Age_Detection_train+test.ipynb

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    },
    "accelerator": "GPU"
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  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "ICnLxDygCSd7",
        "colab_type": "code",
        "outputId": "160f2196-2e14-4817-9044-5cf2d4a78b61",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
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        "colab": {
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          "height": 338
        }
      },
      "source": [
        "!pip install albumentations\n",
        "import albumentations as albu"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: albumentations in /usr/local/lib/python3.6/dist-packages (0.1.12)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.6/dist-packages (from albumentations) (1.4.1)\n",
            "Requirement already satisfied: imgaug<0.2.7,>=0.2.5 in /usr/local/lib/python3.6/dist-packages (from albumentations) (0.2.6)\n",
            "Requirement already satisfied: opencv-python in /usr/local/lib/python3.6/dist-packages (from albumentations) (4.1.2.30)\n",
            "Requirement already satisfied: numpy>=1.11.1 in /usr/local/lib/python3.6/dist-packages (from albumentations) (1.18.4)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.6/dist-packages (from imgaug<0.2.7,>=0.2.5->albumentations) (1.12.0)\n",
            "Requirement already satisfied: scikit-image>=0.11.0 in /usr/local/lib/python3.6/dist-packages (from imgaug<0.2.7,>=0.2.5->albumentations) (0.16.2)\n",
            "Requirement already satisfied: networkx>=2.0 in /usr/local/lib/python3.6/dist-packages (from scikit-image>=0.11.0->imgaug<0.2.7,>=0.2.5->albumentations) (2.4)\n",
            "Requirement already satisfied: PyWavelets>=0.4.0 in /usr/local/lib/python3.6/dist-packages (from scikit-image>=0.11.0->imgaug<0.2.7,>=0.2.5->albumentations) (1.1.1)\n",
            "Requirement already satisfied: pillow>=4.3.0 in /usr/local/lib/python3.6/dist-packages (from scikit-image>=0.11.0->imgaug<0.2.7,>=0.2.5->albumentations) (7.0.0)\n",
            "Requirement already satisfied: imageio>=2.3.0 in /usr/local/lib/python3.6/dist-packages (from scikit-image>=0.11.0->imgaug<0.2.7,>=0.2.5->albumentations) (2.4.1)\n",
            "Requirement already satisfied: matplotlib!=3.0.0,>=2.0.0 in /usr/local/lib/python3.6/dist-packages (from scikit-image>=0.11.0->imgaug<0.2.7,>=0.2.5->albumentations) (3.2.1)\n",
            "Requirement already satisfied: decorator>=4.3.0 in /usr/local/lib/python3.6/dist-packages (from networkx>=2.0->scikit-image>=0.11.0->imgaug<0.2.7,>=0.2.5->albumentations) (4.4.2)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.6/dist-packages (from matplotlib!=3.0.0,>=2.0.0->scikit-image>=0.11.0->imgaug<0.2.7,>=0.2.5->albumentations) (0.10.0)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib!=3.0.0,>=2.0.0->scikit-image>=0.11.0->imgaug<0.2.7,>=0.2.5->albumentations) (1.2.0)\n",
            "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib!=3.0.0,>=2.0.0->scikit-image>=0.11.0->imgaug<0.2.7,>=0.2.5->albumentations) (2.4.7)\n",
            "Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib!=3.0.0,>=2.0.0->scikit-image>=0.11.0->imgaug<0.2.7,>=0.2.5->albumentations) (2.8.1)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UCWsWDq3aVkZ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import os\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import random\n",
        "\n",
        "import torch \n",
        "import torch.nn as nn\n",
        "\n",
        "from torch.utils.data import Dataset, DataLoader\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "\n",
        "from torch.autograd import Variable\n",
        "import torchvision.models as models\n",
        "from torch import optim\n",
        "\n",
        "from PIL import Image\n",
        "from sklearn.model_selection import train_test_split\n",
        "from tqdm.notebook import tqdm"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4M2Usb2RcmVH",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def seed_everything(seed):\n",
        "    random.seed(seed)\n",
        "    os.environ['PYTHONHASHSEED'] = str(seed)\n",
        "    np.random.seed(seed)\n",
        "    torch.manual_seed(seed)\n",
        "    torch.cuda.manual_seed(seed)\n",
        "    torch.backends.cudnn.deterministic = True\n",
        "seed_everything(43)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "U0RahFK9dGbP",
        "colab_type": "code",
        "outputId": "5b4fc1a9-d115-4b49-def7-3e15580ef59e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 347
        }
      },
      "source": [
        "IMG_DIR = \"/content/drive/My Drive/Age Detection/Train/\"\n",
        "train_csv = pd.read_csv(r\"/content/drive/My Drive/Age Detection/train.csv\")\n",
        "test_csv = pd.read_csv(r\"/content/drive/My Drive/Age Detection/test.csv\")\n",
        "train_csv.head(10)"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>ID</th>\n",
              "      <th>Class</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>377.jpg</td>\n",
              "      <td>MIDDLE</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>17814.jpg</td>\n",
              "      <td>YOUNG</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>21283.jpg</td>\n",
              "      <td>MIDDLE</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>16496.jpg</td>\n",
              "      <td>YOUNG</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4487.jpg</td>\n",
              "      <td>MIDDLE</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>6283.jpg</td>\n",
              "      <td>MIDDLE</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>23495.jpg</td>\n",
              "      <td>YOUNG</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>7100.jpg</td>\n",
              "      <td>YOUNG</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>6028.jpg</td>\n",
              "      <td>YOUNG</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>22617.jpg</td>\n",
              "      <td>OLD</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          ID   Class\n",
              "0    377.jpg  MIDDLE\n",
              "1  17814.jpg   YOUNG\n",
              "2  21283.jpg  MIDDLE\n",
              "3  16496.jpg   YOUNG\n",
              "4   4487.jpg  MIDDLE\n",
              "5   6283.jpg  MIDDLE\n",
              "6  23495.jpg   YOUNG\n",
              "7   7100.jpg   YOUNG\n",
              "8   6028.jpg   YOUNG\n",
              "9  22617.jpg     OLD"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_JfxMuMspbs6",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "labels = {\n",
        "    'OLD': 0,\n",
        "    'MIDDLE': 1,\n",
        "    'YOUNG': 2\n",
        "}"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "znHXavSdd3H4",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "class AgeDataset(Dataset):\n",
        "    def __init__(self, csv_file, image_dir, transforms = None):\n",
        "        super(AgeDataset, self).__init__()\n",
        "        self.csv_file = csv_file\n",
        "        self.image_dir = image_dir\n",
        "        self.transforms = transforms\n",
        "\n",
        "    def __len__(self):\n",
        "        return self.csv_file.shape[0]\n",
        "\n",
        "    def __getitem__(self, index):\n",
        "        image = plt.imread(self.image_dir + self.csv_file.iloc[index,0])\n",
        "        label = labels[self.csv_file.iloc[index, 1]]\n",
        "        if self.transforms:\n",
        "            image = self.transforms(image = image)\n",
        "        img = image['image']\n",
        "        \n",
        "        return img, label     "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wyrhDW1ErQKa",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from albumentations.pytorch.transforms import ToTensor\n",
        "def train_transform():\n",
        "    return albu.Compose([\n",
        "        albu.Resize(224,224),\n",
        "        albu.HorizontalFlip(p=0.6),\n",
        "        albu.RGBShift(),\n",
        "        albu.Rotate(60),\n",
        "        albu.RandomBrightness(),\n",
        "        #albu.RandomCrop(200),\n",
        "        albu.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)),\n",
        "        ToTensor()\n",
        "    ])\n",
        "\n",
        "def valid_transform():\n",
        "    return albu.Compose([\n",
        "        albu.Resize(224,224),\n",
        "        albu.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)),\n",
        "        ToTensor()\n",
        "        \n",
        "    ])\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pqyOaqeUrEqk",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_data, valid_data = train_test_split(train_csv, test_size = 0.33, shuffle = True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8voHIb6AtmQJ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_dataset = AgeDataset(train_data, IMG_DIR, train_transform())\n",
        "valid_dataset = AgeDataset(valid_data, IMG_DIR, valid_transform())"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4Dfz2_9iyAV0",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "if torch.cuda.is_available():\n",
        "    device = 'cuda'\n",
        "else:\n",
        "    device = 'cpu'"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-D0dZ79guqmi",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "if device == 'cuda':\n",
        "    workers = 4\n",
        "else:\n",
        "    workers = 0\n",
        "train_loader = DataLoader(\n",
        "    train_dataset,\n",
        "    batch_size = 16,\n",
        "    shuffle = True,\n",
        "    num_workers = workers\n",
        ")\n",
        "\n",
        "valid_loader = DataLoader(\n",
        "    valid_dataset, \n",
        "    batch_size = 16,\n",
        "    shuffle = False,\n",
        "    num_workers = workers\n",
        ")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QBDgczK774lO",
        "colab_type": "code",
        "outputId": "c911e110-d050-4655-9836-bedc5676ad01",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 212
        }
      },
      "source": [
        "!pip install timm\n",
        "import timm\n",
        "model = timm.create_model('efficientnet_b1', pretrained = True)\n",
        "model.train()\n",
        "model.classifier = nn.Linear(in_features=1280, out_features=3, bias=True)"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting timm\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/44/c7/b1ce15cfca60d10d069dafbc2138761940f73b691496fd998a680f04dad1/timm-0.1.26-py3-none-any.whl (179kB)\n",
            "\r\u001b[K     |█▉                              | 10kB 25.9MB/s eta 0:00:01\r\u001b[K     |███▋                            | 20kB 1.7MB/s eta 0:00:01\r\u001b[K     |█████▌                          | 30kB 2.3MB/s eta 0:00:01\r\u001b[K     |███████▎                        | 40kB 2.6MB/s eta 0:00:01\r\u001b[K     |█████████▏                      | 51kB 2.0MB/s eta 0:00:01\r\u001b[K     |███████████                     | 61kB 2.3MB/s eta 0:00:01\r\u001b[K     |████████████▉                   | 71kB 2.5MB/s eta 0:00:01\r\u001b[K     |██████████████▋                 | 81kB 2.7MB/s eta 0:00:01\r\u001b[K     |████████████████▌               | 92kB 2.9MB/s eta 0:00:01\r\u001b[K     |██████████████████▎             | 102kB 2.8MB/s eta 0:00:01\r\u001b[K     |████████████████████            | 112kB 2.8MB/s eta 0:00:01\r\u001b[K     |██████████████████████          | 122kB 2.8MB/s eta 0:00:01\r\u001b[K     |███████████████████████▊        | 133kB 2.8MB/s eta 0:00:01\r\u001b[K     |█████████████████████████▋      | 143kB 2.8MB/s eta 0:00:01\r\u001b[K     |███████████████████████████▍    | 153kB 2.8MB/s eta 0:00:01\r\u001b[K     |█████████████████████████████▎  | 163kB 2.8MB/s eta 0:00:01\r\u001b[K     |███████████████████████████████ | 174kB 2.8MB/s eta 0:00:01\r\u001b[K     |████████████████████████████████| 184kB 2.8MB/s \n",
            "\u001b[?25hRequirement already satisfied: torchvision in /usr/local/lib/python3.6/dist-packages (from timm) (0.6.0+cu101)\n",
            "Requirement already satisfied: torch>=1.0 in /usr/local/lib/python3.6/dist-packages (from timm) (1.5.0+cu101)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.6/dist-packages (from torchvision->timm) (1.18.4)\n",
            "Requirement already satisfied: pillow>=4.1.1 in /usr/local/lib/python3.6/dist-packages (from torchvision->timm) (7.0.0)\n",
            "Requirement already satisfied: future in /usr/local/lib/python3.6/dist-packages (from torch>=1.0->timm) (0.16.0)\n",
            "Installing collected packages: timm\n",
            "Successfully installed timm-0.1.26\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Downloading: \"https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/efficientnet_b1-533bc792.pth\" to /root/.cache/torch/checkpoints/efficientnet_b1-533bc792.pth\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ETO9Hxmtt76o",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model.to(device)\n",
        "optimizer1 = optim.Adam(model.parameters(), lr = 0.001)\n",
        "optimizer2 = optim.SGD(model.parameters(), lr = 0.0001)\n",
        "criterion = nn.CrossEntropyLoss()\n",
        "\n",
        "from torch.optim import lr_scheduler\n",
        "scheduler1 = lr_scheduler.ReduceLROnPlateau(optimizer1,factor=0.33, mode=\"min\", patience=4)\n",
        "scheduler2 = lr_scheduler.ReduceLROnPlateau(optimizer2,factor=0.33, mode=\"min\", patience=4)\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "73AiQnDxtxhI",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def train(model, trainloader, validloader, optimizer, scheduler, criterion, num_epochs = 20):\n",
        "    print(\"Training .....\")\n",
        "    i=0\n",
        "    train_loss_history = []\n",
        "    valid_loss_history = []\n",
        "    best_acc = 0\n",
        "    best_model = None\n",
        "    for epoch in tqdm(range(num_epochs)):\n",
        "        i+=1\n",
        "        running_loss =0\n",
        "        running_corrects =0 \n",
        "        print(\"Epoch: \", i)\n",
        "        for image, label in tqdm(trainloader):\n",
        "            image = image.to(device)\n",
        "            label = label.to(device)\n",
        "            target = model(image)\n",
        "            _,pred = target.max(1)\n",
        "            optimizer.zero_grad()\n",
        "            loss = criterion(target, label)    \n",
        "            running_loss += loss.item()*image.size(0)\n",
        "            running_corrects += (torch.sum(pred == label)) \n",
        "            loss.backward()\n",
        "            optimizer.step()\n",
        "            train_loss_history.append(loss)\n",
        "        \n",
        "        train_loss = running_loss / train_data.shape[0]\n",
        "        train_acc = (running_corrects / float(train_data.shape[0]))\n",
        "        print(\"train_loss: \", train_loss)\n",
        "        print(\"train_accuracy: \", train_acc)\n",
        "        train_loss_history.append(train_loss)\n",
        "        \n",
        "        running_loss =0\n",
        "        running_corrects =0 \n",
        "        for image,label in tqdm(validloader):\n",
        "            image = image.to(device)\n",
        "            label = label.to(device)\n",
        "            target = model(image)\n",
        "            _,pred = target.max(1)\n",
        "            loss = criterion(target, label)    \n",
        "            running_loss += loss.item()*image.size(0)\n",
        "            running_corrects += (torch.sum(pred == label))\n",
        "\n",
        "        valid_loss = running_loss / valid_data.shape[0]\n",
        "        valid_acc =(running_corrects / float(valid_data.shape[0]))\n",
        "        print(\"validation_ loss: \" , valid_loss)\n",
        "        print(\"validation_accuracy: \" , valid_acc)\n",
        "        valid_loss_history.append(valid_loss)\n",
        "        \n",
        "        scheduler.step(valid_loss)\n",
        "\n",
        "        if valid_acc > best_acc:\n",
        "            best_acc = valid_acc\n",
        "            best_model = model\n",
        "\n",
        "    print(\"Best_accuracy: \", best_acc)\n",
        "\n",
        "    print(\"Training Ends\")\n",
        "\n",
        "    return best_model"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "C3cJVJjX7XsW",
        "colab_type": "code",
        "outputId": "527b5424-8bfa-4ac5-db7f-48ece98330d7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
          "referenced_widgets": [
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            "\n",
            "train_loss:  0.07002406361308727\n",
            "train_accuracy:  tensor(0.9745, device='cuda:0')\n"
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            "\n",
            "validation_ loss:  0.3577727733418393\n",
            "validation_accuracy:  tensor(0.8954, device='cuda:0')\n",
            "\n",
            "Best_accuracy:  tensor(0.9004, device='cuda:0')\n",
            "Training Ends\n"
          ],
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      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zuSFpRRcfP6P",
        "colab_type": "code",
        "colab": {}
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      "source": [
        "torch.save(model, '/content/drive/My Drive/Age Detection/effb1_1a.pth')"
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      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Training .....\n"
          ],
          "name": "stdout"
        },
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            "Epoch:  1\n"
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            "\n",
            "train_loss:  0.045442047601894894\n",
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            "\n",
            "validation_ loss:  0.33611012165690424\n",
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            "Epoch:  2\n"
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          "text": [
            "\n",
            "validation_ loss:  0.3378986478814381\n",
            "validation_accuracy:  tensor(0.9062, device='cuda:0')\n",
            "Epoch:  3\n"
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            "\n",
            "train_loss:  0.04430809830006553\n",
            "train_accuracy:  tensor(0.9857, device='cuda:0')\n"
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            "\n",
            "validation_ loss:  0.33942495005412693\n",
            "validation_accuracy:  tensor(0.9074, device='cuda:0')\n",
            "Epoch:  4\n"
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          "output_type": "stream",
          "text": [
            "\n",
            "train_loss:  0.04259369770522895\n",
            "train_accuracy:  tensor(0.9850, device='cuda:0')\n"
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          "name": "stdout"
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            "\n",
            "validation_ loss:  0.33876625611223504\n",
            "validation_accuracy:  tensor(0.9061, device='cuda:0')\n",
            "Epoch:  5\n"
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          "name": "stdout"
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          "metadata": {
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        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "train_loss:  0.045493023733303196\n",
            "train_accuracy:  tensor(0.9839, device='cuda:0')\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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              "HBox(children=(FloatProgress(value=0.0, max=411.0), HTML(value='')))"
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          },
          "metadata": {
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        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "validation_ loss:  0.3384783065994536\n",
            "validation_accuracy:  tensor(0.9076, device='cuda:0')\n",
            "\n",
            "Best_accuracy:  tensor(0.9076, device='cuda:0')\n",
            "Training Ends\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bNPYk1CciD4p",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "torch.save(model, '/content/drive/My Drive/Age Detection/effb1_1b.pth')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xWJ6II9iquiw",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "fc160f08-ae52-4ca2-eefd-70a35d647755"
      },
      "source": [
        "model = torch.load('/content/drive/My Drive/Age Detection/effb1_1b.pth')\n",
        "model.eval()"
      ],
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "EfficientNet(\n",
              "  (conv_stem): Conv2d(3, 32, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
              "  (bn1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "  (act1): Swish()\n",
              "  (blocks): Sequential(\n",
              "    (0): Sequential(\n",
              "      (0): DepthwiseSeparableConv(\n",
              "        (conv_dw): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False)\n",
              "        (bn1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (act1): Swish()\n",
              "        (se): SqueezeExcite(\n",
              "          (avg_pool): AdaptiveAvgPool2d(output_size=1)\n",
              "          (conv_reduce): Conv2d(32, 8, kernel_size=(1, 1), stride=(1, 1))\n",
              "          (act1): Swish()\n",
              "          (conv_expand): Conv2d(8, 32, kernel_size=(1, 1), stride=(1, 1))\n",
              "        )\n",
              "        (conv_pw): Conv2d(32, 16, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn2): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (act2): Identity()\n",
              "      )\n",
              "      (1): DepthwiseSeparableConv(\n",
              "        (conv_dw): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=16, bias=False)\n",
              "        (bn1): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (act1): Swish()\n",
              "        (se): SqueezeExcite(\n",
              "          (avg_pool): AdaptiveAvgPool2d(output_size=1)\n",
              "          (conv_reduce): Conv2d(16, 4, kernel_size=(1, 1), stride=(1, 1))\n",
              "          (act1): Swish()\n",
              "          (conv_expand): Conv2d(4, 16, kernel_size=(1, 1), stride=(1, 1))\n",
              "        )\n",
              "        (conv_pw): Conv2d(16, 16, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn2): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (act2): Identity()\n",
              "      )\n",
              "    )\n",
              "    (1): Sequential(\n",
              "      (0): InvertedResidual(\n",
              "        (conv_pw): Conv2d(16, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn1): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (act1): Swish()\n",
              "        (conv_dw): Conv2d(96, 96, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=96, bias=False)\n",
              "        (bn2): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (act2): Swish()\n",
              "        (se): SqueezeExcite(\n",
              "          (avg_pool): AdaptiveAvgPool2d(output_size=1)\n",
              "          (conv_reduce): Conv2d(96, 4, kernel_size=(1, 1), stride=(1, 1))\n",
              "          (act1): Swish()\n",
              "          (conv_expand): Conv2d(4, 96, kernel_size=(1, 1), stride=(1, 1))\n",
              "        )\n",
              "        (conv_pwl): Conv2d(96, 24, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn3): BatchNorm2d(24, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "      (1): InvertedResidual(\n",
              "        (conv_pw): Conv2d(24, 144, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn1): BatchNorm2d(144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (act1): Swish()\n",
              "        (conv_dw): Conv2d(144, 144, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=144, bias=False)\n",
              "        (bn2): BatchNorm2d(144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (act2): Swish()\n",
              "        (se): SqueezeExcite(\n",
              "          (avg_pool): AdaptiveAvgPool2d(output_size=1)\n",
              "          (conv_reduce): Conv2d(144, 6, kernel_size=(1, 1), stride=(1, 1))\n",
              "          (act1): Swish()\n",
              "          (conv_expand): Conv2d(6, 144, kernel_size=(1, 1), stride=(1, 1))\n",
              "        )\n",
              "        (conv_pwl): Conv2d(144, 24, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn3): BatchNorm2d(24, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "      (2): InvertedResidual(\n",
              "        (conv_pw): Conv2d(24, 144, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn1): BatchNorm2d(144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (act1): Swish()\n",
              "        (conv_dw): Conv2d(144, 144, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=144, bias=False)\n",
              "        (bn2): BatchNorm2d(144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (act2): Swish()\n",
              "        (se): SqueezeExcite(\n",
              "          (avg_pool): AdaptiveAvgPool2d(output_size=1)\n",
              "          (conv_reduce): Conv2d(144, 6, kernel_size=(1, 1), stride=(1, 1))\n",
              "          (act1): Swish()\n",
              "          (conv_expand): Conv2d(6, 144, kernel_size=(1, 1), stride=(1, 1))\n",
              "        )\n",
              "        (conv_pwl): Conv2d(144, 24, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn3): BatchNorm2d(24, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "    )\n",
              "    (2): Sequential(\n",
              "      (0): InvertedResidual(\n",
              "        (conv_pw): Conv2d(24, 144, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn1): BatchNorm2d(144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (act1): Swish()\n",
              "        (conv_dw): Conv2d(144, 144, kernel_size=(5, 5), stride=(2, 2), padding=(2, 2), groups=144, bias=False)\n",
              "        (bn2): BatchNorm2d(144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (act2): Swish()\n",
              "        (se): SqueezeExcite(\n",
              "          (avg_pool): AdaptiveAvgPool2d(output_size=1)\n",
              "          (conv_reduce): Conv2d(144, 6, kernel_size=(1, 1), stride=(1, 1))\n",
              "          (act1): Swish()\n",
              "          (conv_expand): Conv2d(6, 144, kernel_size=(1, 1), stride=(1, 1))\n",
              "        )\n",
              "        (conv_pwl): Conv2d(144, 40, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn3): BatchNorm2d(40, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "      (1): InvertedResidual(\n",
              "        (conv_pw): Conv2d(40, 240, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn1): BatchNorm2d(240, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (act1): Swish()\n",
              "        (conv_dw): Conv2d(240, 240, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=240, bias=False)\n",
              "        (bn2): BatchNorm2d(240, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (act2): Swish()\n",
              "        (se): SqueezeExcite(\n",
              "          (avg_pool): AdaptiveAvgPool2d(output_size=1)\n",
              "          (conv_reduce): Conv2d(240, 10, kernel_size=(1, 1), stride=(1, 1))\n",
              "          (act1): Swish()\n",
              "          (conv_expand): Conv2d(10, 240, kernel_size=(1, 1), stride=(1, 1))\n",
              "        )\n",
              "        (conv_pwl): Conv2d(240, 40, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn3): BatchNorm2d(40, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "      (2): InvertedResidual(\n",
              "        (conv_pw): Conv2d(40, 240, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn1): BatchNorm2d(240, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (act1): Swish()\n",
              "        (conv_dw): Conv2d(240, 240, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=240, bias=False)\n",
              "        (bn2): BatchNorm2d(240, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (act2): Swish()\n",
              "        (se): SqueezeExcite(\n",
              "          (avg_pool): AdaptiveAvgPool2d(output_size=1)\n",
              "          (conv_reduce): Conv2d(240, 10, kernel_size=(1, 1), stride=(1, 1))\n",
              "          (act1): Swish()\n",
              "          (conv_expand): Conv2d(10, 240, kernel_size=(1, 1), stride=(1, 1))\n",
              "        )\n",
              "        (conv_pwl): Conv2d(240, 40, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn3): BatchNorm2d(40, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "    )\n",
              "    (3): Sequential(\n",
              "      (0): InvertedResidual(\n",
              "        (conv_pw): Conv2d(40, 240, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn1): BatchNorm2d(240, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (act1): Swish()\n",
              "        (conv_dw): Conv2d(240, 240, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=240, bias=False)\n",
              "        (bn2): BatchNorm2d(240, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (act2): Swish()\n",
              "        (se): SqueezeExcite(\n",
              "          (avg_pool): AdaptiveAvgPool2d(output_size=1)\n",
              "          (conv_reduce): Conv2d(240, 10, kernel_size=(1, 1), stride=(1, 1))\n",
              "          (act1): Swish()\n",
              "          (conv_expand): Conv2d(10, 240, kernel_size=(1, 1), stride=(1, 1))\n",
              "        )\n",
              "        (conv_pwl): Conv2d(240, 80, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn3): BatchNorm2d(80, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "      (1): InvertedResidual(\n",
              "        (conv_pw): Conv2d(80, 480, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn1): BatchNorm2d(480, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (act1): Swish()\n",
              "        (conv_dw): Conv2d(480, 480, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=480, bias=False)\n",
              "        (bn2): BatchNorm2d(480, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (act2): Swish()\n",
              "        (se): SqueezeExcite(\n",
              "          (avg_pool): AdaptiveAvgPool2d(output_size=1)\n",
              "          (conv_reduce): Conv2d(480, 20, kernel_size=(1, 1), stride=(1, 1))\n",
              "          (act1): Swish()\n",
              "          (conv_expand): Conv2d(20, 480, kernel_size=(1, 1), stride=(1, 1))\n",
              "        )\n",
              "        (conv_pwl): Conv2d(480, 80, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn3): BatchNorm2d(80, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "      (2): InvertedResidual(\n",
              "        (conv_pw): Conv2d(80, 480, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn1): BatchNorm2d(480, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (act1): Swish()\n",
              "        (conv_dw): Conv2d(480, 480, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=480, bias=False)\n",
              "        (bn2): BatchNorm2d(480, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (act2): Swish()\n",
              "        (se): SqueezeExcite(\n",
              "          (avg_pool): AdaptiveAvgPool2d(output_size=1)\n",
              "          (conv_reduce): Conv2d(480, 20, kernel_size=(1, 1), stride=(1, 1))\n",
              "          (act1): Swish()\n",
              "          (conv_expand): Conv2d(20, 480, kernel_size=(1, 1), stride=(1, 1))\n",
              "        )\n",
              "        (conv_pwl): Conv2d(480, 80, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn3): BatchNorm2d(80, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "      (3): InvertedResidual(\n",
              "        (conv_pw): Conv2d(80, 480, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn1): BatchNorm2d(480, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (act1): Swish()\n",
              "        (conv_dw): Conv2d(480, 480, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=480, bias=False)\n",
              "        (bn2): BatchNorm2d(480, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (act2): Swish()\n",
              "        (se): SqueezeExcite(\n",
              "          (avg_pool): AdaptiveAvgPool2d(output_size=1)\n",
              "          (conv_reduce): Conv2d(480, 20, kernel_size=(1, 1), stride=(1, 1))\n",
              "          (act1): Swish()\n",
              "          (conv_expand): Conv2d(20, 480, kernel_size=(1, 1), stride=(1, 1))\n",
              "        )\n",
              "        (conv_pwl): Conv2d(480, 80, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn3): BatchNorm2d(80, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "    )\n",
              "    (4): Sequential(\n",
              "      (0): InvertedResidual(\n",
              "        (conv_pw): Conv2d(80, 480, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn1): BatchNorm2d(480, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (act1): Swish()\n",
              "        (conv_dw): Conv2d(480, 480, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=480, bias=False)\n",
              "        (bn2): BatchNorm2d(480, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (act2): Swish()\n",
              "        (se): SqueezeExcite(\n",
              "          (avg_pool): AdaptiveAvgPool2d(output_size=1)\n",
              "          (conv_reduce): Conv2d(480, 20, kernel_size=(1, 1), stride=(1, 1))\n",
              "          (act1): Swish()\n",
              "          (conv_expand): Conv2d(20, 480, kernel_size=(1, 1), stride=(1, 1))\n",
              "        )\n",
              "        (conv_pwl): Conv2d(480, 112, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn3): BatchNorm2d(112, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "      (1): InvertedResidual(\n",
              "        (conv_pw): Conv2d(112, 672, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn1): BatchNorm2d(672, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (act1): Swish()\n",
              "        (conv_dw): Conv2d(672, 672, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=672, bias=False)\n",
              "        (bn2): BatchNorm2d(672, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (act2): Swish()\n",
              "        (se): SqueezeExcite(\n",
              "          (avg_pool): AdaptiveAvgPool2d(output_size=1)\n",
              "          (conv_reduce): Conv2d(672, 28, kernel_size=(1, 1), stride=(1, 1))\n",
              "          (act1): Swish()\n",
              "          (conv_expand): Conv2d(28, 672, kernel_size=(1, 1), stride=(1, 1))\n",
              "        )\n",
              "        (conv_pwl): Conv2d(672, 112, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn3): BatchNorm2d(112, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "      (2): InvertedResidual(\n",
              "        (conv_pw): Conv2d(112, 672, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn1): BatchNorm2d(672, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (act1): Swish()\n",
              "        (conv_dw): Conv2d(672, 672, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=672, bias=False)\n",
              "        (bn2): BatchNorm2d(672, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (act2): Swish()\n",
              "        (se): SqueezeExcite(\n",
              "          (avg_pool): AdaptiveAvgPool2d(output_size=1)\n",
              "          (conv_reduce): Conv2d(672, 28, kernel_size=(1, 1), stride=(1, 1))\n",
              "          (act1): Swish()\n",
              "          (conv_expand): Conv2d(28, 672, kernel_size=(1, 1), stride=(1, 1))\n",
              "        )\n",
              "        (conv_pwl): Conv2d(672, 112, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn3): BatchNorm2d(112, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "      (3): InvertedResidual(\n",
              "        (conv_pw): Conv2d(112, 672, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn1): BatchNorm2d(672, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (act1): Swish()\n",
              "        (conv_dw): Conv2d(672, 672, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=672, bias=False)\n",
              "        (bn2): BatchNorm2d(672, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (act2): Swish()\n",
              "        (se): SqueezeExcite(\n",
              "          (avg_pool): AdaptiveAvgPool2d(output_size=1)\n",
              "          (conv_reduce): Conv2d(672, 28, kernel_size=(1, 1), stride=(1, 1))\n",
              "          (act1): Swish()\n",
              "          (conv_expand): Conv2d(28, 672, kernel_size=(1, 1), stride=(1, 1))\n",
              "        )\n",
              "        (conv_pwl): Conv2d(672, 112, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn3): BatchNorm2d(112, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "    )\n",
              "    (5): Sequential(\n",
              "      (0): InvertedResidual(\n",
              "        (conv_pw): Conv2d(112, 672, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn1): BatchNorm2d(672, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (act1): Swish()\n",
              "        (conv_dw): Conv2d(672, 672, kernel_size=(5, 5), stride=(2, 2), padding=(2, 2), groups=672, bias=False)\n",
              "        (bn2): BatchNorm2d(672, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (act2): Swish()\n",
              "        (se): SqueezeExcite(\n",
              "          (avg_pool): AdaptiveAvgPool2d(output_size=1)\n",
              "          (conv_reduce): Conv2d(672, 28, kernel_size=(1, 1), stride=(1, 1))\n",
              "          (act1): Swish()\n",
              "          (conv_expand): Conv2d(28, 672, kernel_size=(1, 1), stride=(1, 1))\n",
              "        )\n",
              "        (conv_pwl): Conv2d(672, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn3): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "      (1): InvertedResidual(\n",
              "        (conv_pw): Conv2d(192, 1152, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn1): BatchNorm2d(1152, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (act1): Swish()\n",
              "        (conv_dw): Conv2d(1152, 1152, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=1152, bias=False)\n",
              "        (bn2): BatchNorm2d(1152, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (act2): Swish()\n",
              "        (se): SqueezeExcite(\n",
              "          (avg_pool): AdaptiveAvgPool2d(output_size=1)\n",
              "          (conv_reduce): Conv2d(1152, 48, kernel_size=(1, 1), stride=(1, 1))\n",
              "          (act1): Swish()\n",
              "          (conv_expand): Conv2d(48, 1152, kernel_size=(1, 1), stride=(1, 1))\n",
              "        )\n",
              "        (conv_pwl): Conv2d(1152, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn3): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "      (2): InvertedResidual(\n",
              "        (conv_pw): Conv2d(192, 1152, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn1): BatchNorm2d(1152, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (act1): Swish()\n",
              "        (conv_dw): Conv2d(1152, 1152, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=1152, bias=False)\n",
              "        (bn2): BatchNorm2d(1152, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (act2): Swish()\n",
              "        (se): SqueezeExcite(\n",
              "          (avg_pool): AdaptiveAvgPool2d(output_size=1)\n",
              "          (conv_reduce): Conv2d(1152, 48, kernel_size=(1, 1), stride=(1, 1))\n",
              "          (act1): Swish()\n",
              "          (conv_expand): Conv2d(48, 1152, kernel_size=(1, 1), stride=(1, 1))\n",
              "        )\n",
              "        (conv_pwl): Conv2d(1152, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn3): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "      (3): InvertedResidual(\n",
              "        (conv_pw): Conv2d(192, 1152, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn1): BatchNorm2d(1152, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (act1): Swish()\n",
              "        (conv_dw): Conv2d(1152, 1152, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=1152, bias=False)\n",
              "        (bn2): BatchNorm2d(1152, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (act2): Swish()\n",
              "        (se): SqueezeExcite(\n",
              "          (avg_pool): AdaptiveAvgPool2d(output_size=1)\n",
              "          (conv_reduce): Conv2d(1152, 48, kernel_size=(1, 1), stride=(1, 1))\n",
              "          (act1): Swish()\n",
              "          (conv_expand): Conv2d(48, 1152, kernel_size=(1, 1), stride=(1, 1))\n",
              "        )\n",
              "        (conv_pwl): Conv2d(1152, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn3): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "      (4): InvertedResidual(\n",
              "        (conv_pw): Conv2d(192, 1152, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn1): BatchNorm2d(1152, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (act1): Swish()\n",
              "        (conv_dw): Conv2d(1152, 1152, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=1152, bias=False)\n",
              "        (bn2): BatchNorm2d(1152, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (act2): Swish()\n",
              "        (se): SqueezeExcite(\n",
              "          (avg_pool): AdaptiveAvgPool2d(output_size=1)\n",
              "          (conv_reduce): Conv2d(1152, 48, kernel_size=(1, 1), stride=(1, 1))\n",
              "          (act1): Swish()\n",
              "          (conv_expand): Conv2d(48, 1152, kernel_size=(1, 1), stride=(1, 1))\n",
              "        )\n",
              "        (conv_pwl): Conv2d(1152, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn3): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "    )\n",
              "    (6): Sequential(\n",
              "      (0): InvertedResidual(\n",
              "        (conv_pw): Conv2d(192, 1152, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn1): BatchNorm2d(1152, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (act1): Swish()\n",
              "        (conv_dw): Conv2d(1152, 1152, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=1152, bias=False)\n",
              "        (bn2): BatchNorm2d(1152, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (act2): Swish()\n",
              "        (se): SqueezeExcite(\n",
              "          (avg_pool): AdaptiveAvgPool2d(output_size=1)\n",
              "          (conv_reduce): Conv2d(1152, 48, kernel_size=(1, 1), stride=(1, 1))\n",
              "          (act1): Swish()\n",
              "          (conv_expand): Conv2d(48, 1152, kernel_size=(1, 1), stride=(1, 1))\n",
              "        )\n",
              "        (conv_pwl): Conv2d(1152, 320, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn3): BatchNorm2d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "      (1): InvertedResidual(\n",
              "        (conv_pw): Conv2d(320, 1920, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn1): BatchNorm2d(1920, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (act1): Swish()\n",
              "        (conv_dw): Conv2d(1920, 1920, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=1920, bias=False)\n",
              "        (bn2): BatchNorm2d(1920, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (act2): Swish()\n",
              "        (se): SqueezeExcite(\n",
              "          (avg_pool): AdaptiveAvgPool2d(output_size=1)\n",
              "          (conv_reduce): Conv2d(1920, 80, kernel_size=(1, 1), stride=(1, 1))\n",
              "          (act1): Swish()\n",
              "          (conv_expand): Conv2d(80, 1920, kernel_size=(1, 1), stride=(1, 1))\n",
              "        )\n",
              "        (conv_pwl): Conv2d(1920, 320, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn3): BatchNorm2d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "    )\n",
              "  )\n",
              "  (conv_head): Conv2d(320, 1280, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "  (bn2): BatchNorm2d(1280, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "  (act2): Swish()\n",
              "  (global_pool): SelectAdaptivePool2d (output_size=1, pool_type=avg)\n",
              "  (classifier): Linear(in_features=1280, out_features=3, bias=True)\n",
              ")"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 65
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "B6mDW-AmMw2u",
        "colab_type": "code",
        "outputId": "aeada60f-30c6-4eff-8647-3c7a840d32bb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 197
        }
      },
      "source": [
        "sub = pd.read_csv(r\"/content/drive/My Drive/Age Detection/sample_submission_sDO3m7O.csv\")\n",
        "sub.head()"
      ],
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Class</th>\n",
              "      <th>ID</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>MIDDLE</td>\n",
              "      <td>25321.jpg</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>MIDDLE</td>\n",
              "      <td>989.jpg</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>MIDDLE</td>\n",
              "      <td>19277.jpg</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>MIDDLE</td>\n",
              "      <td>13093.jpg</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>MIDDLE</td>\n",
              "      <td>5367.jpg</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    Class         ID\n",
              "0  MIDDLE  25321.jpg\n",
              "1  MIDDLE    989.jpg\n",
              "2  MIDDLE  19277.jpg\n",
              "3  MIDDLE  13093.jpg\n",
              "4  MIDDLE   5367.jpg"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 66
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AMyDD6y12g_z",
        "colab_type": "code",
        "outputId": "3800f1cd-6315-4af6-b37d-0b3a1b2b5851",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "output = pd.DataFrame(index=sub.index,columns = sub.keys())\n",
        "output['ID'] = sub['ID']\n",
        "output.shape"
      ],
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(6636, 2)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 67
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZWkE_Rd46ghe",
        "colab_type": "code",
        "outputId": "d7c5c8c3-03d8-4de9-c383-1fed067e8b35",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        }
      },
      "source": [
        "sub[sub.loc[:,'ID'] == '25321.jpg'].iloc[:,0]"
      ],
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    MIDDLE\n",
              "Name: Class, dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 68
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iFVI2QAZ5100",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "class AgeTestDataset(Dataset):\n",
        "    def __init__(self, csv_file, image_dir, transforms = None):\n",
        "        self.csv_file = csv_file\n",
        "        self.image_dir = image_dir\n",
        "        self.transforms = transforms\n",
        "\n",
        "    def __len__(self):\n",
        "        return self.csv_file.shape[0]\n",
        "\n",
        "    def __getitem__(self, index):\n",
        "        image = plt.imread(self.image_dir + self.csv_file.iloc[index,1])\n",
        "        label = labels[self.csv_file.iloc[index, 0]]\n",
        "        if self.transforms:\n",
        "            image = self.transforms(image = image)\n",
        "        img = image['image']\n",
        "        \n",
        "        return img, self.csv_file.iloc[index,1]       "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wYN00Qnq3eaO",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "TEST_DIR = \"/content/drive/My Drive/Age Detection/Test/\"\n",
        "testdata = AgeTestDataset(sub, TEST_DIR, valid_transform())\n",
        "testloader =  DataLoader(testdata, batch_size = 32)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oUSo0h-sKTze",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "key_to_label = {\n",
        "    0:'OLD',\n",
        "    1: 'MIDDLE',\n",
        "    2:'YOUNG'\n",
        "}"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Rj3ar7k24PLZ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import time\n",
        "def test_submission(model):\n",
        "    since = time.time()\n",
        "    model.eval()\n",
        "    for image, label in tqdm(testloader):\n",
        "        image = image.to(device)\n",
        "        target = model(image)\n",
        "        #print(label)\n",
        "        _,pred = target.max(1)\n",
        "        #print(pred)\n",
        "        out = [key_to_label[f] for f in pred.cpu().numpy()]\n",
        "        for i in range(len(label)):\n",
        "            output.loc[output['ID'] == label[i], 'Class'] = out[i]\n",
        "    time_elapsed = time.time() - since\n",
        "    print('Run complete in {:.0f}m {:.0f}s'.format(\n",
        "        time_elapsed // 60, time_elapsed % 60))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xImAL-Ki8zeO",
        "colab_type": "code",
        "outputId": "4925a446-58ab-433f-8491-ac3091001b4e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 84,
          "referenced_widgets": [
            "ac6f711f28a4426cb0367d6cbe2987a9",
            "ba257cfaa4f7465b909cbe6b5caeaf49",
            "ee05b89a8b6e4e2ea16ed3759c502d48",
            "8e166883dd17481fa3bf6028f8a67bda",
            "4d65b3a308344795a29c92e07c656fff",
            "cc7220b0c65a46d1bb6555df07bd78c8",
            "eb408cf6d26f4db681efea4eb85c311f",
            "4896e18408a64af7ba915516a181c0a0"
          ]
        }
      },
      "source": [
        "if torch.cuda.is_available():\n",
        "    device = 'cuda'\n",
        "else:\n",
        "    device = 'cpu'\n",
        "model.eval()\n",
        "model = model.to(device)\n",
        "test_submission(model)"
      ],
      "execution_count": 73,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "ac6f711f28a4426cb0367d6cbe2987a9",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=208.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Run complete in 0m 44s\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mQu8DR3O8PUv",
        "colab_type": "code",
        "outputId": "f1c87132-e7b3-4ac1-be08-7db6e709c10a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        }
      },
      "source": [
        "print(sub)"
      ],
      "execution_count": 74,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "       Class         ID\n",
            "0     MIDDLE  25321.jpg\n",
            "1     MIDDLE    989.jpg\n",
            "2     MIDDLE  19277.jpg\n",
            "3     MIDDLE  13093.jpg\n",
            "4     MIDDLE   5367.jpg\n",
            "...      ...        ...\n",
            "6631  MIDDLE   1876.jpg\n",
            "6632  MIDDLE  14940.jpg\n",
            "6633  MIDDLE   3638.jpg\n",
            "6634  MIDDLE    376.jpg\n",
            "6635  MIDDLE   9357.jpg\n",
            "\n",
            "[6636 rows x 2 columns]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "G99vErKX84C3",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "output.to_csv(\"submission_effb1_1d.csv\", index=False)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JnYNWpb9i38m",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}