# clone 項(xiàng)目git clone https://github.com/leijue222/portrait-matting-unet-flask.git
# 進(jìn)入到文件夾中cd portrait-matting-unet-flask/
#?準(zhǔn)備好一張待分割的人像圖片，運(yùn)行下面的代碼即可生成mask并保存python?predict.py?-i?image.jpg?-o?output.jpg

python train.py -e 200 -b 1 -l 0.1 -s 0.5 -v 15.0

class DoubleConv(nn.Module):????"""(convolution?=>?[BN]?=>?ReLU)?*?2"""    def __init__(self, in_channels, out_channels):        super().__init__()        self.double_conv = nn.Sequential(            nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=1),            nn.BatchNorm2d(out_channels),            nn.ReLU(inplace=True),            nn.Conv2d(out_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, use the normal convolutions to reduce the number of channels        if bilinear:            self.up = nn.Upsample(scale_factor=2, mode='bilinear', align_corners=True)        else:            self.up = nn.ConvTranspose2d(in_channels // 2, in_channels // 2, kernel_size=2, stride=2)
        self.conv = DoubleConv(in_channels, out_channels)
    def forward(self, x1, x2):        x1 = self.up(x1)        # input is CHW        diffY = torch.tensor([x2.size()[2] - x1.size()[2]])        diffX = torch.tensor([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.Conv2d(in_channels, out_channels, kernel_size=1)
    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)        self.down4 = Down(512, 512)        self.up1 = Up(1024, 256, bilinear)        self.up2 = Up(512, 128, bilinear)        self.up3 = Up(256, 64, 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