基于Pytorch SSD模型分析


Posted in Python onFebruary 18, 2020

本文参考github上SSD实现,对模型进行分析,主要分析模型组成及输入输出大小.SSD网络结构如下图:

基于Pytorch SSD模型分析

每输入的图像有8732个框输出;

import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
#from layers import *
from data import voc, coco
import os
base = {
 '300': [64, 64, 'M', 128, 128, 'M', 256, 256, 256, 'C', 512, 512, 512, 'M',
   512, 512, 512],
 '512': [],
}
extras = {
 '300': [256, 'S', 512, 128, 'S', 256, 128, 256, 128, 256],
 '512': [],
}
mbox = {
 '300': [4, 6, 6, 6, 4, 4], # number of boxes per feature map location
 '512': [],
}

VGG基础网络结构:

def vgg(cfg, i, batch_norm=False):
 layers = []
 in_channels = i
 for v in cfg:
  if v == 'M':
   layers += [nn.MaxPool2d(kernel_size=2, stride=2)]
  elif v == 'C':
   layers += [nn.MaxPool2d(kernel_size=2, stride=2, ceil_mode=True)]
  else:
   conv2d = nn.Conv2d(in_channels, v, kernel_size=3, padding=1)
   if batch_norm:
    layers += [conv2d, nn.BatchNorm2d(v), nn.ReLU(inplace=True)]
   else:
    layers += [conv2d, nn.ReLU(inplace=True)]
   in_channels = v
 pool5 = nn.MaxPool2d(kernel_size=3, stride=1, padding=1)
 conv6 = nn.Conv2d(512, 1024, kernel_size=3, padding=6, dilation=6)
 conv7 = nn.Conv2d(1024, 1024, kernel_size=1)
 layers += [pool5, conv6,
    nn.ReLU(inplace=True), conv7, nn.ReLU(inplace=True)]
 return layers
size=300
vgg=vgg(base[str(size)], 3)
print(vgg)

输出为:

Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
ReLU(inplace)
Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
ReLU(inplace)
MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
ReLU(inplace)
Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
ReLU(inplace)
MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
ReLU(inplace)
Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
ReLU(inplace)
Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
ReLU(inplace)
MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=True)
Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
ReLU(inplace)
Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
ReLU(inplace)
Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
ReLU(inplace)
MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
ReLU(inplace)
Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
ReLU(inplace)
Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
ReLU(inplace)
MaxPool2d(kernel_size=3, stride=1, padding=1, dilation=1, ceil_mode=False)
Conv2d(512, 1024, kernel_size=(3, 3), stride=(1, 1), padding=(6, 6), dilation=(6, 6))
ReLU(inplace)
Conv2d(1024, 1024, kernel_size=(1, 1), stride=(1, 1))
ReLU(inplace)

SSD中添加的网络

add_extras函数构建基本的卷积层

def add_extras(cfg, i, batch_norm=False):
 # Extra layers added to VGG for feature scaling
 layers = []
 in_channels = i
 flag = False
 for k, v in enumerate(cfg):
  if in_channels != 'S':
   if v == 'S':
    layers += [nn.Conv2d(in_channels, cfg[k + 1],
       kernel_size=(1, 3)[flag], stride=2, padding=1)]
   else:
    layers += [nn.Conv2d(in_channels, v, kernel_size=(1, 3)[flag])]
   flag = not flag
  in_channels = v
 return layers
extra_layers=add_extras(extras[str(size)], 1024)
for layer in extra_layers:
 print(layer)

输出为:

Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1))
Conv2d(256, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1))
Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1))
Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1))
Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1))
Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1))

multibox函数得到每个特征图的默认box的位置计算网络和分类得分网络

def multibox(vgg, extra_layers, cfg, num_classes):
 loc_layers = []
 conf_layers = []
 vgg_source = [21, -2]
 for k, v in enumerate(vgg_source):
  loc_layers += [nn.Conv2d(vgg[v].out_channels,
         cfg[k] * 4, kernel_size=3, padding=1)]
  conf_layers += [nn.Conv2d(vgg[v].out_channels,
      cfg[k] * num_classes, kernel_size=3, padding=1)]
 for k, v in enumerate(extra_layers[1::2], 2):
  loc_layers += [nn.Conv2d(v.out_channels, cfg[k]
         * 4, kernel_size=3, padding=1)]
  conf_layers += [nn.Conv2d(v.out_channels, cfg[k]
         * num_classes, kernel_size=3, padding=1)]
 return vgg, extra_layers, (loc_layers, conf_layers)
base_, extras_, head_ = multibox(vgg(base[str(size)], 3), ## 产生vgg19基本模型
          add_extras(extras[str(size)], 1024), 
          mbox[str(size)], num_classes)
#mbox[str(size)]为:[4, 6, 6, 6, 4, 4]

得到的输出为:

base_为上述描述的vgg网络,extras_为extra_layers网络,head_为:

([Conv2d(512, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)),
 Conv2d(1024, 24, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)),
 Conv2d(512, 24, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)),
 Conv2d(256, 24, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)),
 Conv2d(256, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)),
 Conv2d(256, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))],
 [Conv2d(512, 84, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)),
 Conv2d(1024, 126, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)),
 Conv2d(512, 126, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)),
 Conv2d(256, 126, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)),
 Conv2d(256, 84, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)),
 Conv2d(256, 84, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))])

SSD网络及forward函数为:

class SSD(nn.Module):
 """Single Shot Multibox Architecture
 The network is composed of a base VGG network followed by the
 added multibox conv layers. Each multibox layer branches into
  1) conv2d for class conf scores
  2) conv2d for localization predictions
  3) associated priorbox layer to produce default bounding
   boxes specific to the layer's feature map size.
 See: https://arxiv.org/pdf/1512.02325.pdf for more details.

 Args:
  phase: (string) Can be "test" or "train"
  size: input image size
  base: VGG16 layers for input, size of either 300 or 500
  extras: extra layers that feed to multibox loc and conf layers
  head: "multibox head" consists of loc and conf conv layers
 """

 def __init__(self, phase, size, base, extras, head, num_classes):
  super(SSD, self).__init__()
  self.phase = phase
  self.num_classes = num_classes 
  self.cfg = (coco, voc)[num_classes == 21]
  self.priorbox = PriorBox(self.cfg)
  self.priors = Variable(self.priorbox.forward(), volatile=True)
  self.size = size

  # SSD network
  self.vgg = nn.ModuleList(base)
  # Layer learns to scale the l2 normalized features from conv4_3
  self.L2Norm = L2Norm(512, 20)
  self.extras = nn.ModuleList(extras)

  self.loc = nn.ModuleList(head[0])
  self.conf = nn.ModuleList(head[1])

  if phase == 'test':
   self.softmax = nn.Softmax(dim=-1)
   self.detect = Detect(num_classes, 0, 200, 0.01, 0.45)

 def forward(self, x):
  """Applies network layers and ops on input image(s) x.

  Args:
   x: input image or batch of images. Shape: [batch,3,300,300].

  Return:
   Depending on phase:
   test:
    Variable(tensor) of output class label predictions,
    confidence score, and corresponding location predictions for
    each object detected. Shape: [batch,topk,7]

   train:
    list of concat outputs from:
     1: confidence layers, Shape: [batch*num_priors,num_classes]
     2: localization layers, Shape: [batch,num_priors*4]
     3: priorbox layers, Shape: [2,num_priors*4]
  """
  sources = list()
  loc = list()
  conf = list()

  # apply vgg up to conv4_3 relu
  for k in range(23):
   x = self.vgg[k](x) ##得到的x尺度为[1,512,38,38]

  s = self.L2Norm(x)
  sources.append(s)

  # apply vgg up to fc7
  for k in range(23, len(self.vgg)):
   x = self.vgg[k](x) ##得到的x尺寸为[1,1024,19,19]
  sources.append(x)

  # apply extra layers and cache source layer outputs
  for k, v in enumerate(self.extras):
   x = F.relu(v(x), inplace=True)
   if k % 2 == 1:
    sources.append(x)
  '''
  上述得到的x输出分别为:
  torch.Size([1, 512, 10, 10])
  torch.Size([1, 256, 5, 5])
  torch.Size([1, 256, 3, 3])
  torch.Size([1, 256, 1, 1])
  '''

  # apply multibox head to source layers
  for (x, l, c) in zip(sources, self.loc, self.conf):
   loc.append(l(x).permute(0, 2, 3, 1).contiguous())
   conf.append(c(x).permute(0, 2, 3, 1).contiguous())

  loc = torch.cat([o.view(o.size(0), -1) for o in loc], 1)
  conf = torch.cat([o.view(o.size(0), -1) for o in conf], 1)
  if self.phase == "test":
   output = self.detect(
    loc.view(loc.size(0), -1, 4),     # loc preds
    self.softmax(conf.view(conf.size(0), -1,
        self.num_classes)),    # conf preds
    self.priors.type(type(x.data))     # default boxes
   )
  else:
   output = (
    loc.view(loc.size(0), -1, 4), #[1,8732,4]
    conf.view(conf.size(0), -1, self.num_classes),#[1,8732,21]
    self.priors
   )
  return output

上述代码中sources中保存的数据输出如下,即用于边框提取的特征图:

torch.Size([1, 512, 38, 38])
torch.Size([1, 1024, 19, 19])
torch.Size([1, 512, 10, 10])
torch.Size([1, 256, 5, 5])
torch.Size([1, 256, 3, 3])
torch.Size([1, 256, 1, 1])

模型输入为

x=Variable(torch.randn(1,3,300,300))

以上这篇基于Pytorch SSD模型分析就是小编分享给大家的全部内容了,希望能给大家一个参考,也希望大家多多支持三水点靠木。

Python 相关文章推荐
python列表操作使用示例分享
Feb 21 Python
Python操作CouchDB数据库简单示例
Mar 10 Python
获取python文件扩展名和文件名方法
Feb 02 Python
Python常见排序操作示例【字典、列表、指定元素等】
Aug 15 Python
浅谈python下含中文字符串正则表达式的编码问题
Dec 07 Python
python机器人运动范围问题的解答
Apr 29 Python
Python:Numpy 求平均向量的实例
Jun 29 Python
Python制作微信好友背景墙教程(附完整代码)
Jul 17 Python
python读取文件指定行内容实例讲解
Mar 02 Python
Django 后台带有字典的列表数据与页面js交互实例
Apr 03 Python
Django 5种类型Session使用方法解析
Apr 29 Python
paramiko使用tail实时获取服务器的日志输出详解
Dec 06 Python
Python3使用腾讯云文字识别(腾讯OCR)提取图片中的文字内容实例详解
Feb 18 #Python
Python动态导入模块和反射机制详解
Feb 18 #Python
pytorch进行上采样的种类实例
Feb 18 #Python
new_zeros() pytorch版本的转换方式
Feb 18 #Python
对pytorch的函数中的group参数的作用介绍
Feb 18 #Python
基于python3实现倒叙字符串
Feb 18 #Python
Python日期格式和字符串格式相互转换的方法
Feb 18 #Python
You might like
PHP 和 MySQL 基础教程(二)
2006/10/09 PHP
php file_get_contents函数轻松采集html数据
2010/04/22 PHP
php设计模式 Interpreter(解释器模式)
2011/06/26 PHP
PHP动态创建Web站点的方法
2011/08/14 PHP
PHP的curl实现get,post和cookie(实例介绍)
2013/06/17 PHP
PHP实现抓取Google IP并自动修改hosts文件
2015/02/12 PHP
PHP对象、模式与实践之高级特性分析
2016/12/08 PHP
Javascript学习笔记5 类和对象
2010/01/11 Javascript
当jQuery遭遇CoffeeScript的时候 使用分享
2011/09/17 Javascript
网页右键ie不支持event.preventDefault和event.returnValue (需要加window)
2013/02/22 Javascript
VUE实现日历组件功能
2017/03/13 Javascript
Ionic3 UI组件之Gallery Modal详解
2017/06/07 Javascript
微信小程序使用map组件实现检索(定位位置)周边的POI功能示例
2019/01/23 Javascript
[33:19]完美世界DOTA2联赛PWL S2 PXG vs InkIce 第一场 11.26
2020/11/30 DOTA
python抓取网页图片示例(python爬虫)
2014/04/27 Python
使用python编写批量卸载手机中安装的android应用脚本
2014/07/21 Python
Python中zip()函数用法实例教程
2014/07/31 Python
Python正则表达式匹配HTML页面编码
2015/04/08 Python
python3.5 + PyQt5 +Eric6 实现的一个计算器代码
2017/03/11 Python
在Python中实现字典反转案例
2020/12/05 Python
35款精致的 CSS3 和 HTML5 网页模板 推荐
2012/08/03 HTML / CSS
用CSS3实现背景渐变的方法
2015/07/14 HTML / CSS
Sunglasses Shop荷兰站:英国最大的太阳镜独立在线零售商和供应商
2017/01/08 全球购物
独特的礼品和创新的科技产品:The Grommet
2018/02/24 全球购物
英语师范专业毕业生自荐信
2013/09/21 职场文书
同学聚会老师邀请函
2014/01/28 职场文书
迟到检讨书5000字
2014/01/31 职场文书
函授本科个人自我鉴定
2014/03/25 职场文书
大二学生学年自我鉴定
2014/09/12 职场文书
法人单位适用的授权委托书
2014/09/19 职场文书
项目转让协议书
2014/10/27 职场文书
影视后期实训报告
2014/11/05 职场文书
安全承诺书格式范本
2015/04/28 职场文书
检讨书格式范文
2015/05/07 职场文书
简单总结SpringMVC拦截器的使用方法
2021/06/28 Java/Android
python中出现invalid syntax报错的几种原因分析
2022/02/12 Python