model.py

import os
import numpy as np
import torch
import torch.nn as nn

## 네트워크 구축하기
class UNet(nn.Module):
    def __init__(self):
        super(UNet, self).__init__()

        def CBR2d(in_channels, out_channels, kernel_size=3, stride=1, padding=1, bias=True):
            layers = []
            layers += [nn.Conv2d(in_channels=in_channels, out_channels=out_channels,
                                 kernel_size=kernel_size, stride=stride, padding=padding,bias=bias)]
            layers += [nn.BatchNorm2d(num_features=out_channels)]
            layers += [nn.ReLU()]


            cbr = nn.Sequential(*layers)

            return cbr

        def CBR2d_with_sigmoid(in_channels, out_channels, kernel_size=3, stride=1, padding=1, bias=True):
            layers = []
            layers += [nn.Conv2d(in_channels=in_channels, out_channels=out_channels,
                                 kernel_size=kernel_size, stride=stride, padding=padding,bias=bias)]
            layers += [nn.BatchNorm2d(num_features=out_channels)]
            layers += [nn.Sigmoid()]

            cbr = nn.Sequential(*layers)

            return cbr


        # Contracting path = encoder part
        # self.enc1_1 = CBR2d(in_channels=1, out_channels=64, kernel_size=3, stride=1, padding=1, bias=True)
        # self.enc1_2 = CBR2d(in_channels=64, out_channels=64, kernel_size=3, stride=1, padding=1, bias=True)
        # kernel_size, stride, padding, bias는 모두 실험 전에 pretrain으로 설정한 값임.
        # 따라서 코드를 작성할 때, 지워줘도 무방하다.
        self.enc1_1 = CBR2d(in_channels=1, out_channels=64)
        self.enc1_2 = CBR2d(in_channels=64, out_channels=64)

        self.pool1 = nn.MaxPool2d(kernel_size=2)

        self.enc2_1 = CBR2d(in_channels=64, out_channels=128)
        self.enc2_2 = CBR2d(in_channels=128, out_channels=128)

        self.pool2 = nn.MaxPool2d(kernel_size=2)

        self.enc3_1 = CBR2d(in_channels=128, out_channels=256)
        self.enc3_2 = CBR2d(in_channels=256, out_channels=256)

        self.pool3 = nn.MaxPool2d(kernel_size=2)

        self.enc4_1 = CBR2d(in_channels=256, out_channels=512)
        self.enc4_2 = CBR2d(in_channels=512, out_channels=512)

        self.pool4 = nn.MaxPool2d(kernel_size=2)

        self.enc5_1 = CBR2d(in_channels=512, out_channels=1024)

        # Expansive path = decoder part
        self.dec5_1 = CBR2d(in_channels=1024, out_channels=512)

        self.unpool4 = nn.ConvTranspose2d(in_channels=512, out_channels=512,
                                          kernel_size=2, stride=2, padding=0, bias=True)

        self.dec4_2 = CBR2d(in_channels=2*512, out_channels=512)
        self.dec4_1 = CBR2d(in_channels=512, out_channels=256)

        self.unpool3 = nn.ConvTranspose2d(in_channels=256, out_channels=256,
                                          kernel_size=2, stride=2, padding=0, bias=True)
        self.dec3_2 = CBR2d(in_channels=2*256, out_channels=256)
        self.dec3_1 = CBR2d(in_channels=256, out_channels=128)

        self.unpool2 = nn.ConvTranspose2d(in_channels=128, out_channels=128,
                                          kernel_size=2, stride=2, padding=0, bias=True)

        self.dec2_2 = CBR2d(in_channels=2 * 128, out_channels=128)
        self.dec2_1 = CBR2d(in_channels=128, out_channels=64)

        self.unpool1 = nn.ConvTranspose2d(in_channels=64, out_channels=64,
                                          kernel_size=2, stride=2, padding=0, bias=True)

        self.dec1_2 = CBR2d(in_channels=2 * 64, out_channels=64)
        # self.dec1_1 = CBR2d(in_channels=64, out_channels=64)
        self.dec1_1 = CBR2d_with_sigmoid(in_channels=64, out_channels=64)

        ## Segmentation에 필요한 N개의 클래스를 만들어주기 위한 1*1 conv 설정

        self.fc = nn.Conv2d(in_channels=64, out_channels=1, kernel_size=1, stride=1, padding=0, bias=True)

    # Unet 레이어 연걸하기
    # x는 input 이미지
    def forward(self, x):
        enc1_1 = self.enc1_1(x)
        enc1_2 = self.enc1_2(enc1_1)
        pool1 = self.pool1(enc1_2)

        enc2_1 = self.enc2_1(pool1)
        enc2_2 = self.enc2_2(enc2_1)
        pool2 = self.pool2(enc2_2)

        enc3_1 = self.enc3_1(pool2)
        enc3_2 = self.enc3_2(enc3_1)
        pool3 = self.pool3(enc3_2)

        enc4_1 = self.enc4_1(pool3)
        enc4_2 = self.enc4_2(enc4_1)
        pool4 = self.pool4(enc4_2)

        enc5_1 = self.enc5_1(pool4)

        dec5_1 = self.dec5_1(enc5_1)

        unpool4 = self.unpool4(dec5_1)
        cat4 = torch.cat((unpool4, enc4_2), dim=1) # skip connection을 뜻하는 부분. # dim=1은 channel을 의미함.
        dec4_2 = self.dec4_2(cat4)
        dec4_1 = self.dec4_1(dec4_2)

        unpool3 = self.unpool3(dec4_1)
        cat3 = torch.cat((unpool3, enc3_2), dim=1)
        dec3_2 = self.dec3_2(cat3)
        dec3_1 = self.dec3_1(dec3_2)

        unpool2 = self.unpool2(dec3_1)
        cat2 = torch.cat((unpool2, enc2_2), dim=1)
        dec2_2 = self.dec2_2(cat2)
        dec2_1 = self.dec2_1(dec2_2)

        unpool1 = self.unpool1(dec2_1)
        cat1 = torch.cat((unpool1, enc1_2), dim=1)
        dec1_2 = self.dec1_2(cat1)
        dec1_1 = self.dec1_1(dec1_2)

        x = self.fc(dec1_1)

        return x