网络构架图：
文章来源：https://www.toymoban.com/news/detail-674771.html
文章来源地址https://www.toymoban.com/news/detail-674771.html
代码实现：
import math
from collections import OrderedDict
import torch.nn as nn
#---------------------------------------------------------------------#
# 残差结构
# 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 += residual
return out
class DarkNet(nn.Module):
def __init__(self, layers):
super(DarkNet, self).__init__()
self.inplanes = 32
# 416,416,3 -> 416,416,32
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)
# 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
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 = []
# 下采样，步长为2，卷积核大小为3
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]
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
from collections import OrderedDict
import torch
import torch.nn as nn
from nets.darknet import darknet53
def conv2d(filter_in, filter_out, kernel_size):
pad = (kernel_size - 1) // 2 if kernel_size else 0
return nn.Sequential(OrderedDict([
("conv", nn.Conv2d(filter_in, filter_out, kernel_size=kernel_size, stride=1, padding=pad, bias=False)),
("bn", nn.BatchNorm2d(filter_out)),
("relu", nn.LeakyReLU(0.1)),
]))
#------------------------------------------------------------------------#
# make_last_layers里面一共有七个卷积，前五个用于提取特征。
# 后两个用于获得yolo网络的预测结果
#------------------------------------------------------------------------#
def make_last_layers(filters_list, in_filters, out_filter):
m = nn.Sequential(
conv2d(in_filters, filters_list[0], 1),
conv2d(filters_list[0], filters_list[1], 3),
conv2d(filters_list[1], filters_list[0], 1),
conv2d(filters_list[0], filters_list[1], 3),
conv2d(filters_list[1], filters_list[0], 1),
conv2d(filters_list[0], filters_list[1], 3),
nn.Conv2d(filters_list[1], out_filter, kernel_size=1, stride=1, padding=0, bias=True)
)
return m
class YoloBody(nn.Module):
def __init__(self, anchors_mask, num_classes):
super(YoloBody, self).__init__()
#---------------------------------------------------#
# 生成darknet53的主干模型
# 获得三个有效特征层，他们的shape分别是：
# 52,52,256
# 26,26,512
# 13,13,1024
#---------------------------------------------------#
self.backbone = darknet53()
#---------------------------------------------------#
# out_filters : [64, 128, 256, 512, 1024]
#---------------------------------------------------#
out_filters = self.backbone.layers_out_filters
#------------------------------------------------------------------------#
# 计算yolo_head的输出通道数，对于voc数据集而言
# final_out_filter0 = final_out_filter1 = final_out_filter2 = 75
#------------------------------------------------------------------------#
self.last_layer0 = make_last_layers([512, 1024], out_filters[-1], len(anchors_mask[0]) * (num_classes + 5))
self.last_layer1_conv = conv2d(512, 256, 1)
self.last_layer1_upsample = nn.Upsample(scale_factor=2, mode='nearest')
self.last_layer1 = make_last_layers([256, 512], out_filters[-2] + 256, len(anchors_mask[1]) * (num_classes + 5))
self.last_layer2_conv = conv2d(256, 128, 1)
self.last_layer2_upsample = nn.Upsample(scale_factor=2, mode='nearest')
self.last_layer2 = make_last_layers([128, 256], out_filters[-3] + 128, len(anchors_mask[2]) * (num_classes + 5))
def forward(self, x):
#---------------------------------------------------#
# 获得三个有效特征层，他们的shape分别是：
# 52,52,256；26,26,512；13,13,1024
#---------------------------------------------------#
x2, x1, x0 = self.backbone(x)
#---------------------------------------------------#
# 第一个特征层
# out0 = (batch_size,255,13,13)
#---------------------------------------------------#
# 13,13,1024 -> 13,13,512 -> 13,13,1024 -> 13,13,512 -> 13,13,1024 -> 13,13,512
out0_branch = self.last_layer0[:5](x0)
out0 = self.last_layer0[5:](out0_branch)
# 13,13,512 -> 13,13,256 -> 26,26,256
x1_in = self.last_layer1_conv(out0_branch)
x1_in = self.last_layer1_upsample(x1_in)
# 26,26,256 + 26,26,512 -> 26,26,768
x1_in = torch.cat([x1_in, x1], 1)
#---------------------------------------------------#
# 第二个特征层
# out1 = (batch_size,255,26,26)
#---------------------------------------------------#
# 26,26,768 -> 26,26,256 -> 26,26,512 -> 26,26,256 -> 26,26,512 -> 26,26,256
out1_branch = self.last_layer1[:5](x1_in)
out1 = self.last_layer1[5:](out1_branch)
# 26,26,256 -> 26,26,128 -> 52,52,128
x2_in = self.last_layer2_conv(out1_branch)
x2_in = self.last_layer2_upsample(x2_in)
# 52,52,128 + 52,52,256 -> 52,52,384
x2_in = torch.cat([x2_in, x2], 1)
#---------------------------------------------------#
# 第三个特征层
# out3 = (batch_size,255,52,52)
#---------------------------------------------------#
# 52,52,384 -> 52,52,128 -> 52,52,256 -> 52,52,128 -> 52,52,256 -> 52,52,128
out2 = self.last_layer2(x2_in)
return out0, out1, out2
到了这里，关于【目标检测】理论篇（2）YOLOv3网络构架及其代码实现的文章就介绍完了。如果您还想了解更多内容，请在右上角搜索TOY模板网以前的文章或继续浏览下面的相关文章，希望大家以后多多支持TOY模板网！
