nn.Upsample(scale_factor=2, mode='bilinear') # 14*14
self.hd5_UT_hd4_conv = nn.Conv2d(filters[4], self.CatChannels, 3, padding=1)
self.hd5_UT_hd4_bn = nn.BatchNorm2d(self.CatChannels)
self.hd5_UT_hd4_relu = nn.ReLU(inplace=True)
# fusion(h1_PT_hd4, h2_PT_hd4, h3_PT_hd4, h4_Cat_hd4, hd5_UT_hd4)
self.conv4d_1 = nn.Conv2d(self.UpChannels, self.UpChannels, 3, padding=1) # 16
self.bn4d_1 = nn.BatchNorm2d(self.UpChannels)
self.relu4d_1 = nn.ReLU(inplace=True)
'''stage 3d'''
# h1->320*320, hd3->80*80, Pooling 4 times
self.h1_PT_hd3 = nn.MaxPool2d(4, 4, ceil_mode=True)
self.h1_PT_hd3_conv = nn.Conv2d(filters[0], self.CatChannels, 3, padding=1)
self.h1_PT_hd3_bn = nn.BatchNorm2d(self.CatChannels)
self.h1_PT_hd3_relu = nn.ReLU(inplace=True)
# h2->160*160, hd3->80*80, Pooling 2 times
self.h2_PT_hd3 = nn.MaxPool2d(2, 2, ceil_mode=True)
self.h2_PT_hd3_conv = nn.Conv2d(filters[1], self.CatChannels, 3, padding=1)
self.h2_PT_hd3_bn = nn.BatchNorm2d(self.CatChannels)
self.h2_PT_hd3_relu = nn.ReLU(inplace=True)
# h3->80*80, hd3->80*80, Concatenation
self.h3_Cat_hd3_conv = nn.Conv2d(filters[2], self.CatChannels, 3, padding=1)
self.h3_Cat_hd3_bn = nn.BatchNorm2d(self.CatChannels)
self.h3_Cat_hd3_relu = nn.ReLU(inplace=True)
# hd4->40*40, hd4->80*80, Upsample 2 times
self.hd4_UT_hd3 = nn.Upsample(scale_factor=2, mode='bilinear') # 14*14
self.hd4_UT_hd3_conv = nn.Conv2d(self.UpChannels, self.CatChannels, 3, padding=1)
self.hd4_UT_hd3_bn = nn.BatchNorm2d(self.CatChannels)
self.hd4_UT_hd3_relu = nn.ReLU(inplace=True)
# hd5->20*20, hd4->80*80, Upsample 4 times
self.hd5_UT_hd3 = nn.Upsample(scale_factor=4, mode='bilinear') # 14*14
self.hd5_UT_hd3_conv = nn.Conv2d(filters[4], self.CatChannels, 3, padding=1)
self.hd5_UT_hd3_bn = nn.BatchNorm2d(self.CatChannels)
self.hd5_UT_hd3_relu = nn.ReLU(inplace=True)
# fusion(h1_PT_hd3, h2_PT_hd3, h3_Cat_hd3, hd4_UT_hd3, hd5_UT_hd3)
self.conv3d_1 = nn.Conv2d(self.UpChannels, self.UpChannels, 3, padding=1) # 16
self.bn3d_1 = nn.BatchNorm2d(self.UpChannels)
self.relu3d_1 = nn.ReLU(inplace=True)
'''stage 2d '''
# h1->320*320, hd2->160*160, Pooling 2 times
self.h1_PT_hd2 = nn.MaxPool2d(2, 2, ceil_mode=True)
self.h1_PT_hd2_conv = nn.Conv2d(filters[0], self.CatChannels, 3, padding=1)
self.h1_PT_hd2_bn = nn.BatchNorm2d(self.CatChannels)
self.h1_PT_hd2_relu = nn.ReLU(inplace=True)
# h2->160*160, hd2->160*160, Concatenation
self.h2_Cat_hd2_conv = nn.Conv2d(filters[1], self.CatChannels, 3, padding=1)
self.h2_Cat_hd2_bn = nn.BatchNorm2d(self.CatChannels)
self.h2_Cat_hd2_relu = nn.ReLU(inplace=True)
# hd3->80*80, hd2->160*160, Upsample 2 times
self.hd3_UT_hd2 = nn.Upsample(scale_factor=2, mode='bilinear') # 14*14
self.hd3_UT_hd2_conv = nn.Conv2d(self.UpChannels, self.CatChannels, 3, padding=1)
self.hd3_UT_hd2_bn = nn.BatchNorm2d(self.CatChannels)
self.hd3_UT_hd2_relu = nn.ReLU(inplace=True)
# hd4->40*40, hd2->160*160, Upsample 4 times
self.hd4_UT_hd2 = nn.Upsample(scale_factor=4, mode='bilinear') # 14*14
self.hd4_UT_hd2_conv = nn.Conv2d(self.UpChannels, self.CatChannels, 3, padding=1)
self.hd4_UT_hd2_bn = nn.BatchNorm2d(self.CatChannels)
self.hd4_UT_hd2_relu = nn.ReLU(inplace=True)
# hd5->20*20, hd2->160*160, Upsample 8 times
self.hd5_UT_hd2 = nn.Upsample(scale_factor=8, mode='bilinear') # 14*14
self.hd5_UT_hd2_conv = nn.Conv2d(filters[4], self.CatChannels, 3, padding=1)
self.hd5_UT_hd2_bn = nn.BatchNorm2d(self.CatChannels)
self.hd5_UT_hd2_relu = nn.ReLU(inplace=True)
# fusion(h1_PT_hd2, h2_Cat_hd2, hd3_UT_hd2, hd4_UT_hd2, hd5_UT_hd2)
self.conv2d_1 = nn.Conv2d(self.UpChannels, self.UpChannels, 3, padding=1) # 16
self.bn2d_1 = nn.BatchNorm2d(self.UpChannels)
self.relu2d_1 = nn.ReLU(inplace=True)
'''stage 1d'''
# h1->320*320, hd1->320*320, Concatenation
self.h1_Cat_hd1_conv = nn.Conv2d(filters[0], self.CatChannels, 3, padding=1)
self.h1_Cat_hd1_bn = nn.BatchNorm2d(self.CatChannels)
self.h1_Cat_hd1_relu = nn.ReLU(inplace=True)
# hd2->160*160, hd1->320*320, Upsample 2 times
self.hd2_UT_hd1 = nn.Upsample(scale_factor=2, mode='bilinear') # 14*14
self.hd2_UT_hd1_conv = nn.Conv2d(self.UpChannels, self.CatChannels, 3, padding=1)
self.hd2_UT_hd1_bn = nn.BatchNorm2d(self.CatChannels)
self.hd2_UT_hd1_relu = nn.ReLU(inplace=True)
# hd3->80*80, hd1->320*320, Upsample 4 times
self.hd3_UT_hd1 = nn.Upsample(scale_factor=4, mode='bilinear') # 14*14
self.hd3_UT_hd1_conv = nn.Conv2d(self.UpChannels, self.CatChannels, 3, padding=1)
self.hd3_UT_hd1_bn = nn.BatchNorm2d(self.CatChannels)
self.hd3_UT_hd1_relu = nn.ReLU(inplace=True)
# hd4->40*40, hd1->320*320, Upsample 8 times
self.hd4_UT_hd1 = nn.Upsample(scale_factor=8, mode='bilinear') # 14*14
self.hd4_UT_hd1_conv = nn.Conv2d(self.UpChannels, self.CatChannels, 3, padding=1)
self.hd4_UT_hd1_bn = nn.BatchNorm2d(self.CatChannels)
self.hd4_UT_hd1_relu = nn.ReLU(inplace=True)
# hd5->20*20, hd1->320*320, Upsample 16 times
self.hd5_UT_hd1 = nn.Upsample(scale_factor=16, mode='bilinear') # 14*14
self.hd5_UT_hd1_conv = nn.Conv2d(filters[4], self.CatChannels, 3, padding=1)
self.hd5_UT_hd1_bn = nn.BatchNorm2d(self.CatChannels)
self.hd5_UT_hd1_relu = nn.ReLU(inplace=True)
# fusion(h1_Cat_hd1, hd2_UT_hd1, hd3_UT_hd1, hd4_UT_hd1, hd5_UT_hd1)
self.conv1d_1 = nn.Conv2d(self.UpChannels, self.UpChannels, 3, padding=1) # 16
self.bn1d_1 = nn.BatchNorm2d(self.UpChannels)
self.relu1d_1 = nn.ReLU(inplace=True)
# output
self.outconv1 = nn.Conv2d(self.UpChannels, n_classes, 3, padding=1)
# initialise weights
for m in self.modules():
if isinstance(m, nn.Conv2d):
init_weights(m, init_type='kaiming')
elif isinstance(m, nn.BatchNorm2d):
init_weights(m, init_type='kaiming')
def forward(self, inputs):
## -------------Encoder-------------
h1 = self.conv1(inputs) # h1->320*320*64
h2 = self.maxpool1(h1)
h2 = self.conv2(h2) # h2->160*160*128
h3 = self.maxpool2(h2)
h3 = self.conv3(h3) # h3->80*80*256
h4 = self.maxpool3(h3)
h4 = self.conv4(h4) # h4->40*40*512
h5 = self.maxpool4(h4)
hd5 = self.conv5(h5) # h5->20*20*1024
## -------------Decoder-------------
h1_PT_hd4 = self.h1_PT_hd4_relu(self.h1_PT_hd4_bn(self.h1_PT_hd4_conv(self.h1_PT_hd4(h1))))
h2_PT_hd4 = self.h2_PT_hd4_relu(self.h2_PT_hd4_bn(self.h2_PT_hd4_conv(self.h2_PT_hd4(h2))))
h3_PT_hd4 = self.h3_PT_hd4_relu(self.h3_PT_hd4_bn(self.h3_PT_hd4_conv(self.h3_PT_hd4(h3))))
h4_Cat_hd4 = self.h4_Cat_hd4_relu(self.h4_Cat_hd4_bn(self.h4_Cat_hd4_conv(h4)))
hd5_UT_hd4 = self.hd5_UT_hd4_relu(self.hd5_UT_hd4_bn(self.hd5_UT_hd4_conv(self.hd5_UT_hd4(hd5))))
hd4 = self.relu4d_1(self.bn4d_1(self.conv4d_1(
torch.cat((h1_PT_hd4, h2_PT_hd4, h3_PT_hd4, h4_Cat_hd4, hd5_UT_hd4), 1)))) # hd4->40*40*UpChannels
h1_PT_hd3 = self.h1_PT_hd3_relu(self.h1_PT_hd3_bn(self.h1_PT_hd3_conv(self.h1_PT_hd3(h1))))
h2_PT_hd3 = self.h2_PT_hd3_relu(self.h2_PT_hd3_bn(self.h2_PT_hd3_conv(self.h2_PT_hd3(h2))))
h3_Cat_hd3 = self.h3_Cat_hd3_relu(self.h3_Cat_hd3_bn(self.h3_Cat_hd3_conv(h3)))
hd4_UT_hd3 = self.hd4_UT_hd3_relu(self.hd4_UT_hd3_bn(self.hd4_UT_hd3_conv(self.hd4_UT_hd3(hd4))))
hd5_UT_hd3 = self.hd5_UT_hd3_relu(self.hd5_UT_hd3_bn(self.hd5_UT_hd3_conv(self.hd5_UT_hd3(hd5))))
hd3 = self.relu3d_1(self.bn3d_1(self.conv3d_1(
torch.cat((h1_PT_hd3, h2_PT_hd3, h3_Cat_hd3, hd4_UT_hd3, hd5_UT_hd3), 1)))) # hd3->80*80*UpChannels
h1_PT_hd2 = self.h1_PT_hd2_relu(self.h1_PT_hd2_bn(self.h1_PT_hd2_conv(self.h1_PT_hd2(h1))))
h2_Cat_hd2 = self.h2_Cat_hd2_relu(self.h2_Cat_hd2_bn(self.h2_Cat_hd2_conv(h2)))
hd3_UT_hd2 = self.hd3_UT_hd2_relu(self.hd3_UT_hd2_bn(self.hd3_UT_hd2_conv(self.hd3_UT_hd2(hd3))))
hd4_UT_hd2 = self.hd4_UT_hd2_relu(self.hd4_UT_hd2_bn(self.hd4_UT_hd2_conv(self.hd4_UT_hd2(hd4))))
hd5_UT_hd2 = self.hd5_UT_hd2_relu(self.hd5_UT_hd2_bn(self.hd5_UT_hd2_conv(self.hd5_UT_hd2(hd5))))
hd2 = self.relu2d_1(self.bn2d_1(self.conv2d_1(
torch.cat((h1_PT_hd2, h2_Cat_hd2, hd3_UT_hd2, hd4_UT_hd2, hd5_UT_hd2), 1)))) # hd2->160*160*UpChannels
h1_Cat_hd1 = self.h1_Cat_hd1_relu(self.h1_Cat_hd1_bn(self.h1_Cat_hd1_conv(h1)))
hd2_UT_hd1 = self.hd2_UT_hd1_relu(self.hd2_UT_hd1_bn(self.hd2_UT_hd1_conv(self.hd2_UT_hd1(hd2))))
hd3_UT_hd1 = self.hd3_UT_hd1_relu(self.hd3_UT_hd1_bn(self.hd3_UT_hd1_conv(self.hd3_UT_hd1(hd3))))
hd4_UT_hd1 = self.hd4_UT_hd1_relu(self.hd4_UT_hd1_bn(self.hd4_UT_hd1_conv(self.hd4_UT_hd1(hd4))))
hd5_UT_hd1 = self.hd5_UT_hd1_relu(self.hd5_UT_hd1_bn(self.hd5_UT_hd1_conv(self.hd5_UT_hd1(hd5))))
hd1 = self.relu1d_1(self.bn1d_1(self.conv1d_1(
torch.cat((h1_Cat_hd1, hd2_UT_hd1, hd3_UT_hd1, hd4_UT_hd1, hd5_UT_hd1), 1)))) # hd1->320*320*UpChannels
d1 = self.outconv1(hd1) # d1->320*320*n_classes
return d1
'''
UNet 3+ with deep supervision
'''
class UNet_3Plus_DeepSup(nn.Module):
def __init__(self, in_channels=3, n_classes=1, feature_scale=4, is_deconv=True, is_batchnorm=True):
super(UNet_3Plus_DeepSup, self).__init__()
self.is_deconv = is_deconv
self.in_channels = in_channels
self.is_batchnorm = is_batchnorm
self.feature_scale = feature_scale
filters = [32, 64, 128, 256, 512]
## -------------Encoder--------------
self.conv1 = unetConv2(self.in_channels, filters[0], self.is_batchnorm)
self.maxpool1 = nn.MaxPool2d(kernel_size=2)
self.conv2 = unetConv2(filters[0], filters[1], self.is_batchnorm)
self.maxpool2 = nn.MaxPool2d(kernel_size=2)
self.conv3 = unetConv2(filters[1], filters[2], self.is_batchnorm)
self.maxpool3 = nn.MaxPool2d(kernel_size=2)
self.conv4 = unetConv2(filters[2], filters[3], self.is_batchnorm)
self.maxpool4 = nn.MaxPool2d(kernel_size=2)
self.conv5 = unetConv2(filters[3], filters[4], self.is_batchnorm)
## -------------Decoder--------------
self.CatChannels = filters[0]
self.CatBlocks = 5
self.UpChannels = self.CatChannels * self.CatBlocks
'''stage 4d'''
# h1->320*320, hd4->40*40, Pooling 8 times
self.h1_PT_hd4 = nn.MaxPool2d(8, 8, ceil_mode=True)
self.h1_PT_hd4_conv = nn.Conv2d(filters[0], self.CatChannels, 3, padding=1)
self.h1_PT_hd4_bn = nn.BatchNorm2d(self.CatChannels)
self.h1_PT_hd4_relu = nn.ReLU(inplace=True)
# h2->160*160, hd4->40*40, Pooling 4 times
self.h2_PT_hd4 = nn.MaxPool2d(4, 4, ceil_mode=True)
self.h2_PT_hd4_conv = nn.Conv2d(filters[1], self.CatChannels, 3, padding=1)
self.h2_PT_hd4_bn = nn.BatchNorm2d(self.CatChannels)
self.h2_PT_hd4_relu = nn.ReLU(inplace=True)
# h3->80*80, hd4->40*40, Pooling 2 times
self.h3_PT_hd4 = nn.MaxPool2d(2, 2, ceil_mode=True)
self.h3_PT_hd4_conv = nn.Conv2d(filters[2], self.CatChannels, 3, padding=1)
self.h3_PT_hd4_bn = nn.BatchNorm2d(self.CatChannels)
self.h3_PT_hd4_relu = nn.ReLU(inplace=True)
# h4->40*40, hd4->40*40, Concatenation
self.h4_Cat_hd4_conv = nn.Conv2d(filters[3], self.CatChannels, 3, padding=1)
self.h4_Cat_hd4_bn = nn.BatchNorm2d(self.CatChannels)
self.h4_Cat_hd4_relu = nn.ReLU(inplace=True)
# hd5->20*20, hd4->40*40, Upsample 2 times
self.hd5_UT_hd4 = nn.Upsample(scale_factor=2, mode='bilinear') # 14*14
self.hd5_UT_hd4_conv = nn.Conv2d(filters[4], self.CatChannels, 3, padding=1)
self.hd5_UT_hd4_bn = nn.BatchNorm2d(self.CatChannels)
self.hd5_UT_hd4_relu = nn.ReLU(inplace=True)
# fusion(h1_PT_hd4, h2_PT_hd4, h3_PT_hd4, h4_Cat_hd4, hd5_UT_hd4)
self.conv4d_1 = nn.Conv2d(self.UpChannels, self.UpChannels, 3, padding=1) # 16
self.bn4d_1 = nn.BatchNorm2d(self.UpChannels)
self.relu4d_1 = nn.ReLU(inplace=True)
'''stage 3d'''
# h1->320*320, hd3->80*80, Pooling 4 times
self.h1_PT_hd3 = nn.MaxPool2d(4, 4, ceil_mode=True)
self.h1_PT_hd3_conv = nn.Conv2d(filters[0], self.CatChannels, 3, padding=1)
self.h1_PT_hd3_bn = nn.BatchNorm2d(self.CatChannels)
self.h1_PT_hd3_relu = nn.ReLU(inplace=True)
# h2->160*160, hd3->80*80, Pooling 2 times
self.h2_PT_hd3 = nn.MaxPool2d(2, 2, ceil_mode=True)
self.h2_PT_hd3_conv = nn.Conv2d(filters[1], self.CatChannels, 3, padding=1)
self.h2_PT_hd3_bn = nn.BatchNorm2d(self.CatChannels)
self.h2_PT_hd3_relu = nn.ReLU(inplace=True)
# h3->80*80, hd3->80*80, Concatenation
self.h3_Cat_hd3_conv = nn.Conv2d(filters[2], self.CatChannels, 3, padding=1)
self.h3_Cat_hd3_bn = nn.BatchNorm2d(self.CatChannels)
self.h3_Cat_hd3_relu = nn.ReLU(inplace=True)
# hd4->40*40, hd4->80*80, Upsample 2 times
self.hd4_UT_hd3 = nn.Upsample(scale_factor=2, mode='bilinear') # 14*14
self.hd4_UT_hd3_conv = nn.Conv2d(self.UpChannels, self.CatChannels, 3, padding=1)
self.hd4_UT_hd3_bn = nn.BatchNorm2d(self.CatChannels)
self.hd4_UT_hd3_relu = nn.ReLU(inplace=True)
# hd5->20*20, hd4->80*80, Upsample 4 times
self.hd5_UT_hd3 = nn.Upsample(scale_factor=4, mode='bilinear') # 14*14
self.hd5_UT_hd3_conv = nn.Conv2d(filters[4], self.CatChannels, 3, padding=1)
self.hd5_UT_hd3_bn = nn.BatchNorm2d(self.CatChannels)
self.hd5_UT_hd3_relu = nn.ReLU(inplace=True)
# fusion(h1_PT_hd3, h2_PT_hd3, h3_Cat_hd3, hd4_UT_hd3, hd5_UT_hd3)
self.conv3d_1 = nn.Conv2d(self.UpChannels, self.UpChannels, 3, padding=1) # 16
self.bn3d_1 = nn.BatchNorm2d(self.UpChannels)
self.relu3d_1 = nn.ReLU(inplace=True)
'''stage 2d '''
# h1->320*320, hd2->160*160, Pooling 2 times
self.h1_PT_hd2 = nn.MaxPool2d(2, 2, ceil_mode=True)
self.h1_PT_hd2_conv = nn.Conv2d(filters[0], self.CatChannels, 3, padding=1)
self.h1_PT_hd2_bn = nn.BatchNorm2d(self.CatChannels)
self.h1_PT_hd2_relu = nn.ReLU(inplace=True)
# h2->160*160, hd2->160*160, Concatenation
self.h2_Cat_hd2_conv = nn.Conv2d(filters[1], self.CatChannels, 3, padding=1)
self.h2_Cat_hd2_bn = nn.BatchNorm2d(self.CatChannels)
self.h2_Cat_hd2_relu = nn.ReLU(inplace=True)
# hd3->80*80, hd2->160*160, Upsample 2 times
self.hd3_UT_hd2 = nn.Upsample(scale_factor=2, mode='bilinear') # 14*14
self.hd3_UT_hd2_conv = nn.Conv2d(self.UpChannels, self.CatChannels, 3, padding=1)
self.hd3_UT_hd2_bn = nn.BatchNorm2d(self.CatChannels)
self.hd3_UT_hd2_relu = nn.ReLU(inplace=True)
# hd4->40*40, hd2->160*160, Upsample 4 times
self.hd4_UT_hd2 = nn.Upsample(scale_factor=4, mode='bilinear') # 14*14
self.hd4_UT_hd2_conv = nn.Conv2d(self.UpChannels, self.CatChannels, 3, padding=1)
self.hd4_UT_hd2_bn = nn.BatchNorm2d(self.CatChannels)
self.hd4_UT_hd2_relu = nn.ReLU(inplace=True)
# hd5->20*20, hd2->160*160, Upsample 8 times
self.hd5_UT_hd2 = nn.Upsample(scale_factor=8, mode='bilinear') # 14*14
self.hd5_UT_hd2_conv = nn.Conv2d(filters[4], self.CatChannels, 3, padding=1)
self.hd5_UT_hd2_bn = nn.BatchNorm2d(self.CatChannels)
self.hd5_UT_hd2_relu = nn.ReLU(inplace=True)
# fusion(h1_PT_hd2, h2_Cat_hd2, hd3_UT_hd2, hd4_UT_hd2, hd5_UT_hd2)
self.conv2d_1 = nn.Conv2d(self.UpChannels, self.UpChannels, 3, padding=1) # 16
self.bn2d_1 = nn.BatchNorm2d(self.UpChannels)
self.relu2d_1 = nn.ReLU(inplace=True)
'''stage 1d'''
# h1->320*320, hd1->320*320, Concatenation
self.h1_Cat_hd1_conv = nn.Conv2d(filters[0], self.CatChannels, 3, padding=1)
self.h1_Cat_hd1_bn = nn.BatchNorm2d(self.CatChannels)
self.h1_Cat_hd1_relu = nn.ReLU(inplace=True)
# hd2->160*160, hd1->320*320, Upsample 2 times
self.hd2_UT_hd1 = nn.Upsample(scale_factor=2, mode='bilinear') # 14*14
self.hd2_UT_hd1_conv = nn.Conv2d(self.UpChannels, self.CatChannels, 3, padding=1)
self.hd2_UT_hd1_bn = nn.BatchNorm2d(self.CatChannels)
self.hd2_UT_hd1_relu = nn.ReLU(inplace=True)
# hd3->80*80, hd1->320*320, Upsample 4 times
self.hd3_UT_hd1 = nn.Upsample(scale_factor=4, mode='bilinear') # 14*14
self.hd3_UT_hd1_conv = nn.Conv2d(self.UpChannels, self.CatChannels, 3, padding=1)
self.hd3_UT_hd1_bn = nn.BatchNorm2d(self.CatChannels)
self.hd3_UT_hd1_relu = nn.ReLU(inplace=True)
# hd4->40*40, hd1->320*320, Upsample 8 times
self.hd4_UT_hd1 = nn.Upsample(scale_factor=8, mode='bilinear') # 14*14
self.hd4_UT_hd1_conv = nn.Conv2d(self.UpChannels, self.CatChannels, 3, padding=1)
self.hd4_UT_hd1_bn = nn.BatchNorm2d(self.CatChannels)
self.hd4_UT_hd1_relu = nn.ReLU(inplace=True)
# hd5->20*20, hd1->320*320, Upsample 16 times
self.hd5_UT_hd1 = nn.Upsample(scale_factor=16, mode='bilinear') # 14*14
self.hd5_UT_hd1_conv = nn.Conv2d(filters[4], self.CatChannels, 3, padding=1)
self.hd5_UT_hd1_bn = nn.BatchNorm2d(self.CatChannels)
self.hd5_UT_hd1_relu = nn.ReLU(inplace=True)
# fusion(h1_Cat_hd1, hd2_UT_hd1, hd3_UT_hd1, hd4_UT_hd1, hd5_UT_hd1)
self.conv1d_1 = nn.Conv2d(self.UpChannels, self.UpChannels, 3, padding=1) # 16
self.bn1d_1 = nn.BatchNorm2d(self.UpChannels)
self.relu1d_1 = nn.ReLU(inplace=True)
# -------------Bilinear Upsampling--------------
self.upscore6 = nn.Upsample(scale_factor=32, mode='bilinear') ###
self.upscore5 = nn.Upsample(scale_factor=16, mode='bilinear')
self.upscore4 = nn.Upsample(scale_factor=8, mode='bilinear')
self.upscore3 = nn.Upsample(scale_factor=4, mode='bilinear')
self.upscore2 = nn.Upsample(scale_factor=2, mode='bilinear')
# DeepSup
self.outconv1 = nn.Conv2d(self.UpChannels, n_classes, 3, padding=1)
self.outconv2 = nn.Conv2d(self.UpChannels, n_classes, 3, padding=1)
self.outconv3 = nn.Conv2d(self.UpChannels, n_classes, 3, padding=1)
self.outconv4 = nn.Conv2d(self.UpChannels, n_classes, 3, padding=1)
self.outconv5 = nn.Conv2d(filters[4], n_classes, 3, padding=1)
# initialise weights
for m in self.modules():
if isinstance(m, nn.Conv2d):
init_weights(m, init_type='kaiming')
elif isinstance(m, nn.BatchNorm2d):
init_weights(m, init_type='kaiming')
def forward(self, inputs):
## -------------Encoder-------------
h1 = self.conv1(inputs) # h1->320*320*64
h2 = self.maxpool1(h1)
h2 = self.conv2(h2) # h2->160*160*128
h3 = self.maxpool2(h2)
h3 = self.conv3(h3) # h3->80*80*256
h4 = self.maxpool3(h3)
h4 = self.conv4(h4) # h4->40*40*512
h5 = self.maxpool4(h4)
hd5 = self.conv5(h5) # h5->20*20*1024
## -------------Decoder-------------
h1_PT_hd4 = self.h1_PT_hd4_relu(self.h1_PT_hd4_bn(self.h1_PT_hd4_conv(self.h1_PT_hd4(h1))))
h2_PT_hd4 = self.h2_PT_hd4_relu(self.h2_PT_hd4_bn(self.h2_PT_hd4_conv(self.h2_PT_hd4(h2))))
h3_PT_hd4 = self.h3_PT_hd4_relu(self.h3_PT_hd4_bn(self.h3_PT_hd4_conv(self.h3_PT_hd4(h3))))
h4_Cat_hd4 = self.h4_Cat_hd4_relu(self.h4_Cat_hd4_bn(self.h4_Cat_hd4_conv(h4)))
hd5_UT_hd4 = self.hd5_UT_hd4_relu(self.hd5_UT_hd4_bn(self.hd5_UT_hd4_conv(self.hd5_UT_hd4(hd5))))
hd4 = self.relu4d_1(self.bn4d_1(self.conv4d_1(
torch.cat((h1_PT_hd4, h2_PT_hd4, h3_PT_hd4, h4_Cat_hd4, hd5_UT_hd4), 1)))) # hd4->40*40*UpChannels
h1_PT_hd3 = self.h1_PT_hd3_relu(self.h1_PT_hd3_bn(self.h1_PT_hd3_conv(self.h1_PT_hd3(h1))))
h2_PT_hd3 = self.h2_PT_hd3_relu(self.h2_PT_hd3_bn(self.h2_PT_hd3_conv(self.h2_PT_hd3(h2))))
h3_Cat_hd3 = self.h3_Cat_hd3_relu(self.h3_Cat_hd3_bn(self.h3_Cat_hd3_conv(h3)))
hd4_UT_hd3 = self.hd4_UT_hd3_relu(self.hd4_UT_hd3_bn(self.hd4_UT_hd3_conv(self.hd4_UT_hd3(hd4))))
hd5_UT_hd3 = self.hd5_UT_hd3_relu(self.hd5_UT_hd3_bn(self.hd5_UT_hd3_conv(self.hd5_UT_hd3(hd5))))
hd3 = self.relu3d_1(self.bn3d_1(self.conv3d_1(
torch.cat((h1_PT_hd3, h2_PT_hd3, h3_Cat_hd3, hd4_UT_hd3, hd5_UT_hd3), 1)))) # hd3->80*80*UpChannels
h1_PT_hd2 = self.h1_PT_hd2_relu(self.h1_PT_hd2_bn(self.h1_PT_hd2_conv(self.h1_PT_hd2(h1))))
h2_Cat_hd2 = self.h2_Cat_hd2_relu(self.h2_Cat_hd2_bn(self.h2_Cat_hd2_conv(h2)))
hd3_UT_hd2 = self.hd3_UT_hd2_relu(self.hd3_UT_hd2_bn(self.hd3_UT_hd2_conv(self.hd3_UT_hd2(hd3))))
hd4_UT_hd2 = self.hd4_UT_hd2_relu(self.hd4_UT_hd2_bn(self.hd4_UT_hd2_conv(self.hd4_UT_hd2(hd4))))
hd5_UT_hd2 = self.hd5_UT_hd2_relu(self.hd5_UT_hd2_bn(self.hd5_UT_hd2_conv(self.hd5_UT_hd2(hd5))))
hd2 = self.relu2d_1(self.bn2d_1(self.conv2d_1(
torch.cat((h1_PT_hd2, h2_Cat_hd2, hd3_UT_hd2, hd4_UT_hd2, hd5_UT_hd2), 1)))) # hd2->160*160*UpChannels
h1_Cat_hd1 = self.h1_Cat_hd1_relu(self.h1_Cat_hd1_bn(self.h1_Cat_hd1_conv(h1)))
hd2_UT_hd1 = self.hd2_UT_hd1_relu(self.hd2_UT_hd1_bn(self.hd2_UT_hd1_conv(self.hd2_UT_hd1(hd2))))
hd3_UT_hd1 = self.hd3_UT_hd1_relu(self.hd3_UT_hd1_bn(self.hd3_UT_hd1_conv(self.hd3_UT_hd1(hd3))))
hd4_UT_hd1 = self.hd4_UT_hd1_relu(self.hd4_UT_hd1_bn(self.hd4_UT_hd1_conv(self.hd4_UT_hd1(hd4))))
hd5_UT_hd1 = self.hd5_UT_hd1_relu(self.hd5_UT_hd1_bn(self.hd5_UT_hd1_conv(self.hd5_UT_hd1(hd5))))
hd1 = self.relu1d_1(self.bn1d_1(self.conv1d_1(
torch.cat((h1_Cat_hd1, hd2_UT_hd1, hd3_UT_hd1, hd4_UT_hd1, hd5_UT_hd1), 1)))) # hd1->320*320*UpChannels
d5 = self.outconv5(hd5)
d5 = self.upscore5(d5) # 16->256
d4 = self.outconv4(hd4)
d4 = self.upscore4(d4) # 32->256
d3 = self.outconv3(hd3)
d3 = self.upscore3(d3) # 64->256
d2 = self.outconv2(hd2)
d2 = self.upscore2(d2) # 128->256
d1 = self.outconv1(hd1) # 256
return F.sigmoid(d1), F.sigmoid(d2), F.sigmoid(d3), F.sigmoid(d4), F.sigmoid(d5)
'''
UNet 3+ with deep supervision and class-guided module
'''
class UNet_3Plus_DeepSup_CGM(nn.Module):
def __init__(self, in_channels=3, n_classes=1, feature_scale=4, is_deconv=True, is_batchnorm=True):
super(UNet_3Plus_DeepSup_CGM, self).__init__()
self.is_deconv = is_deconv
self.in_channels = in_channels
self.is_batchnorm = is_batchnorm
self.feature_scale = feature_scale
filters = [64, 128, 256, 512, 1024]
## -------------Encoder--------------
self.conv1 = unetConv2(self.in_channels, filters[0], self.is_batchnorm)
self.maxpool1 = nn.MaxPool2d(kernel_size=2)
self.conv2 = unetConv2(filters[0], filters[1], self.is_batchnorm)
self.maxpool2 = nn.MaxPool2d(kernel_size=2)
self.conv3 = unetConv2(filters[1], filters[2], self.is_batchnorm)
self.maxpool3 = nn.MaxPool2d(kernel_size=2)
self.conv4 = unetConv2(filters[2], filters[3], self.is_batchnorm)
self.maxpool4 = nn.MaxPool2d(kernel_size=2)
self.conv5 = unetConv2(filters[3], filters[4], self.is_batchnorm)
## -------------Decoder--------------
self.CatChannels = filters[0]
self.CatBlocks = 5
self.UpChannels = self.CatChannels * self.CatBlocks
'''stage 4d'''
# h1->320*320, hd4->40*40, Pooling 8 times
self.h1_PT_hd4 = nn.MaxPool2d(8, 8, ceil_mode=True)
self.h1_PT_hd4_conv = nn.Conv2d(filters[0], self.CatChannels, 3, padding=1)
self.h1_PT_hd4_bn = nn.BatchNorm2d(self.CatChannels)
self.h1_PT_hd4_relu = nn.ReLU(inplace=True)
# h2->160*160, hd4->40*40, Pooling 4 times
self.h2_PT_hd4 = nn.MaxPool2d(4, 4, ceil_mode=True)
self.h2_PT_hd4_conv = nn.Conv2d(filters[1], self.CatChannels, 3, padding=1)
self.h2_PT_hd4_bn = nn.BatchNorm2d(self.CatChannels)
self.h2_PT_hd4_relu = nn.ReLU(inplace=True)
# h3->80*80, hd4->40*40, Pooling 2 times
self.h3_PT_hd4 = nn.MaxPool2d(2, 2, ceil_mode=True)
self.h3_PT_hd4_conv = nn.Conv2d(filters[2], self.CatChannels, 3, padding=1)
self.h3_PT_hd4_bn = nn.BatchNorm2d(self.CatChannels)
self.h3_PT_hd4_relu = nn.ReLU(inplace=True)
# h4->40*40, hd4->40*40, Concatenation
self.h4_Cat_hd4_conv = nn.Conv2d(filters[3], self.CatChannels, 3, padding=1)
self.h4_Cat_hd4_bn = nn.BatchNorm2d(self.CatChannels)
self.h4_Cat_hd4_relu = nn.ReLU(inplace=True)
# hd5->20*20, hd4->40*40, Upsample 2 times
self.hd5_UT_hd4 = nn.Upsample(scale_factor=2, mode='bilinear') # 14*14
self.hd5_UT_hd4_conv = nn.Conv2d(filters[4], self.CatChannels, 3, padding=1)
self.hd5_UT_hd4_bn = nn.BatchNorm2d(self.CatChannels)
self.hd5_UT_hd4_relu = nn.ReLU(inplace=True)
# fusion(h1_PT_hd4, h2_PT_hd4, h3_PT_hd4, h4_Cat_hd4, hd5_UT_hd4)
self.conv4d_1 = nn.Conv2d(self.UpChannels, self.UpChannels, 3, padding=1) # 16
self.bn4d_1 = nn.BatchNorm2d(self.UpChannels)
self.relu4d_1 = nn.ReLU(inplace=True)
'''stage 3d'''
# h1->320*320, hd3->80*80, Pooling 4 times
self.h1_PT_hd3 = nn.MaxPool2d(4, 4, ceil_mode=True)
self.h1_PT_hd3_conv = nn.Conv2d(filters[0], self.CatChannels, 3, padding=1)
self.h1_PT_hd3_bn = nn.BatchNorm2d(self.CatChannels)
self.h1_PT_hd3_relu = nn.ReLU(inplace=True)
# h2->160*160, hd3->80*80, Pooling 2 times
self.h2_PT_hd3 = nn.MaxPool2d(2, 2, ceil_mode=True)
self.h2_PT_hd3_conv = nn.Conv2d(filters[1], self.CatChannels, 3, padding=1)
self.h2_PT_hd3_bn = nn.BatchNorm2d(self.CatChannels)
self.h2_PT_hd3_relu = nn.ReLU(inplace=True)
# h3->80*80, hd3->80*80, Concatenation
self.h3_Cat_hd3_conv = nn.Conv2d(filters[2], self.CatChannels, 3, padding=1)
self.h3_Cat_hd3_bn = nn.BatchNorm2d(self.CatChannels)
self.h3_Cat_hd3_relu = nn.ReLU(inplace=True)
# hd4->40*40, hd4->80*80, Upsample 2 times
self.hd4_UT_hd3 = nn.Upsample(scale_factor=2, mode='bilinear') # 14*14
self.hd4_UT_hd3_conv = nn.Conv2d(self.UpChannels, self.CatChannels, 3, padding=1)
self.hd4_UT_hd3_bn = nn.BatchNorm2d(self.CatChannels)
self.hd4_UT_hd3_relu = nn.ReLU(inplace=True)
# hd5->20*20, hd4->80*80, Upsample 4 times
self.hd5_UT_hd3 = nn.Upsample(scale_factor=4, mode='bilinear') # 14*14
self.hd5_UT_hd3_conv = nn.Conv2d(filters[4], self.CatChannels, 3, padding=1)
self.hd5_UT_hd3_bn = nn.BatchNorm2d(self.CatChannels)
self.hd5_UT_hd3_relu = nn.ReLU(inplace=True)
# fusion(h1_PT_hd3, h2_PT_hd3, h3_Cat_hd3, hd4_UT_hd3, hd5_UT_hd3)
self.conv3d_1 = nn.Conv2d(self.UpChannels, self.UpChannels, 3, padding=1) # 16
self.bn3d_1 = nn.BatchNorm2d(self.UpChannels)
self.relu3d_1 = nn.ReLU(inplace=True)
'''stage 2d '''
# h1->320*320, hd2->160*160, Pooling 2 times
self.h1_PT_hd2 = nn.MaxPool2d(2, 2, ceil_mode=True)
self.h1_PT_hd2_conv = nn.Conv2d(filters[0], self.CatChannels, 3, padding=1)
self.h1_PT_hd2_bn = nn.BatchNorm2d(self.CatChannels)
self.h1_PT_hd2_relu = nn.ReLU(inplace=True)
# h2->160*160, hd2->160*160, Concatenation
self.h2_Cat_hd2_conv = nn.Conv2d(filters[1], self.CatChannels, 3, padding=1)
self.h2_Cat_hd2_bn = nn.BatchNorm2d(self.CatChannels)
self.h2_Cat_hd2_relu = nn.ReLU(inplace=True)
# hd3->80*80, hd2->160*160, Upsample 2 times
self.hd3_UT_hd2 = nn.Upsample(scale_factor=2, mode='bilinear') # 14*14
self.hd3_UT_hd2_conv = nn.Conv2d(self.UpChannels, self.CatChannels, 3, padding=1)
self.hd3_UT_hd2_bn = nn.BatchNorm2d(self.CatChannels)
self.hd3_UT_hd2_relu = nn.ReLU(inplace=True)
# hd4->40*40, hd2->160*160, Upsample 4 times
self.hd4_UT_hd2 = nn.Upsample(scale_factor=4, mode='bilinear') # 14*14
self.hd4_UT_hd2_conv = nn.Conv2d(self.UpChannels, self.CatChannels, 3, padding=1)
self.hd4_UT_hd2_bn = nn.BatchNorm2d(self.CatChannels)
self.hd4_UT_hd2_relu = nn.ReLU(inplace=True)
# hd5->20*20, hd2->160*160, Upsample 8 times
self.hd5_UT_hd2 = nn.Upsample(scale_factor=8, mode='bilinear') # 14*14
self.hd5_UT_hd2_conv = nn.Conv2d(filters[4], self.CatChannels, 3, padding=1)
self.hd5_UT_hd2_bn = nn.BatchNorm2d(self.CatChannels)
self.hd5_UT_hd2_relu = nn.ReLU(inplace=True)
# fusion(h1_PT_hd2, h2_Cat_hd2, hd3_UT_hd2, hd4_UT_hd2, hd5_UT_hd2)
self.conv2d_1 = nn.Conv2d(self.UpChannels, self.UpChannels, 3, padding=1) # 16
self.bn2d_1 = nn.BatchNorm2d(self.UpChannels)
self.relu2d_1 = nn.ReLU(inplace=True)
'''stage 1d'''
# h1->320*320, hd1->320*320, Concatenation
self.h1_Cat_hd1_conv = nn.Conv2d(filters[0], self.CatChannels, 3, padding=1)
self.h1_Cat_hd1_bn = nn.BatchNorm2d(self.CatChannels)
self.h1_Cat_hd1_relu = nn.ReLU(inplace=True)
# hd2->160*160, hd1->320*320, Upsample 2 times
self.hd2_UT_hd1 = nn.Upsample(scale_factor=2, mode='bilinear') # 14*14
self.hd2_UT_hd1_conv = nn.Conv2d(self.UpChannels, self.CatChannels, 3, padding=1)
self.hd2_UT_hd1_bn = nn.BatchNorm2d(self.CatChannels)
self.hd2_UT_hd1_relu = nn.ReLU(inplace=True)
# hd3->80*80, hd1->320*320, Upsample 4 times
self.hd3_UT_hd1 = nn.Upsample(scale_factor=4, mode='bilinear') # 14*14
self.hd3_UT_hd1_conv = nn.Conv2d(self.UpChannels, self.CatChannels, 3, padding=1)
self.hd3_UT_hd1_bn = nn.BatchNorm2d(self.CatChannels)
self.hd3_UT_hd1_relu = nn.ReLU(inplace=True)
# hd4->40*40, hd1->320*320, Upsample 8 times
self.hd4_UT_hd1 = nn.Upsample(scale_factor=8, mode='bilinear') # 14*14
self.hd4_UT_hd1_conv = nn.Conv2d(self.UpChannels, self.CatChannels, 3, padding=1)
self.hd4_UT_hd1_bn = nn.BatchNorm2d(self.CatChannels)
self.hd4_UT_hd1_relu = nn.ReLU(inplace=True)
# hd5->20*20, hd1->320*320, Upsample 16 times
self.hd5_UT_hd1 = nn.Upsample(scale_factor=16, mode='bilinear') # 14*14
self.hd5_UT_hd1_conv = nn.Conv2d(filters[4], self.CatChannels, 3, padding=1)
self.hd5_UT_hd1_bn = nn.BatchNorm2d(self.CatChannels)
self.hd5_UT_hd1_relu = nn.ReLU(inplace=True)
# fusion(h1_Cat_hd1, hd2_UT_hd1, hd3_UT_hd1, hd4_UT_hd1, hd5_UT_hd1)
self.conv1d_1 = nn.Conv2d(self.UpChannels, self.UpChannels, 3, padding=1) # 16
self.bn1d_1 = nn.BatchNorm2d(self.UpChannels)
self.relu1d_1 = nn.ReLU(inplace=True)
# -------------Bilinear Upsampling--------------
self.upscore6 = nn.Upsample(scale_factor=32, mode='bilinear') ###
self.upscore5 = nn.Upsample(scale_factor=16, mode='bilinear')
self.upscore4 = nn.Upsample(scale_factor=8, mode='bilinear')
self.upscore3 = nn.Upsample(scale_factor=4, mode='bilinear')
self.upscore2 = nn.Upsample(scale_factor=2, mode='bilinear')
# DeepSup
self.outconv1 = nn.Conv2d(self.UpChannels, n_classes, 3, padding=1)
self.outconv2 = nn.Conv2d(self.UpChannels, n_classes, 3, padding=1)
self.outconv3 = nn.Conv2d(self.UpChannels, n_classes, 3, padding=1)
self.outconv4 = nn.Conv2d(self.UpChannels, n_classes, 3, padding=1)
self.outconv5 = nn.Conv2d(filters[4], n_classes, 3, padding=1)
self.cls = nn.Sequential(
nn.Dropout(p=0.5),
nn.Conv2d(filters[4], 2, 1),
nn.AdaptiveMaxPool2d(1),
nn.Sigmoid())
# initialise weights
for m in self.modules():
if isinstance(m, nn.Conv2d):
init_weights(m, init_type='kaiming')
elif isinstance(m, nn.BatchNorm2d):
init_weights(m, init_type='kaiming')
def dotProduct(self, seg, cls):
B, N, H, W = seg.size()
seg = seg.view(B, N, H * W)
final = torch.einsum("ijk,ij->ijk", [seg, cls])
final = final.view(B, N, H, W)
return final
def forward(self, inputs):
## -------------Encoder-------------
h1 = self.conv1(inputs) # h1->320*320*64
h2 = self.maxpool1(h1)
h2 = self.conv2(h2) # h2->160*160*128
h3 = self.maxpool2(h2)
h3 = self.conv3(h3) # h3->80*80*256
h4 = self.maxpool3(h3)
h4 = self.conv4(h4) # h4->40*40*512
h5 = self.maxpool4(h4)
hd5 = self.conv5(h5) # h5->20*20*1024
# -------------Classification-------------
cls_branch = self.cls(hd5).squeeze(3).squeeze(2) # (B,N,1,1)->(B,N)
cls_branch_max = cls_branch.argmax(dim=1)
cls_branch_max = cls_branch_max[:, np.newaxis].float()
## -------------Decoder-------------
h1_PT_hd4 = self.h1_PT_hd4_relu(self.h1_PT_hd4_bn(self.h1_PT_hd4_conv(self.h1_PT_hd4(h1))))
h2_PT_hd4 = self.h2_PT_hd4_relu(self.h2_PT_hd4_bn(self.h2_PT_hd4_conv(self.h2_PT_hd4(h2))))
h3_PT_hd4 = self.h3_PT_hd4_relu(self.h3_PT_hd4_bn(self.h3_PT_hd4_conv(self.h3_PT_hd4(h3))))
h4_Cat_hd4 = self.h4_Cat_hd4_relu(self.h4_Cat_hd4_bn(self.h4_Cat_hd4_conv(h4)))
hd5_UT_hd4 = self.hd5_UT_hd4_relu(self.hd5_UT_hd4_bn(self.hd5_UT_hd4_conv(self.hd5_UT_hd4(hd5))))
hd4 = self.relu4d_1(self.bn4d_1(self.conv4d_1(
torch.cat((h1_PT_hd4, h2_PT_hd4, h3_PT_hd4, h4_Cat_hd4, hd5_UT_hd4), 1)))) # hd4->40*40*UpChannels
h1_PT_hd3 = self.h1_PT_hd3_relu(self.h1_PT_hd3_bn(self.h1_PT_hd3_conv(self.h1_PT_hd3(h1))))
h2_PT_hd3 = self.h2_PT_hd3_relu(self.h2_PT_hd3_bn(self.h2_PT_hd3_conv(self.h2_PT_hd3(h2))))
h3_Cat_hd3 = self.h3_Cat_hd3_relu(self.h3_Cat_hd3_bn(self.h3_Cat_hd3_conv(h3)))
hd4_UT_hd3 = self.hd4_UT_hd3_relu(self.hd4_UT_hd3_bn(self.hd4_UT_hd3_conv(self.hd4_UT_hd3(hd4))))
hd5_UT_hd3 = self.hd5_UT_hd3_relu(self.hd5_UT_hd3_bn(self.hd5_UT_hd3_conv(self.hd5_UT_hd3(hd5))))
hd3 = self.relu3d_1(self.bn3d_1(self.conv3d_1(
torch.cat((h1_PT_hd3, h2_PT_hd3, h3_Cat_hd3, hd4_UT_hd3, hd5_UT_hd3), 1)))) # hd3->80*80*UpChannels
h1_PT_hd2 = self.h1_PT_hd2_relu(self.h1_PT_hd2_bn(self.h1_PT_hd2_conv(self.h1_PT_hd2(h1))))
h2_Cat_hd2 = self.h2_Cat_hd2_relu(self.h2_Cat_hd2_bn(self.h2_Cat_hd2_conv(h2)))
hd3_UT_hd2 = self.hd3_UT_hd2_relu(self.hd3_UT_hd2_bn(self.hd3_UT_hd2_conv(self.hd3_UT_hd2(hd3))))
hd4_UT_hd2 = self.hd4_UT_hd2_relu(self.hd4_UT_hd2_bn(self.hd4_UT_hd2_conv(self.hd4_UT_hd2(hd4))))
hd5_UT_hd2 = self.hd5_UT_hd2_relu(self.hd5_UT_hd2_bn(self.hd5_UT_hd2_conv(self.hd5_UT_hd2(hd5))))
hd2 = self.relu2d_1(self.bn2d_1(self.conv2d_1(
torch.cat((h1_PT_hd2, h2_Cat_hd2, hd3_UT_hd2, hd4_UT_hd2, hd5_UT_hd2), 1)))) # hd2->160*160*UpChannels
h1_Cat_hd1 = self.h1_Cat_hd1_relu(self.h1_Cat_hd1_bn(self.h1_Cat_hd1_conv(h1)))
hd2_UT_hd1 = self.hd2_UT_hd1_relu(self.hd2_UT_hd1_bn(self.hd2_UT_hd1_conv(self.hd2_UT_hd1(hd2))))
hd3_UT_hd1 = self.hd3_UT_hd1_relu(self.hd3_UT_hd1_bn(self.hd3_UT_hd1_conv(self.hd3_UT_hd1(hd3))))
hd4_UT_hd1 = self.hd4_UT_hd1_relu(self.hd4_UT_hd1_bn(self.hd4_UT_hd1_conv(self.hd4_UT_hd1(hd4))))
hd5_UT_hd1 = self.hd5_UT_hd1_relu(self.hd5_UT_hd1_bn(self.hd5_UT_hd1_conv(self.hd5_UT_hd1(hd5))))
hd1 = self.relu1d_1(self.bn1d_1(self.conv1d_1(
torch.cat((h1_Cat_hd1, hd2_UT_hd1, hd3_UT_hd1, hd4_UT_hd1, hd5_UT_hd1), 1)))) # hd1->320*320*UpChannels
d5 = self.outconv5(hd5)
d5 = self.upscore5(d5) # 16->256
d4 = self.outconv4(hd4)
d4 = self.upscore4(d4) # 32->256
d3 = self.outconv3(hd3)
d3 = self.upscore3(d3) # 64->256
d2 = self.outconv2(hd2)
d2 = self.upscore2(d2) # 128->256
d1 = self.outconv1(hd1) # 256
d1 = self.dotProduct(d1, cls_branch_max)
d2 = self.dotProduct(d2, cls_branch_max)
d3 = self.dotProduct(d3, cls_branch_max)
d4 = self.dotProduct(d4, cls_branch_max)
d5 = self.dotProduct(d5, cls_branch_max)
return F.sigmoid(d1), F.sigmoid(d2), F.sigmoid(d3), F.sigmoid(d4), F.sigmoid(d5)
if __name__ == '__main__':
a_net= UNet_3Plus(3).cuda()
input = torch.randn(1, 3, 320, 320).cuda()
# y = model(input)
# print(y)
# inputs = torch.randn(1, 3, 640, 640).cuda()
for i in range(5):
start = time.time()
output = a_net(input)
print('output.size ', time.time() - start, output[0].size())