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{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "65e35379",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<All keys matched successfully>"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import os\n",
    "os.environ['HF_ENDPOINT'] = 'https://hf-mirror.com'\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import timm\n",
    "torch.manual_seed(3407)\n",
    "torch.cuda.manual_seed(3407)\n",
    "# 定义GELU_PyTorch_Tanh类\n",
    "class GELU_PyTorch_Tanh(nn.Module):\n",
    "    def forward(self, x):\n",
    "        return 0.5 * x * (1 + torch.tanh((2 / torch.pi) ** 0.5 * (x + 0.044715 * x ** 3)))\n",
    "\n",
    "# 定义CustomModel类\n",
    "class CustomModel(nn.Module):\n",
    "    def __init__(self, base_model):\n",
    "        super(CustomModel, self).__init__()\n",
    "        self.base_model = base_model\n",
    "        \n",
    "        # 定义新的分类层\n",
    "        self.fc1 = nn.Linear(1536, 2048)  # EfficientNet-B1的输出维度是1536\n",
    "        self.bn1 = nn.BatchNorm1d(2048)\n",
    "        self.dropout1 = nn.Dropout(p=0.5)        \n",
    "        self.fc2 = nn.Linear(2048, 2048)\n",
    "        self.bn2 = nn.BatchNorm1d(2048)\n",
    "        self.gelu = GELU_PyTorch_Tanh()\n",
    "        self.dropout2 = nn.Dropout(p=0.5)\n",
    "        \n",
    "        self.final_fc = nn.Linear(2048, 1)  # 二分类问题,输出维度为1\n",
    "        self.sigmoid = nn.Sigmoid()\n",
    "        \n",
    "    def forward(self, x):\n",
    "        x = self.base_model(x)\n",
    "        x = self.fc1(x)\n",
    "        x = self.bn1(x)\n",
    "        x = self.gelu(x)\n",
    "        x = self.dropout1(x)\n",
    "        x = self.fc2(x)\n",
    "        x = self.bn2(x)\n",
    "        x = self.gelu(x)\n",
    "        x = self.dropout2(x)\n",
    "        x = self.final_fc(x)\n",
    "        x = self.sigmoid(x)\n",
    "        return x\n",
    "\n",
    "# 创建基础模型\n",
    "base_model = timm.create_model('tf_efficientnet_b3.ns_jft_in1k', pretrained=True, num_classes=0)\n",
    "base_model = base_model.cuda()\n",
    "\n",
    "# 创建自定义模型\n",
    "custom_model = CustomModel(base_model)\n",
    "custom_model = custom_model.cuda()\n",
    "\n",
    "# 加载保存的模型权重\n",
    "checkpoint_path = \"E:\\\\下载\\\\custom_model_96.64.pt\"\n",
    "custom_model.load_state_dict(torch.load(checkpoint_path))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "3f695d9a",
   "metadata": {},
   "outputs": [],
   "source": [
    "from PIL import Image\n",
    "from torchvision import transforms\n",
    "custom_model.eval()\n",
    "transform = transforms.Compose([\n",
    "    transforms.Resize((300, 300)),\n",
    "    transforms.ToTensor(),\n",
    "    transforms.Normalize(\n",
    "        mean=[0.485, 0.456, 0.406],\n",
    "        std=[0.229, 0.224, 0.225]\n",
    "    )\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a8a96208",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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      "Image: 299.png, probabilities: tensor([[0.1865]], device='cuda:0')\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Image: 301.png, probabilities: tensor([[0.9997]], device='cuda:0')\n",
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      "Image: 806.png, probabilities: tensor([[0.4562]], device='cuda:0')\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Image: 809.png, probabilities: tensor([[0.9955]], device='cuda:0')\n",
      "Image: 81.png, probabilities: tensor([[0.9188]], device='cuda:0')\n",
      "Image: 810.png, probabilities: tensor([[0.3929]], device='cuda:0')\n",
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      "Image: 828.png, probabilities: tensor([[0.1760]], device='cuda:0')\n",
      "Image: 829.png, probabilities: tensor([[0.9984]], device='cuda:0')\n",
      "Image: 833.png, probabilities: tensor([[0.3641]], device='cuda:0')\n",
      "Image: 835.png, probabilities: tensor([[0.6530]], device='cuda:0')\n",
      "Image: 836.png, probabilities: tensor([[0.9552]], device='cuda:0')\n",
      "Image: 837.png, probabilities: tensor([[0.1598]], device='cuda:0')\n",
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      "Image: 841.png, probabilities: tensor([[0.4956]], device='cuda:0')\n",
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      "Image: 884.png, probabilities: tensor([[0.9993]], device='cuda:0')\n",
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      "Image: 9.png, probabilities: tensor([[1.0000]], device='cuda:0')\n",
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      "Image: 959.png, probabilities: tensor([[0.5024]], device='cuda:0')\n",
      "Image: 961.png, probabilities: tensor([[0.9981]], device='cuda:0')\n",
      "Image: 962.png, probabilities: tensor([[0.4659]], device='cuda:0')\n",
      "Image: 968.png, probabilities: tensor([[0.9481]], device='cuda:0')\n",
      "Image: 972.png, probabilities: tensor([[0.9998]], device='cuda:0')\n",
      "Image: 973.png, probabilities: tensor([[0.9747]], device='cuda:0')\n",
      "Image: 976.png, probabilities: tensor([[0.9994]], device='cuda:0')\n",
      "Image: 978.png, probabilities: tensor([[1.0000]], device='cuda:0')\n",
      "Image: 979.png, probabilities: tensor([[0.8647]], device='cuda:0')\n",
      "Image: 987.png, probabilities: tensor([[0.9550]], device='cuda:0')\n",
      "Image: 989.png, probabilities: tensor([[0.9988]], device='cuda:0')\n",
      "Image: 99.png, probabilities: tensor([[0.7989]], device='cuda:0')\n",
      "Image: 990.png, probabilities: tensor([[0.5491]], device='cuda:0')\n",
      "Image: 991.png, probabilities: tensor([[0.9999]], device='cuda:0')\n",
      "Image: 996.png, probabilities: tensor([[0.9424]], device='cuda:0')\n",
      "Image: 997.png, probabilities: tensor([[0.5517]], device='cuda:0')\n",
      "Image: 999.png, probabilities: tensor([[0.7581]], device='cuda:0')\n",
      "Number of outputs greater than 0.07: 581\n"
     ]
    }
   ],
   "source": [
    "#probabilities_list = []\n",
    "image_folder = r\"E:\\qq文件\\任务2\\提取图片\"\n",
    "image_files = [f for f in os.listdir(image_folder) if os.path.isfile(os.path.join(image_folder, f))]\n",
    "\n",
    "count_above_threshold = 0  # 计数大于0.5的输出数量\n",
    "\n",
    "for image_file in image_files:\n",
    "    image_path = os.path.join(image_folder, image_file)\n",
    "    image = Image.open(image_path).convert('RGB')\n",
    "    input_tensor = transform(image).unsqueeze(0)  # 增加batch维度\n",
    "    input_tensor = input_tensor.cuda()  # 将输入张量转移到GPU\n",
    "\n",
    "    with torch.no_grad():\n",
    "        output = custom_model(input_tensor)\n",
    "        \n",
    "    if output.item() >= 0.07:\n",
    "        count_above_threshold += 1\n",
    "        print(f\"Image: {image_file}, probabilities: {output}\")\n",
    "\n",
    "print(f\"Number of outputs greater than 0.07: {count_above_threshold}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "142a9b6f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Image: 0.png, probabilities: tensor([[0.9870]], device='cuda:0')\n",
      "Image: 1.png, probabilities: tensor([[0.9999]], device='cuda:0')\n",
      "Image: 100.png, probabilities: tensor([[0.7772]], device='cuda:0')\n",
      "Image: 1000.png, probabilities: tensor([[0.9953]], device='cuda:0')\n",
      "Image: 1001.png, probabilities: tensor([[1.0000]], device='cuda:0')\n",
      "Image: 1002.png, probabilities: tensor([[0.9997]], device='cuda:0')\n",
      "Image: 1003.png, probabilities: tensor([[0.9995]], device='cuda:0')\n",
      "Image: 1004.png, probabilities: tensor([[1.0000]], device='cuda:0')\n",
      "Image: 1005.png, probabilities: tensor([[0.9994]], device='cuda:0')\n",
      "Image: 1006.png, probabilities: tensor([[1.0000]], device='cuda:0')\n",
      "Image: 1007.png, probabilities: tensor([[0.9998]], device='cuda:0')\n",
      "Image: 1008.png, probabilities: tensor([[0.3588]], device='cuda:0')\n",
      "Image: 101.png, probabilities: tensor([[0.9978]], device='cuda:0')\n",
      "Image: 1010.png, probabilities: tensor([[0.9953]], device='cuda:0')\n",
      "Image: 1012.png, probabilities: tensor([[0.4747]], device='cuda:0')\n",
      "Image: 1013.png, probabilities: tensor([[0.8764]], device='cuda:0')\n",
      "Image: 1017.png, probabilities: tensor([[0.3660]], device='cuda:0')\n",
      "Image: 1018.png, probabilities: tensor([[0.9980]], device='cuda:0')\n",
      "Image: 1019.png, probabilities: tensor([[1.0000]], device='cuda:0')\n",
      "Image: 102.png, probabilities: tensor([[0.9619]], device='cuda:0')\n",
      "Image: 1021.png, probabilities: tensor([[0.9998]], device='cuda:0')\n",
      "Image: 1022.png, probabilities: tensor([[1.0000]], device='cuda:0')\n",
      "Image: 1023.png, probabilities: tensor([[0.9999]], device='cuda:0')\n",
      "Image: 1025.png, probabilities: tensor([[0.1526]], device='cuda:0')\n",
      "Image: 1027.png, probabilities: tensor([[0.1112]], device='cuda:0')\n",
      "Image: 1029.png, probabilities: tensor([[0.9998]], device='cuda:0')\n",
      "Image: 103.png, probabilities: tensor([[0.9975]], device='cuda:0')\n",
      "Image: 1030.png, probabilities: tensor([[1.0000]], device='cuda:0')\n",
      "Image: 1031.png, probabilities: tensor([[0.9871]], device='cuda:0')\n",
      "Image: 1033.png, probabilities: tensor([[0.7425]], device='cuda:0')\n",
      "Image: 1034.png, probabilities: tensor([[0.8482]], device='cuda:0')\n",
      "Image: 1035.png, probabilities: tensor([[1.0000]], device='cuda:0')\n",
      "Image: 1036.png, probabilities: tensor([[0.8803]], device='cuda:0')\n",
      "Image: 1037.png, probabilities: tensor([[0.9883]], device='cuda:0')\n",
      "Image: 1038.png, probabilities: tensor([[0.8936]], device='cuda:0')\n",
      "Image: 1039.png, probabilities: tensor([[0.6551]], device='cuda:0')\n",
      "Image: 104.png, probabilities: tensor([[0.9625]], device='cuda:0')\n",
      "Image: 1040.png, probabilities: tensor([[0.6643]], device='cuda:0')\n",
      "Image: 1041.png, probabilities: tensor([[0.7153]], device='cuda:0')\n",
      "Image: 1044.png, probabilities: tensor([[0.8434]], device='cuda:0')\n",
      "Image: 1045.png, probabilities: tensor([[0.7953]], device='cuda:0')\n",
      "Image: 1046.png, probabilities: tensor([[0.9999]], device='cuda:0')\n",
      "Image: 1047.png, probabilities: tensor([[0.9726]], device='cuda:0')\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Image: 832.png, probabilities: tensor([[0.7373]], device='cuda:0')\n",
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      "Image: 97.png, probabilities: tensor([[0.3735]], device='cuda:0')\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Image: 970.png, probabilities: tensor([[0.9907]], device='cuda:0')\n",
      "Image: 971.png, probabilities: tensor([[0.9974]], device='cuda:0')\n",
      "Image: 972.png, probabilities: tensor([[0.9998]], device='cuda:0')\n",
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      "Image: 983.png, probabilities: tensor([[0.9217]], device='cuda:0')\n",
      "Image: 984.png, probabilities: tensor([[0.5426]], device='cuda:0')\n",
      "Image: 985.png, probabilities: tensor([[0.9996]], device='cuda:0')\n",
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      "Image: 988.png, probabilities: tensor([[1.0000]], device='cuda:0')\n",
      "Image: 989.png, probabilities: tensor([[0.9988]], device='cuda:0')\n",
      "Image: 99.png, probabilities: tensor([[0.7989]], device='cuda:0')\n",
      "Image: 990.png, probabilities: tensor([[0.5491]], device='cuda:0')\n",
      "Image: 991.png, probabilities: tensor([[0.9999]], device='cuda:0')\n",
      "Image: 994.png, probabilities: tensor([[1.0000]], device='cuda:0')\n",
      "Image: 996.png, probabilities: tensor([[0.9424]], device='cuda:0')\n",
      "Image: 997.png, probabilities: tensor([[0.5517]], device='cuda:0')\n",
      "Image: 998.png, probabilities: tensor([[0.9832]], device='cuda:0')\n",
      "Image: 999.png, probabilities: tensor([[0.7581]], device='cuda:0')\n",
      "Number of outputs greater than 0.07: 1635\n"
     ]
    }
   ],
   "source": [
    "image_folder = r\"E:\\qq文件\\任务2\\任务2\"\n",
    "image_files = [f for f in os.listdir(image_folder) if os.path.isfile(os.path.join(image_folder, f))]\n",
    "\n",
    "count_above_threshold = 0  # 计数大于0.5的输出数量\n",
    "\n",
    "for image_file in image_files:\n",
    "    image_path = os.path.join(image_folder, image_file)\n",
    "    image = Image.open(image_path).convert('RGB')\n",
    "    input_tensor = transform(image).unsqueeze(0)  # 增加batch维度\n",
    "    input_tensor = input_tensor.cuda()  # 将输入张量转移到GPU\n",
    "\n",
    "    with torch.no_grad():\n",
    "        output = custom_model(input_tensor)\n",
    "        \n",
    "    if output.item() >= 0.07:\n",
    "        count_above_threshold += 1\n",
    "        print(f\"Image: {image_file}, probabilities: {output}\")\n",
    "\n",
    "print(f\"Number of outputs greater than 0.07: {count_above_threshold}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "f943aa30",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classification results saved to classification_results.csv\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "image_folder = r\"E:\\qq文件\\任务2\\任务2\"\n",
    "image_files = [f for f in os.listdir(image_folder) if os.path.isfile(os.path.join(image_folder, f))]\n",
    "\n",
    "results = []  # 用于存储结果的列表\n",
    "\n",
    "for image_file in image_files:\n",
    "    image_path = os.path.join(image_folder, image_file)\n",
    "    image = Image.open(image_path).convert('RGB')\n",
    "    input_tensor = transform(image).unsqueeze(0)  # 增加batch维度\n",
    "    input_tensor = input_tensor.cuda()  # 将输入张量转移到GPU\n",
    "\n",
    "    with torch.no_grad():\n",
    "        output = custom_model(input_tensor)\n",
    "\n",
    "    classification = 1 if output.item() >= 0.07 else 0  # 根据阈值进行分类\n",
    "    results.append([image_file, classification])\n",
    "\n",
    "# 将结果保存到CSV文件\n",
    "df = pd.DataFrame(results, columns=['Image Name', 'Classification'])\n",
    "df.to_csv('classification_results_0.07.csv', index=False) \n",
    "\n",
    "print(\"Classification results saved to classification_results.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "333ab682",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "def sort_by_number(filename):\n",
    "  \"\"\"提取文件名中的数字部分。\"\"\"\n",
    "  return int(filename.split('.')[0])\n",
    "\n",
    "def remove_png(filename):\n",
    "  \"\"\"移除 .png 后缀\"\"\"\n",
    "  return filename[:-4]\n",
    "\n",
    "# 读取 CSV 文件, 指定列名和标题行\n",
    "df = pd.read_csv('classification_results_0.07.csv', names=['Image Name', 'Classification'], header=0) \n",
    "\n",
    "# 移除 .png 后缀\n",
    "df['Image Name'] = df['Image Name'].apply(remove_png) \n",
    "\n",
    "# 按照数字大小排序\n",
    "df['number'] = df['Image Name'].apply(sort_by_number)\n",
    "df.sort_values(by='number', inplace=True)\n",
    "\n",
    "# 删除辅助列\n",
    "df.drop('number', axis=1, inplace=True)  \n",
    "\n",
    "# 修改标题\n",
    "df.columns = ['id', 'label'] \n",
    "\n",
    "# 保存排序后的文件列表\n",
    "df.to_csv('sorted_file_list.csv', header=True, index=False)  # 保存时保留标题行"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "81f3c5a7",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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