Untitled1-Copy1.ipynb

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      "./dataset/pork2\\class2\\IMG_20230329_185825.jpg\n",
      "./dataset/pork2\\class2\\IMG_20230329_185834.jpg\n",
      "./dataset/pork2\\class2\\IMG_20230329_185907.jpg\n",
      "./dataset/pork2\\class2\\IMG_20230329_185921.jpg\n",
      "./dataset/pork2\\class2\\IMG_20230329_185930.jpg\n",
      "./dataset/pork2\\class2\\IMG_20230329_185947.jpg\n",
      "./dataset/pork2\\class2\\IMG_2338.jpg\n",
      "./dataset/pork2\\class2\\IMG_2339.jpg\n",
      "./dataset/pork2\\class2\\IMG_2340.jpg\n",
      "./dataset/pork2\\class2\\IMG_2341.jpg\n",
      "./dataset/pork2\\class2\\IMG_2342.jpg\n",
      "./dataset/pork2\\class2\\IMG_2343.jpg\n",
      "./dataset/pork2\\class2\\IMG_2344.jpg\n",
      "./dataset/pork2\\class2\\IMG_2355.jpg\n",
      "./dataset/pork2\\class2\\IMG_2356.jpg\n",
      "./dataset/pork2\\class2\\IMG_2357.jpg\n",
      "./dataset/pork2\\class2\\IMG_2363.jpg\n",
      "./dataset/pork2\\class2\\IMG_2364.jpg\n",
      "./dataset/pork2\\class2\\IMG_2365.jpg\n",
      "./dataset/pork2\\class2\\IMG_2469.jpg\n",
      "./dataset/pork2\\class2\\IMG_2470.jpg\n",
      "./dataset/pork2\\class2\\IMG_2471.jpg\n",
      "./dataset/pork2\\class2\\IMG_2472.jpg\n",
      "./dataset/pork2\\class2\\IMG_2473.jpg\n",
      "./dataset/pork2\\class2\\IMG_2474.jpg\n",
      "./dataset/pork2\\class2\\IMG_2475.jpg\n",
      "./dataset/pork2\\class2\\IMG_2476.jpg\n",
      "./dataset/pork2\\class2\\IMG_2477.jpg\n",
      "./dataset/pork2\\class2\\IMG_2478.jpg\n",
      "./dataset/pork2\\class2\\IMG_2479.jpg\n",
      "./dataset/pork2\\class2\\IMG_2480.jpg\n",
      "./dataset/pork2\\class2\\微信图片_2023040417535511.jpg\n",
      "./dataset/pork2\\class2\\微信图片_2023040417535512.jpg\n",
      "./dataset/pork2\\class2\\微信图片_2023040417535613.jpg\n",
      "./dataset/pork2\\class2\\微信图片_2023040417535614.jpg\n",
      "./dataset/pork2\\class2\\微信图片_2023040417535615.jpg\n",
      "./dataset/pork2\\class2\\微信图片_2023040417535644.jpg\n",
      "./dataset/pork2\\class2\\微信图片_2023040417535645.jpg\n",
      "./dataset/pork2\\class2\\微信图片_2023040417535646.jpg\n",
      "./dataset/pork2\\class2\\微信图片_2023040417535647.jpg\n",
      "./dataset/pork2\\class2\\微信图片_2023040417535648.jpg\n",
      "./dataset/pork2\\class2\\微信图片_2023040417535710.jpg\n",
      "./dataset/pork2\\class2\\微信图片_2023040417535711.jpg\n",
      "./dataset/pork2\\class2\\微信图片_2023040417535712.jpg\n",
      "611\n",
      "None\n",
      "{'class1': 0, 'class2': 1}\n",
      "{0: 'class1', 1: 'class2'}\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([4, 3, 256, 256])\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x800 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "488\n",
      "**********\n",
      "488\n",
      "**********\n"
     ]
    }
   ],
   "source": [
    "import glob\n",
    "import torch\n",
    "from torch.utils import data\n",
    "from PIL import Image\n",
    "import numpy as np\n",
    "from torchvision import transforms\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# 通过创建data.Dataset子类Mydataset来创建输入\n",
    "class Mydataset(data.Dataset):\n",
    "\t# init() 初始化方法，传入数据文件夹路径\n",
    "\tdef __init__(self, root):\n",
    "\t\tself.imgs_path = root\n",
    "\n",
    "\t# getitem() 切片方法，根据索引下标，获得相应的图片\n",
    "\tdef __getitem__(self, index):\n",
    "\t\timg_path = self.imgs_path[index]\n",
    "\n",
    "\t# len() 计算长度方法，返回整个数据文件夹下所有文件的个数\n",
    "\tdef __len__(self):\n",
    "\t\treturn len(self.imgs_path)\n",
    "\t\n",
    "\n",
    "# 使用glob方法来获取数据图片的所有路径\n",
    "all_imgs_path = glob.glob(r\"./dataset/pork2/*/*.jpg\")  # 数据文件夹路径\n",
    "\n",
    "for var in all_imgs_path:\n",
    "\tprint(var)\n",
    "\n",
    "# 利用自定义类Mydataset创建对象brake_dataset\n",
    "brake_dataset = Mydataset(all_imgs_path)\n",
    "print(len(brake_dataset))  # 返回文件夹中图片总个数\n",
    "print(brake_dataset[12:14])  # 切片，显示第12张、第十三张图片，python左闭右开\n",
    "brake_dataloader = torch.utils.data.DataLoader(brake_dataset, batch_size=4)  # 每次迭代时返回4个数据\n",
    "# print(next(iter(break_dataloader)))\n",
    "\n",
    "# 为每张图片制作对应标签\n",
    "species = ['class1', 'class2']\n",
    "species_to_id = dict((c, i) for i, c in enumerate(species))\n",
    "print(species_to_id)\n",
    "\n",
    "id_to_species = dict((v, k) for k, v in species_to_id.items())\n",
    "print(id_to_species)\n",
    "\n",
    "# 对所有图片路径进行迭代\n",
    "all_labels = []\n",
    "for img in all_imgs_path:\n",
    "\t# 区分出每个img，应该属于什么类别\n",
    "\tfor i, c in enumerate(species):\n",
    "\t\tif c in img:\n",
    "\t\t\tall_labels.append(i)\n",
    "print(all_labels)\n",
    "\n",
    "import os\n",
    "os.environ[\"KMP_DUPLICATE_LIB_OK\"]=\"TRUE\"\n",
    "\n",
    "# 对数据进行转换处理\n",
    "transform = transforms.Compose([\n",
    "\ttransforms.Resize((256, 256)),  # 做的第一步转换\n",
    "\ttransforms.ToTensor()  # 第二步转换，作用：第一转换成Tensor，第二将图片取值范围转换成0-1之间，第三会将channel置前\n",
    "])\n",
    "\n",
    "\n",
    "class Mydatasetpro(data.Dataset):\n",
    "\tdef __init__(self, img_paths, labels, transform):\n",
    "\t\tself.imgs = img_paths\n",
    "\t\tself.labels = labels\n",
    "\t\tself.transforms = transform\n",
    "\n",
    "\t# 进行切片\n",
    "\tdef __getitem__(self, index):\n",
    "\t\timg = self.imgs[index]\n",
    "\t\tlabel = self.labels[index]\n",
    "\t\tpil_img = Image.open(img)  # pip install pillow\n",
    "\t\tpil_img = pil_img.convert('RGB')\n",
    "\t\tdata = self.transforms(pil_img)\n",
    "\t\treturn data, label\n",
    "\n",
    "\t# 返回长度\n",
    "\tdef __len__(self):\n",
    "\t\treturn len(self.imgs)\n",
    "\n",
    "BATCH_SIZE = 4\n",
    "brake_dataset = Mydatasetpro(all_imgs_path, all_labels, transform)\n",
    "brake_dataloader = data.DataLoader(\n",
    "\tbrake_dataset,\n",
    "\tbatch_size=BATCH_SIZE,\n",
    "\tshuffle=True\n",
    ")\n",
    "\n",
    "imgs_batch, labels_batch = next(iter(brake_dataloader))\n",
    "print(imgs_batch.shape)\n",
    "\n",
    "plt.figure(figsize=(12, 8))\n",
    "for i, (img, label) in enumerate(zip(imgs_batch[:6], labels_batch[:6])):\n",
    "\timg = img.permute(1, 2, 0).numpy()\n",
    "\tplt.subplot(2, 3, i + 1)\n",
    "\tplt.title(id_to_species.get(label.item()))\n",
    "\tplt.imshow(img)\n",
    "plt.show()  # 展示图片\n",
    "\n",
    "\n",
    "# 划分数据集和测试集\n",
    "index = np.random.permutation(len(all_imgs_path))\n",
    "\n",
    "all_imgs_path = np.array(all_imgs_path)[index]\n",
    "all_labels = np.array(all_labels)[index]\n",
    "\n",
    "# 80%做训练集\n",
    "s = int(len(all_imgs_path) * 0.8)\n",
    "print(s)\n",
    "\n",
    "train_imgs = all_imgs_path[:s]\n",
    "train_labels = all_labels[:s]\n",
    "test_imgs = all_imgs_path[s:]\n",
    "test_labels = all_labels[s:]\n",
    "\n",
    "train_ds = Mydatasetpro(train_imgs, train_labels, transform)  # TrainSet TensorData\n",
    "test_ds = Mydatasetpro(test_imgs, test_labels, transform)  # TestSet TensorData\n",
    "# print(train_ds)\n",
    "# print(test_ds)\n",
    "print(\"**********\")\n",
    "train_dl = data.DataLoader(train_ds, batch_size=BATCH_SIZE, shuffle=True)  # TrainSet Labels\n",
    "test_dl = data.DataLoader(train_ds, batch_size=BATCH_SIZE, shuffle=True)  # TestSet Labels\n",
    "# 划分数据集和测试集\n",
    "index = np.random.permutation(len(all_imgs_path))\n",
    "\n",
    "all_imgs_path = np.array(all_imgs_path)[index]\n",
    "all_labels = np.array(all_labels)[index]\n",
    "\n",
    "# 80%做训练集\n",
    "s = int(len(all_imgs_path) * 0.8)\n",
    "print(s)\n",
    "\n",
    "train_imgs = all_imgs_path[:s]\n",
    "train_labels = all_labels[:s]\n",
    "test_imgs = all_imgs_path[s:]\n",
    "test_labels = all_labels[s:]\n",
    "\n",
    "train_ds = Mydatasetpro(train_imgs, train_labels, transform)  # 训练集数据\n",
    "test_ds = Mydatasetpro(test_imgs, test_labels, transform)  # 测试集数据\n",
    "# print(train_ds)\n",
    "# print(test_ds)\n",
    "print(\"**********\")\n",
    "train_dl = data.DataLoader(train_ds, batch_size=BATCH_SIZE, shuffle=True)  # 训练集标签\n",
    "test_dl = data.DataLoader(train_ds, batch_size=BATCH_SIZE, shuffle=True)  # 测试集标签\n",
    "# print(train_dl)\n",
    "# print(test_dl)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "b16d54e6",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\beichen\\AppData\\Local\\Temp\\ipykernel_14016\\2589124253.py:40: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
      "  labels = torch.tensor(labels, dtype=torch.long)\n",
      "C:\\Users\\beichen\\AppData\\Local\\Temp\\ipykernel_14016\\2589124253.py:56: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
      "  labels = torch.tensor(labels, dtype=torch.long)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/50.. Train loss: 1.432.. Test loss: 0.756.. Test accuracy: 0.742\n",
      "Epoch 1/50.. Train loss: 0.935.. Test loss: 0.358.. Test accuracy: 0.838\n",
      "Epoch 1/50.. Train loss: 1.063.. Test loss: 0.332.. Test accuracy: 0.842\n",
      "Epoch 1/50.. Train loss: 0.682.. Test loss: 0.438.. Test accuracy: 0.822\n",
      "Epoch 1/50.. Train loss: 0.556.. Test loss: 0.976.. Test accuracy: 0.543\n",
      "Epoch 1/50.. Train loss: 0.853.. Test loss: 1.076.. Test accuracy: 0.742\n",
      "Epoch 1/50.. Train loss: 0.508.. Test loss: 0.355.. Test accuracy: 0.861\n",
      "Epoch 1/50.. Train loss: 0.683.. Test loss: 1.004.. Test accuracy: 0.742\n",
      "Epoch 1/50.. Train loss: 0.297.. Test loss: 0.388.. Test accuracy: 0.838\n",
      "Epoch 1/50.. Train loss: 0.267.. Test loss: 0.870.. Test accuracy: 0.742\n",
      "Epoch 1/50.. Train loss: 0.947.. Test loss: 0.545.. Test accuracy: 0.756\n",
      "Epoch 1/50.. Train loss: 0.735.. Test loss: 0.320.. Test accuracy: 0.900\n",
      "Epoch 1/50.. Train loss: 0.656.. Test loss: 0.353.. Test accuracy: 0.824\n",
      "Epoch 1/50.. Train loss: 0.568.. Test loss: 0.339.. Test accuracy: 0.816\n",
      "Epoch 1/50.. Train loss: 0.472.. Test loss: 0.307.. Test accuracy: 0.865\n",
      "Epoch 2/50.. Train loss: 0.466.. Test loss: 0.308.. Test accuracy: 0.883\n",
      "Epoch 2/50.. Train loss: 0.399.. Test loss: 0.288.. Test accuracy: 0.895\n",
      "Epoch 2/50.. Train loss: 0.553.. Test loss: 0.295.. Test accuracy: 0.904\n",
      "Epoch 2/50.. Train loss: 0.399.. Test loss: 0.315.. Test accuracy: 0.830\n",
      "Epoch 2/50.. Train loss: 0.418.. Test loss: 0.347.. Test accuracy: 0.855\n",
      "Epoch 2/50.. Train loss: 0.485.. Test loss: 0.281.. Test accuracy: 0.861\n",
      "Epoch 2/50.. Train loss: 0.432.. Test loss: 0.298.. Test accuracy: 0.883\n",
      "Epoch 2/50.. Train loss: 0.326.. Test loss: 0.235.. Test accuracy: 0.898\n",
      "Epoch 2/50.. Train loss: 0.403.. Test loss: 0.229.. Test accuracy: 0.904\n",
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      "Epoch 2/50.. Train loss: 0.554.. Test loss: 0.443.. Test accuracy: 0.791\n",
      "Epoch 2/50.. Train loss: 0.505.. Test loss: 0.287.. Test accuracy: 0.865\n",
      "Epoch 2/50.. Train loss: 0.340.. Test loss: 0.254.. Test accuracy: 0.895\n",
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      "Epoch 2/50.. Train loss: 0.335.. Test loss: 0.252.. Test accuracy: 0.902\n",
      "Epoch 3/50.. Train loss: 0.359.. Test loss: 0.216.. Test accuracy: 0.912\n",
      "Epoch 3/50.. Train loss: 0.505.. Test loss: 0.434.. Test accuracy: 0.809\n",
      "Epoch 3/50.. Train loss: 0.413.. Test loss: 0.230.. Test accuracy: 0.908\n",
      "Epoch 3/50.. Train loss: 0.481.. Test loss: 0.270.. Test accuracy: 0.885\n",
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      "Epoch 3/50.. Train loss: 0.222.. Test loss: 0.231.. Test accuracy: 0.889\n",
      "Epoch 3/50.. Train loss: 0.357.. Test loss: 0.254.. Test accuracy: 0.891\n",
      "Epoch 3/50.. Train loss: 0.405.. Test loss: 0.471.. Test accuracy: 0.805\n",
      "Epoch 3/50.. Train loss: 0.331.. Test loss: 0.247.. Test accuracy: 0.893\n",
      "Epoch 3/50.. Train loss: 0.373.. Test loss: 0.244.. Test accuracy: 0.902\n",
      "Epoch 3/50.. Train loss: 0.574.. Test loss: 0.379.. Test accuracy: 0.824\n",
      "Epoch 3/50.. Train loss: 0.476.. Test loss: 0.277.. Test accuracy: 0.869\n",
      "Epoch 3/50.. Train loss: 0.357.. Test loss: 0.251.. Test accuracy: 0.912\n",
      "Epoch 4/50.. Train loss: 0.482.. Test loss: 0.219.. Test accuracy: 0.916\n",
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      "Epoch 4/50.. Train loss: 0.257.. Test loss: 0.230.. Test accuracy: 0.912\n",
      "Epoch 5/50.. Train loss: 0.298.. Test loss: 0.213.. Test accuracy: 0.908\n",
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      "Epoch 5/50.. Train loss: 0.442.. Test loss: 0.211.. Test accuracy: 0.916\n",
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      "Epoch 5/50.. Train loss: 0.377.. Test loss: 0.233.. Test accuracy: 0.910\n",
      "Epoch 6/50.. Train loss: 0.332.. Test loss: 0.279.. Test accuracy: 0.877\n",
      "Epoch 6/50.. Train loss: 0.306.. Test loss: 0.202.. Test accuracy: 0.914\n",
      "Epoch 6/50.. Train loss: 0.453.. Test loss: 0.198.. Test accuracy: 0.912\n",
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      "Epoch 6/50.. Train loss: 0.544.. Test loss: 0.349.. Test accuracy: 0.826\n",
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      "Epoch 6/50.. Train loss: 0.415.. Test loss: 0.320.. Test accuracy: 0.906\n",
      "Epoch 6/50.. Train loss: 0.345.. Test loss: 0.256.. Test accuracy: 0.879\n",
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      "Epoch 7/50.. Train loss: 0.439.. Test loss: 0.214.. Test accuracy: 0.914\n",
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      "Epoch 7/50.. Train loss: 0.291.. Test loss: 0.178.. Test accuracy: 0.926\n",
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      "Epoch 7/50.. Train loss: 0.290.. Test loss: 0.228.. Test accuracy: 0.900\n",
      "Epoch 7/50.. Train loss: 0.186.. Test loss: 0.190.. Test accuracy: 0.922\n",
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      "Epoch 7/50.. Train loss: 0.281.. Test loss: 0.171.. Test accuracy: 0.924\n",
      "Epoch 7/50.. Train loss: 0.148.. Test loss: 0.176.. Test accuracy: 0.920\n",
      "Epoch 7/50.. Train loss: 0.330.. Test loss: 0.194.. Test accuracy: 0.918\n",
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      "Epoch 8/50.. Train loss: 0.465.. Test loss: 0.279.. Test accuracy: 0.900\n",
      "Epoch 8/50.. Train loss: 0.434.. Test loss: 0.311.. Test accuracy: 0.816\n",
      "Epoch 8/50.. Train loss: 0.397.. Test loss: 0.213.. Test accuracy: 0.924\n",
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      "Epoch 8/50.. Train loss: 0.380.. Test loss: 0.191.. Test accuracy: 0.924\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 8/50.. Train loss: 0.231.. Test loss: 0.188.. Test accuracy: 0.930\n",
      "Epoch 8/50.. Train loss: 0.403.. Test loss: 0.189.. Test accuracy: 0.926\n",
      "Epoch 8/50.. Train loss: 0.197.. Test loss: 0.179.. Test accuracy: 0.930\n",
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      "Epoch 8/50.. Train loss: 0.285.. Test loss: 0.194.. Test accuracy: 0.922\n",
      "Epoch 9/50.. Train loss: 0.286.. Test loss: 0.175.. Test accuracy: 0.926\n",
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      "Epoch 9/50.. Train loss: 0.176.. Test loss: 0.169.. Test accuracy: 0.932\n",
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      "Epoch 9/50.. Train loss: 0.236.. Test loss: 0.198.. Test accuracy: 0.914\n",
      "Epoch 10/50.. Train loss: 0.345.. Test loss: 0.174.. Test accuracy: 0.934\n",
      "Epoch 10/50.. Train loss: 0.267.. Test loss: 0.176.. Test accuracy: 0.928\n",
      "Epoch 10/50.. Train loss: 0.406.. Test loss: 0.181.. Test accuracy: 0.930\n",
      "Epoch 10/50.. Train loss: 0.226.. Test loss: 0.223.. Test accuracy: 0.895\n",
      "Epoch 10/50.. Train loss: 0.338.. Test loss: 0.183.. Test accuracy: 0.924\n",
      "Epoch 10/50.. Train loss: 0.456.. Test loss: 0.339.. Test accuracy: 0.832\n",
      "Epoch 10/50.. Train loss: 0.570.. Test loss: 0.213.. Test accuracy: 0.932\n",
      "Epoch 10/50.. Train loss: 0.410.. Test loss: 0.202.. Test accuracy: 0.918\n",
      "Epoch 10/50.. Train loss: 0.222.. Test loss: 0.168.. Test accuracy: 0.932\n",
      "Epoch 10/50.. Train loss: 0.331.. Test loss: 0.218.. Test accuracy: 0.900\n",
      "Epoch 10/50.. Train loss: 0.331.. Test loss: 0.201.. Test accuracy: 0.910\n",
      "Epoch 10/50.. Train loss: 0.404.. Test loss: 0.197.. Test accuracy: 0.918\n",
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      "Epoch 10/50.. Train loss: 0.298.. Test loss: 0.173.. Test accuracy: 0.926\n",
      "Epoch 10/50.. Train loss: 0.261.. Test loss: 0.162.. Test accuracy: 0.928\n",
      "Epoch 10/50.. Train loss: 0.311.. Test loss: 0.159.. Test accuracy: 0.928\n",
      "Epoch 11/50.. Train loss: 0.395.. Test loss: 0.173.. Test accuracy: 0.930\n",
      "Epoch 11/50.. Train loss: 0.325.. Test loss: 0.184.. Test accuracy: 0.928\n",
      "Epoch 11/50.. Train loss: 0.346.. Test loss: 0.175.. Test accuracy: 0.936\n",
      "Epoch 11/50.. Train loss: 0.424.. Test loss: 0.206.. Test accuracy: 0.918\n",
      "Epoch 11/50.. Train loss: 0.422.. Test loss: 0.177.. Test accuracy: 0.930\n",
      "Epoch 11/50.. Train loss: 0.195.. Test loss: 0.235.. Test accuracy: 0.887\n",
      "Epoch 11/50.. Train loss: 0.340.. Test loss: 0.172.. Test accuracy: 0.920\n",
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      "Epoch 11/50.. Train loss: 0.249.. Test loss: 0.178.. Test accuracy: 0.932\n",
      "Epoch 11/50.. Train loss: 0.174.. Test loss: 0.158.. Test accuracy: 0.926\n",
      "Epoch 11/50.. Train loss: 0.349.. Test loss: 0.169.. Test accuracy: 0.930\n",
      "Epoch 11/50.. Train loss: 0.298.. Test loss: 0.157.. Test accuracy: 0.932\n",
      "Epoch 11/50.. Train loss: 0.174.. Test loss: 0.149.. Test accuracy: 0.936\n",
      "Epoch 11/50.. Train loss: 0.268.. Test loss: 0.148.. Test accuracy: 0.939\n",
      "Epoch 11/50.. Train loss: 0.384.. Test loss: 0.226.. Test accuracy: 0.893\n",
      "Epoch 12/50.. Train loss: 0.352.. Test loss: 0.166.. Test accuracy: 0.932\n",
      "Epoch 12/50.. Train loss: 0.226.. Test loss: 0.158.. Test accuracy: 0.926\n",
      "Epoch 12/50.. Train loss: 0.381.. Test loss: 0.166.. Test accuracy: 0.924\n",
      "Epoch 12/50.. Train loss: 0.388.. Test loss: 0.186.. Test accuracy: 0.918\n",
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      "Epoch 12/50.. Train loss: 0.204.. Test loss: 0.149.. Test accuracy: 0.932\n",
      "Epoch 12/50.. Train loss: 0.531.. Test loss: 0.163.. Test accuracy: 0.934\n",
      "Epoch 12/50.. Train loss: 0.299.. Test loss: 0.217.. Test accuracy: 0.904\n",
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      "Epoch 12/50.. Train loss: 0.481.. Test loss: 0.168.. Test accuracy: 0.930\n",
      "Epoch 13/50.. Train loss: 0.247.. Test loss: 0.185.. Test accuracy: 0.920\n",
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      "Epoch 13/50.. Train loss: 0.580.. Test loss: 0.233.. Test accuracy: 0.885\n",
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      "Epoch 13/50.. Train loss: 0.371.. Test loss: 0.208.. Test accuracy: 0.934\n",
      "Epoch 13/50.. Train loss: 0.440.. Test loss: 0.216.. Test accuracy: 0.895\n",
      "Epoch 14/50.. Train loss: 0.400.. Test loss: 0.190.. Test accuracy: 0.924\n",
      "Epoch 14/50.. Train loss: 0.281.. Test loss: 0.173.. Test accuracy: 0.941\n",
      "Epoch 14/50.. Train loss: 0.286.. Test loss: 0.162.. Test accuracy: 0.939\n",
      "Epoch 14/50.. Train loss: 0.339.. Test loss: 0.169.. Test accuracy: 0.936\n",
      "Epoch 14/50.. Train loss: 0.279.. Test loss: 0.165.. Test accuracy: 0.932\n",
      "Epoch 14/50.. Train loss: 0.297.. Test loss: 0.157.. Test accuracy: 0.928\n",
      "Epoch 14/50.. Train loss: 0.183.. Test loss: 0.222.. Test accuracy: 0.904\n",
      "Epoch 14/50.. Train loss: 0.662.. Test loss: 0.261.. Test accuracy: 0.877\n",
      "Epoch 14/50.. Train loss: 0.218.. Test loss: 0.226.. Test accuracy: 0.895\n",
      "Epoch 14/50.. Train loss: 0.323.. Test loss: 0.181.. Test accuracy: 0.924\n",
      "Epoch 14/50.. Train loss: 0.493.. Test loss: 0.230.. Test accuracy: 0.945\n",
      "Epoch 14/50.. Train loss: 0.254.. Test loss: 0.268.. Test accuracy: 0.875\n",
      "Epoch 14/50.. Train loss: 0.183.. Test loss: 0.204.. Test accuracy: 0.910\n",
      "Epoch 14/50.. Train loss: 0.200.. Test loss: 0.170.. Test accuracy: 0.930\n",
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      "Epoch 14/50.. Train loss: 0.466.. Test loss: 0.235.. Test accuracy: 0.902\n",
      "Epoch 15/50.. Train loss: 0.301.. Test loss: 0.240.. Test accuracy: 0.904\n",
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      "Epoch 15/50.. Train loss: 0.317.. Test loss: 0.161.. Test accuracy: 0.939\n",
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      "Epoch 15/50.. Train loss: 0.157.. Test loss: 0.155.. Test accuracy: 0.941\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 15/50.. Train loss: 0.212.. Test loss: 0.151.. Test accuracy: 0.934\n",
      "Epoch 15/50.. Train loss: 0.316.. Test loss: 0.151.. Test accuracy: 0.939\n",
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      "Epoch 15/50.. Train loss: 0.392.. Test loss: 0.250.. Test accuracy: 0.895\n",
      "Epoch 16/50.. Train loss: 0.378.. Test loss: 0.224.. Test accuracy: 0.922\n",
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      "Epoch 16/50.. Train loss: 0.548.. Test loss: 0.158.. Test accuracy: 0.941\n",
      "Epoch 16/50.. Train loss: 0.310.. Test loss: 0.182.. Test accuracy: 0.926\n",
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      "Epoch 16/50.. Train loss: 0.291.. Test loss: 0.202.. Test accuracy: 0.916\n",
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      "Epoch 16/50.. Train loss: 0.320.. Test loss: 0.164.. Test accuracy: 0.932\n",
      "Epoch 16/50.. Train loss: 0.262.. Test loss: 0.158.. Test accuracy: 0.941\n",
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      "Epoch 16/50.. Train loss: 0.274.. Test loss: 0.219.. Test accuracy: 0.906\n",
      "Epoch 17/50.. Train loss: 0.313.. Test loss: 0.183.. Test accuracy: 0.926\n",
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      "Epoch 18/50.. Train loss: 0.461.. Test loss: 0.165.. Test accuracy: 0.941\n",
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      "Epoch 18/50.. Train loss: 0.287.. Test loss: 0.214.. Test accuracy: 0.912\n",
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      "Epoch 19/50.. Train loss: 0.209.. Test loss: 0.166.. Test accuracy: 0.934\n",
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      "Epoch 20/50.. Train loss: 0.195.. Test loss: 0.172.. Test accuracy: 0.926\n",
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      "Epoch 20/50.. Train loss: 0.380.. Test loss: 0.209.. Test accuracy: 0.906\n",
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      "Epoch 20/50.. Train loss: 0.252.. Test loss: 0.184.. Test accuracy: 0.930\n",
      "Epoch 20/50.. Train loss: 0.399.. Test loss: 0.174.. Test accuracy: 0.936\n",
      "Epoch 20/50.. Train loss: 0.210.. Test loss: 0.304.. Test accuracy: 0.865\n",
      "Epoch 20/50.. Train loss: 0.340.. Test loss: 0.167.. Test accuracy: 0.930\n",
      "Epoch 21/50.. Train loss: 0.305.. Test loss: 0.149.. Test accuracy: 0.945\n",
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      "Epoch 21/50.. Train loss: 0.331.. Test loss: 0.160.. Test accuracy: 0.936\n",
      "Epoch 21/50.. Train loss: 0.174.. Test loss: 0.146.. Test accuracy: 0.943\n",
      "Epoch 21/50.. Train loss: 0.172.. Test loss: 0.196.. Test accuracy: 0.914\n",
      "Epoch 21/50.. Train loss: 0.394.. Test loss: 0.286.. Test accuracy: 0.877\n",
      "Epoch 21/50.. Train loss: 0.309.. Test loss: 0.163.. Test accuracy: 0.947\n",
      "Epoch 21/50.. Train loss: 0.342.. Test loss: 0.161.. Test accuracy: 0.939\n",
      "Epoch 21/50.. Train loss: 0.205.. Test loss: 0.165.. Test accuracy: 0.932\n",
      "Epoch 21/50.. Train loss: 0.347.. Test loss: 0.151.. Test accuracy: 0.934\n",
      "Epoch 21/50.. Train loss: 0.276.. Test loss: 0.177.. Test accuracy: 0.934\n",
      "Epoch 21/50.. Train loss: 0.288.. Test loss: 0.173.. Test accuracy: 0.934\n",
      "Epoch 21/50.. Train loss: 0.514.. Test loss: 0.209.. Test accuracy: 0.930\n",
      "Epoch 21/50.. Train loss: 0.364.. Test loss: 0.193.. Test accuracy: 0.910\n",
      "Epoch 21/50.. Train loss: 0.303.. Test loss: 0.192.. Test accuracy: 0.914\n",
      "Epoch 22/50.. Train loss: 0.256.. Test loss: 0.159.. Test accuracy: 0.932\n",
      "Epoch 22/50.. Train loss: 0.335.. Test loss: 0.173.. Test accuracy: 0.926\n",
      "Epoch 22/50.. Train loss: 0.354.. Test loss: 0.158.. Test accuracy: 0.936\n",
      "Epoch 22/50.. Train loss: 0.330.. Test loss: 0.159.. Test accuracy: 0.939\n",
      "Epoch 22/50.. Train loss: 0.275.. Test loss: 0.148.. Test accuracy: 0.943\n",
      "Epoch 22/50.. Train loss: 0.184.. Test loss: 0.208.. Test accuracy: 0.906\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp\\ipykernel_14016\\2589124253.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     53\u001b[0m                         \u001b[1;32mwith\u001b[0m \u001b[0mtorch\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mno_grad\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     54\u001b[0m                                 \u001b[1;31m# 遍历测试集数据\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 55\u001b[1;33m                                 \u001b[1;32mfor\u001b[0m \u001b[0mimgs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlabels\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mtest_dl\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     56\u001b[0m                                         \u001b[0mlabels\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtorch\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtensor\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtorch\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlong\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     57\u001b[0m                                         \u001b[0mimgs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlabels\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mimgs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mto\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mDEVICE\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlabels\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mto\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mDEVICE\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\torch\\utils\\data\\dataloader.py\u001b[0m in \u001b[0;36m__next__\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    519\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_sampler_iter\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    520\u001b[0m                 \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_reset\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 521\u001b[1;33m             \u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_next_data\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    522\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_num_yielded\u001b[0m \u001b[1;33m+=\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    523\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_dataset_kind\u001b[0m \u001b[1;33m==\u001b[0m \u001b[0m_DatasetKind\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mIterable\u001b[0m \u001b[1;32mand\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m\\\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\torch\\utils\\data\\dataloader.py\u001b[0m in \u001b[0;36m_next_data\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    559\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_next_data\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    560\u001b[0m         \u001b[0mindex\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_next_index\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m  \u001b[1;31m# may raise StopIteration\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 561\u001b[1;33m         \u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_dataset_fetcher\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfetch\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m)\u001b[0m  \u001b[1;31m# may raise StopIteration\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    562\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_pin_memory\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    563\u001b[0m             \u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_utils\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpin_memory\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpin_memory\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\torch\\utils\\data\\_utils\\fetch.py\u001b[0m in \u001b[0;36mfetch\u001b[1;34m(self, possibly_batched_index)\u001b[0m\n\u001b[0;32m     47\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mfetch\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mpossibly_batched_index\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     48\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mauto_collation\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 49\u001b[1;33m             \u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdataset\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0midx\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0midx\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mpossibly_batched_index\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     50\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     51\u001b[0m             \u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdataset\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mpossibly_batched_index\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\torch\\utils\\data\\_utils\\fetch.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[1;34m(.0)\u001b[0m\n\u001b[0;32m     47\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mfetch\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mpossibly_batched_index\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     48\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mauto_collation\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 49\u001b[1;33m             \u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdataset\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0midx\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0midx\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mpossibly_batched_index\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     50\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     51\u001b[0m             \u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdataset\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mpossibly_batched_index\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\Temp\\ipykernel_14016\\3909330983.py\u001b[0m in \u001b[0;36m__getitem__\u001b[1;34m(self, index)\u001b[0m\n\u001b[0;32m     73\u001b[0m                 \u001b[0mlabel\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlabels\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     74\u001b[0m                 \u001b[0mpil_img\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mImage\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mopen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mimg\u001b[0m\u001b[1;33m)\u001b[0m  \u001b[1;31m# pip install pillow\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 75\u001b[1;33m                 \u001b[0mpil_img\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpil_img\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconvert\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'RGB'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     76\u001b[0m                 \u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtransforms\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpil_img\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     77\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\PIL\\Image.py\u001b[0m in \u001b[0;36mconvert\u001b[1;34m(self, mode, matrix, dither, palette, colors)\u001b[0m\n\u001b[0;32m   1028\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1029\u001b[0m         \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1030\u001b[1;33m             \u001b[0mim\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mim\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconvert\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmode\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdither\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1031\u001b[0m         \u001b[1;32mexcept\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1032\u001b[0m             \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "import torch\n",
    "import torchvision.models as models\n",
    "from torch import nn\n",
    "from torch import optim\n",
    "#from data_production import brake_dataloader\n",
    "#from data_production import train_dl, test_dl\n",
    "\n",
    "# 判断是否使用GPU\n",
    "DEVICE = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    "\n",
    "model_ft = models.resnet50(pretrained=True)  # 使用迁移学习，加载预训练权\n",
    "\n",
    "\n",
    "in_features = model_ft.fc.in_features\n",
    "model_ft.fc = nn.Sequential(nn.Linear(in_features, 256),\n",
    "\t\t\t\t\t\t\tnn.ReLU(),\n",
    "\t\t\t\t\t\t\t# nn.Dropout(0, 4),\n",
    "\t\t\t\t\t\t\tnn.Linear(256, 4),\n",
    "\t\t\t\t\t\t\tnn.LogSoftmax(dim=1))\n",
    "\n",
    "model_ft = model_ft.to(DEVICE)  # 将模型迁移到gpu\n",
    "\n",
    "# 优化器\n",
    "loss_fn = nn.CrossEntropyLoss()\n",
    "loss_fn = loss_fn.to(DEVICE)  # 将loss_fn迁移到GPU\n",
    "# Adam损失函数\n",
    "optimizer = optim.Adam(model_ft.fc.parameters(), lr=0.003)\n",
    "\n",
    "epochs = 50  # 迭代次数\n",
    "steps = 0\n",
    "running_loss = 0\n",
    "print_every = 10\n",
    "train_losses, test_losses = [], []\n",
    "\n",
    "for epoch in range(epochs):\n",
    "\tmodel_ft.train()\n",
    "\t# 遍历训练集数据\n",
    "\tfor imgs, labels in brake_dataloader:\n",
    "\t\tsteps += 1\n",
    "\t\tlabels = torch.tensor(labels, dtype=torch.long)\n",
    "\t\timgs, labels = imgs.to(DEVICE), labels.to(DEVICE)\n",
    "\t\toptimizer.zero_grad()  # 梯度归零\n",
    "\t\toutputs = model_ft(imgs)\n",
    "\t\tloss = loss_fn(outputs, labels)\n",
    "\t\tloss.backward()  # 反向传播计算梯度\n",
    "\t\toptimizer.step()  # 梯度优化\n",
    "\t\trunning_loss += loss.item()\n",
    "\n",
    "\t\tif steps % print_every == 0:\n",
    "\t\t\ttest_loss = 0\n",
    "\t\t\taccuracy = 0\n",
    "\t\t\tmodel_ft.eval()\n",
    "\t\t\twith torch.no_grad():\n",
    "\t\t\t\t# 遍历测试集数据\n",
    "\t\t\t\tfor imgs, labels in test_dl:\n",
    "\t\t\t\t\tlabels = torch.tensor(labels, dtype=torch.long)\n",
    "\t\t\t\t\timgs, labels = imgs.to(DEVICE), labels.to(DEVICE)\n",
    "\t\t\t\t\toutputs = model_ft(imgs)\n",
    "\t\t\t\t\tloss = loss_fn(outputs, labels)\n",
    "\t\t\t\t\ttest_loss += loss.item()\n",
    "\n",
    "\t\t\t\t\tps = torch.exp(outputs)\n",
    "\t\t\t\t\ttop_p, top_class = ps.topk(1, dim=1)\n",
    "\n",
    "\t\t\t\t\tequals = top_class == labels.view(*top_class.shape)\n",
    "\t\t\t\t\taccuracy += torch.mean(equals.type(torch.FloatTensor)).item()\n",
    "\n",
    "\t\t\ttrain_losses.append(running_loss / len(train_dl))\n",
    "\t\t\ttest_losses.append(test_loss / len(test_dl))\n",
    "\n",
    "\t\t\tprint(f\"Epoch {epoch + 1}/{epochs}.. \"\n",
    "\t\t\t\t  f\"Train loss: {running_loss / print_every:.3f}.. \"\n",
    "\t\t\t\t  f\"Test loss: {test_loss / len(test_dl):.3f}.. \"\n",
    "\t\t\t\t  f\"Test accuracy: {accuracy / len(test_dl):.3f}\")\n",
    "\n",
    "\t\t\trunning_loss = 0\n",
    "\t\t\tmodel_ft.train()\n",
    "torch.save(model_ft, \"aerialmodel2.pth\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "17e6ed3e",
   "metadata": {},
   "outputs": [],
   "source": [
    "torch.save(model_ft, \"aerialmodel2.pth\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "41f3b5fd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4890bf9bef5c435fb8b19fc1eb1efd5e_1000.webp\n",
      "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=800x800 at 0x2BD68C30FA0>\n",
      "torch.Size([3, 256, 256])\n",
      "tensor([[-1.3629e-01, -2.0604e+00, -1.4404e+02, -1.4285e+02]])\n",
      "第1张图片预测为：class1\n",
      "5bc952271e16c1d100f8c5383447e2be_O1CN010bUu5L28vIkQOX8Ps_!!0-item_pic.jpg\n",
      "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=800x800 at 0x2BD6A33B6D0>\n",
      "torch.Size([3, 256, 256])\n",
      "tensor([[-3.0086e+00, -5.0619e-02, -1.0002e+02, -9.9214e+01]])\n",
      "第2张图片预测为：class2\n",
      "d5240b7a87cf37bb9f25ef92b4384a15_c72943edf38d676d.jpg\n",
      "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=800x800 at 0x2BD6A361940>\n",
      "torch.Size([3, 256, 256])\n",
      "tensor([[-4.0885e-03, -5.5016e+00, -1.9030e+02, -1.8938e+02]])\n",
      "第3张图片预测为：class1\n",
      "IMG20230327143157.jpg\n",
      "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=3468x4624 at 0x2BD6A2E9E80>\n",
      "torch.Size([3, 256, 256])\n",
      "tensor([[-1.9656e-04, -8.5349e+00, -2.1461e+02, -2.1135e+02]])\n",
      "第4张图片预测为：class1\n",
      "IMG20230327143157.jpg.JPG\n",
      "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=150x201 at 0x2BD6A33B7C0>\n",
      "torch.Size([3, 256, 256])\n",
      "tensor([[-2.6849e-03, -5.9215e+00, -2.0698e+02, -2.0371e+02]])\n",
      "第5张图片预测为：class1\n",
      "IMG_20230327_154836.jpg\n",
      "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=3072x4096 at 0x2BD6A33B6A0>\n",
      "torch.Size([3, 256, 256])\n",
      "tensor([[-1.1921e-07, -1.5785e+01, -2.1473e+02, -2.1234e+02]])\n",
      "第6张图片预测为：class1\n",
      "IMG_20230327_154838.jpg\n",
      "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=3072x4096 at 0x2BD6A2E95E0>\n",
      "torch.Size([3, 256, 256])\n",
      "tensor([[-4.2915e-06, -1.2347e+01, -1.9938e+02, -1.9708e+02]])\n",
      "第7张图片预测为：class1\n",
      "IMG_20230327_154841.jpg\n",
      "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=3072x4096 at 0x2BD6A33BDF0>\n",
      "torch.Size([3, 256, 256])\n",
      "tensor([[-4.5775e-05, -9.9916e+00, -1.9109e+02, -1.8890e+02]])\n",
      "第8张图片预测为：class1\n",
      "IMG_20230327_154844.jpg\n",
      "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=3072x4096 at 0x2BD6A3616D0>\n",
      "torch.Size([3, 256, 256])\n",
      "tensor([[-1.1921e-06, -1.3608e+01, -1.9069e+02, -1.8853e+02]])\n",
      "第9张图片预测为：class1\n",
      "IMG_20230327_154847.jpg\n",
      "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=3072x4096 at 0x2BD60D22640>\n",
      "torch.Size([3, 256, 256])\n",
      "tensor([[-1.5497e-06, -1.3351e+01, -2.1146e+02, -2.0898e+02]])\n",
      "第10张图片预测为：class1\n",
      "IMG_20230327_154850.jpg\n",
      "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=3072x4096 at 0x2BD6A33B280>\n",
      "torch.Size([3, 256, 256])\n",
      "tensor([[-1.0729e-06, -1.3741e+01, -2.0580e+02, -2.0331e+02]])\n",
      "第11张图片预测为：class1\n",
      "IMG_20230329_072342.jpg\n",
      "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=4096x3072 at 0x2BD60D36DC0>\n",
      "torch.Size([3, 256, 256])\n",
      "tensor([[-1.1921e-07, -1.6250e+01, -2.0193e+02, -1.9927e+02]])\n",
      "第12张图片预测为：class1\n",
      "IMG_20230329_072345(1).jpg\n",
      "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=4096x3072 at 0x2BD60D22730>\n",
      "torch.Size([3, 256, 256])\n",
      "tensor([[-1.7881e-06, -1.3212e+01, -1.8929e+02, -1.8712e+02]])\n",
      "第13张图片预测为：class1\n",
      "IMG_20230329_072345.jpg\n",
      "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=4096x3072 at 0x2BD6A361C10>\n",
      "torch.Size([3, 256, 256])\n",
      "tensor([[-1.7881e-06, -1.3212e+01, -1.8929e+02, -1.8712e+02]])\n",
      "第14张图片预测为：class1\n",
      "IMG_20230329_072351(1).jpg\n",
      "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=4096x3072 at 0x2BD60D36DC0>\n",
      "torch.Size([3, 256, 256])\n",
      "tensor([[-9.5367e-07, -1.3907e+01, -2.1542e+02, -2.1274e+02]])\n",
      "第15张图片预测为：class1\n",
      "IMG_20230329_072356(1).jpg\n",
      "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=4096x3072 at 0x2BD6A3615E0>\n",
      "torch.Size([3, 256, 256])\n",
      "tensor([[-1.1921e-07, -1.6609e+01, -2.1333e+02, -2.1069e+02]])\n",
      "第16张图片预测为：class1\n",
      "IMG_20230329_072356.jpg\n",
      "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=4096x3072 at 0x2BD60D22370>\n",
      "torch.Size([3, 256, 256])\n",
      "tensor([[-1.1921e-07, -1.6609e+01, -2.1333e+02, -2.1069e+02]])\n",
      "第17张图片预测为：class1\n",
      "IMG_20230329_072404.jpg\n",
      "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=3072x4096 at 0x2BD69754E50>\n",
      "torch.Size([3, 256, 256])\n",
      "tensor([[-1.5497e-06, -1.3351e+01, -1.9449e+02, -1.9205e+02]])\n",
      "第18张图片预测为：class1\n",
      "IMG_20230329_072414(1).jpg\n",
      "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=3072x4096 at 0x2BD6A3495E0>\n",
      "torch.Size([3, 256, 256])\n",
      "tensor([[-9.1791e-06, -1.1597e+01, -2.4321e+02, -2.3996e+02]])\n",
      "第19张图片预测为：class1\n",
      "IMG_20230329_072417(1).jpg\n",
      "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=3072x4096 at 0x2BD6CF423D0>\n",
      "torch.Size([3, 256, 256])\n",
      "tensor([[-9.1791e-06, -1.1599e+01, -2.0009e+02, -1.9770e+02]])\n",
      "第20张图片预测为：class1\n",
      "IMG_20230329_072419(1).jpg\n",
      "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=3072x4096 at 0x2BD69754D30>\n",
      "torch.Size([3, 256, 256])\n",
      "tensor([[-2.8610e-06, -1.2749e+01, -2.0077e+02, -1.9851e+02]])\n",
      "第21张图片预测为：class1\n",
      "IMG_20230329_072422.jpg\n",
      "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=3072x4096 at 0x2BD6A3495B0>\n",
      "torch.Size([3, 256, 256])\n",
      "tensor([[-2.3842e-07, -1.5256e+01, -2.0919e+02, -2.0681e+02]])\n",
      "第22张图片预测为：class1\n",
      "IMG_20230329_072426.jpg\n",
      "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=3072x4096 at 0x2BD60D87E50>\n",
      "torch.Size([3, 256, 256])\n",
      "tensor([[-2.3842e-06, -1.2934e+01, -1.9386e+02, -1.9166e+02]])\n",
      "第23张图片预测为：class1\n",
      "IMG_20230329_072844.jpg\n",
      "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=4096x3072 at 0x2BD15C7CF40>\n",
      "torch.Size([3, 256, 256])\n",
      "tensor([[-8.3446e-07, -1.4035e+01, -1.9276e+02, -1.9054e+02]])\n",
      "第24张图片预测为：class1\n",
      "IMG_20230329_072848.jpg\n",
      "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=4096x3072 at 0x2BD69737790>\n",
      "torch.Size([3, 256, 256])\n",
      "tensor([[-3.5763e-07, -1.4779e+01, -2.0215e+02, -1.9978e+02]])\n",
      "第25张图片预测为：class1\n",
      "IMG_20230329_072853.jpg\n",
      "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=3072x4096 at 0x2BD0C50A2E0>\n",
      "torch.Size([3, 256, 256])\n",
      "tensor([[-3.5763e-07, -1.4863e+01, -1.9779e+02, -1.9576e+02]])\n",
      "第26张图片预测为：class1\n",
      "IMG_20230329_072857.jpg\n",
      "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=3072x4096 at 0x2BD69754430>\n",
      "torch.Size([3, 256, 256])\n",
      "tensor([[   0.0000,  -17.2412, -203.5294, -201.2963]])\n",
      "第27张图片预测为：class1\n",
      "IMG_20230329_072905.jpg\n",
      "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=3072x4096 at 0x2BD69737790>\n",
      "torch.Size([3, 256, 256])\n",
      "tensor([[-1.1921e-07, -1.5539e+01, -1.9127e+02, -1.8888e+02]])\n",
      "第28张图片预测为：class1\n",
      "IMG_20230329_072914(1).jpg\n",
      "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=4096x3072 at 0x2BD6CF5EA30>\n",
      "torch.Size([3, 256, 256])\n",
      "tensor([[-7.1526e-07, -1.4237e+01, -1.9550e+02, -1.9314e+02]])\n",
      "第29张图片预测为：class1\n",
      "IMG_20230329_072922.jpg\n",
      "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=3072x4096 at 0x2BD697542B0>\n",
      "torch.Size([3, 256, 256])\n",
      "tensor([[   0.0000,  -17.1239, -199.2642, -197.2182]])\n",
      "第30张图片预测为：class1\n",
      "IMG_20230329_072926.jpg\n",
      "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=4096x3072 at 0x2BD69737160>\n",
      "torch.Size([3, 256, 256])\n",
      "tensor([[-2.3842e-07, -1.5114e+01, -1.8306e+02, -1.8082e+02]])\n",
      "第31张图片预测为：class1\n",
      "IMG_20230329_072930.jpg\n",
      "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=4096x3072 at 0x2BD6A2E9880>\n",
      "torch.Size([3, 256, 256])\n",
      "tensor([[-2.3842e-07, -1.5527e+01, -1.9294e+02, -1.9086e+02]])\n",
      "第32张图片预测为：class1\n",
      "IMG_20230329_072933.jpg\n",
      "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=4096x3072 at 0x2BD69754D30>\n",
      "torch.Size([3, 256, 256])\n",
      "tensor([[-1.0729e-06, -1.3747e+01, -1.7590e+02, -1.7392e+02]])\n",
      "第33张图片预测为：class1\n",
      "IMG_20230329_072936.jpg\n",
      "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=3072x4096 at 0x2BD69737850>\n",
      "torch.Size([3, 256, 256])\n",
      "tensor([[-2.3842e-07, -1.5050e+01, -1.8880e+02, -1.8665e+02]])\n",
      "第34张图片预测为：class1\n",
      "IMG_20230329_073000.jpg\n",
      "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=4096x3072 at 0x2BD6A2E9F40>\n",
      "torch.Size([3, 256, 256])\n",
      "tensor([[-1.1921e-07, -1.5924e+01, -1.8521e+02, -1.8319e+02]])\n",
      "第35张图片预测为：class1\n",
      "IMG_20230329_073008(1).jpg\n",
      "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=3072x4096 at 0x2BD697541F0>\n",
      "torch.Size([3, 256, 256])\n",
      "tensor([[-1.1921e-07, -1.6383e+01, -1.9052e+02, -1.8836e+02]])\n",
      "第36张图片预测为：class1\n",
      "IMG_20230329_073008.jpg\n",
      "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=3072x4096 at 0x2BD69737F70>\n",
      "torch.Size([3, 256, 256])\n",
      "tensor([[-1.1921e-07, -1.6383e+01, -1.9052e+02, -1.8836e+02]])\n",
      "第37张图片预测为：class1\n",
      "IMG_20230329_073012.jpg\n",
      "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=3072x4096 at 0x2BD60D769A0>\n",
      "torch.Size([3, 256, 256])\n",
      "tensor([[-6.3181e-06, -1.1977e+01, -1.9552e+02, -1.9346e+02]])\n",
      "第38张图片预测为：class1\n",
      "IMG_20230329_074926.jpg\n",
      "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=4096x3072 at 0x2BD697541F0>\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([3, 256, 256])\n",
      "tensor([[-1.1921e-06, -1.3620e+01, -2.7168e+02, -2.6833e+02]])\n",
      "第39张图片预测为：class1\n",
      "IMG_20230329_074928.jpg\n",
      "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=4096x3072 at 0x2BD69737F70>\n",
      "torch.Size([3, 256, 256])\n",
      "tensor([[-2.3842e-07, -1.5153e+01, -2.5835e+02, -2.5521e+02]])\n",
      "第40张图片预测为：class1\n",
      "IMG_20230329_074930.jpg\n",
      "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=4096x3072 at 0x2BD6A2E9370>\n",
      "torch.Size([3, 256, 256])\n",
      "tensor([[-1.5497e-06, -1.3355e+01, -2.5374e+02, -2.5023e+02]])\n",
      "第41张图片预测为：class1\n",
      "IMG_20230329_074935.jpg\n",
      "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=3072x4096 at 0x2BD60D76CA0>\n",
      "torch.Size([3, 256, 256])\n",
      "tensor([[-4.4107e-06, -1.2336e+01, -2.1523e+02, -2.1244e+02]])\n",
      "第42张图片预测为：class1\n",
      "IMG_20230329_074937.jpg\n",
      "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=3072x4096 at 0x2BD6A2D3430>\n",
      "torch.Size([3, 256, 256])\n",
      "tensor([[-1.1921e-07, -1.6124e+01, -2.6319e+02, -2.5967e+02]])\n",
      "第43张图片预测为：class1\n",
      "IMG_20230329_075018.jpg\n",
      "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=1459x1553 at 0x2BD0C50AD30>\n",
      "torch.Size([3, 256, 256])\n",
      "tensor([[-2.3842e-07, -1.5054e+01, -2.1412e+02, -2.1081e+02]])\n",
      "第44张图片预测为：class1\n",
      "IMG_20230329_075018.jpg.JPG\n",
      "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=162x172 at 0x2BD6A2E9E80>\n",
      "torch.Size([3, 256, 256])\n",
      "tensor([[-1.0729e-06, -1.3704e+01, -2.1290e+02, -2.0943e+02]])\n",
      "第45张图片预测为：class1\n",
      "IMG_20230329_075028.jpg\n",
      "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=1332x1280 at 0x2BD6A2D3430>\n",
      "torch.Size([3, 256, 256])\n",
      "tensor([[-7.0333e-06, -1.1868e+01, -2.2167e+02, -2.1886e+02]])\n",
      "第46张图片预测为：class1\n",
      "IMG_20230329_075028.jpg.JPG\n",
      "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=167x160 at 0x2BD60995CD0>\n",
      "torch.Size([3, 256, 256])\n",
      "tensor([[-5.7220e-06, -1.2062e+01, -2.2454e+02, -2.2134e+02]])\n",
      "第47张图片预测为：class1\n",
      "IMG_20230329_075032.jpg\n",
      "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=4096x3072 at 0x2BD6CF42D00>\n",
      "torch.Size([3, 256, 256])\n",
      "tensor([[-1.1921e-07, -1.5954e+01, -2.3034e+02, -2.2755e+02]])\n",
      "第48张图片预测为：class1\n",
      "IMG_20230329_075037.jpg\n",
      "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=3072x4096 at 0x2BD6A2E9F40>\n",
      "torch.Size([3, 256, 256])\n",
      "tensor([[   0.0000,  -19.0151, -221.1167, -218.4042]])\n",
      "第49张图片预测为：class1\n",
      "IMG_20230329_075041.jpg\n",
      "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=3072x4096 at 0x2BD0C50AAC0>\n",
      "torch.Size([3, 256, 256])\n",
      "tensor([[-2.0266e-06, -1.3082e+01, -2.0634e+02, -2.0405e+02]])\n",
      "第50张图片预测为：class1\n",
      "IMG_20230329_075046.jpg\n",
      "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=4096x3072 at 0x2BD6CF42370>\n",
      "torch.Size([3, 256, 256])\n",
      "tensor([[-8.3085e-05, -9.3956e+00, -2.2648e+02, -2.2365e+02]])\n",
      "第51张图片预测为：class1\n",
      "IMG_20230329_075047.jpg\n",
      "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=4096x3072 at 0x2BD697544F0>\n",
      "torch.Size([3, 256, 256])\n",
      "tensor([[-5.9605e-07, -1.4342e+01, -2.5919e+02, -2.5569e+02]])\n",
      "第52张图片预测为：class1\n",
      "IMG_20230329_075049.jpg\n",
      "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=4096x3072 at 0x2BD6A2D3D00>\n",
      "torch.Size([3, 256, 256])\n",
      "tensor([[-1.6689e-06, -1.3308e+01, -2.5608e+02, -2.5273e+02]])\n",
      "第53张图片预测为：class1\n",
      "IMG_20230329_075056.jpg\n",
      "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=3072x4096 at 0x2BD6CF42370>\n",
      "torch.Size([3, 256, 256])\n",
      "tensor([[   0.0000,  -17.6132, -204.7228, -202.0991]])\n",
      "第54张图片预测为：class1\n",
      "IMG_20230329_075102.jpg\n",
      "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=3072x4096 at 0x2BD6CF42490>\n",
      "torch.Size([3, 256, 256])\n",
      "tensor([[-3.5763e-07, -1.4712e+01, -2.4291e+02, -2.4019e+02]])\n",
      "第55张图片预测为：class1\n",
      "IMG_20230329_075104.jpg\n",
      "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=3072x4096 at 0x2BD6A2D3610>\n",
      "torch.Size([3, 256, 256])\n",
      "tensor([[-1.8596e-05, -1.0894e+01, -2.5134e+02, -2.4838e+02]])\n",
      "第56张图片预测为：class1\n",
      "IMG_20230329_075109.jpg\n",
      "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=4096x3072 at 0x2BD60D99340>\n",
      "torch.Size([3, 256, 256])\n",
      "tensor([[-4.0531e-06, -1.2419e+01, -2.1652e+02, -2.1412e+02]])\n",
      "第57张图片预测为：class1\n",
      "IMG_20230329_075139.jpg\n",
      "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=3072x4096 at 0x2BD6A2D3400>\n",
      "torch.Size([3, 256, 256])\n",
      "tensor([[-4.0054e-05, -1.0126e+01, -2.0788e+02, -2.0581e+02]])\n",
      "第58张图片预测为：class1\n",
      "IMG_20230329_075150.jpg\n",
      "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=3072x4096 at 0x2BD6A2D3160>\n",
      "torch.Size([3, 256, 256])\n",
      "tensor([[-1.7762e-05, -1.0935e+01, -2.1287e+02, -2.1022e+02]])\n",
      "第59张图片预测为：class1\n",
      "IMG_20230329_075153.jpg\n",
      "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=3072x4096 at 0x2BD6CF5E5E0>\n",
      "torch.Size([3, 256, 256])\n",
      "tensor([[-1.1921e-07, -1.6026e+01, -2.2726e+02, -2.2461e+02]])\n",
      "第60张图片预测为：class1\n",
      "IMG_20230329_075200.jpg\n",
      "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=1107x1625 at 0x2BD60D99280>\n",
      "torch.Size([3, 256, 256])\n",
      "tensor([[-9.2145e-05, -9.2919e+00, -3.0522e+02, -3.0177e+02]])\n",
      "第61张图片预测为：class1\n",
      "IMG_20230329_075200.jpg.JPG\n",
      "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=158x232 at 0x2BD6A349E50>\n",
      "torch.Size([3, 256, 256])\n",
      "tensor([[-4.5790e-04, -7.6890e+00, -3.0652e+02, -3.0276e+02]])\n",
      "第62张图片预测为：class1\n",
      "IMG_20230329_075215.jpg\n",
      "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=1138x1318 at 0x2BD6CF5E5E0>\n",
      "torch.Size([3, 256, 256])\n",
      "tensor([[-5.6573e-04, -7.4776e+00, -2.9987e+02, -2.9705e+02]])\n",
      "第63张图片预测为：class1\n",
      "IMG_20230329_075215.jpg.JPG\n",
      "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=162x188 at 0x2BD69754370>\n",
      "torch.Size([3, 256, 256])\n",
      "tensor([[-4.6570e-03, -5.3717e+00, -2.9860e+02, -2.9552e+02]])\n",
      "第64张图片预测为：class1\n",
      "IMG_20230329_075224.jpg\n",
      "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=702x927 at 0x2BD6A349AC0>\n",
      "torch.Size([3, 256, 256])\n",
      "tensor([[-1.7761e-03, -6.3343e+00, -3.0309e+02, -3.0024e+02]])\n",
      "第65张图片预测为：class1\n",
      "IMG_20230329_075224.jpg.JPG\n",
      "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=176x232 at 0x2BD608C3AC0>\n",
      "torch.Size([3, 256, 256])\n",
      "tensor([[-6.7230e-03, -5.0056e+00, -3.0322e+02, -3.0017e+02]])\n",
      "第66张图片预测为：class1\n",
      "IMG_20230329_075238.jpg\n",
      "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=1054x1549 at 0x2BD69754970>\n",
      "torch.Size([3, 256, 256])\n",
      "tensor([[-1.3434e-04, -8.9149e+00, -2.9117e+02, -2.8808e+02]])\n",
      "第67张图片预测为：class1\n",
      "IMG_20230329_075238.jpg.JPG\n",
      "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=150x221 at 0x2BD6CF42D00>\n",
      "torch.Size([3, 256, 256])\n",
      "tensor([[-2.1436e-03, -6.1463e+00, -2.8755e+02, -2.8435e+02]])\n",
      "第68张图片预测为：class1\n",
      "IMG_20230329_075838.jpg\n",
      "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=1613x1253 at 0x2BD15C7C610>\n",
      "torch.Size([3, 256, 256])\n",
      "tensor([[-7.9870e-06, -1.1735e+01, -2.9663e+02, -2.9296e+02]])\n",
      "第69张图片预测为：class1\n",
      "IMG_20230329_075838.jpg.JPG\n",
      "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=202x157 at 0x2BD69754130>\n",
      "torch.Size([3, 256, 256])\n",
      "tensor([[-3.6477e-05, -1.0220e+01, -2.9598e+02, -2.9198e+02]])\n",
      "第70张图片预测为：class1\n",
      "IMG_20230329_080650.jpg\n",
      "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=1281x896 at 0x2BD60D87DF0>\n",
      "torch.Size([3, 256, 256])\n",
      "tensor([[-5.3762e-05, -9.8309e+00, -3.1020e+02, -3.0672e+02]])\n",
      "第71张图片预测为：class1\n",
      "IMG_20230329_080650.jpg.JPG\n",
      "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=256x179 at 0x2BD60D36E50>\n",
      "torch.Size([3, 256, 256])\n",
      "tensor([[-5.5908e-05, -9.7924e+00, -3.1244e+02, -3.0873e+02]])\n",
      "第72张图片预测为：class1\n",
      "IMG_20230329_080716.jpg\n",
      "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=1334x813 at 0x2BD608C38E0>\n",
      "torch.Size([3, 256, 256])\n",
      "tensor([[-2.0347e-04, -8.5002e+00, -3.0373e+02, -3.0060e+02]])\n",
      "第73张图片预测为：class1\n",
      "IMG_20230329_080716.jpg.JPG\n",
      "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=266x162 at 0x2BD60D87F10>\n",
      "torch.Size([3, 256, 256])\n",
      "tensor([[-3.1478e-04, -8.0638e+00, -3.0957e+02, -3.0584e+02]])\n",
      "第74张图片预测为：class1\n",
      "IMG_20230329_080728.jpg\n",
      "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=1989x990 at 0x2BD6A2E9850>\n",
      "torch.Size([3, 256, 256])\n",
      "tensor([[-5.8411e-05, -9.7479e+00, -3.0937e+02, -3.0594e+02]])\n",
      "第75张图片预测为：class1\n",
      "IMG_20230329_080728.jpg.JPG\n",
      "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=331x165 at 0x2BD60D76670>\n",
      "torch.Size([3, 256, 256])\n",
      "tensor([[-1.3982e-04, -8.8754e+00, -3.0909e+02, -3.0539e+02]])\n",
      "第76张图片预测为：class1\n",
      "IMG_20230329_080747.jpg\n",
      "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=1915x1405 at 0x2BD69754B80>\n",
      "torch.Size([3, 256, 256])\n",
      "tensor([[-6.9141e-06, -1.1882e+01, -2.8786e+02, -2.8446e+02]])\n",
      "第77张图片预测为：class1\n",
      "IMG_20230329_080747.jpg.JPG\n",
      "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=212x156 at 0x2BD6A2E9250>\n",
      "torch.Size([3, 256, 256])\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[-6.7949e-06, -1.1897e+01, -2.8974e+02, -2.8613e+02]])\n",
      "第78张图片预测为：class1\n",
      "IMG_20230329_080801.jpg\n",
      "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=1574x1159 at 0x2BD60D76670>\n",
      "torch.Size([3, 256, 256])\n",
      "tensor([[-2.7418e-06, -1.2821e+01, -3.0548e+02, -3.0159e+02]])\n",
      "第79张图片预测为：class1\n",
      "IMG_20230329_080801.jpg.JPG\n",
      "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=224x165 at 0x2BD69754B80>\n",
      "torch.Size([3, 256, 256])\n",
      "tensor([[-2.5034e-06, -1.2875e+01, -3.0877e+02, -3.0443e+02]])\n",
      "第80张图片预测为：class1\n",
      "IMG_20230329_080818.jpg\n",
      "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=1327x1201 at 0x2BD6A2E9880>\n",
      "torch.Size([3, 256, 256])\n",
      "tensor([[-3.2186e-06, -1.2641e+01, -3.0831e+02, -3.0453e+02]])\n",
      "第81张图片预测为：class1\n",
      "IMG_20230329_080818.jpg.JPG\n",
      "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=166x151 at 0x2BD60D76220>\n",
      "torch.Size([3, 256, 256])\n",
      "tensor([[-2.8371e-05, -1.0470e+01, -3.0688e+02, -3.0281e+02]])\n",
      "第82张图片预测为：class1\n",
      "IMG_20230329_080849.jpg\n",
      "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=2351x1231 at 0x2BD69754850>\n",
      "torch.Size([3, 256, 256])\n",
      "tensor([[-5.1260e-06, -1.2172e+01, -2.8324e+02, -2.7992e+02]])\n",
      "第83张图片预测为：class1\n",
      "IMG_20230329_080849.jpg.JPG\n",
      "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=294x154 at 0x2BD60D363A0>\n",
      "torch.Size([3, 256, 256])\n",
      "tensor([[-5.1259e-05, -9.8776e+00, -2.8214e+02, -2.7859e+02]])\n",
      "第84张图片预测为：class1\n",
      "IMG_20230329_080903.jpg\n",
      "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=1455x968 at 0x2BD60D707F0>\n",
      "torch.Size([3, 256, 256])\n",
      "tensor([[-1.7047e-05, -1.0981e+01, -2.9933e+02, -2.9615e+02]])\n",
      "第85张图片预测为：class1\n",
      "IMG_20230329_080903.jpg.JPG\n",
      "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=242x161 at 0x2BD60D70760>\n",
      "torch.Size([3, 256, 256])\n",
      "tensor([[-6.8305e-05, -9.5910e+00, -2.9982e+02, -2.9629e+02]])\n",
      "第86张图片预测为：class1\n",
      "IMG_20230329_185825.jpg\n",
      "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=1423x1274 at 0x2BD60D2A3D0>\n",
      "torch.Size([3, 256, 256])\n",
      "tensor([[-1.1026e-04, -9.1124e+00, -2.8933e+02, -2.8550e+02]])\n",
      "第87张图片预测为：class1\n",
      "IMG_20230329_185834.jpg\n",
      "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=1625x1633 at 0x2BD60D360D0>\n",
      "torch.Size([3, 256, 256])\n",
      "tensor([[-5.6028e-06, -1.2096e+01, -2.7148e+02, -2.6782e+02]])\n",
      "第88张图片预测为：class1\n",
      "IMG_20230329_185834.jpg.JPG\n",
      "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=162x163 at 0x2BD0C50A760>\n",
      "torch.Size([3, 256, 256])\n",
      "tensor([[-1.0610e-05, -1.1454e+01, -2.6669e+02, -2.6285e+02]])\n",
      "第89张图片预测为：class1\n",
      "IMG_20230329_185907.jpg\n",
      "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=2046x1070 at 0x2BD60D70580>\n",
      "torch.Size([3, 256, 256])\n",
      "tensor([[-2.0146e-05, -1.0812e+01, -1.5293e+02, -1.5134e+02]])\n",
      "第90张图片预测为：class1\n",
      "IMG_20230329_185907.jpg.JPG\n",
      "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=292x152 at 0x2BD15C7C730>\n",
      "torch.Size([3, 256, 256])\n",
      "tensor([[-8.5830e-06, -1.1666e+01, -1.6054e+02, -1.5860e+02]])\n",
      "第91张图片预测为：class1\n",
      "IMG_20230329_185921.jpg\n",
      "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=1592x1050 at 0x2BD0C50AAC0>\n",
      "torch.Size([3, 256, 256])\n",
      "tensor([[-5.2452e-06, -1.2161e+01, -1.6043e+02, -1.5895e+02]])\n",
      "第92张图片预测为：class1\n",
      "IMG_20230329_185930.jpg\n",
      "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=2170x1209 at 0x2BD0C50AD90>\n",
      "torch.Size([3, 256, 256])\n",
      "tensor([[-2.4676e-05, -1.0611e+01, -1.4634e+02, -1.4519e+02]])\n",
      "第93张图片预测为：class1\n",
      "IMG_20230329_185947.jpg\n",
      "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=1621x1936 at 0x2BD69754EB0>\n",
      "torch.Size([3, 256, 256])\n",
      "tensor([[-1.0251e-04, -9.1858e+00, -1.7857e+02, -1.7740e+02]])\n",
      "第94张图片预测为：class1\n",
      "IMG_2338.jpg\n",
      "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=2951x2214 at 0x2BD0C50A100>\n",
      "torch.Size([3, 256, 256])\n",
      "tensor([[-9.8581e-05, -9.2247e+00, -1.4208e+02, -1.4139e+02]])\n",
      "第95张图片预测为：class1\n",
      "IMG_2339.jpg\n",
      "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=2951x2214 at 0x2BD60D87F70>\n",
      "torch.Size([3, 256, 256])\n",
      "tensor([[-1.0371e-05, -1.1472e+01, -1.6274e+02, -1.6161e+02]])\n",
      "第96张图片预测为：class1\n",
      "IMG_2340.jpg\n",
      "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=2951x2214 at 0x2BD6CF42790>\n",
      "torch.Size([3, 256, 256])\n",
      "tensor([[-4.5299e-06, -1.2293e+01, -1.5578e+02, -1.5461e+02]])\n",
      "第97张图片预测为：class1\n",
      "IMG_2341.jpg\n",
      "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=2951x2214 at 0x2BD69754160>\n",
      "torch.Size([3, 256, 256])\n",
      "tensor([[-4.8876e-06, -1.2240e+01, -1.7250e+02, -1.7121e+02]])\n",
      "第98张图片预测为：class1\n",
      "IMG_2342.jpg\n",
      "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=2951x2214 at 0x2BD60D87F70>\n",
      "torch.Size([3, 256, 256])\n",
      "tensor([[-3.3379e-06, -1.2612e+01, -1.9895e+02, -1.9668e+02]])\n",
      "第99张图片预测为：class1\n",
      "IMG_2343.jpg\n",
      "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=2951x2214 at 0x2BD6CF42DF0>\n",
      "torch.Size([3, 256, 256])\n",
      "tensor([[-1.1921e-07, -1.5846e+01, -2.0854e+02, -2.0613e+02]])\n",
      "第100张图片预测为：class1\n",
      "IMG_2344.jpg\n",
      "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=2951x2214 at 0x2BD60D706D0>\n",
      "torch.Size([3, 256, 256])\n",
      "tensor([[-1.3113e-05, -1.1239e+01, -1.9824e+02, -1.9609e+02]])\n",
      "第101张图片预测为：class1\n",
      "IMG_2355.jpg\n",
      "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=2951x2214 at 0x2BD60D70910>\n",
      "torch.Size([3, 256, 256])\n",
      "tensor([[-7.0689e-05, -9.5576e+00, -1.4375e+02, -1.4316e+02]])\n",
      "第102张图片预测为：class1\n",
      "IMG_2356.jpg\n",
      "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=2951x2214 at 0x2BD69754730>\n",
      "torch.Size([3, 256, 256])\n",
      "tensor([[-4.2680e-04, -7.7595e+00, -1.4188e+02, -1.4165e+02]])\n",
      "第103张图片预测为：class1\n",
      "IMG_2357.jpg\n",
      "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=2951x2214 at 0x2BD6CF421C0>\n",
      "torch.Size([3, 256, 256])\n",
      "tensor([[-4.6848e-05, -9.9690e+00, -1.5322e+02, -1.5201e+02]])\n",
      "第104张图片预测为：class1\n",
      "IMG_2363.jpg\n",
      "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=2951x2214 at 0x2BD6A2D30A0>\n",
      "torch.Size([3, 256, 256])\n",
      "tensor([[-7.1882e-03, -4.9389e+00, -1.7959e+02, -1.7782e+02]])\n",
      "第105张图片预测为：class1\n",
      "IMG_2364.jpg\n",
      "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=2951x2214 at 0x2BD60D2A400>\n",
      "torch.Size([3, 256, 256])\n",
      "tensor([[-1.7524e-05, -1.0950e+01, -1.7258e+02, -1.7080e+02]])\n",
      "第106张图片预测为：class1\n",
      "IMG_2365.jpg\n",
      "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=2951x2214 at 0x2BD60D76100>\n",
      "torch.Size([3, 256, 256])\n",
      "tensor([[-1.1968e-04, -9.0311e+00, -2.4558e+02, -2.4262e+02]])\n",
      "第107张图片预测为：class1\n",
      "IMG_2469.jpg\n",
      "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=2951x2214 at 0x2BD60D363A0>\n",
      "torch.Size([3, 256, 256])\n",
      "tensor([[-1.5497e-06, -1.3351e+01, -1.8597e+02, -1.8351e+02]])\n",
      "第108张图片预测为：class1\n",
      "IMG_2470.jpg\n",
      "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=2951x2214 at 0x2BD60D70F40>\n",
      "torch.Size([3, 256, 256])\n",
      "tensor([[-8.2254e-06, -1.1715e+01, -1.8065e+02, -1.7851e+02]])\n",
      "第109张图片预测为：class1\n",
      "IMG_2471.jpg\n",
      "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=2951x2214 at 0x2BD60D76F70>\n",
      "torch.Size([3, 256, 256])\n",
      "tensor([[-6.0797e-06, -1.2002e+01, -1.5503e+02, -1.5320e+02]])\n",
      "第110张图片预测为：class1\n",
      "IMG_2472.jpg\n",
      "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=2951x2214 at 0x2BD60D363A0>\n",
      "torch.Size([3, 256, 256])\n",
      "tensor([[-5.3404e-05, -9.8369e+00, -1.5107e+02, -1.5017e+02]])\n",
      "第111张图片预测为：class1\n",
      "IMG_2473.jpg\n",
      "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=2951x2214 at 0x2BD60D87FA0>\n",
      "torch.Size([3, 256, 256])\n",
      "tensor([[-5.9722e-05, -9.7259e+00, -1.5437e+02, -1.5341e+02]])\n",
      "第112张图片预测为：class1\n",
      "IMG_2474.jpg\n",
      "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=2951x2214 at 0x2BD60D76EB0>\n",
      "torch.Size([3, 256, 256])\n",
      "tensor([[-6.7949e-06, -1.1896e+01, -1.9398e+02, -1.9178e+02]])\n",
      "第113张图片预测为：class1\n",
      "IMG_2475.jpg\n",
      "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=2951x2214 at 0x2BD6A3492B0>\n",
      "torch.Size([3, 256, 256])\n",
      "tensor([[-8.3446e-06, -1.1698e+01, -2.0241e+02, -1.9986e+02]])\n",
      "第114张图片预测为：class1\n",
      "IMG_2476.jpg\n",
      "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=2951x2214 at 0x2BD60D87F70>\n",
      "torch.Size([3, 256, 256])\n",
      "tensor([[-4.0531e-06, -1.2406e+01, -1.7525e+02, -1.7347e+02]])\n",
      "第115张图片预测为：class1\n",
      "IMG_2477.jpg\n",
      "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=2951x2214 at 0x2BD6A342940>\n",
      "torch.Size([3, 256, 256])\n",
      "tensor([[-4.0530e-05, -1.0112e+01, -1.5026e+02, -1.4900e+02]])\n",
      "第116张图片预测为：class1\n",
      "IMG_2478.jpg\n",
      "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=2951x2214 at 0x2BD60D704C0>\n",
      "torch.Size([3, 256, 256])\n",
      "tensor([[-1.6808e-05, -1.0992e+01, -1.4869e+02, -1.4743e+02]])\n",
      "第117张图片预测为：class1\n",
      "IMG_2479.jpg\n",
      "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=2951x2214 at 0x2BD60D70640>\n",
      "torch.Size([3, 256, 256])\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[-9.0599e-06, -1.1609e+01, -2.2666e+02, -2.2390e+02]])\n",
      "第118张图片预测为：class1\n",
      "IMG_2480.jpg\n",
      "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=2951x2214 at 0x2BD6A3422B0>\n",
      "torch.Size([3, 256, 256])\n",
      "tensor([[-5.1259e-05, -9.8777e+00, -2.2521e+02, -2.2196e+02]])\n",
      "第119张图片预测为：class1\n",
      "微信图片_20230329154823.jpg\n",
      "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=2304x4096 at 0x2BD60D87DC0>\n",
      "torch.Size([3, 256, 256])\n",
      "tensor([[-4.7684e-07, -1.4635e+01, -2.5613e+02, -2.5199e+02]])\n",
      "第120张图片预测为：class1\n",
      "微信图片_20230329154824.jpg\n",
      "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=2304x4096 at 0x2BD60D707F0>\n",
      "torch.Size([3, 256, 256])\n",
      "tensor([[-9.4175e-06, -1.1568e+01, -2.5082e+02, -2.4737e+02]])\n",
      "第121张图片预测为：class1\n",
      "微信图片_20230329154825.jpg\n",
      "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=2304x4096 at 0x2BD6A3422B0>\n",
      "torch.Size([3, 256, 256])\n",
      "tensor([[-3.5763e-07, -1.5001e+01, -2.5208e+02, -2.4822e+02]])\n",
      "第122张图片预测为：class1\n",
      "微信图片_20230329154903.jpg\n",
      "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=2304x4096 at 0x2BD60D708E0>\n",
      "torch.Size([3, 256, 256])\n",
      "tensor([[-6.3298e-05, -9.6680e+00, -2.5960e+02, -2.5617e+02]])\n",
      "第123张图片预测为：class1\n",
      "微信图片_20230329154904.jpg\n",
      "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=2304x4096 at 0x2BD60D706D0>\n",
      "torch.Size([3, 256, 256])\n",
      "tensor([[-3.5763e-07, -1.4994e+01, -2.6613e+02, -2.6265e+02]])\n",
      "第124张图片预测为：class1\n",
      "微信图片_20230329154905.jpg\n",
      "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=2304x4096 at 0x2BD15C7C880>\n",
      "torch.Size([3, 256, 256])\n",
      "tensor([[-1.9550e-05, -1.0841e+01, -2.4998e+02, -2.4703e+02]])\n",
      "第125张图片预测为：class1\n",
      "微信图片_20230329154906.jpg\n",
      "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=4096x2304 at 0x2BD15C7C940>\n",
      "torch.Size([3, 256, 256])\n",
      "tensor([[  -0.3136,   -1.3125, -130.9316, -129.5850]])\n",
      "第126张图片预测为：class1\n",
      "微信图片_20230329154907.jpg\n",
      "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=2304x4096 at 0x2BD60D87EE0>\n",
      "torch.Size([3, 256, 256])\n",
      "tensor([[-2.5034e-06, -1.2913e+01, -2.5598e+02, -2.5277e+02]])\n",
      "第127张图片预测为：class1\n",
      "微信图片_20230329160231.jpg\n",
      "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=3072x4096 at 0x2BD69737DF0>\n",
      "torch.Size([3, 256, 256])\n",
      "tensor([[-1.2555e-02, -4.3839e+00, -2.2480e+02, -2.2220e+02]])\n",
      "第128张图片预测为：class1\n",
      "微信图片_20230329160313.jpg\n",
      "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=3072x4096 at 0x2BD60D76D90>\n",
      "torch.Size([3, 256, 256])\n",
      "tensor([[-3.5429e-03, -5.6446e+00, -1.6847e+02, -1.6665e+02]])\n",
      "第129张图片预测为：class1\n",
      "微信图片_20230329160314.jpg\n",
      "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=3072x4096 at 0x2BD60D2A400>\n",
      "torch.Size([3, 256, 256])\n",
      "tensor([[-2.3052e-04, -8.3750e+00, -1.6611e+02, -1.6414e+02]])\n",
      "第130张图片预测为：class1\n",
      "微信图片_20230329160540.jpg\n",
      "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=3072x4096 at 0x2BD6A342940>\n",
      "torch.Size([3, 256, 256])\n",
      "tensor([[-1.2755e-05, -1.1269e+01, -1.8468e+02, -1.8247e+02]])\n",
      "第131张图片预测为：class1\n",
      "微信图片_20230329160541.jpg\n",
      "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=3072x4096 at 0x2BD60D76B50>\n",
      "torch.Size([3, 256, 256])\n",
      "tensor([[-4.0650e-05, -1.0111e+01, -2.0553e+02, -2.0282e+02]])\n",
      "第132张图片预测为：class1\n",
      "微信图片_20230329160542.jpg\n",
      "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=3072x4096 at 0x2BD69754F40>\n",
      "torch.Size([3, 256, 256])\n",
      "tensor([[-5.0068e-06, -1.2205e+01, -1.7363e+02, -1.7179e+02]])\n",
      "第133张图片预测为：class1\n",
      "微信图片_20230329160543.jpg\n",
      "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=3072x4096 at 0x2BD60995820>\n",
      "torch.Size([3, 256, 256])\n",
      "tensor([[-1.3289e-03, -6.6241e+00, -1.5653e+02, -1.5512e+02]])\n",
      "第134张图片预测为：class1\n",
      "微信图片_20230329160544.jpg\n",
      "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=3072x4096 at 0x2BD15C7C3D0>\n",
      "torch.Size([3, 256, 256])\n",
      "tensor([[-9.0964e-03, -4.7044e+00, -1.9427e+02, -1.9221e+02]])\n",
      "第135张图片预测为：class1\n",
      "微信图片_20230329160545.jpg\n",
      "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=3072x4096 at 0x2BD69754F40>\n",
      "torch.Size([3, 256, 256])\n",
      "tensor([[-3.1828e-05, -1.0357e+01, -1.7979e+02, -1.7772e+02]])\n",
      "第136张图片预测为：class1\n",
      "微信图片_20230329160546.jpg\n",
      "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=3072x4096 at 0x2BD60995820>\n",
      "torch.Size([3, 256, 256])\n",
      "tensor([[-3.2186e-05, -1.0344e+01, -1.7038e+02, -1.6828e+02]])\n",
      "第137张图片预测为：class1\n",
      "微信图片_20230329160547.jpg\n",
      "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=3072x4096 at 0x2BD69737F70>\n",
      "torch.Size([3, 256, 256])\n",
      "tensor([[-1.6368e-03, -6.4158e+00, -1.3345e+02, -1.3156e+02]])\n",
      "第138张图片预测为：class1\n",
      "微信图片_20230329160623.jpg\n",
      "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=4096x3072 at 0x2BD60D70F70>\n",
      "torch.Size([3, 256, 256])\n",
      "tensor([[-2.3842e-07, -1.5367e+01, -2.4683e+02, -2.4380e+02]])\n",
      "第139张图片预测为：class1\n",
      "微信图片_20230329160626.jpg\n",
      "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=4096x3072 at 0x2BD6A2E95E0>\n",
      "torch.Size([3, 256, 256])\n",
      "tensor([[   0.0000,  -17.5620, -246.2863, -243.8123]])\n",
      "第140张图片预测为：class1\n",
      "微信图片_20230329160629.jpg\n",
      "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=4096x3072 at 0x2BD69737790>\n",
      "torch.Size([3, 256, 256])\n",
      "tensor([[-1.1921e-07, -1.6215e+01, -2.5395e+02, -2.5126e+02]])\n",
      "第141张图片预测为：class1\n",
      "微信图片_20230329160631.jpg\n",
      "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=4096x3072 at 0x2BD69754F40>\n",
      "torch.Size([3, 256, 256])\n",
      "tensor([[-1.0573e-04, -9.1541e+00, -2.6096e+02, -2.5769e+02]])\n",
      "第142张图片预测为：class1\n",
      "微信图片_20230329160634.jpg\n",
      "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=4096x3072 at 0x2BD69754BE0>\n",
      "torch.Size([3, 256, 256])\n",
      "tensor([[-1.5497e-06, -1.3406e+01, -2.3308e+02, -2.3101e+02]])\n",
      "第143张图片预测为：class1\n",
      "微信图片_20230329160638.jpg\n",
      "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=3072x4096 at 0x2BD69737790>\n",
      "torch.Size([3, 256, 256])\n",
      "tensor([[-1.4328e-04, -8.8504e+00, -2.5152e+02, -2.4823e+02]])\n",
      "第144张图片预测为：class1\n",
      "微信图片_20230329160639.jpg\n",
      "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=3072x4096 at 0x2BD697543A0>\n",
      "torch.Size([3, 256, 256])\n",
      "tensor([[-1.0610e-05, -1.1451e+01, -2.2395e+02, -2.2088e+02]])\n",
      "第145张图片预测为：class1\n",
      "微信图片_20230329160714.jpg\n",
      "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=4096x3072 at 0x2BD69754FA0>\n",
      "torch.Size([3, 256, 256])\n",
      "tensor([[-9.5720e-05, -9.2535e+00, -2.7649e+02, -2.7297e+02]])\n",
      "第146张图片预测为：class1\n",
      "微信图片_20230329160716.jpg\n",
      "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=4096x3072 at 0x2BD15C7CD00>\n",
      "torch.Size([3, 256, 256])\n",
      "tensor([[-1.5973e-04, -8.7420e+00, -2.0460e+02, -2.0163e+02]])\n",
      "第147张图片预测为：class1\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import torch\n",
    "from PIL import Image\n",
    "from torch import nn\n",
    "from torchvision import transforms, models\n",
    "\n",
    "i = 0  # 识别图片计数\n",
    "# 这里最好新建一个test_data文件随机放一些上面整理好的图片进去\n",
    "root_path = r\"C:\\Users\\beichen\\dataset\\test\"  # 待测试文件夹\n",
    "names = os.listdir(root_path)\n",
    "\n",
    "for name in names:\n",
    "\tprint(name)\n",
    "\ti = i + 1\n",
    "\tdata_class = ['class1','class2']  # 按文件索引顺序排列\n",
    "\timage_path = os.path.join(root_path, name)\n",
    "\timage = Image.open(image_path)\n",
    "\tprint(image)\n",
    "\ttransform = transforms.Compose([transforms.Resize((256, 256)),\n",
    "\t\t\t\t\t\t\t\t\ttransforms.ToTensor()])\n",
    "\n",
    "\timage = transform(image)\n",
    "\tprint(image.shape)\n",
    "\n",
    "\tmodel_ft = models.resnet50()\n",
    "\tin_features = model_ft.fc.in_features\n",
    "\tmodel_ft.fc = nn.Sequential(nn.Linear(in_features, 256),\n",
    "\t\t\t\t\t\t\t\tnn.ReLU(),\n",
    "\t\t\t\t\t\t\t\t# nn.Dropout(0, 4),\n",
    "\t\t\t\t\t\t\t\tnn.Linear(256, 4),\n",
    "\t\t\t\t\t\t\t\tnn.LogSoftmax(dim=1))\n",
    "\n",
    "\tmodel = torch.load(\"aerialmodel2.pth\", map_location=torch.device(\"cpu\"))\n",
    "\timage = torch.reshape(image, (1, 3, 256, 256))  # 修改待预测图片尺寸，需要与训练时一致\n",
    "\tmodel.eval()\n",
    "\twith torch.no_grad():\n",
    "\t\toutput = model(image)\n",
    "\tprint(output)  # 输出预测结果\n",
    "\t# print(int(output.argmax(1)))\n",
    "\t# 对结果进行处理，使直接显示出预测的种类\n",
    "\tprint(\"第{}张图片预测为：{}\".format(i, data_class[int(output.argmax(1))]))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c4c3e6ef",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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