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competition/科大讯飞AI开发者大赛2023/自动驾驶疲劳检测挑战赛_baseline.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "c8d01073-09a8-4c68-b93b-aef463738bd0",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "%pylab is deprecated, use %matplotlib inline and import the required libraries.\n",
+      "Populating the interactive namespace from numpy and matplotlib\n"
+     ]
+    }
+   ],
+   "source": [
+    "import os, sys, glob, argparse\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "from tqdm import tqdm\n",
+    "\n",
+    "%pylab inline\n",
+    "\n",
+    "import cv2\n",
+    "from PIL import Image\n",
+    "from sklearn.model_selection import train_test_split, StratifiedKFold, KFold\n",
+    "\n",
+    "import torch\n",
+    "torch.manual_seed(0)\n",
+    "torch.backends.cudnn.deterministic = False\n",
+    "torch.backends.cudnn.benchmark = True\n",
+    "\n",
+    "import torchvision.models as models\n",
+    "import torchvision.transforms as transforms\n",
+    "import torchvision.datasets as datasets\n",
+    "import torch.nn as nn\n",
+    "import torch.nn.functional as F\n",
+    "import torch.optim as optim\n",
+    "from torch.autograd import Variable\n",
+    "from torch.utils.data.dataset import Dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "70d2263d-916c-431e-aca7-3e5bcd867f9b",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "train_path = glob.glob('./自动驾驶疲劳检测挑战赛公开数据/train/*/*')\n",
+    "test_path = glob.glob('./自动驾驶疲劳检测挑战赛公开数据/test/*')\n",
+    "\n",
+    "np.random.shuffle(train_path)\n",
+    "np.random.shuffle(test_path)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "584f2188-cf45-4d72-abee-4dae0d362926",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "DATA_CACHE = {}\n",
+    "class XunFeiDataset(Dataset):\n",
+    "    def __init__(self, img_path, transform=None):\n",
+    "        self.img_path = img_path\n",
+    "        if transform is not None:\n",
+    "            self.transform = transform\n",
+    "        else:\n",
+    "            self.transform = None\n",
+    "    \n",
+    "    def __getitem__(self, index):\n",
+    "        img = cv2.imread(self.img_path[index])\n",
+    "            \n",
+    "        if self.transform is not None:\n",
+    "            img = self.transform(image = img)['image']\n",
+    "        \n",
+    "        img = img.transpose([2,0,1])\n",
+    "        img = img.astype(np.float32)\n",
+    "        return img, torch.from_numpy(np.array(int('non-sleepy' in self.img_path[index])))\n",
+    "    \n",
+    "    def __len__(self):\n",
+    "        return len(self.img_path)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "bd5a22b6-3a32-4039-bcc7-a6c6f5f9cbaf",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "class XunFeiNet(nn.Module):\n",
+    "    def __init__(self):\n",
+    "        super(XunFeiNet, self).__init__()\n",
+    "                \n",
+    "        model = models.resnet18(True)\n",
+    "        model.avgpool = nn.AdaptiveAvgPool2d(1)\n",
+    "        model.fc = nn.Linear(512, 2)\n",
+    "        self.resnet = model\n",
+    "        \n",
+    "    def forward(self, img):        \n",
+    "        out = self.resnet(img)\n",
+    "        return out"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "47539bef-14a7-4881-85e9-0882ff295e6a",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "def train(train_loader, model, criterion, optimizer):\n",
+    "    model.train()\n",
+    "    train_loss = 0.0\n",
+    "    for i, (input, target) in enumerate(train_loader):\n",
+    "        input = input.cuda(non_blocking=True)\n",
+    "        target = target.cuda(non_blocking=True)\n",
+    "\n",
+    "        # compute output\n",
+    "        output = model(input)\n",
+    "        loss = criterion(output, target)\n",
+    "\n",
+    "        # compute gradient and do SGD step\n",
+    "        optimizer.zero_grad()\n",
+    "        loss.backward()\n",
+    "        optimizer.step()\n",
+    "\n",
+    "        if i % 100 == 0:\n",
+    "            print(loss.item())\n",
+    "            \n",
+    "        train_loss += loss.item()\n",
+    "    \n",
+    "    return train_loss/len(train_loader)\n",
+    "            \n",
+    "def validate(val_loader, model, criterion):\n",
+    "    model.eval()\n",
+    "    \n",
+    "    val_acc = 0.0\n",
+    "    \n",
+    "    with torch.no_grad():\n",
+    "        end = time.time()\n",
+    "        for i, (input, target) in enumerate(val_loader):\n",
+    "            input = input.cuda()\n",
+    "            target = target.cuda()\n",
+    "\n",
+    "            # compute output\n",
+    "            output = model(input)\n",
+    "            loss = criterion(output, target)\n",
+    "            \n",
+    "            val_acc += (output.argmax(1) == target).sum().item()\n",
+    "            \n",
+    "    return val_acc / len(val_loader.dataset)\n",
+    "\n",
+    "def predict(test_loader, model, criterion):\n",
+    "    model.eval()\n",
+    "    val_acc = 0.0\n",
+    "    \n",
+    "    test_pred = []\n",
+    "    with torch.no_grad():\n",
+    "        end = time.time()\n",
+    "        for i, (input, target) in enumerate(test_loader):\n",
+    "            input = input.cuda()\n",
+    "            target = target.cuda()\n",
+    "\n",
+    "            # compute output\n",
+    "            output = model(input)\n",
+    "            test_pred.append(output.data.cpu().numpy())\n",
+    "            \n",
+    "    return np.vstack(test_pred)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "c14b410f-de94-4f52-be5b-1377c34bda4a",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/lyz/.local/lib/python3.9/site-packages/albumentations/augmentations/transforms.py:1639: FutureWarning: RandomContrast has been deprecated. Please use RandomBrightnessContrast\n",
+      "  warnings.warn(\n"
+     ]
+    }
+   ],
+   "source": [
+    "import albumentations as A\n",
+    "\n",
+    "train_loader = torch.utils.data.DataLoader(\n",
+    "    XunFeiDataset(train_path[:-10],\n",
+    "            A.Compose([\n",
+    "            A.RandomRotate90(),\n",
+    "            A.Resize(128, 128),\n",
+    "            A.RandomCrop(120, 120),\n",
+    "            A.HorizontalFlip(p=0.5),\n",
+    "            A.RandomContrast(p=0.5),\n",
+    "            A.RandomBrightnessContrast(p=0.5),\n",
+    "        ])\n",
+    "    ), batch_size=20, shuffle=True, num_workers=1, pin_memory=False\n",
+    ")\n",
+    "\n",
+    "val_loader = torch.utils.data.DataLoader(\n",
+    "    XunFeiDataset(train_path[-10:],\n",
+    "            A.Compose([\n",
+    "            A.Resize(128, 128),\n",
+    "            A.RandomCrop(120, 120),\n",
+    "            # A.HorizontalFlip(p=0.5),\n",
+    "            # A.RandomContrast(p=0.5),\n",
+    "        ])\n",
+    "    ), batch_size=20, shuffle=False, num_workers=1, pin_memory=False\n",
+    ")\n",
+    "\n",
+    "test_loader = torch.utils.data.DataLoader(\n",
+    "    XunFeiDataset(test_path,\n",
+    "            A.Compose([\n",
+    "            A.Resize(128, 128),\n",
+    "            A.HorizontalFlip(p=0.5),\n",
+    "            A.RandomContrast(p=0.5),\n",
+    "        ])\n",
+    "    ), batch_size=20, shuffle=False, num_workers=1, pin_memory=False\n",
+    ")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "7f7ee8cb-cfc0-4eb8-96ea-7302144ae7a4",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/lyz/.local/lib/python3.9/site-packages/torchvision/models/_utils.py:135: UserWarning: Using 'weights' as positional parameter(s) is deprecated since 0.13 and will be removed in 0.15. Please use keyword parameter(s) instead.\n",
+      "  warnings.warn(\n",
+      "/home/lyz/.local/lib/python3.9/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and will be removed in 0.15. The current behavior is equivalent to passing `weights=ResNet18_Weights.IMAGENET1K_V1`. You can also use `weights=ResNet18_Weights.DEFAULT` to get the most up-to-date weights.\n",
+      "  warnings.warn(msg)\n"
+     ]
+    }
+   ],
+   "source": [
+    "model = XunFeiNet()\n",
+    "model = model.to('cuda')\n",
+    "criterion = nn.CrossEntropyLoss().cuda()\n",
+    "optimizer = torch.optim.AdamW(model.parameters(), 0.001)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "b2faad78-e794-4f31-8b56-1a64dbf89ccc",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.8071246147155762\n",
+      "0.40889906883239746\n",
+      "0.20135173201560974\n",
+      "0.4055064118653536 0.8306613226452906 0.8\n"
+     ]
+    }
+   ],
+   "source": [
+    "for _  in range(1):\n",
+    "    train_loss = train(train_loader, model, criterion, optimizer)\n",
+    "    val_acc  = validate(val_loader, model, criterion)\n",
+    "    train_acc = validate(train_loader, model, criterion)\n",
+    "    \n",
+    "    print(train_loss, train_acc, val_acc)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "6787d12a-b923-4777-abf5-4d90904551d1",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "pred = None\n",
+    "\n",
+    "for _ in range(10):\n",
+    "    if pred is None:\n",
+    "        pred = predict(test_loader, model, criterion)\n",
+    "    else:\n",
+    "        pred += predict(test_loader, model, criterion)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "1743990f-d27d-43cd-85fd-23a0ff0f2c36",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "submit = pd.DataFrame(\n",
+    "    {\n",
+    "        'name': [x.split('/')[-1] for x in test_path],\n",
+    "        'label': pred.argmax(1)\n",
+    "})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "cc565971-7a5f-4bb0-8ab5-c33281bc469c",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "submit['label'] = submit['label'].map({1:'non-sleepy', 0: 'sleepy'})\n",
+    "submit.to_csv('submit.csv', index=None)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3fc9e8ed-2c50-4f92-ad5b-d53083b20895",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3.10"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.10"
+  },
+  "widgets": {
+   "application/vnd.jupyter.widget-state+json": {
+    "state": {},
+    "version_major": 2,
+    "version_minor": 0
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}