deeplabv3+系列之deeplabv3网络搭建

发布时间:2022-12-24 10:30

使用paddlepaddle2.0版本实现deeplabv3网络搭建。

基于paddlepaddle2.0版本的搭建。最近飞桨2.0版本出来啦!也挺好用的,所以就参考一些其他版本的代码,用paddlepaddle2.0版本重新写一下deeplabv3+ResNet网络。这篇文章为deeplabv3中ASPP等模块的搭建,以及整合前面的ResNet网络,骨干网络部分可以看上一篇文章deeplabv3+系列之ResNet骨干网络
原论文地址:Rethinking Atrous Convolution for Semantic Image Segmentation
paddlepaddle2.0版本安装教程

一.deeplabv3模块(参考飞桨七日图像分割打卡课程搭建的)

deeplabv3+系列之deeplabv3网络搭建_第1张图片

deeplabv3+系列之deeplabv3网络搭建_第2张图片

deeplabv3+系列之deeplabv3网络搭建_第3张图片

二.可直接运行的代码:

## layers
## 一些子层
import paddle
import paddle.nn as nn
import paddle.nn.functional as F
class ConvBNReLU(nn.Layer):
    def __init__(self,
                 in_channels,
                 out_channels,
                 kernel_size,
                 padding='same',
                 **kwargs):
        super().__init__()

        self._conv = nn.Conv2D(
            in_channels, out_channels, kernel_size, padding=padding, **kwargs)

        self._batch_norm = SyncBatchNorm(out_channels)

    def forward(self, x):
        x = self._conv(x)
        x = self._batch_norm(x)
        x = F.relu(x)
        return x
class ConvBN(nn.Layer):
    def __init__(self,
                 in_channels,
                 out_channels,
                 kernel_size,
                 padding='same',
                 **kwargs):
        super().__init__()
        self._conv = nn.Conv2D(
            in_channels, out_channels, kernel_size, padding=padding, **kwargs)
        self._batch_norm = SyncBatchNorm(out_channels)

    def forward(self, x):
        x = self._conv(x)
        x = self._batch_norm(x)
        return x

class SeparableConvBNReLU(nn.Layer):
    def __init__(self,
                 in_channels,
                 out_channels,
                 kernel_size,
                 padding='same',
                 **kwargs):
        super().__init__()
        self.depthwise_conv = ConvBN(
            in_channels,
            out_channels=in_channels,
            kernel_size=kernel_size,
            padding=padding,
            groups=in_channels,
            **kwargs)
        self.piontwise_conv = ConvBNReLU(
            in_channels, out_channels, kernel_size=1, groups=1)

    def forward(self, x):
        x = self.depthwise_conv(x)
        x = self.piontwise_conv(x)
        return x

## ASPPModule

#from paddle import nn
#from paddleseg.models import layers

class ASPPModule(nn.Layer):
    """
    ASPP模块
    """
    def __init__(self,
                 aspp_ratios,   #1 6 12 18
                 in_channels,
                 out_channels,
                 align_corners,
                 use_sep_conv=False,
                 image_pooling=False):
        super().__init__()

        self.align_corners = align_corners
        self.aspp_blocks = nn.LayerList()
        
        #Features
        #ASPPConv
        for ratio in aspp_ratios:
            if use_sep_conv and ratio > 1:
                conv_func = SeparableConvBNReLU#3x3空洞卷积(不同系数)+bn+relu
            else:
                conv_func = ConvBNReLU#1x1卷积+bn+relu

            block = conv_func(
                in_channels=in_channels,
                out_channels=out_channels,
                kernel_size=1 if ratio == 1 else 3,
                dilation=ratio,
                padding=0 if ratio == 1 else ratio)
            self.aspp_blocks.append(block)

        out_size = len(self.aspp_blocks)
        
        #ASPPpooling
        if image_pooling:
            #顺序容器。子Layer将按构造函数参数的顺序添加到此容器中。传递给构造函数的参数可以Layers或可迭代的name Layer元组。
            self.global_avg_pool = nn.Sequential(
                nn.AdaptiveAvgPool2D(output_size=(1, 1)),
                ConvBNReLU(
                    in_channels, out_channels, kernel_size=1, bias_attr=False))
            out_size += 1
        self.image_pooling = image_pooling

        self.conv_bn_relu = ConvBNReLU(
            in_channels=out_channels * out_size,
            out_channels=out_channels,
            kernel_size=1)

        self.dropout = nn.Dropout(p=0.1)  # drop rate

    def forward(self, x):
        outputs = []
        for block in self.aspp_blocks:
            y = block(x)
            y = F.interpolate(
                y,
                x.shape[2:],
                mode='bilinear',
                align_corners=self.align_corners)
            outputs.append(y)

        if self.image_pooling:
            img_avg = self.global_avg_pool(x)
            img_avg = F.interpolate(
                img_avg,
                x.shape[2:],
                mode='bilinear',
                align_corners=self.align_corners)
            outputs.append(img_avg)

        x = paddle.concat(outputs, axis=1)
        x = self.conv_bn_relu(x)
        x = self.dropout(x)

        return x
class DeepLabV3Head(nn.Layer):
    """
    deeplabv3Head模块:ASPP模块+分类模块
    """
    def __init__(self, num_classes, backbone_indices, backbone_channels,
                 aspp_ratios, aspp_out_channels, align_corners):
        super().__init__()

        self.aspp = ASPPModule(
            aspp_ratios,
            backbone_channels[0],
            aspp_out_channels,
            align_corners,
            use_sep_conv=False,
            image_pooling=True)

        self.cls = nn.Conv2D(
            in_channels=aspp_out_channels,
            out_channels=num_classes,
            kernel_size=1)

        self.backbone_indices = backbone_indices

    def forward(self, feat_list):
        logit_list = []
        x = feat_list
        x = self.aspp(x)
        logit = self.cls(x)
        logit_list.append(logit)

        return logit_list


class DeepLabV3(nn.Layer):
    """
    deeplabv3模块:骨干网络(ResNet)+deeplabv3Head模块+插值模块
    """

    def __init__(self,
                 num_classes,
                 backbone_indices=(3, ),
                 aspp_ratios=(1, 6, 12, 18),
                 aspp_out_channels=256,
                 align_corners=False,
                 pretrained=None):
        super().__init__()

        backbone = ResNet(layers=101,duplicate_blocks=True)
        backbone_channels = [backbone.out_channels[3] * 4]

        self.head = DeepLabV3Head(num_classes, backbone_indices,
                                  backbone_channels, aspp_ratios,
                                  aspp_out_channels, align_corners)
        self.align_corners = align_corners
        self.pretrained = pretrained
        self.init_weight()

        backbone = ResNet(layers=101,duplicate_blocks=True)
        self.layer1 = backbone.layer1
        self.layer2 = backbone.layer2
        self.layer3 = backbone.layer3
        self.layer4 = backbone.layer4
        self.layer5 = backbone.layer5
        self.layer6 = backbone.layer6
        self.layer7 = backbone.layer7
        self.conv = backbone.conv
        self.pool2d_max = backbone.pool2d_max
        
    def forward(self, inputs):
        x = self.conv(inputs)
        x = self.pool2d_max(x)

        #print(x.shape)
        x = self.layer1(x)
        #print(x.shape)
        x = self.layer2(x)
        #print(x.shape)
        x = self.layer3(x)
        #print(x.shape)
        x = self.layer4(x)
        #print(x.shape)
        x = self.layer5(x)
        x = self.layer6(x)
        x = self.layer7(x)
        logit_list = self.head(x)
        
        return [
            F.interpolate(
                logit,
                [inputs.shape[2],inputs.shape[3]],
                #x.shape[2:],
                mode='bilinear',
                align_corners=self.align_corners) for logit in logit_list
        ]
    def init_weight(self):
        if self.pretrained is not None:
            utils.load_entire_model(self, self.pretrained)

x_data = np.random.rand(2, 3, 224, 224).astype(np.float32)
x = to_variable(x_data)
model = DeepLabV3(59)
model.eval()
pred = model(x)
print('dilated resnet50: pred.shape = ', pred[0].shape)

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