FaceClassification_CNN_GPU.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "f5a97c7c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using device: cuda\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "from torch.utils.data import Dataset, DataLoader\n",
    "from torchvision import transforms\n",
    "from PIL import Image\n",
    "import numpy as np\n",
    "from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay\n",
    "import matplotlib.pyplot as plt\n",
    "from tqdm import tqdm\n",
    "\n",
    "\n",
    "# 检查是否有可用的GPU\n",
    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    "print(f\"Using device: {device}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "29bf867e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 自定义数据集类\n",
    "class FaceDataset(Dataset):\n",
    "    def __init__(self, data_file, raw_data_dir, transform=None):\n",
    "        self.data_file = data_file\n",
    "        self.raw_data_dir = raw_data_dir\n",
    "        self.transform = transform\n",
    "        self.samples = self._load_samples()\n",
    "\n",
    "    def _load_samples(self):\n",
    "        samples = []\n",
    "        with open(self.data_file, 'r') as f:\n",
    "            for line_number, line in enumerate(f, start=1):\n",
    "                parts = line.strip().split()\n",
    "                if len(parts) < 5:\n",
    "                    print(f\"Skipping incomplete line {line_number}: {line}\")\n",
    "                    continue  # 跳过不完整或错误的数据行\n",
    "\n",
    "                try:\n",
    "                    image_id = parts[0]\n",
    "                    sex = parts[2].strip('()')\n",
    "                    age = parts[4].strip('()')\n",
    "                    race = parts[6].strip('()')\n",
    "                    face = parts[8].strip('()')\n",
    "                    samples.append((image_id, sex, age, race, face))\n",
    "                except IndexError as e:\n",
    "                    print(f\"Skipping malformed line {line_number} due to error: {e}\\nLine content: {line}\")\n",
    "\n",
    "        return samples\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.samples)\n",
    "\n",
    "    def __getitem__(self, idx):\n",
    "        image_id, sex, age, race, face = self.samples[idx]\n",
    "        image_path = os.path.join(self.raw_data_dir, image_id)\n",
    "\n",
    "        file_size_kb = os.path.getsize(image_path) / 1024\n",
    "\n",
    "        if file_size_kb < 20:\n",
    "            with open(image_path, 'rb') as f:\n",
    "                img = Image.fromarray(np.reshape(np.frombuffer(f.read(), dtype=np.uint8), (128, 128)))\n",
    "        else:\n",
    "            with open(image_path, 'rb') as f:\n",
    "                img = Image.fromarray(np.reshape(np.frombuffer(f.read(), dtype=np.uint8), (512, 512)))\n",
    "                img = img.resize((128, 128))\n",
    "        if self.transform:\n",
    "            img = self.transform(img)\n",
    "\n",
    "        # 将类别标签转换为整数\n",
    "        sex_label = {'male': 0, 'female': 1}.get(sex, -1)\n",
    "        age_label = {'child': 0, 'teen': 1, 'adult': 2, 'senior': 3}.get(age, -1)\n",
    "        race_label = {'white': 0, 'yellow': 1, 'black': 2, 'hispanic': 0, 'asian': 1, 'other': 0}.get(race, -1)\n",
    "        face_label = {'smiling': 0, 'serious': 1, 'funny': 2}.get(face, -1)\n",
    "\n",
    "        labels = [sex_label, age_label, race_label, face_label]\n",
    "        if any(label == -1 for label in labels):\n",
    "            print(f\"Invalid label in sample: {self.samples[idx]}\")\n",
    "            raise ValueError(f\"Invalid label in sample: {self.samples[idx]}\")\n",
    "\n",
    "        return img, torch.tensor(labels, dtype=torch.long)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "8caaf701",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Skipping incomplete line 6:  1228 (_missing descriptor)\n",
      "\n",
      "Skipping incomplete line 10:  1232 (_missing descriptor)\n",
      "\n",
      "Skipping incomplete line 586:  1808 (_missing descriptor)\n",
      "\n",
      "Skipping incomplete line 834:  4056 (_missing descriptor)\n",
      "\n",
      "Skipping incomplete line 913:  4135 (_missing descriptor)\n",
      "\n",
      "Skipping incomplete line 914:  4136 (_missing descriptor)\n",
      "\n",
      "Skipping incomplete line 1782:  5004 (_missing descriptor)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# 数据变换，包括数据增强\n",
    "train_transform = transforms.Compose([\n",
    "    transforms.Resize((128, 128)),\n",
    "    transforms.RandomHorizontalFlip(),\n",
    "    transforms.ColorJitter(brightness=0.1, contrast=0.1, saturation=0.1),\n",
    "    transforms.ToTensor(),\n",
    "])\n",
    "\n",
    "test_transform = transforms.Compose([\n",
    "    transforms.Resize((128, 128)),\n",
    "    transforms.ToTensor(),\n",
    "])\n",
    "\n",
    "# 加载数据集\n",
    "train_dataset = FaceDataset(data_file='../人脸图像识别/face/faceDR', raw_data_dir='../人脸图像识别/face/rawdata', transform=train_transform)\n",
    "test_dataset = FaceDataset(data_file='../人脸图像识别/face/faceDS', raw_data_dir='../人脸图像识别/face/rawdata', transform=test_transform)\n",
    "train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True)\n",
    "test_loader = DataLoader(test_dataset, batch_size=32, shuffle=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "b7a1208d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 定义卷积神经网络模型\n",
    "class SimpleCNN(nn.Module):\n",
    "    def __init__(self):\n",
    "        super(SimpleCNN, self).__init__()\n",
    "        self.conv1 = nn.Conv2d(1, 32, kernel_size=3, stride=1, padding=1)\n",
    "        self.relu = nn.ReLU()\n",
    "        self.pool = nn.MaxPool2d(kernel_size=2, stride=2, padding=0)\n",
    "        self.conv2 = nn.Conv2d(32, 64, kernel_size=3, stride=1, padding=1)\n",
    "        self.fc1 = nn.Linear(64 * 32 * 32, 256)\n",
    "        self.fc2 = nn.Linear(256, 2)  # 性别\n",
    "        self.fc3 = nn.Linear(256, 4)  # 年龄\n",
    "        self.fc4 = nn.Linear(256, 3)  # 种族\n",
    "        self.fc5 = nn.Linear(256, 3)  # 表情\n",
    "\n",
    "    def forward(self, x):\n",
    "        x = self.pool(self.relu(self.conv1(x)))\n",
    "        x = self.pool(self.relu(self.conv2(x)))\n",
    "        x = x.view(-1, 64 * 32 * 32)\n",
    "        x = self.relu(self.fc1(x))\n",
    "        sex_output = self.fc2(x)\n",
    "        age_output = self.fc3(x)\n",
    "        race_output = self.fc4(x)\n",
    "        face_output = self.fc5(x)\n",
    "        return sex_output, age_output, race_output, face_output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "9f982989cd1e017a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-01-07T18:43:14.842257Z",
     "start_time": "2025-01-07T18:41:48.311105Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  2%|▏         | 1/50 [00:03<02:42,  3.32s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [1/50], Loss: 2.7665\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  4%|▍         | 2/50 [00:06<02:33,  3.20s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [2/50], Loss: 2.4966\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  6%|▌         | 3/50 [00:09<02:26,  3.11s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [3/50], Loss: 2.2923\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  8%|▊         | 4/50 [00:12<02:20,  3.05s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [4/50], Loss: 2.1156\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 10%|█         | 5/50 [00:15<02:15,  3.01s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [5/50], Loss: 1.9654\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 12%|█▏        | 6/50 [00:18<02:11,  2.98s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [6/50], Loss: 1.8737\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 14%|█▍        | 7/50 [00:21<02:07,  2.97s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [7/50], Loss: 1.6744\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 16%|█▌        | 8/50 [00:24<02:04,  2.96s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [8/50], Loss: 1.5499\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 18%|█▊        | 9/50 [00:27<02:02,  2.99s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [9/50], Loss: 1.4424\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 20%|██        | 10/50 [00:30<01:59,  2.98s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [10/50], Loss: 1.3553\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 22%|██▏       | 11/50 [00:33<01:56,  2.98s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [11/50], Loss: 1.2736\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 24%|██▍       | 12/50 [00:36<01:54,  3.02s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [12/50], Loss: 1.2451\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 26%|██▌       | 13/50 [00:39<01:52,  3.03s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [13/50], Loss: 1.1851\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 28%|██▊       | 14/50 [00:42<01:52,  3.13s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [14/50], Loss: 1.1524\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 30%|███       | 15/50 [00:45<01:51,  3.17s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [15/50], Loss: 1.0292\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 32%|███▏      | 16/50 [00:49<01:50,  3.26s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [16/50], Loss: 0.9924\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 34%|███▍      | 17/50 [00:52<01:50,  3.35s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [17/50], Loss: 0.8970\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 36%|███▌      | 18/50 [00:56<01:48,  3.40s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [18/50], Loss: 0.8579\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 38%|███▊      | 19/50 [00:59<01:43,  3.35s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [19/50], Loss: 0.8602\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 40%|████      | 20/50 [01:02<01:38,  3.30s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [20/50], Loss: 0.7794\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 42%|████▏     | 21/50 [01:06<01:34,  3.26s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [21/50], Loss: 0.7252\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 44%|████▍     | 22/50 [01:09<01:31,  3.26s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [22/50], Loss: 0.6581\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 46%|████▌     | 23/50 [01:12<01:26,  3.21s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [23/50], Loss: 0.6462\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 48%|████▊     | 24/50 [01:15<01:22,  3.18s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [24/50], Loss: 0.5440\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 50%|█████     | 25/50 [01:18<01:19,  3.16s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [25/50], Loss: 0.5115\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 52%|█████▏    | 26/50 [01:21<01:16,  3.17s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [26/50], Loss: 0.5759\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 54%|█████▍    | 27/50 [01:25<01:13,  3.18s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [27/50], Loss: 0.4566\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 56%|█████▌    | 28/50 [01:28<01:09,  3.17s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [28/50], Loss: 0.4483\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 58%|█████▊    | 29/50 [01:31<01:07,  3.21s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [29/50], Loss: 0.3770\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 60%|██████    | 30/50 [01:35<01:06,  3.32s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [30/50], Loss: 0.3880\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 62%|██████▏   | 31/50 [01:38<01:03,  3.34s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [31/50], Loss: 0.3189\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 64%|██████▍   | 32/50 [01:42<01:01,  3.44s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [32/50], Loss: 0.3195\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 66%|██████▌   | 33/50 [01:45<00:58,  3.42s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [33/50], Loss: 0.2851\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 68%|██████▊   | 34/50 [01:48<00:54,  3.42s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [34/50], Loss: 0.2904\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 70%|███████   | 35/50 [01:52<00:53,  3.56s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [35/50], Loss: 0.2718\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 72%|███████▏  | 36/50 [01:56<00:51,  3.66s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [36/50], Loss: 0.1867\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 74%|███████▍  | 37/50 [02:00<00:46,  3.55s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [37/50], Loss: 0.1662\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 76%|███████▌  | 38/50 [02:03<00:41,  3.45s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [38/50], Loss: 0.1850\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 78%|███████▊  | 39/50 [02:06<00:37,  3.38s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [39/50], Loss: 0.2199\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 80%|████████  | 40/50 [02:09<00:33,  3.32s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [40/50], Loss: 0.1838\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 82%|████████▏ | 41/50 [02:12<00:29,  3.33s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [41/50], Loss: 0.1416\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 84%|████████▍ | 42/50 [02:16<00:26,  3.37s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [42/50], Loss: 0.1908\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 86%|████████▌ | 43/50 [02:20<00:24,  3.44s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [43/50], Loss: 0.1198\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 88%|████████▊ | 44/50 [02:23<00:20,  3.48s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [44/50], Loss: 0.1091\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 90%|█████████ | 45/50 [02:27<00:17,  3.54s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [45/50], Loss: 0.1036\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 92%|█████████▏| 46/50 [02:30<00:14,  3.53s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [46/50], Loss: 0.1787\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 94%|█████████▍| 47/50 [02:34<00:10,  3.46s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [47/50], Loss: 0.1301\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 96%|█████████▌| 48/50 [02:37<00:06,  3.44s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [48/50], Loss: 0.0898\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 98%|█████████▊| 49/50 [02:40<00:03,  3.46s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [49/50], Loss: 0.0707\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 50/50 [02:44<00:00,  3.29s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [50/50], Loss: 0.0796\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of the model on the test images: 82.60%\n",
      "Accuracy of the model on the test images for each category:\n",
      "male Accuracy: 82.85%\n",
      "female Accuracy: 77.75%\n",
      "Accuracy of the model on the test images for each category:\n",
      "child Accuracy: 19.12%\n",
      "teen Accuracy: 7.23%\n",
      "adult Accuracy: 99.02%\n",
      "senior Accuracy: 20.87%\n",
      "Accuracy of the model on the test images for each category:\n",
      "white Accuracy: 97.44%\n",
      "yellow Accuracy: 26.92%\n",
      "black Accuracy: 10.44%\n",
      "Accuracy of the model on the test images for each category:\n",
      "smiling Accuracy: 76.20%\n",
      "serious Accuracy: 79.49%\n",
      "funny Accuracy: 4.76%\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 初始化模型、损失函数和优化器\n",
    "model = SimpleCNN().to(device)\n",
    "criterion = nn.CrossEntropyLoss().to(device)\n",
    "# optimizer = optim.Adam(model.parameters(), lr=0.005)\n",
    "optimizer = optim.SGD(model.parameters(), lr=0.005, momentum=0.9)\n",
    "# 训练模型\n",
    "num_epochs = 50\n",
    "for epoch in tqdm(range(num_epochs)):\n",
    "    model.train()\n",
    "    running_loss = 0.0\n",
    "    for images, labels in train_loader:\n",
    "        images = images.to(device)\n",
    "        labels = labels.to(device)\n",
    "\n",
    "        optimizer.zero_grad()\n",
    "        sex_outputs, age_outputs, race_outputs, face_outputs = model(images)\n",
    "        loss = criterion(sex_outputs, labels[:, 0]) + \\\n",
    "               criterion(age_outputs, labels[:, 1]) + \\\n",
    "               criterion(race_outputs, labels[:, 2]) + \\\n",
    "               criterion(face_outputs, labels[:, 3])\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "        running_loss += loss.item()\n",
    "    print(f'Epoch [{epoch+1}/{num_epochs}], Loss: {running_loss/len(train_loader):.4f}')\n",
    "\n",
    "# 评估模型\n",
    "model.eval()\n",
    "total = 0\n",
    "correct = 0\n",
    "\n",
    "# 存储所有真实标签和预测标签\n",
    "true_labels = {'sex': [], 'age': [], 'race': [], 'face': []}\n",
    "pred_labels = {'sex': [], 'age': [], 'race': [], 'face': []}\n",
    "\n",
    "with torch.no_grad():\n",
    "    for images, labels in test_loader:\n",
    "        images = images.to(device)\n",
    "        labels = labels.to(device)\n",
    "\n",
    "        sex_outputs, age_outputs, race_outputs, face_outputs = model(images)\n",
    "        _, predicted_sex = torch.max(sex_outputs, 1)\n",
    "        _, predicted_age = torch.max(age_outputs, 1)\n",
    "        _, predicted_race = torch.max(race_outputs, 1)\n",
    "        _, predicted_face = torch.max(face_outputs, 1)\n",
    "\n",
    "        true_labels['sex'].extend(labels[:, 0].cpu().numpy())\n",
    "        pred_labels['sex'].extend(predicted_sex.cpu().numpy())\n",
    "        true_labels['age'].extend(labels[:, 1].cpu().numpy())\n",
    "        pred_labels['age'].extend(predicted_age.cpu().numpy())\n",
    "        true_labels['race'].extend(labels[:, 2].cpu().numpy())\n",
    "        pred_labels['race'].extend(predicted_race.cpu().numpy())\n",
    "        true_labels['face'].extend(labels[:, 3].cpu().numpy())\n",
    "        pred_labels['face'].extend(predicted_face.cpu().numpy())\n",
    "\n",
    "        total += labels.size(0)\n",
    "\n",
    "        correct += ((predicted_sex == labels[:, 0]).sum().item() +\n",
    "                    (predicted_age == labels[:, 1]).sum().item() +\n",
    "                    (predicted_race == labels[:, 2]).sum().item() +\n",
    "                    (predicted_face == labels[:, 3]).sum().item()) / 4\n",
    "\n",
    "print(f'Accuracy of the model on the test images: {100 * correct / total:.2f}%')\n",
    "\n",
    "# 计算并打印各个类别的准确率\n",
    "def calculate_accuracy(true_labels, pred_labels, num_classes, class_names):\n",
    "    cm = confusion_matrix(true_labels, pred_labels, labels=list(range(num_classes)))\n",
    "    row_sums = cm.sum(axis=1)\n",
    "    accuracies = np.zeros(num_classes)\n",
    "    for i in range(num_classes):\n",
    "        if row_sums[i] > 0:\n",
    "            accuracies[i] = cm[i, i] / row_sums[i]\n",
    "    print(f'Accuracy of the model on the test images for each category:')\n",
    "    for i, acc in enumerate(accuracies):\n",
    "        print(f'{class_names[i]} Accuracy: {100 * acc:.2f}%')\n",
    "\n",
    "calculate_accuracy(true_labels['sex'], pred_labels['sex'], 2, ['male', 'female'])\n",
    "calculate_accuracy(true_labels['age'], pred_labels['age'], 4, ['child', 'teen', 'adult', 'senior'])\n",
    "calculate_accuracy(true_labels['race'], pred_labels['race'], 3, ['white', 'yellow', 'black'])\n",
    "calculate_accuracy(true_labels['face'], pred_labels['face'], 3, ['smiling', 'serious', 'funny'])\n",
    "\n",
    "# 绘制混淆矩阵\n",
    "def plot_confusion_matrix(true_labels, pred_labels, num_classes, class_names, title):\n",
    "    cm = confusion_matrix(true_labels, pred_labels, labels=list(range(num_classes)))\n",
    "    disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=class_names)\n",
    "    disp.plot(cmap=plt.cm.Blues)\n",
    "    plt.title(title)\n",
    "    plt.show()\n",
    "\n",
    "plot_confusion_matrix(true_labels['sex'], pred_labels['sex'], 2, ['male', 'female'], 'Sex Confusion Matrix')\n",
    "plot_confusion_matrix(true_labels['age'], pred_labels['age'], 4, ['child', 'teen', 'adult', 'senior'], 'Age Confusion Matrix')\n",
    "plot_confusion_matrix(true_labels['race'], pred_labels['race'], 3, ['white', 'yellow', 'black'], 'Race Confusion Matrix')\n",
    "plot_confusion_matrix(true_labels['face'], pred_labels['face'], 3, ['smiling', 'serious', 'funny'], 'Face Confusion Matrix')\n",
    "\n",
    "\n",
    "\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "AI-base",
   "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",
   "version": "3.10.16"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}