YOLOV3网络搭建 and 代码复现Pytorch and 元器件检测

目录

一. YOLOV3 网络

1.主干网络DarkNet53

二. 代码复现        

1.DarkNet53搭建

2.特征加强 and 结果预测​编辑

3. 源码地址

三. 检测效果

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一. YOLOV3 网络

1.主干网络DarkNet53

YoloV3所使用的主干特征提取网络为Darknet53，它具有两个重要特点：
1、Darknet53具有一个重要特点是使用了残差网络Residual（残差结构可以在"分类"栏中的ResNet中了解）...... 。通过不断的1X1卷积和3X3卷积以及残差边的叠加，我们便大幅度的加深了网络。残差网络的特点是容易优化，并且能够通过增加相当的深度来提高准确率。其内部的残差块使用了跳跃连接，缓解了在深度神经网络中增加深度带来的梯度消失问题。

2、Darknet53的每一个卷积部分使用了特有的DarknetConv2D结构，每一次卷积的时候进行l2正则化，完成卷积后进行BatchNormalization标准化与LeakyReLU。普通的ReLU是将所有的负值都设为零，Leaky ReLU则是给所有负值赋予一个非零斜率。以数学的方式我们可以表示为：

Fig1. DarkNet53结构表 

画成流程图如下，更好理解：

上述流程图的关键字均与下面列出的代码中的参数名一样， 对照网络结构看代码 更易于理解。 

二. 代码复现
        

1.DarkNet53搭建

import math
from collections import OrderedDict

import torch.nn as nn


#---------------------------------------------------------------------#
#   残差块结构
#   利用一个1x1卷积下降通道数，然后利用一个3x3卷积提取特征并且上升通道数
#   最后接上一个残差边
#---------------------------------------------------------------------#
class BasicBlock(nn.Module):
    def __init__(self, inplanes, planes):
        super(BasicBlock, self).__init__()
        self.conv1 = nn.Conv2d(inplanes, planes[0], kernel_size=1, stride=1, padding=0, bias=False)
        self.bn1   = nn.BatchNorm2d(planes[0])
        self.relu1 = nn.LeakyReLU(0.1)

        self.conv2 = nn.Conv2d(planes[0], planes[1], kernel_size=3, stride=1, padding=1, bias=False)
        self.bn2   = nn.BatchNorm2d(planes[1])
        self.relu2 = nn.LeakyReLU(0.1)

    def forward(self, x):
        residual = x

        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu1(out)

        out = self.conv2(out)
        out = self.bn2(out)
        out = self.relu2(out)

        out = out + residual

        return out

class DarkNet(nn.Module):
    def __init__(self, layers):
        super(DarkNet, self).__init__()   #调用父类nn.Mudule的构造函数
        self.inplanes = 32                #输入通道

        # 416,416,3 -> 416,416,32 Conv2D 32x3x3   卷积核          步长       填充       偏置
        self.conv1 = nn.Conv2d(3, self.inplanes, kernel_size=3, stride=1, padding=1, bias=False)
        self.bn1   = nn.BatchNorm2d(self.inplanes)
        self.relu1 = nn.LeakyReLU(0.1)    #和cnn的Relu()不同 此LeakyReLU不为0->0.1*x

        # 416,416,32 -> 208,208,64
        self.layer1 = self._make_layer([32, 64], layers[0])
        # 208,208,64 -> 104,104,128
        self.layer2 = self._make_layer([64, 128], layers[1])
        # 104,104,128 -> 52,52,256
        self.layer3 = self._make_layer([128, 256], layers[2])
        # 52,52,256 -> 26,26,512
        self.layer4 = self._make_layer([256, 512], layers[3])
        # 26,26,512 -> 13,13,1024
        self.layer5 = self._make_layer([512, 1024], layers[4])
        self.layers_out_filters = [64, 128, 256, 512, 1024]

        #进行权值初始化
        # isinstance(m, nn.Conv2d) 判断 m 是否是 Conv2d
        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
                m.weight.data.normal_(0, math.sqrt(2. / n))
            elif isinstance(m, nn.BatchNorm2d):
                m.weight.data.fill_(1)
                m.bias.data.zero_()


#---------------------------------------------------------------------#
#   在每一个layer里面，首先利用一个步长为2的3x3卷积进行下采样
#   然后进行残差结构的堆叠
#---------------------------------------------------------------------#
    def _make_layer(self, planes, blocks):
        layers = []
        # 步长为2 的 下采样  ， 卷积核为3x3
        layers.append(("ds_conv", nn.Conv2d(self.inplanes, planes[1], kernel_size=3, stride=2, padding=1, bias=False)))
        layers.append(("ds_bn", nn.BatchNorm2d(planes[1])))
        layers.append(("ds_relu", nn.LeakyReLU(0.1)))

        #加入残差结构
        self.inplanes = planes[1]    #更新当前的输入通道数
        for i in range(0, blocks):
            layers.append(("residual_{}".format(i), BasicBlock(self.inplanes, planes)))
        return nn.Sequential(OrderedDict(layers))

    def forward(self, x):
        x = self.conv1(x)
        x = self.bn1(x)
        x = self.relu1(x)

        x = self.layer1(x)
        x = self.layer2(x)
        out3 = self.layer3(x)
        out4 = self.layer4(out3)
        out5 = self.layer5(out4)

        return out3, out4, out5

def darknet53():
    model = DarkNet([1, 2, 8, 8, 4])
    return model

2.特征加强 and 结果预测

 从特征获取预测结果的过程可以分为两个部分，分别是：

• 构建FPN特征金字塔进行加强特征提取（红色部分）
• 利用Yolo Head对三个有效特征层进行预测（绿色部分）

from tensorflow.keras.layers import Concatenate, Input, Lambda, UpSampling2D
from tensorflow.keras.models import Model
from utils.utils import compose

from nets.darknet import DarknetConv2D, DarknetConv2D_BN_Leaky, darknet_body
from nets.yolo_training import yolo_loss


#---------------------------------------------------#
#   特征层->最后的输出
#---------------------------------------------------#
def make_five_conv(x, num_filters, weight_decay=5e-4):
    x = DarknetConv2D_BN_Leaky(num_filters, (1,1), weight_decay=weight_decay)(x)
    x = DarknetConv2D_BN_Leaky(num_filters*2, (3,3), weight_decay=weight_decay)(x)
    x = DarknetConv2D_BN_Leaky(num_filters, (1,1), weight_decay=weight_decay)(x)
    x = DarknetConv2D_BN_Leaky(num_filters*2, (3,3), weight_decay=weight_decay)(x)
    x = DarknetConv2D_BN_Leaky(num_filters, (1,1), weight_decay=weight_decay)(x)
    return x

def make_yolo_head(x, num_filters, out_filters, weight_decay=5e-4):
    y = DarknetConv2D_BN_Leaky(num_filters*2, (3,3), weight_decay=weight_decay)(x)
    # 255->3, 85->3, 4 + 1 + 80
    y = DarknetConv2D(out_filters, (1,1), weight_decay=weight_decay)(y)
    return y

#---------------------------------------------------#
#   FPN网络的构建，并且获得预测结果
#---------------------------------------------------#
def yolo_body(input_shape, anchors_mask, num_classes, weight_decay=5e-4):
    inputs      = Input(input_shape)
    #---------------------------------------------------#   
    #   生成darknet53的主干模型
    #   获得三个有效特征层，他们的shape分别是：
    #   C3 为 52,52,256
    #   C4 为 26,26,512
    #   C5 为 13,13,1024
    #---------------------------------------------------#
    C3, C4, C5  = darknet_body(inputs, weight_decay)

    #---------------------------------------------------#
    #   第一个特征层
    #   y1=(batch_size,13,13,3,85)
    #---------------------------------------------------#
    # 13,13,1024 -> 13,13,512 -> 13,13,1024 -> 13,13,512 -> 13,13,1024 -> 13,13,512
    x   = make_five_conv(C5, 512, weight_decay)
    P5  = make_yolo_head(x, 512, len(anchors_mask[0]) * (num_classes+5), weight_decay)

    # 13,13,512 -> 13,13,256 -> 26,26,256
    x   = compose(DarknetConv2D_BN_Leaky(256, (1,1), weight_decay=weight_decay), UpSampling2D(2))(x)

    # 26,26,256 + 26,26,512 -> 26,26,768
    x   = Concatenate()([x, C4])
    #---------------------------------------------------#
    #   第二个特征层
    #   y2=(batch_size,26,26,3,85)
    #---------------------------------------------------#
    # 26,26,768 -> 26,26,256 -> 26,26,512 -> 26,26,256 -> 26,26,512 -> 26,26,256
    x   = make_five_conv(x, 256, weight_decay)
    P4  = make_yolo_head(x, 256, len(anchors_mask[1]) * (num_classes+5), weight_decay)

    # 26,26,256 -> 26,26,128 -> 52,52,128
    x   = compose(DarknetConv2D_BN_Leaky(128, (1,1), weight_decay=weight_decay), UpSampling2D(2))(x)
    # 52,52,128 + 52,52,256 -> 52,52,384
    x   = Concatenate()([x, C3])
    #---------------------------------------------------#
    #   第三个特征层
    #   y3=(batch_size,52,52,3,85)
    #---------------------------------------------------#
    # 52,52,384 -> 52,52,128 -> 52,52,256 -> 52,52,128 -> 52,52,256 -> 52,52,128
    x   = make_five_conv(x, 128, weight_decay)
    P3  = make_yolo_head(x, 128, len(anchors_mask[2]) * (num_classes+5), weight_decay)
    return Model(inputs, [P5, P4, P3])


def get_train_model(model_body, input_shape, num_classes, anchors, anchors_mask):
    y_true = [Input(shape = (input_shape[0] // {0:32, 1:16, 2:8}[l], input_shape[1] // {0:32, 1:16, 2:8}[l], \
                                len(anchors_mask[l]), num_classes + 5)) for l in range(len(anchors_mask))]
    model_loss  = Lambda(
        yolo_loss, 
        output_shape    = (1, ), 
        name            = 'yolo_loss', 
        arguments       = {
            'input_shape'       : input_shape, 
            'anchors'           : anchors, 
            'anchors_mask'      : anchors_mask, 
            'num_classes'       : num_classes, 
            'balance'           : [0.4, 1.0, 4],
            'box_ratio'         : 0.05,
            'obj_ratio'         : 5 * (input_shape[0] * input_shape[1]) / (416 ** 2), 
            'cls_ratio'         : 1 * (num_classes / 80)
        }
    )([*model_body.output, *y_true])
    model       = Model([model_body.input, *y_true], model_loss)
    return model

3. 源码地址

https://github.com/mcuwangzaiacm/YOLOV3_Pytorch/tree/main/utils

三. 检测效果

训练采用5000张电路板， 每张电路板上标记了4-30个的电容，4个种类（上，下，左， 右），检测电容极性，检测效果如下