图像分类_ResNet18模型_CIFAR10数据集_精度90%.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 基于FPGA硬件加速的深度学习系统设计——图像分类"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Auther:李远东\n",
    "\n",
    "Student Number: 171180531\n",
    "\n",
    "Date: 2020/11/17"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 一.实验要求："
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "仿照示例，使用jupyter notebook+pytorch完成一个网络的训练。\n",
    "要求：\n",
    "1.\t使用自己定义的网络结构（可以更改网络的层数、卷积尺寸、channel数等等）\n",
    "2.\t使用cifar10数据集\n",
    "3.\t最后的分类精度至少达到0.9\n",
    "4.\t写一份报告，介绍一下自己的网络结构，最后实现的分类效果等，附上源代码。\n",
    "5.\t下节课前，交到之前交作业的邮箱"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 二.实验准备："
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "硬件条件：\n",
    "\n",
    "独立显卡型号：NVIDIA GeForce 940MX × 1\n",
    "\n",
    "独立显卡显存：2048 MB\n",
    "\n",
    "软件条件：\n",
    "\n",
    "语言环境：Python 3.7.2\n",
    "\n",
    "实验工具：Jupyter Notebook\n",
    "\n",
    "深度学习框架库：PyTorch"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 三.实验过程："
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "数据集: CIFAR10\n",
    "\n",
    "网络模型: ResNet18(有所修改)\n",
    "\n",
    "源代码及模型的描述均以注释形式展示如下。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1.导入相关模块"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.7.0\n",
      "0.8.1\n",
      "cuda\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import time\n",
    "import numpy as np\n",
    "import torch\n",
    "from torch import nn, optim\n",
    "import torch.nn.functional as F\n",
    "import torch.backends.cudnn as cudnn\n",
    "import torchvision\n",
    "import torchvision.transforms as transforms\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "import sys\n",
    "sys.path.append(\"D:\\课程与教材\\基于FPGA硬件加速的深度学习系统设计\\Dive-into-DL-PyTorch-master\\code\")\n",
    "import d2lzh_pytorch as d2l\n",
    "\n",
    "os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"0\"\n",
    "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
    "\n",
    "print(torch.__version__)\n",
    "print(torchvision.__version__)\n",
    "print(device)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "torch版本1.7.0   \n",
    "\n",
    "torchvision版本0.8.1    \n",
    "\n",
    "运算设备cuda"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2.获取数据集并做预处理（CIFAR10）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#图像预处理变换的定义\n",
    "transform_train = transforms.Compose([\n",
    "    transforms.RandomCrop(32, padding=4),         #在一个随机的位置进行裁剪，32正方形裁剪，每个边框上填充4\n",
    "    transforms.RandomHorizontalFlip(),            #以给定的概率随机水平翻转给定的PIL图像，默认值为0.5\n",
    "    transforms.ToTensor(),                        #将PIL Image或者 ndarray 转换为tensor，并且归一化至[0-1]\n",
    "    transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),#用平均值和标准偏差归一化张量图像，\n",
    "                                                                             #(M1,…,Mn)和(S1,…,Sn)将标准化输入的每个通道\n",
    "])\n",
    "transform_test = transforms.Compose([             #测试集同样进行图像预处理\n",
    "    transforms.ToTensor(),\n",
    "    transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),\n",
    "])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "对图像进行预处理变换，可以一定程度提高模型精度，提升收敛速度。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Files already downloaded and verified\n",
      "Files already downloaded and verified\n"
     ]
    }
   ],
   "source": [
    "#下载数据集并设置存放目录，训练与否，下载与否，数据预处理转换方式等参数，得到cifar10_train训练数据集与cifar10_test测试数据集\n",
    "cifar10_train = torchvision.datasets.CIFAR10(root='~/Datasets/CIFAR10', train=True, download=True, transform=transform_train)\n",
    "cifar10_test = torchvision.datasets.CIFAR10(root='~/Datasets/CIFAR10', train=False, download=True, transform=transform_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "这里使用的CIFAR10数据集已经下载至本地，所以不需要在线下载，直接取用即可。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'torchvision.datasets.cifar.CIFAR10'>\n",
      "50000 10000\n"
     ]
    }
   ],
   "source": [
    "#展示数据集类型及数据集的大小\n",
    "print(type(cifar10_train))\n",
    "print(len(cifar10_train), len(cifar10_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([3, 32, 32]) torch.float32\n",
      "4\n"
     ]
    }
   ],
   "source": [
    "#打印其中一个数据，得到其图像尺寸，数据类型与对应的标签\n",
    "feature, label = cifar10_train[3]\n",
    "print(feature.shape, feature.dtype)\n",
    "print(label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['airplane', 'automobile', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']\n"
     ]
    }
   ],
   "source": [
    "#此函数使输入数字标签，返回字符串型标签，便于标签可读性\n",
    "def get_CIFAR10_labels(labels):\n",
    "    text_labels = ['airplane', 'automobile', 'bird', 'cat', 'deer',\n",
    "                   'dog', 'frog', 'horse', 'ship', 'truck']\n",
    "    return [text_labels[int(i)] for i in labels]\n",
    "print(get_CIFAR10_labels([0,1,2,3,4,5,6,7,8,9]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "#此函数用于显示图像与对应标签\n",
    "def show_cifar10(images, labels):\n",
    "    d2l.use_svg_display()                                   #图像以可缩放矢量图形式显示\n",
    "    _, figs = plt.subplots(1, len(images), figsize=(13, 13))#返回第一个变量为画布，第二个变量为1行，len(images)列，13大小的图片的一个图片画布\n",
    "                                                            #这里的_表示我们忽略（不使用）的变量\n",
    "    for f, img, lbl in zip(figs, images, labels):           #zip为打包，把figs, images, labels中的每个元素对应打包在一起\n",
    "        img=torchvision.utils.make_grid(img).numpy()        #make_grid将多张图片拼在一起，这里img只有一张图片\n",
    "        f.imshow(np.transpose(img,(1,2,0)))                 #将图片的维度调换，从[C,W,H]转换为[W,H,C]，以便于显示\n",
    "        f.set_title(lbl)                                    #画布上设置标题为标签\n",
    "        f.axes.get_xaxis().set_visible(False)               #X轴刻度关闭\n",
    "        f.axes.get_yaxis().set_visible(False)               #Y轴刻度关闭\n",
    "    plt.show()                                              #绘图"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3.定义ResNet18模型"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "本实验为了达到精度0.9的要求，尝试使用过LeNet，VGG-16作为网络模型，但测试集精度最优分别只达到了0.68与0.85。\n",
    "\n",
    "之后使用了更先进的MSRA何凯明团队的残差网络(Residual Networks)，综合考虑硬件条件约束后，采用简单的18层ResNet-18作为网络模型进行训练，测试集精度勉强达到0.9。\n",
    "\n",
    "对于更加复杂先进的网络结构，可以得到更高的测试集精度，但由于硬件显卡性能瓶颈，需要非常长的训练时间甚至会显存不够而报错，所以没有进一步实验。\n",
    "\n",
    "由于原始的ResNet是基于ImageNet的图像尺寸而定义的，为了适应CIFAR10数据集，我做了一些改动。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "#定义带两个卷积路径和一条捷径的残差基本块类\n",
    "class BasicBlock(nn.Module):\n",
    "    expansion = 1\n",
    "\n",
    "    def __init__(self, in_planes, planes, stride=1):       #初始化函数，in_planes为输入通道数，planes为输出通道数，步长默认为1\n",
    "        super(BasicBlock, self).__init__()\n",
    "        #定义第一个卷积，默认卷积前后图像大小不变但可修改stride使其变化，通道可能改变\n",
    "        self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=3, stride=stride, padding=1, bias=False)\n",
    "        #定义第一个批归一化\n",
    "        self.bn1 = nn.BatchNorm2d(planes)\n",
    "        #定义第二个卷积，卷积前后图像大小不变，通道数不变\n",
    "        self.conv2 = nn.Conv2d(planes, planes, kernel_size=3,stride=1, padding=1, bias=False)\n",
    "        #定义第二个批归一化\n",
    "        self.bn2 = nn.BatchNorm2d(planes)\n",
    "\n",
    "        #定义一条捷径，若两个卷积前后的图像尺寸有变化(stride不为1导致图像大小变化或通道数改变)，捷径通过1×1卷积用stride修改大小\n",
    "        #以及用expansion修改通道数，以便于捷径输出和两个卷积的输出尺寸匹配相加\n",
    "        self.shortcut = nn.Sequential()         \n",
    "        if stride != 1 or in_planes != self.expansion*planes:\n",
    "            self.shortcut = nn.Sequential(\n",
    "                nn.Conv2d(in_planes, self.expansion*planes,kernel_size=1, stride=stride, bias=False),\n",
    "                nn.BatchNorm2d(self.expansion*planes)\n",
    "            )\n",
    "\n",
    "    #定义前向传播函数，输入图像为x，输出图像为out\n",
    "    def forward(self, x):\n",
    "        out = F.relu(self.bn1(self.conv1(x)))   #第一个卷积和第一个批归一化后用ReLU函数激活\n",
    "        out = self.bn2(self.conv2(out))       \n",
    "        out += self.shortcut(x)                 #第二个卷积和第二个批归一化后与捷径相加\n",
    "        out = F.relu(out)                       #两个卷积路径输出与捷径输出相加后用ReLU激活\n",
    "        return out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "#定义残差网络ResNet18\n",
    "class ResNet(nn.Module):\n",
    "    #定义初始函数，输入参数为残差块，残差块数量，默认参数为分类数10\n",
    "    def __init__(self, block, num_blocks, num_classes=10):\n",
    "        super(ResNet, self).__init__()\n",
    "        #设置第一层的输入通道数\n",
    "        self.in_planes = 64\n",
    "\n",
    "        #定义输入图片先进行一次卷积与批归一化，使图像大小不变，通道数由3变为64得两个操作\n",
    "        self.conv1 = nn.Conv2d(3, 64, kernel_size=3,stride=1, padding=1, bias=False)\n",
    "        self.bn1 = nn.BatchNorm2d(64)\n",
    "        \n",
    "        #定义第一层，输入通道数64，有num_blocks[0]个残差块，残差块中第一个卷积步长自定义为1\n",
    "        self.layer1 = self._make_layer(block, 64, num_blocks[0], stride=1)\n",
    "        #定义第二层，输入通道数128，有num_blocks[1]个残差块，残差块中第一个卷积步长自定义为2\n",
    "        self.layer2 = self._make_layer(block, 128, num_blocks[1], stride=2)\n",
    "        #定义第三层，输入通道数256，有num_blocks[2]个残差块，残差块中第一个卷积步长自定义为2\n",
    "        self.layer3 = self._make_layer(block, 256, num_blocks[2], stride=2)\n",
    "        #定义第四层，输入通道数512，有num_blocks[3]个残差块，残差块中第一个卷积步长自定义为2\n",
    "        self.layer4 = self._make_layer(block, 512, num_blocks[3], stride=2)\n",
    "        \n",
    "        #定义全连接层，输入512*block.expansion个神经元，输出10个分类神经元\n",
    "        self.linear = nn.Linear(512*block.expansion, num_classes)\n",
    "\n",
    "    #定义创造层的函数，在同一层中通道数相同，输入参数为残差块，通道数，残差块数量，步长\n",
    "    def _make_layer(self, block, planes, num_blocks, stride):\n",
    "        #strides列表第一个元素stride表示第一个残差块第一个卷积步长，其余元素表示其他残差块第一个卷积步长为1\n",
    "        strides = [stride] + [1]*(num_blocks-1)\n",
    "        #创建一个空列表用于放置层\n",
    "        layers = []\n",
    "        #遍历strides列表，对本层不同的残差块设置不同的stride\n",
    "        for stride in strides:\n",
    "            layers.append(block(self.in_planes, planes, stride)) #创建残差块添加进本层\n",
    "            self.in_planes = planes * block.expansion            #更新本层下一个残差块的输入通道数或本层遍历结束后作为下一层的输入通道数\n",
    "        return nn.Sequential(*layers)                            #返回层列表\n",
    "\n",
    "    #定义前向传播函数，输入图像为x，输出预测数据\n",
    "    def forward(self, x):\n",
    "        out = F.relu(self.bn1(self.conv1(x)))  #第一个卷积和第一个批归一化后用ReLU函数激活\n",
    "        out = self.layer1(out)                 #第一层传播\n",
    "        out = self.layer2(out)                 #第二层传播\n",
    "        out = self.layer3(out)                 #第三层传播\n",
    "        out = self.layer4(out)                 #第四层传播\n",
    "        out = F.avg_pool2d(out, 4)             #经过一次4×4的平均池化\n",
    "        out = out.view(out.size(0), -1)        #将数据flatten平坦化\n",
    "        out = self.linear(out)                 #全连接传播\n",
    "        return out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ResNet(\n",
      "  (conv1): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "  (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "  (layer1): Sequential(\n",
      "    (0): BasicBlock(\n",
      "      (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (shortcut): Sequential()\n",
      "    )\n",
      "    (1): BasicBlock(\n",
      "      (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (shortcut): Sequential()\n",
      "    )\n",
      "  )\n",
      "  (layer2): Sequential(\n",
      "    (0): BasicBlock(\n",
      "      (conv1): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (shortcut): Sequential(\n",
      "        (0): Conv2d(64, 128, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
      "        (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      )\n",
      "    )\n",
      "    (1): BasicBlock(\n",
      "      (conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (shortcut): Sequential()\n",
      "    )\n",
      "  )\n",
      "  (layer3): Sequential(\n",
      "    (0): BasicBlock(\n",
      "      (conv1): Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (shortcut): Sequential(\n",
      "        (0): Conv2d(128, 256, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
      "        (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      )\n",
      "    )\n",
      "    (1): BasicBlock(\n",
      "      (conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (shortcut): Sequential()\n",
      "    )\n",
      "  )\n",
      "  (layer4): Sequential(\n",
      "    (0): BasicBlock(\n",
      "      (conv1): Conv2d(256, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (shortcut): Sequential(\n",
      "        (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
      "        (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      )\n",
      "    )\n",
      "    (1): BasicBlock(\n",
      "      (conv1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (shortcut): Sequential()\n",
      "    )\n",
      "  )\n",
      "  (linear): Linear(in_features=512, out_features=10, bias=True)\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "#将ResNet类(参数为BasicBlock基本残差块，[2,2,2,2]四层中每层2个基本残差块)赋给对象net\n",
    "net = ResNet(BasicBlock, [2, 2, 2, 2])\n",
    "#打印net，查看网络结构\n",
    "print(net)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "可以看到网络结构由六大部分组成：\n",
    "\n",
    "1.最初的一个卷积层与批归一化层，输入图片[3,32,32]，输出图片[64,32,32]\n",
    "\n",
    "2.第一层，包括两个残差块\n",
    "\n",
    "    第一个残差块包含两个卷积与两个批归一化，输入图片[64,32,32]，输出图片[64,32,32],捷径未加卷积修改\n",
    "    第二个残差块包含两个卷积与两个批归一化，输入图片[64,32,32]，输出图片[64,32,32],捷径未加卷积修改\n",
    "\n",
    "3.第二层，包括两个残差块\n",
    "\n",
    "    第一个残差块包含两个卷积与两个批归一化，输入图片[64,32,32]，输出图片[128,16,16],捷径通过步长为2的1×1卷积修改输出为[128,16,16]\n",
    "    第二个残差块包含两个卷积与两个批归一化，输入图片[128,16,16]，输出图片[128,16,16],捷径未加卷积修改\n",
    "\n",
    "4.第三层，包括两个残差块\n",
    "\n",
    "    第一个残差块包含两个卷积与两个批归一化，输入图片[128,16,16]，输出图片[256,9,9],捷径通过步长为2的1×1卷积修改输出为[256,9,9]\n",
    "    第二个残差块包含两个卷积与两个批归一化，输入图片[256,9,9]，输出图片[256,9,9],捷径未加卷积修改\n",
    "\n",
    "5.第四层，包括两个残差块\n",
    "\n",
    "    第一个残差块包含两个卷积与两个批归一化，输入图片[256,9,9]，输出图片[512,5,5],捷径通过步长为2的1×1卷积修改输出为[512,5,5]\n",
    "    第二个残差块包含两个卷积与两个批归一化，输入图片[512,5,5]，输出图片[512,5,5],捷径未加卷积修改\n",
    "\n",
    "6.全连接层，输入神经元节点数为512 * 5 * 5，输出神经元节点数为10 \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4.训练模型准备"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "#定义一个训练批量的样本数\n",
    "batch_size=128\n",
    "#构建可迭代的数据装载器(参数为数据集，一个批量的样本数，是否乱序，工作线程数)\n",
    "train_iter = torch.utils.data.DataLoader(cifar10_train, batch_size=128, shuffle=True, num_workers=2)\n",
    "test_iter = torch.utils.data.DataLoader(cifar10_test, batch_size=100, shuffle=False, num_workers=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "#多GPU训练及优化\n",
    "if device == 'cuda':\n",
    "    #对象net可进行多GPU并行处理\n",
    "    net = torch.nn.DataParallel(net)\n",
    "    #cudnn是英伟达为深度神经网络开发的GPU加速库,让内置的cudnn的auto-tuner自动寻找最适合当前配置的高效算法,来达到优化运行效率的问题。\n",
    "    cudnn.benchmark = True          "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "创建了一个准确度评估的函数，主要用来在训练过程中将每轮所得网络模型输入测试集数据，进而获得其泛化精度"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "#定义准确度评估函数(参数为数据装载器，网络模型，运算设备)\n",
    "def evaluate_accuracy(data_iter, net, device=None):\n",
    "    # 如果没指定device就使用net的device\n",
    "    if device is None and isinstance(net, torch.nn.Module):\n",
    "        device = list(net.parameters())[0].device\n",
    "    #累计正确样本设为0.0，累计预测样本数设为0\n",
    "    acc_sum, n = 0.0, 0\n",
    "    #准确度评估阶段，with torch.no_grad()封装内关闭梯度计算功能\n",
    "    with torch.no_grad():\n",
    "        #从数据加载器上批量读取数据X与标签y\n",
    "        for X, y in data_iter:\n",
    "            if isinstance(net, torch.nn.Module):#若网络模型继承自torch.nn.Module\n",
    "                net.eval()#进入评估模式, 这会关闭dropout，以CPU进行准确度累加计算\n",
    "                #判断net(X.to(device)).argmax(dim=1)即X经net后输出的批量预测列表中每一个样本输出的最大值和y.to(device)此样本真实标签是否相同，\n",
    "                #若相同则表示预测正确，等号表达式为True，值为1；否则表达式为False，值为0。将批量所有样本的等号表达式值求和后再加给acc_sum\n",
    "                #每一次acc_sum增加一批量中预测正确的样本个数，随着不断的遍历，acc_sum表示累计的所有预测正确的样本个数\n",
    "                acc_sum += (net(X.to(device)).argmax(dim=1) == y.to(device)).float().sum().cpu().item()\n",
    "                net.train()# 改回训练模式\n",
    "            else: #若使用自定义的模型\n",
    "                #查看net对象中是否有变量名为is_training的参数，若有将is_training设置成False后遍历累加每一批量中的预测正确的样本数量\n",
    "                if('is_training' in net.__code__.co_varnames): \n",
    "                    acc_sum += (net(X, is_training=False).argmax(dim=1) == y).float().sum().item()\n",
    "                #若net对象中没有变量名为is_training的参数，则遍历累加每一批量中的预测正确的样本数量\n",
    "                else:\n",
    "                    acc_sum += (net(X).argmax(dim=1) == y).float().sum().item() \n",
    "            n += y.shape[0]        #每一次y.shape[0]表示一批量中标签个数，也就是样本个数。所以n表示累计的所有预测过的样本个数，无论正确与否\n",
    "    return acc_sum / n             #用累计的所有预测正确的样本个数除以累计的所有预测过的样本个数得到准确率"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "创建了一个训练函数，主要用来进行循环训练，并在每轮训练结束后，打印本轮的训练集精度与测试集精度等训练信息"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "#定义训练函数(参数为网络模型，训练加载器，测试加载器，批量样本数，优化器，运算设备，训练回合数)\n",
    "def train(net, train_iter, test_iter, batch_size, optimizer, device, num_epochs):\n",
    "    net = net.to(device)                               #网络模型搬移至指定的设备上\n",
    "    print(\"training on \", device)                      #查看当前训练所用的设备\n",
    "    loss = torch.nn.CrossEntropyLoss()                 #损失函数loss使用交叉熵损失函数\n",
    "    batch_count = 0                                    #批量计数器设为0\n",
    "    for epoch in range(num_epochs):#循环训练回合，每回合会以批量为单位训练完整个训练集，一共训练num_epochs个回合\n",
    "        #每一训练回合初始化累计训练损失函数为0.0，累计训练正确样本数为0.0，训练样本总数为0，start为开始计时的时间点\n",
    "        train_l_sum, train_acc_sum, n, start = 0.0, 0.0, 0, time.time()\n",
    "        for X, y in train_iter:              #循环每次取一批量的图像与标签\n",
    "            X = X.to(device)                 #将图像搬移至指定设备上\n",
    "            y = y.to(device)                 #将标签搬移至指定设备上\n",
    "            y_hat = net(X)                   #将批量图像数据X输入网络模型net，得到输出批量预测数据y_hat\n",
    "            l = loss(y_hat, y)               #计算批量预测标签y_hat与批量真实标签y之间的损失函数l\n",
    "            optimizer.zero_grad()            #优化器的梯度清零\n",
    "            l.backward()                     #对批量损失函数l进行反向传播计算梯度\n",
    "            optimizer.step()                 #优化器的梯度进行更新，训练所得参数也更新\n",
    "            train_l_sum += l.cpu().item()    #将本批量损失函数l加至训练损失函数累计train_l_sum中\n",
    "            train_acc_sum += (y_hat.argmax(dim=1) == y).sum().cpu().item()#将本批量预测正确的样本数加至累计预测正确样本数train_acc_sum中\n",
    "            n += y.shape[0]                  #将本批量训练的样本数，加至训练样本总数\n",
    "            batch_count += 1                 #批量计数器加1\n",
    "        test_acc = evaluate_accuracy(test_iter, net)#对本回合训练所得网络模型参数，以批量为单位，测试全集去验证，得到测试集预测准确度\n",
    "        #打印回合数，每回合平均损失函数，每回合的训练集准确度，每回合的测试集准确度，每回合用时\n",
    "        print('epoch %d, loss %.4f, train acc %.3f, test acc %.3f, time %.1f sec'\n",
    "              % (epoch + 1, train_l_sum / batch_count, train_acc_sum / n, test_acc, time.time() - start))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5.训练模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "training on  cuda\n",
      "epoch 1, loss 1.8085, train acc 0.337, test acc 0.420, time 391.4 sec\n",
      "epoch 2, loss 0.6419, train acc 0.532, test acc 0.601, time 382.5 sec\n",
      "epoch 3, loss 0.3349, train acc 0.640, test acc 0.659, time 381.2 sec\n",
      "epoch 4, loss 0.2087, train acc 0.706, test acc 0.734, time 381.7 sec\n",
      "epoch 5, loss 0.1401, train acc 0.755, test acc 0.751, time 382.8 sec\n",
      "epoch 6, loss 0.0990, train acc 0.792, test acc 0.782, time 382.9 sec\n",
      "epoch 7, loss 0.0743, train acc 0.819, test acc 0.806, time 381.3 sec\n",
      "epoch 8, loss 0.0572, train acc 0.841, test acc 0.826, time 381.2 sec\n",
      "epoch 9, loss 0.0463, train acc 0.855, test acc 0.840, time 381.5 sec\n",
      "epoch 10, loss 0.0380, train acc 0.867, test acc 0.839, time 381.6 sec\n",
      "epoch 11, loss 0.0315, train acc 0.881, test acc 0.855, time 382.6 sec\n",
      "epoch 12, loss 0.0265, train acc 0.887, test acc 0.844, time 381.8 sec\n",
      "epoch 13, loss 0.0230, train acc 0.896, test acc 0.852, time 382.6 sec\n",
      "epoch 14, loss 0.0198, train acc 0.903, test acc 0.867, time 381.5 sec\n",
      "epoch 15, loss 0.0172, train acc 0.910, test acc 0.863, time 381.7 sec\n",
      "epoch 16, loss 0.0149, train acc 0.916, test acc 0.887, time 381.8 sec\n",
      "epoch 17, loss 0.0132, train acc 0.921, test acc 0.875, time 385.0 sec\n",
      "epoch 18, loss 0.0116, train acc 0.927, test acc 0.879, time 381.6 sec\n",
      "epoch 19, loss 0.0104, train acc 0.931, test acc 0.884, time 381.3 sec\n",
      "epoch 20, loss 0.0092, train acc 0.935, test acc 0.885, time 382.1 sec\n",
      "epoch 21, loss 0.0082, train acc 0.940, test acc 0.895, time 381.3 sec\n",
      "epoch 22, loss 0.0074, train acc 0.943, test acc 0.894, time 382.1 sec\n",
      "epoch 23, loss 0.0065, train acc 0.947, test acc 0.892, time 383.7 sec\n",
      "epoch 24, loss 0.0059, train acc 0.950, test acc 0.896, time 385.9 sec\n",
      "epoch 25, loss 0.0055, train acc 0.951, test acc 0.893, time 384.1 sec\n",
      "epoch 26, loss 0.0050, train acc 0.955, test acc 0.888, time 382.0 sec\n",
      "epoch 27, loss 0.0046, train acc 0.957, test acc 0.903, time 381.7 sec\n",
      "epoch 28, loss 0.0041, train acc 0.958, test acc 0.881, time 381.5 sec\n",
      "epoch 29, loss 0.0040, train acc 0.960, test acc 0.900, time 382.5 sec\n",
      "epoch 30, loss 0.0035, train acc 0.963, test acc 0.902, time 381.4 sec\n",
      "epoch 31, loss 0.0032, train acc 0.967, test acc 0.905, time 381.4 sec\n",
      "epoch 32, loss 0.0030, train acc 0.965, test acc 0.896, time 381.6 sec\n",
      "epoch 33, loss 0.0028, train acc 0.968, test acc 0.900, time 381.4 sec\n",
      "epoch 34, loss 0.0027, train acc 0.968, test acc 0.892, time 380.8 sec\n",
      "epoch 35, loss 0.0025, train acc 0.970, test acc 0.907, time 381.3 sec\n",
      "epoch 36, loss 0.0022, train acc 0.973, test acc 0.905, time 381.0 sec\n",
      "epoch 37, loss 0.0021, train acc 0.972, test acc 0.908, time 380.7 sec\n",
      "epoch 38, loss 0.0020, train acc 0.973, test acc 0.911, time 380.4 sec\n",
      "epoch 39, loss 0.0019, train acc 0.974, test acc 0.907, time 380.9 sec\n",
      "epoch 40, loss 0.0018, train acc 0.975, test acc 0.906, time 380.8 sec\n",
      "epoch 41, loss 0.0018, train acc 0.973, test acc 0.911, time 380.8 sec\n",
      "epoch 42, loss 0.0016, train acc 0.978, test acc 0.904, time 382.2 sec\n",
      "epoch 43, loss 0.0014, train acc 0.979, test acc 0.893, time 380.6 sec\n",
      "epoch 44, loss 0.0015, train acc 0.978, test acc 0.901, time 380.6 sec\n",
      "epoch 45, loss 0.0014, train acc 0.979, test acc 0.907, time 380.6 sec\n",
      "epoch 46, loss 0.0014, train acc 0.978, test acc 0.907, time 380.6 sec\n",
      "epoch 47, loss 0.0012, train acc 0.981, test acc 0.903, time 380.7 sec\n",
      "epoch 48, loss 0.0011, train acc 0.982, test acc 0.911, time 381.1 sec\n",
      "epoch 49, loss 0.0011, train acc 0.981, test acc 0.901, time 380.9 sec\n",
      "epoch 50, loss 0.0011, train acc 0.981, test acc 0.904, time 380.9 sec\n"
     ]
    }
   ],
   "source": [
    "#设置学习率lr，训练回合数num_epochs\n",
    "lr, num_epochs = 0.01, 50\n",
    "#设置优化器为Adam优化器，参数为网络模型的参数和学习率\n",
    "optimizer = torch.optim.Adam(net.parameters(), lr=lr)\n",
    "#开始训练模型，参数为网络模型，训练加载器，测试加载器，批量大小，优化器，运算设备，训练回合数\n",
    "train(net, train_iter, test_iter, batch_size, optimizer, device, num_epochs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "一共循环了50个回合，损失函数已经几乎不再减小了，训练集精度也趋于恒定。\n",
    "\n",
    "可以从测试准确度test acc上看到，该模型的饱和测试集训练精度可以勉强达到0.9，满足了实验要求。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "#保存网络模型的参数至mymodel.pth文件\n",
    "torch.save(net.state_dict(),'mymodel.pth')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 6.验证模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Files already downloaded and verified\n",
      "Files already downloaded and verified\n"
     ]
    }
   ],
   "source": [
    "#重新加载数据集与测试集，但不做预处理变形，便于图片展示。\n",
    "cifar10_train = torchvision.datasets.CIFAR10(root='~/Datasets/CIFAR10', train=True, download=True, transform=transforms.ToTensor())\n",
    "cifar10_test = torchvision.datasets.CIFAR10(root='~/Datasets/CIFAR10', train=False, download=True, transform=transforms.ToTensor())\n",
    "#重新构建训练加载器与测试加载器，但批量大小为6，便于图片展示。\n",
    "train_iter = torch.utils.data.DataLoader(cifar10_train, batch_size=6, shuffle=True, num_workers=2)\n",
    "test_iter = torch.utils.data.DataLoader(cifar10_test, batch_size=6, shuffle=False, num_workers=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Files already downloaded and verified\n",
      "Files already downloaded and verified\n"
     ]
    }
   ],
   "source": [
    "#新建另一个训练集与测试集，做预处理变形，便于将其输入已训练好的网络模型来展示其输出的预测标签\n",
    "cifar10_train_tran = torchvision.datasets.CIFAR10(root='~/Datasets/CIFAR10', train=True, download=True, transform=transform_train)\n",
    "cifar10_test_tran = torchvision.datasets.CIFAR10(root='~/Datasets/CIFAR10', train=False, download=True, transform=transform_test)\n",
    "#新建新数据集的训练加载器与测试加载器，但批量大小为6，便于标签展示。\n",
    "train_iter_tran = torch.utils.data.DataLoader(cifar10_train_tran, batch_size=6, shuffle=True, num_workers=2)\n",
    "test_iter_tran = torch.utils.data.DataLoader(cifar10_test_tran, batch_size=6, shuffle=False, num_workers=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
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       "   </g>\r\n",
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       " <defs>\r\n",
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       " </defs>\r\n",
       "</svg>\r\n"
      ],
      "text/plain": [
       "<Figure size 936x936 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GroundTruth:  ['cat'] ['ship'] ['ship'] ['airplane'] ['frog'] ['frog']\n",
      "Predicted:  ['cat'] ['ship'] ['ship'] ['airplane'] ['frog'] ['frog']\n"
     ]
    }
   ],
   "source": [
    "dataiter = iter(test_iter)       #创建无预处理的测试加载器的迭代器\n",
    "images, labels = dataiter.next() #返回batch_size幅图片与标签，即6幅图片images与标签labels\n",
    "show_cifar10(images, get_CIFAR10_labels(labels))#展示测试集的这六幅图片与标签\n",
    "\n",
    "print('GroundTruth: ', ' '.join('%5s' % get_CIFAR10_labels([labels[j].numpy()]) for j in range(6)))#打印真实的标签GroundTruth\n",
    "dataiter_tran = iter(test_iter_tran)           #创建有预处理测试集的测试加载器的迭代器\n",
    "images_tran, labels_tran = dataiter_tran.next()#返回batch_size幅图片与标签，即6幅图片images与标签labels\n",
    "images_tran=images_tran.to(device)             #将图片搬移至GPU\n",
    "labels_tran=labels_tran.to(device)             #将标签搬移至GPU\n",
    "outputs = net(images_tran)                     #将有预处理的测试集批量图片输入已经训练好的网络模型,输出批量预测数据outputs\n",
    "_, predicted = torch.max(outputs.data, 1)      #得到批量预测数据outputs.data的最大值索引predicted列表\n",
    "print('Predicted: ', ' '.join('%5s' % get_CIFAR10_labels([predicted[j].cpu().numpy()]) for j in range(6)))#打印预测的标签Predicted"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "可以看到，对测试集中的六幅图片，通过训练好的模型预测所得的标签与真实的标签完全一致，实现了图像分类的目的，实验完成。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 四.实验总结"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "本次试验中以ResNet18残差模型为图像识别的网络模型，其测试精度达到了约0.9，但由于其层数较浅，并未完全发挥残差网络的优势。\n",
    "相比较LeNet，AlexNet，VGG16，GoogLeNet等模型，更深的网络层数带来的不仅仅是精度的缺失，模型的训练也更加困难，梯度消失导致反向传播无法继续。\n",
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "而何凯明博士发明的残差网络使得网络层数加深的同时准确度反而出现了提升。例如在较浅的饱和网络后面再加上几个恒等映射层（Identity mapping，也即y=x，输出等于输入），这样就增加了网络的深度，并且误差不会增加，也即更深的网络不会带来训练集上误差的上升。而这里提到的使用恒等映射直接将前一层输出传到后面的思想，便是著名深度残差网络ResNet的灵感来源。只需要将残差F(x)=H(x)-x训练至趋于零，就可以实现加深网络层数而不降低准确度。\n",
    "![image.png](attachment:image.png)\n",
    "由于残差网络的出现，深度学习神经网络的层数瞬间从几十层深入到上百层，甚至可以达到更深的上千层。目前先进的一些网络结构均采用深度网络模型，用于达到更高的准确度。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "对于CIFAR10数据集，一些常见深度网络进度如下：\n",
    "\n",
    "VGG16\t              92.64%\n",
    "\n",
    "ResNet18\t           93.02%\n",
    "\n",
    "ResNet50\t           93.62%\n",
    "\n",
    "ResNet101\t           93.75%\n",
    "\n",
    "RegNetX_200MF\t        94.24%\n",
    "\n",
    "RegNetY_400MF\t        94.29%\n",
    "\n",
    "MobileNetV2\t           94.43%\n",
    "\n",
    "ResNeXt29(32x4d)        94.73%\n",
    "\n",
    "ResNeXt29(2x64d)\t     94.82%\n",
    "\n",
    "DenseNet121\t           95.04%\n",
    "\n",
    "PreActResNet18\t        95.11%\n",
    "\n",
    "DPN92\t             95.16%"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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 },
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}