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# -*- coding: utf-8 -*-
# @Time : 2024/8/7 下午3:51
# @Author : xiaoshun
# @Email : [email protected]
# @File : kappamask.py.py
# @Software: PyCharm
import torch
from torch import nn as nn
from torch.nn import functional as F
class KappaMask(nn.Module):
def __init__(self, num_classes=2, in_channels=3):
super().__init__()
self.conv1 = nn.Sequential(
nn.Conv2d(in_channels, 64, 3, 1, 1),
nn.ReLU(inplace=True),
nn.Conv2d(64, 64, 3, 1, 1),
nn.ReLU(inplace=True),
)
self.conv2 = nn.Sequential(
nn.Conv2d(64, 128, 3, 1, 1),
nn.ReLU(inplace=True),
nn.Conv2d(128, 128, 3, 1, 1),
nn.ReLU(inplace=True),
)
self.conv3 = nn.Sequential(
nn.Conv2d(128, 256, 3, 1, 1),
nn.ReLU(inplace=True),
nn.Conv2d(256, 256, 3, 1, 1),
nn.ReLU(inplace=True),
)
self.conv4 = nn.Sequential(
nn.Conv2d(256, 512, 3, 1, 1),
nn.ReLU(inplace=True),
nn.Conv2d(512, 512, 3, 1, 1),
nn.ReLU(inplace=True),
)
self.drop4 = nn.Dropout(0.5)
self.conv5 = nn.Sequential(
nn.Conv2d(512, 1024, 3, 1, 1),
nn.ReLU(inplace=True),
nn.Conv2d(1024, 1024, 3, 1, 1),
nn.ReLU(inplace=True),
)
self.drop5 = nn.Dropout(0.5)
self.up6 = nn.Sequential(
nn.Upsample(scale_factor=2),
nn.ZeroPad2d((0, 1, 0, 1)),
nn.Conv2d(1024, 512, 2),
nn.ReLU(inplace=True)
)
self.conv6 = nn.Sequential(
nn.Conv2d(1024, 512, 3, 1, 1),
nn.ReLU(inplace=True),
nn.Conv2d(512, 512, 3, 1, 1),
nn.ReLU(inplace=True),
)
self.up7 = nn.Sequential(
nn.Upsample(scale_factor=2),
nn.ZeroPad2d((0, 1, 0, 1)),
nn.Conv2d(512, 256, 2),
nn.ReLU(inplace=True)
)
self.conv7 = nn.Sequential(
nn.Conv2d(512, 256, 3, 1, 1),
nn.ReLU(inplace=True),
nn.Conv2d(256, 256, 3, 1, 1),
nn.ReLU(inplace=True),
)
self.up8 = nn.Sequential(
nn.Upsample(scale_factor=2),
nn.ZeroPad2d((0, 1, 0, 1)),
nn.Conv2d(256, 128, 2),
nn.ReLU(inplace=True)
)
self.conv8 = nn.Sequential(
nn.Conv2d(256, 128, 3, 1, 1),
nn.ReLU(inplace=True),
nn.Conv2d(128, 128, 3, 1, 1),
nn.ReLU(inplace=True),
)
self.up9 = nn.Sequential(
nn.Upsample(scale_factor=2),
nn.ZeroPad2d((0, 1, 0, 1)),
nn.Conv2d(128, 64, 2),
nn.ReLU(inplace=True)
)
self.conv9 = nn.Sequential(
nn.Conv2d(128, 64, 3, 1, 1),
nn.ReLU(inplace=True),
nn.Conv2d(64, 64, 3, 1, 1),
nn.ReLU(inplace=True),
nn.Conv2d(64, 2, 3, 1, 1),
nn.ReLU(inplace=True),
)
self.conv10 = nn.Conv2d(2, num_classes, 1)
self.__init_weights()
def __init_weights(self):
for m in self.modules():
if isinstance(m, nn.Conv2d):
nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
def forward(self, x):
conv1 = self.conv1(x)
pool1 = F.max_pool2d(conv1, 2, 2)
conv2 = self.conv2(pool1)
pool2 = F.max_pool2d(conv2, 2, 2)
conv3 = self.conv3(pool2)
pool3 = F.max_pool2d(conv3, 2, 2)
conv4 = self.conv4(pool3)
drop4 = self.drop4(conv4)
pool4 = F.max_pool2d(drop4, 2, 2)
conv5 = self.conv5(pool4)
drop5 = self.drop5(conv5)
up6 = self.up6(drop5)
merge6 = torch.cat((drop4, up6), dim=1)
conv6 = self.conv6(merge6)
up7 = self.up7(conv6)
merge7 = torch.cat((conv3, up7), dim=1)
conv7 = self.conv7(merge7)
up8 = self.up8(conv7)
merge8 = torch.cat((conv2, up8), dim=1)
conv8 = self.conv8(merge8)
up9 = self.up9(conv8)
merge9 = torch.cat((conv1, up9), dim=1)
conv9 = self.conv9(merge9)
output = self.conv10(conv9)
return output
if __name__ == '__main__':
model = KappaMask(num_classes=2, in_channels=3)
fake_data = torch.rand(2, 3, 256, 256)
output = model(fake_data)
print(output.shape) |