【Darknet-53】YOLOv3 backbone Darknet-53 详解
文章目录
- 1 模型计算量与参数量
- 2 Darknet-53网络
- 3 感谢链接
1 模型计算量与参数量
模型计算量与参数量的计算方式主要有两种，一种是使用thop库，一种是使用torchsummaryX。
- 使用
pip install thop安装thop库 - 使用
pip install torchsummaryX安装torchsummaryX库
2 Darknet-53网络
可直接运行下方代码，结合注释和结果理解
本例中darknet53主要用于yolov3中的主干网络
import math
from collections import OrderedDict
import torch.nn as nn
import torch
from torchsummaryX import summary
from thop import profile
# ---------------------------------------------------------------------#
# 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
# ---------------------------------------------------------------------#
# ---------------基本残差块-----------------
class BasicBlock(nn.Module):
def __init__(self, inplanes, planes): # inplanes是一个int，planes是一个list，包括两个数字
super(BasicBlock, self).__init__() # 1x1卷积下降通道数，3x3卷积上升通道数，减少参数
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
# H,W,C：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)
# ---------------------------------------#
# 下面利用_make_layer进行残差块堆叠
# ---------------------------------------#
# 416,416,32 -> 208,208,64
self.layer1 = self._make_layer([32, 64], layers[0]) # self._make_layer(planes, blocks)
# 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] # 表示几个结构块的输出通道数
# 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): # blocks表示堆叠该结构块次数
layers = []
# ---------------------------------------------#
# 下采样，步长为2，卷积核大小为3，
# 特征图长和宽压缩、通道数扩张到planes[1]
# ---------------------------------------------#
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，利用一个1x1卷积下降通道数到planes[0]，
# 然后利用一个3x3卷积提取特征并且上升通道数到planes[1]
# ---------------------------------------------#
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)
# -----------------------------------------------------#
# 这样区分写，是因为在yolov3中需要使用这三个输出特征层进行后续处理
# -----------------------------------------------------#
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
if __name__ == '__main__':
model = darknet53()
print(model)
# --------------------------------------#
# 使用thop计算模型计算量与参数量
# --------------------------------------#
input = torch.randn(1, 3, 416, 416) # batch_size, channels, height, weight, NCHW
flops, params = profile(model, inputs=(input,)) # 计算量，参数量
print(flops, params)
# --------------------------------------#
# 使用TorchsummaryX计算模型计算量与参数量
# --------------------------------------#
summary(model, torch.zeros(1, 3, 416, 416))
thop库输出结果：警告表示该部分计算量与参数量的对待方式(不算在内)
[INFO] Register count_convNd() for .
[WARN] Cannot find rule for . Treat it as zero Macs and zero Params.
[WARN] Cannot find rule for . Treat it as zero Macs and zero Params.
[WARN] Cannot find rule for . Treat it as zero Macs and zero Params.
[WARN] Cannot find rule for . Treat it as zero Macs and zero Params.
[WARN] Cannot find rule for . Treat it as zero Macs and zero Params.
24515805184.0 40549216.0 # 计算量 参数量
torchsummaryX库输出结果：可展示每层的情况，参数量和计算量
====================================================================================================
Kernel Shape Output Shape \
Layer
0_conv1 [3, 32, 3, 3] [1, 32, 416, 416]
1_bn1 [32] [1, 32, 416, 416]
2_relu1 - [1, 32, 416, 416]
3_layer1.Conv2d_ds_conv [32, 64, 3, 3] [1, 64, 208, 208]
4_layer1.BatchNorm2d_ds_bn [64] [1, 64, 208, 208]
...
Params Mult-Adds
Layer
0_conv1 864.0 149.520384M
1_bn1 64.0 32.0
2_relu1 - -
3_layer1.Conv2d_ds_conv 18.432k 797.442048M
...
153_layer5.residual_3.Conv2d_conv2 4.718592M 797.442048M
154_layer5.residual_3.BatchNorm2d_bn2 2.048k 1.024k
155_layer5.residual_3.LeakyReLU_relu2 - -
----------------------------------------------------------------------------------------------------
Totals
Total params 40.584928M # 参数量
Trainable params 40.584928M
Non-trainable params 0.0
Mult-Adds 24.51582304G # 计算量
====================================================================================================
3 感谢链接
https://blog.csdn.net/weixin_44791964/article/details/105310627