resnet50.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 人脸识别比赛"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 数据集\n",
    "\n",
    "1、上传zip格式压缩的数据集压缩包\n",
    "\n",
    "2、运行`! unzip dataset.zip`\n",
    "\n",
    "3、按上面的方法上传test.zip\n",
    "\n",
    "4、将数据集放置在根目录下，与work目录同级"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import torch\n",
    "import time\n",
    "from torch import nn, optim\n",
    "from torch.utils.data import Dataset, DataLoader\n",
    "import torchvision\n",
    "from torchvision.datasets import ImageFolder\n",
    "from torchvision import transforms\n",
    "from torchvision import models\n",
    "import os  \n",
    "\n",
    "import sys\n",
    "sys.path.append(\"..\") \n",
    "# import d2lzh_pytorch as d2l\n",
    "\n",
    "device = torch.device('cuda:2' if torch.cuda.is_available() else 'cpu')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_data_face(batch_size):\n",
    "    transform = torchvision.transforms.Compose([\n",
    "       # torchvision.transforms.Grayscale(num_output_channels=1), # 彩色图像转灰度图像num_output_channels默认1\n",
    "        torchvision.transforms.RandomHorizontalFlip(),\n",
    "        torchvision.transforms.Resize([330,330]),\n",
    "        torchvision.transforms.CenterCrop([224, 224]),\n",
    "        \n",
    "        torchvision.transforms.ToTensor()\n",
    "    ])\n",
    "    train_imgs = torchvision.datasets.ImageFolder('./dataset/train', transform=transform)\n",
    "    test_imgs = torchvision.datasets.ImageFolder('./dataset/test', transform=transform)\n",
    "    train_iter = torch.utils.data.DataLoader(train_imgs, batch_size=batch_size, shuffle=True, num_workers=4)\n",
    "    test_iter = torch.utils.data.DataLoader(test_imgs, batch_size=batch_size, shuffle=False, num_workers=4)\n",
    "\n",
    "    return train_iter, test_iter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "batch_size = 32\n",
    "# 如出现“out of memory”的报错信息，可减小batch_size或resize\n",
    "train_iter, test_iter = load_data_face(batch_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 获取并保存人名和索引的对应关系，用于测试过程中将索引映射为人名\n",
    "import pickle\n",
    "transform = torchvision.transforms.Compose([\n",
    "    #torchvision.transforms.Grayscale(num_output_channels=1), # 彩色图像转灰度图像num_output_channels默认1\n",
    "    torchvision.transforms.RandomHorizontalFlip(),\n",
    "    torchvision.transforms.Resize([330,330]),\n",
    "    torchvision.transforms.CenterCrop([224, 224]),\n",
    "    torchvision.transforms.ToTensor()\n",
    "])\n",
    "test_imgs = torchvision.datasets.ImageFolder('./dataset/test', transform=transform)\n",
    "label = test_imgs.class_to_idx\n",
    "label = {value:key for key, value in label.items()}\n",
    "# print(len(label))\n",
    "# 写入文件\n",
    "label_hal = open('label.pkl', 'wb')\n",
    "s = pickle.dumps(label)\n",
    "label_hal.write(s)\n",
    "label_hal.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#修改net，pretrained，正则化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "net=models.resnet50(pretrained=True)\n",
    "# print(net)\n",
    "net.fc = nn.Sequential(\n",
    "                       nn.ReLU(),\n",
    "                       nn.Dropout(0.6),\n",
    "                       nn.Linear(2048, 311)\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 训练"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def evaluate_accuracy(data_iter, net, device=None):\n",
    "    if device is None and isinstance(net, torch.nn.Module):\n",
    "        # 如果没指定device就使用net的device\n",
    "        device = list(net.parameters())[0].device \n",
    "    acc_sum, n = 0.0, 0\n",
    "    with torch.no_grad():\n",
    "        for X, y in data_iter:\n",
    "            if isinstance(net, torch.nn.Module):\n",
    "                net.eval() # 评估模式, 这会关闭dropout\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: # 自定义的模型, 3.13节之后不会用到, 不考虑GPU\n",
    "                if('is_training' in net.__code__.co_varnames): # 如果有is_training这个参数\n",
    "                    # 将is_training设置成False\n",
    "                    acc_sum += (net(X, is_training=False).argmax(dim=1) == y).float().sum().item() \n",
    "                else:\n",
    "                    acc_sum += (net(X).argmax(dim=1) == y).float().sum().item() \n",
    "            n += y.shape[0]\n",
    "    return acc_sum / n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_acc_list = []\n",
    "test_acc_list = []\n",
    "def train_ch5(net, train_iter, test_iter, batch_size, optimizer, device, num_epochs):\n",
    "    net = net.to(device)\n",
    "    print(\"training on \", device)\n",
    "    loss = torch.nn.CrossEntropyLoss()\n",
    "    for epoch in range(num_epochs):\n",
    "        train_l_sum, train_acc_sum, n, batch_count, start = 0.0, 0.0, 0, 0, time.time()\n",
    "        for X, y in train_iter:\n",
    "            X = X.to(device)\n",
    "            y = y.to(device)\n",
    "            y_hat = net(X)\n",
    "            l = loss(y_hat, y)\n",
    "            optimizer.zero_grad()\n",
    "            l.backward()\n",
    "            optimizer.step()\n",
    "            train_l_sum += l.cpu().item()\n",
    "            train_acc_sum += (y_hat.argmax(dim=1) == y).sum().cpu().item()\n",
    "            n += y.shape[0]\n",
    "            batch_count += 1\n",
    "#         train_iter,test_iter1 = load_data_face(batch_size)\n",
    "        test_acc = evaluate_accuracy(test_iter, net)\n",
    "        train_acc_list.append(train_acc_sum / n)\n",
    "        test_acc_list.append(test_acc)\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": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "training on  cuda:2\n",
      "epoch 1, loss 0.7742, train acc 0.815, test acc 0.661, time 31.4 sec\n",
      "epoch 2, loss 0.6757, train acc 0.829, test acc 0.669, time 30.0 sec\n",
      "epoch 3, loss 0.4927, train acc 0.878, test acc 0.741, time 29.8 sec\n",
      "epoch 4, loss 0.3249, train acc 0.922, test acc 0.803, time 30.2 sec\n"
     ]
    }
   ],
   "source": [
    "lr, num_epochs = 0.01,4\n",
    "optimizer = torch.optim.SGD(net.parameters(), lr=lr, momentum=0.9,weight_decay=1e-4)\n",
    "train_ch5(net, train_iter, test_iter, batch_size, optimizer, device, num_epochs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "training on  cuda:2\n",
      "epoch 1, loss 1.0158, train acc 0.763, test acc 0.697, time 28.1 sec\n",
      "epoch 2, loss 0.8901, train acc 0.800, test acc 0.724, time 27.7 sec\n",
      "epoch 3, loss 0.8476, train acc 0.811, test acc 0.735, time 28.4 sec\n",
      "epoch 4, loss 0.7879, train acc 0.822, test acc 0.749, time 27.9 sec\n",
      "epoch 5, loss 0.7773, train acc 0.832, test acc 0.763, time 28.1 sec\n"
     ]
    }
   ],
   "source": [
    "lr, num_epochs = 0.001,5\n",
    "optimizer = torch.optim.SGD(net.parameters(), lr=lr, momentum=0.5,weight_decay=1e-4)\n",
    "train_ch5(net, train_iter, test_iter, batch_size, optimizer, device, num_epochs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "training on  cuda:2\n",
      "epoch 1, loss 0.7255, train acc 0.846, test acc 0.772, time 29.5 sec\n",
      "epoch 2, loss 0.6788, train acc 0.864, test acc 0.784, time 30.3 sec\n",
      "epoch 3, loss 0.6229, train acc 0.877, test acc 0.784, time 30.2 sec\n",
      "epoch 4, loss 0.5861, train acc 0.880, test acc 0.793, time 30.5 sec\n",
      "epoch 5, loss 0.5545, train acc 0.899, test acc 0.799, time 31.6 sec\n"
     ]
    }
   ],
   "source": [
    "lr, num_epochs = 0.0005,5\n",
    "optimizer = torch.optim.SGD(net.parameters(), lr=lr, momentum=0.9,weight_decay=1e-4)\n",
    "train_ch5(net, train_iter, test_iter, batch_size, optimizer, device, num_epochs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<generator object Module.parameters at 0x7f5dcc20d570>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "net.parameters()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 自己加个画图\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "x = np.arange(1, len(train_acc_list)+1, 1)\n",
    "y1 = np.array(train_acc_list)\n",
    "y2 = np.array(test_acc_list)\n",
    "plt.plot(x, y1, label=\"train\")\n",
    "plt.plot(x, y2, linestyle = \"--\", label=\"test\")\n",
    "plt.xlabel(\"x\")\n",
    "plt.ylabel(\"y\")\n",
    "plt.title('train & test')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 保存模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "torch.save(net, './resnet50.pkl')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 生成识别结果文件测试"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 读取训练好的模型\n",
    "model = torch.load('./resnet50.pkl').eval()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 获取本次训练的人名和索引的对应关系\n",
    "label = {}\n",
    "with open('label.pkl','rb') as file:\n",
    "    label = pickle.loads(file.read())\n",
    "# print(label)\n",
    "# 测试集label对应关系\n",
    "import pickle\n",
    "label_answer = {}\n",
    "with open('label_answer.pkl','rb') as file:\n",
    "    label_answer = pickle.loads(file.read())\n",
    "label_answer = {value:key for key, value in label_answer.items()}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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    }
   ],
   "source": [
    "# 加载测试数据（在test目录下）\n",
    "from PIL import Image\n",
    "import numpy as np\n",
    "\n",
    "transform = torchvision.transforms.Compose([\n",
    "#     torchvision.transforms.Grayscale(num_output_channels=1), # 彩色图像转灰度图像num_output_channels默认1\n",
    "    torchvision.transforms.RandomHorizontalFlip(),\n",
    "    torchvision.transforms.CenterCrop([224, 224]),\n",
    "#     torchvision.transforms.Resize([224, 224]),\n",
    "    torchvision.transforms.ToTensor()\n",
    "])\n",
    "# 生成测试结果文件\n",
    "path = os.listdir('test')\n",
    "r_d = {}\n",
    "for f in path:\n",
    "#     print('test/' + f)\n",
    "    img = Image.open('test/' + f)\n",
    "    test_imgs = transform(img).unsqueeze(0)\n",
    "    test_imgs = test_imgs.to(device)\n",
    "    y = model(test_imgs)\n",
    "    pred = torch.argmax(y, dim = 1)\n",
    "    r = label_answer[label[int(pred)]]\n",
    "    r_d[int(f.strip('.jpg'))] = r\n",
    "#     print(1)\n",
    "# 写入结果文件\n",
    "r_d = sorted(r_d.items(), key=lambda a:a[0])\n",
    "r_d = dict(r_d)\n",
    "ret = open(\"result.csv\",\"w\")\n",
    "for key, value in r_d.items():\n",
    "    print(\"%d,%s\"%(key, value), file=ret)\n",
    "ret.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "finish\n"
     ]
    }
   ],
   "source": [
    "# 根据生成识别文件的代码，自行编写main.py文件，要求文件可生成结果文件result.csv\n",
    "# 已知的坑：main.py中需增加模型类的定义\n",
    "\n",
    "# 测试main.py生成result.csv\n",
    "!python main.py\n",
    "# 生成后自行验证"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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