brain.py

from config import *

#==========================================================================
class BrainDataset(Dataset):
    '''main class of deep learning to creat and manage final datasets'''
    
    def __init__(self, df, transform=None):
        self.df = df # dataFrame which contains all datasets
        self.transform = transform
        
    def __len__(self):
        '''return the size of datasets'''
        return len(self.df)
    
    def __getitem__(self, idx):
        '''gathering data from dataFrame'''
        
        image = cv2.imread(self.df.iloc[idx, 0])
        image = np.array(image) / 255.
        mask = cv2.imread(self.df.iloc[idx, 1], 0)
        mask = np.array(mask) / 255.
        
        if self.transform != None : 
            augment = self.transform(image=image, mask=mask)
            image = augment['image']
            mask = augment['mask']
            
        # else try to transform the matrix of images :
        image = image.transpose((2,0,1))
        image = torch.from_numpy(image).type(torch.float32)
        image = tt.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225))(image)
        mask = np.expand_dims(mask, axis=-1).transpose((2,0,1))
        mask = torch.from_numpy(mask).type(torch.float32)
        
        return image, mask # final data
    
#==========================================================================
# in this case we are Defining the UNET model

class DoubleConv(nn.Module):
    """(convolution => [BN] => ReLU) * 2"""
    def __init__(self, in_channels, out_channels, mid_channels=None):
        super().__init__()
        if not mid_channels:
            mid_channels = out_channels
        self.double_conv = nn.Sequential(
            nn.Conv2d(in_channels, mid_channels, kernel_size=3, padding=1),
            nn.BatchNorm2d(mid_channels),
            nn.ReLU(inplace=True),
            nn.Conv2d(mid_channels, out_channels, kernel_size=3, padding=1),
            nn.BatchNorm2d(out_channels),
            nn.ReLU(inplace=True))
    def forward(self, x):
        return self.double_conv(x)
    
class Down(nn.Module):
    """Downscaling with maxpool then double conv"""
    def __init__(self, in_channels, out_channels):
        super().__init__()
        self.maxpool_conv = nn.Sequential(
            nn.MaxPool2d(2),
            DoubleConv(in_channels, out_channels))
    def forward(self, x):
        return self.maxpool_conv(x)
    
class Up(nn.Module):
    """Upscaling then double conv"""
    def __init__(self, in_channels, out_channels, bilinear=True):
        super().__init__()
        if bilinear:
            self.up = nn.Upsample(scale_factor=2, mode='bilinear', align_corners=True)
            self.conv = DoubleConv(in_channels, out_channels, in_channels//2)
        else:
            self.up = nn.ConvTranspose2d(in_channels, in_channels//2, kernel_size=2, stride=2)
            self.conv = DoubleConv(in_channels, out_channels)
    def forward(self, x1, x2):
        x1 = self.up(x1)
        diffY = x2.size()[2] - x1.size()[2]
        diffX = x2.size()[3] - x1.size()[3]
        
        x1 = F.pad(x1, [diffX//2, diffX-diffX//2,
                        diffY//2, diffY-diffY//2])
        x = torch.cat([x2, x1], dim=1)
        return self.conv(x)

class OutConv(nn.Module):
    def __init__(self, in_channels, out_channels):
        super(OutConv, self).__init__()
        self.conv = nn.Sequential(
            nn.Conv2d(in_channels, out_channels, kernel_size=1),
            nn.Sigmoid())
    def forward(self, x):
        return self.conv(x)

class UNet(nn.Module):
    def __init__(self, n_channels, n_classes, bilinear=True):
        super(UNet, self).__init__()
        self.n_channels = n_channels
        self.n_classes = n_classes
        self.bilinear = bilinear
        
        self.inc = DoubleConv(n_channels, 64)
        self.down1 = Down(64, 128)
        self.down2 = Down(128, 256)
        self.down3 = Down(256, 512)
        factor = 2 if bilinear else 1
        self.down4 = Down(512, 1024//factor)
        self.up1 = Up(1024, 512//factor, bilinear)
        self.up2 = Up(512, 256//factor, bilinear)        
        self.up3 = Up(256, 128//factor, bilinear)        
        self.up4 = Up(128, 64, bilinear)        
        self.outc = OutConv(64, n_classes)
    
    def forward(self, x):
        x1 = self.inc(x)
        x2 = self.down1(x1)
        x3 = self.down2(x2)
        x4 = self.down3(x3)
        x5 = self.down4(x4)
        x = self.up1(x5, x4)
        x = self.up2(x, x3)
        x = self.up3(x, x2)
        x = self.up4(x, x1)
        logits = self.outc(x)
        return logits