如何用基于resnet的Unet进行图像分割 基于Pytorch-0.5版本

1、关于Unet

      Unet主要用于医学图像的很多论文中，以及Kaggle竞赛和一些其他竞赛中“少类别”的图像分割。从我做实验的经验来说，像VOC这种类别比较多的分割任务，不容易收敛，效果较为差。

2、Resnet34

     我们的encode部分选择resnet34，decode部分为每一个block制作三层卷积，其中每个的第二层为upsample（bilinear/deconv）

      

#基本的block
class DecoderBlock(nn.Module):
    def __init__(self,
                 in_channels=512,
                 n_filters=256,
                 kernel_size=3,
                 is_deconv=False,
                 ):
        super().__init__()

        if kernel_size == 3:
            conv_padding = 1
        elif kernel_size == 1:
            conv_padding = 0

        # B, C, H, W -> B, C/4, H, W
        self.conv1 = nn.Conv2d(in_channels,
                               in_channels // 4,
                               kernel_size,
                               padding=1,bias=False)
        self.norm1 = nn.BatchNorm2d(in_channels // 4)
        self.relu1 = nonlinearity(inplace=True)

        # B, C/4, H, W -> B, C/4, H, W
        if is_deconv == True:
            self.deconv2 = nn.ConvTranspose2d(in_channels // 4,
                                              in_channels // 4,
                                              3,
                                              stride=2,
                                              padding=1,
                                              output_padding=conv_padding,bias=False)
        else:
            self.deconv2 = nn.Upsample(scale_factor=2,**up_kwargs)

        self.norm2 = nn.BatchNorm2d(in_channels // 4)
        self.relu2 = nonlinearity(inplace=True)

        # B, C/4, H, W -> B, C, H, W
        self.conv3 = nn.Conv2d(in_channels // 4,
                               n_filters,
                               kernel_size,
                               padding=conv_padding,bias=False)
        self.norm3 = nn.BatchNorm2d(n_filters)
        self.relu3 = nonlinearity(inplace=True)

    def forward(self, x):
        x = self.conv1(x)
        x = self.norm1(x)
        x = self.relu1(x)
        x = self.deconv2(x)
        x = self.norm2(x)
        x = self.relu2(x)
        x = self.conv3(x)
        x = self.norm3(x)
        x = self.relu3(x)
        return x


class BasicConv2d(nn.Module):
    def __init__(self, in_planes, out_planes, kernel_size, stride, padding=0):
        super(BasicConv2d, self).__init__()
        self.conv = nn.Conv2d(in_planes, out_planes,
                              kernel_size=kernel_size, stride=stride,
                              padding=padding, bias=False)  # verify bias false
        self.bn = nn.BatchNorm2d(out_planes,
                                 eps=0.001,  # value found in tensorflow
                                 momentum=0.1,  # default pytorch value
                                 affine=True)
        self.relu = nn.ReLU(inplace=False)

    def forward(self, x):
        x = self.conv(x)
        x = self.bn(x)
        x = self.relu(x)
        return x

---

接下来，就是模型代码了。

class ResNet34Unet(nn.Module):
    def __init__(self,
                 num_classes,
                 num_channels=3,
                 is_deconv=False,
                 decoder_kernel_size=3,
                 ):
        super().__init__()

        self.mean = (0.485, 0.456, 0.406)
        self.std = (0.229, 0.224, 0.225)

        filters = [64, 128, 256, 512]
        resnet = models.resnet34(pretrained=True)
        self.base_size=512
        self.crop_size=512
        self._up_kwargs={'mode': 'bilinear', 'align_corners': True}
        # self.firstconv = resnet.conv1
        # assert num_channels == 3, "num channels not used now. to use changle first conv layer to support num channels other then 3"
        # try to use 8-channels as first input
        if num_channels == 3:
            self.firstconv = resnet.conv1
        else:
            self.firstconv = nn.Conv2d(num_channels, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3),bias=False)

        self.firstbn = resnet.bn1
        self.firstrelu = resnet.relu
        self.firstmaxpool = resnet.maxpool
        self.encoder1 = resnet.layer1
        self.encoder2 = resnet.layer2
        self.encoder3 = resnet.layer3
        self.encoder4 = resnet.layer4

        # Decoder
        self.center = DecoderBlock(in_channels=filters[3],
                                   n_filters=filters[3],
                                   kernel_size=decoder_kernel_size,
                                   is_deconv=is_deconv)
        self.decoder4 = DecoderBlock(in_channels=filters[3] + filters[2],
                                     n_filters=filters[2],
                                     kernel_size=decoder_kernel_size,
                                     is_deconv=is_deconv)
        self.decoder3 = DecoderBlock(in_channels=filters[2] + filters[1],
                                     n_filters=filters[1],
                                     kernel_size=decoder_kernel_size,
                                     is_deconv=is_deconv)
        self.decoder2 = DecoderBlock(in_channels=filters[1] + filters[0],
                                     n_filters=filters[0],
                                     kernel_size=decoder_kernel_size,
                                     is_deconv=is_deconv)
        self.decoder1 = DecoderBlock(in_channels=filters[0] + filters[0],
                                     n_filters=filters[0],
                                     kernel_size=decoder_kernel_size,
                                     is_deconv=is_deconv)


        self.finalconv = nn.Sequential(nn.Conv2d(filters[0], 32, 3, padding=1, bias=False),
                                       nn.BatchNorm2d(32),
                                       nn.ReLU(),
                                       nn.Dropout2d(0.1, False),
                                       nn.Conv2d(32, num_classes, 1))

    def require_encoder_grad(self, requires_grad):
        blocks = [self.firstconv,
                  self.encoder1,
                  self.encoder2,
                  self.encoder3,
                  self.encoder4]

        for block in blocks:
            for p in block.parameters():
                p.requires_grad = requires_grad

    def forward(self, x):
        # stem
        x = self.firstconv(x)
        x = self.firstbn(x)
        x = self.firstrelu(x)
        x_ = self.firstmaxpool(x)

        # Encoder
        e1 = self.encoder1(x_)
        e2 = self.encoder2(e1)
        e3 = self.encoder3(e2)
        e4 = self.encoder4(e3)

        center = self.center(e4)

        d4 = self.decoder4(torch.cat([center, e3], 1))
        d3 = self.decoder3(torch.cat([d4, e2], 1))
        d2 = self.decoder2(torch.cat([d3, e1], 1))
        d1 = self.decoder1(torch.cat([d2, x], 1))

        f= self.finalconv(d1)
        return tuple([f])