train_effi_b3.ipynb

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   "source": [
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.image as mpimg\n",
    "import torch\n",
    "import os\n",
    "from tqdm.auto import tqdm\n",
    "from glob import glob\n",
    "import cv2\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import PIL \n",
    "import urllib\n",
    "from torch import nn\n",
    "from torch import optim\n",
    "import torch.nn.functional as F\n",
    "from torchvision import datasets, transforms\n",
    "from random import uniform\n",
    "from imgaug import augmenters as iaa\n",
    "import torch.utils.data as td\n",
    "import torchvision as tv\n",
    "from PIL import Image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from google.colab import drive\n",
    "drive.mount._DEBUG = True\n",
    "drive.mount('/content/drive', force_remount=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "os.chdir('./drive/MyDrive/hack/')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%time\n",
    "!unzip \"./data.zip\" -d \"/content/drive/MyDrive/hack/data/\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "executionInfo": {
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     "timestamp": 1635529197449,
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    "id": "iGvdXw8zVOgU"
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   "outputs": [],
   "source": [
    "def imshow(img):\n",
    "  img = img   # unnormalize\n",
    "  \n",
    "  # cv2_imshow(images[0][0])\n",
    "  plt.imshow(np.transpose(img, (1, 2, 0)))  # convert from Tensor image\n",
    "  \n",
    "  plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "executionInfo": {
     "elapsed": 3,
     "status": "ok",
     "timestamp": 1635529197449,
     "user": {
      "displayName": "최재홍",
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      "userId": "10030380872100387179"
     },
     "user_tz": -540
    },
    "id": "xlMYdzukM1DD"
   },
   "outputs": [],
   "source": [
    "transform = transforms.Compose([\n",
    "                                transforms.ColorJitter(brightness=.3),\n",
    "#                                 transforms.RandomRotation(20),\n",
    "                                transforms.Resize((256,256)), \n",
    "                                transforms.RandomVerticalFlip(0.3),\n",
    "                                transforms.RandomHorizontalFlip(0.3),\n",
    "                                transforms.ToTensor(),\n",
    "                                transforms.Normalize([0.485 , 0.456, 0.406], [0.229, 0.224 , 0.225]),                                \n",
    "                               ])\n",
    "\n",
    "\n",
    "valid_transforms = transforms.Compose([\n",
    "    transforms.Resize((256,256)),\n",
    "    transforms.ToTensor(),\n",
    "    transforms.Normalize([0.485 , 0.456, 0.406], [0.229, 0.224 , 0.225]), \n",
    "    ])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "executionInfo": {
     "elapsed": 492,
     "status": "ok",
     "timestamp": 1635529197938,
     "user": {
      "displayName": "최재홍",
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      "userId": "10030380872100387179"
     },
     "user_tz": -540
    },
    "id": "CRn2FrYENyI_"
   },
   "outputs": [],
   "source": [
    "train_dataset=datasets.ImageFolder('./data1/train/',transform=transform)\n",
    "valid_dataset=datasets.ImageFolder('./data1/valid/',transform=valid_transforms)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 4,
     "status": "ok",
     "timestamp": 1635529198921,
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    "outputId": "15d661d5-cada-40ed-bcb1-cce00848a4d1"
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "1016"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(valid_dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "executionInfo": {
     "elapsed": 2,
     "status": "ok",
     "timestamp": 1635529198921,
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   "source": [
    "# prepare data loaders (combine dataset and sampler)\n",
    "batch_size=24\n",
    "train_dataloader = torch.utils.data.DataLoader(train_dataset, batch_size=batch_size,shuffle=True)\n",
    "valid_dataloader = torch.utils.data.DataLoader(valid_dataset, batch_size=batch_size, shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
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     "height": 34
    },
    "executionInfo": {
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     "timestamp": 1635529250676,
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    "id": "uF7S12XoVL3U",
    "outputId": "2ef7c762-3bd7-4c60-d19b-7f0a6e4c63bd"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'cuda'"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "device = 'cpu'\n",
    "if torch.cuda.is_available():\n",
    "    device = 'cuda'\n",
    "    \n",
    "device"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
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     "status": "ok",
     "timestamp": 1635529257865,
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    },
    "id": "h6tfMMfuNUa1",
    "outputId": "f6b8d3da-58e6-4f3e-daa2-10943e207761"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: efficientnet_pytorch in c:\\users\\user\\anaconda3\\lib\\site-packages (0.7.1)\n",
      "Requirement already satisfied: torch in c:\\users\\user\\anaconda3\\lib\\site-packages (from efficientnet_pytorch) (1.9.0)\n",
      "Requirement already satisfied: typing_extensions in c:\\users\\user\\anaconda3\\lib\\site-packages (from torch->efficientnet_pytorch) (3.7.4.3)\n"
     ]
    }
   ],
   "source": [
    "!pip install efficientnet_pytorch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
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      "b6e2d8cf4a3d4c849139be628217428c"
     ]
    },
    "executionInfo": {
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     "status": "ok",
     "timestamp": 1635529263760,
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    "id": "H2fhneAuNclg",
    "outputId": "0ab0f268-9653-4829-e280-247601824e59"
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loaded pretrained weights for efficientnet-b3\n"
     ]
    }
   ],
   "source": [
    "from efficientnet_pytorch import EfficientNet\n",
    "model = EfficientNet.from_pretrained('efficientnet-b3', num_classes=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "executionInfo": {
     "elapsed": 298,
     "status": "ok",
     "timestamp": 1635529266024,
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     },
     "user_tz": -540
    },
    "id": "_vX786Yie2fg"
   },
   "outputs": [],
   "source": [
    "import torch.optim as optim\n",
    "\n",
    "criterion = nn.CrossEntropyLoss()\n",
    "optimizer = optim.Adam(model.parameters(),lr=0.001)\n",
    "scheduler=optim.lr_scheduler.ReduceLROnPlateau(optimizer,mode='min')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
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    },
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    "outputId": "4d62ef90-533b-4c3a-9001-eb240a1f717c"
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   "outputs": [
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    {
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     "text": [
      "Epoch: 0 \tTraining Loss: 0.108537 \tValidation Loss: 0.093714\n",
      "Epoch: 0 \tTraining accuracy: 0.966597 \tValidation accuracy: 0.979651\n",
      "Validation loss decreased (inf --> 0.093714).  Saving model ...\n"
     ]
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    {
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     "text": [
      "Epoch: 1 \tTraining Loss: 0.060653 \tValidation Loss: 0.041470\n",
      "Epoch: 1 \tTraining accuracy: 0.981093 \tValidation accuracy: 0.988372\n",
      "Validation loss decreased (0.093714 --> 0.041470).  Saving model ...\n"
     ]
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     "text": [
      "Epoch: 2 \tTraining Loss: 0.034761 \tValidation Loss: 0.066994\n",
      "Epoch: 2 \tTraining accuracy: 0.989356 \tValidation accuracy: 0.984496\n"
     ]
    },
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     "text": [
      "Epoch: 3 \tTraining Loss: 0.049124 \tValidation Loss: 0.067834\n",
      "Epoch: 3 \tTraining accuracy: 0.983964 \tValidation accuracy: 0.980620\n"
     ]
    },
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     "text": [
      "Epoch: 4 \tTraining Loss: 0.039509 \tValidation Loss: 0.046953\n",
      "Epoch: 4 \tTraining accuracy: 0.987045 \tValidation accuracy: 0.981589\n"
     ]
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     "text": [
      "Epoch: 5 \tTraining Loss: 0.032008 \tValidation Loss: 0.053049\n",
      "Epoch: 5 \tTraining accuracy: 0.989986 \tValidation accuracy: 0.989341\n"
     ]
    },
    {
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     "text": [
      "Epoch: 6 \tTraining Loss: 0.039603 \tValidation Loss: 0.048865\n",
      "Epoch: 6 \tTraining accuracy: 0.987255 \tValidation accuracy: 0.988372\n"
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     "text": [
      "Epoch: 7 \tTraining Loss: 0.022380 \tValidation Loss: 0.097961\n",
      "Epoch: 7 \tTraining accuracy: 0.993697 \tValidation accuracy: 0.976744\n"
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      "Epoch: 8 \tTraining accuracy: 0.992227 \tValidation accuracy: 0.994186\n",
      "Validation loss decreased (0.041470 --> 0.031893).  Saving model ...\n"
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 194 \tTraining Loss: 0.000157 \tValidation Loss: 0.067930\n",
      "Epoch: 194 \tTraining accuracy: 1.000000 \tValidation accuracy: 0.993217\n"
     ]
    },
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       "version_major": 2,
       "version_minor": 0
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     },
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      ]
     },
     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 195 \tTraining Loss: 0.000636 \tValidation Loss: 0.067280\n",
      "Epoch: 195 \tTraining accuracy: 0.999860 \tValidation accuracy: 0.993217\n"
     ]
    },
    {
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 196 \tTraining Loss: 0.000186 \tValidation Loss: 0.067708\n",
      "Epoch: 196 \tTraining accuracy: 1.000000 \tValidation accuracy: 0.992248\n"
     ]
    },
    {
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       "model_id": "c21ac19a4ce44ed6936f3eeeb259f62a",
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       "version_minor": 0
      },
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     },
     "metadata": {},
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 197 \tTraining Loss: 0.000506 \tValidation Loss: 0.068148\n",
      "Epoch: 197 \tTraining accuracy: 0.999860 \tValidation accuracy: 0.993217\n"
     ]
    },
    {
     "data": {
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       "model_id": "9b445588afc54ab6b9021cbbe83cd6fd",
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       "version_minor": 0
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     },
     "metadata": {},
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    {
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       "model_id": "235c522e3aaf4335b50d792824a97a60",
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 198 \tTraining Loss: 0.000296 \tValidation Loss: 0.067709\n",
      "Epoch: 198 \tTraining accuracy: 0.999860 \tValidation accuracy: 0.994186\n"
     ]
    },
    {
     "data": {
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       "model_id": "5e04acc962ba4fb2b796f7750a88aa39",
       "version_major": 2,
       "version_minor": 0
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1d0548dde6f74bc386d0074ac1d15df1",
       "version_major": 2,
       "version_minor": 0
      },
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 199 \tTraining Loss: 0.000116 \tValidation Loss: 0.068269\n",
      "Epoch: 199 \tTraining accuracy: 1.000000 \tValidation accuracy: 0.993217\n"
     ]
    }
   ],
   "source": [
    "# number of epochs to train the model\n",
    "n_epochs = 200\n",
    "\n",
    "valid_loss_min = np.Inf # track change in validation loss\n",
    "\n",
    "# keep track of training and validation loss\n",
    "train_loss = torch.zeros(n_epochs)\n",
    "valid_loss = torch.zeros(n_epochs)\n",
    "\n",
    "train_acc = torch.zeros(n_epochs)\n",
    "valid_acc = torch.zeros(n_epochs)\n",
    "model.to(device)\n",
    "\n",
    "for e in range(0, n_epochs):\n",
    "\n",
    "   \n",
    "    ###################\n",
    "    # train the model #\n",
    "    ###################\n",
    "    model.train()\n",
    "    for data, labels in tqdm(train_dataloader):\n",
    "        # move tensors to GPU if CUDA is available\n",
    "        data, labels = data.to(device), labels.to(device)\n",
    "        # clear the gradients of all optimized variables\n",
    "        \n",
    "        optimizer.zero_grad()\n",
    "        # forward pass: compute predicted outputs by passing inputs to the model\n",
    "        logits = model(data)\n",
    "        # calculate the batch loss\n",
    "        loss = criterion(logits, labels)\n",
    "        # backward pass: compute gradient of the loss with respect to model parameters\n",
    "        loss.backward()\n",
    "        # perform a single optimization step (parameter update)\n",
    "        optimizer.step()\n",
    "        # update training loss\n",
    "        train_loss[e] += loss.item()\n",
    "        \n",
    "        ps = F.softmax(logits, dim=1)\n",
    "        top_p, top_class = ps.topk(1, dim=1)\n",
    "        equals = top_class == labels.reshape(top_class.shape)\n",
    "        train_acc[e] += torch.mean(equals.type(torch.float)).detach().cpu()\n",
    "    \n",
    "    train_loss[e] /= len(train_dataloader)\n",
    "    train_acc[e] /= len(train_dataloader)\n",
    "        \n",
    "        \n",
    "    ######################    \n",
    "    # validate the model #\n",
    "    ######################\n",
    "    with torch.no_grad(): \n",
    "        model.eval()\n",
    "        for data, labels in tqdm(valid_dataloader):\n",
    "            # move tensors to GPU if CUDA is available\n",
    "            data, labels = data.to(device), labels.to(device)\n",
    "            # forward pass: compute predicted outputs by passing inputs to the model\n",
    "            logits = model(data)\n",
    "            # calculate the batch loss\n",
    "            loss = criterion(logits, labels)\n",
    "            # update average validation loss \n",
    "            valid_loss[e] += loss.item()\n",
    "            \n",
    "            ps = F.softmax(logits, dim=1)\n",
    "            top_p, top_class = ps.topk(1, dim=1)\n",
    "            equals = top_class == labels.reshape(top_class.shape)\n",
    "            valid_acc[e] += torch.mean(equals.type(torch.float)).detach().cpu()\n",
    "            \n",
    "    \n",
    "    # calculate average losses\n",
    "    valid_loss[e] /= len(valid_dataloader)\n",
    "    valid_acc[e] /= len(valid_dataloader)\n",
    "    \n",
    "    scheduler.step(valid_loss[e])    \n",
    "    # print training/validation statistics \n",
    "    print('Epoch: {} \\tTraining Loss: {:.6f} \\tValidation Loss: {:.6f}'.format(\n",
    "        e, train_loss[e], valid_loss[e]))\n",
    "    \n",
    "    # print training/validation statistics \n",
    "    print('Epoch: {} \\tTraining accuracy: {:.6f} \\tValidation accuracy: {:.6f}'.format(\n",
    "        e, train_acc[e], valid_acc[e]))\n",
    "    \n",
    "    # save model if validation loss has decreased\n",
    "    if valid_loss[e] <= valid_loss_min:\n",
    "        print('Validation loss decreased ({:.6f} --> {:.6f}).  Saving model ...'.format(\n",
    "        valid_loss_min,\n",
    "        valid_loss[e]))\n",
    "        torch.save(model.state_dict(), 'hackaton1.pt')\n",
    "        valid_loss_min = valid_loss[e]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "id": "l-xxf-72VZRa"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x15241200be0>]"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(train_loss)\n",
    "plt.plot(valid_loss)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_transform = transforms.Compose([transforms.Resize((256,256)),\n",
    "                                     transforms.ToTensor(),\n",
    "                                     transforms.Normalize([0.485 , 0.456, 0.406], [0.229, 0.224 , 0.225])\n",
    "                                    ])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_dataset=datasets.ImageFolder('./test/',transform=test_transform)\n",
    "test_dataloader = torch.utils.data.DataLoader(test_dataset, batch_size=1,shuffle=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b31b06298b6d4118b8994164b054ec3a",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/89 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "리멤버 team test accuracy: 0.955056\n"
     ]
    }
   ],
   "source": [
    "acc=0\n",
    "model.to(device)\n",
    "with torch.no_grad(): \n",
    "    model.eval()\n",
    "    for data, labels in tqdm(test_dataloader):\n",
    "        # move tensors to GPU if CUDA is available\n",
    "        data, labels = data.to(device), labels.to(device)\n",
    "        # forward pass: compute predicted outputs by passing inputs to the model\n",
    "        logits = model(data)\n",
    "        ps = F.softmax(logits, dim=1)\n",
    "        top_p, top_class = ps.topk(1, dim=1)\n",
    "        equals = top_class == labels.reshape(top_class.shape)\n",
    "        acc += torch.mean(equals.type(torch.float)).detach().cpu()\n",
    "        \n",
    "# calculate average losses\n",
    "acc /= len(test_dataloader)\n",
    "print('리멤버 team test accuracy: {:.6f}'.format(acc))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<All keys matched successfully>"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.load_state_dict(torch.load('hackaton1.pt'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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