Classification/Wide_ResNet(2016).ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Wide_ResNet(2016).ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyMkmZrvK8XavfVd2igLApIs",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Seonghoon-Yu/Paper_Review_and_Implementation_in_PyTorch/blob/master/Classification/Wide_ResNet(2016).ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jxmekiB2-nHV",
        "outputId": "f96171fd-5896-4b2a-e5b8-68c7d54da344"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('wrn')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mounted at wrn\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0FFCJdTZDczm"
      },
      "source": [
        "# import package\n",
        "\n",
        "# model\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "from torchsummary import summary\n",
        "from torch import optim\n",
        "\n",
        "# dataset and transformation\n",
        "from torchvision import datasets\n",
        "import torchvision.transforms as transforms\n",
        "from torch.utils.data import DataLoader\n",
        "import os\n",
        "\n",
        "# display images\n",
        "from torchvision import utils\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "\n",
        "# utils\n",
        "import numpy as np\n",
        "from torchsummary import summary\n",
        "import time\n",
        "import copy"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VADDggncDbud"
      },
      "source": [
        "# 1. 데이터셋 불러오기"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CUGL8OSN-7Fb",
        "outputId": "b325d4a0-2ff2-4afb-9e80-366ddf27aa95"
      },
      "source": [
        "# specify path to data\n",
        "path2data = '/content/wrn/MyDrive/data'\n",
        "\n",
        "# if not exists the path, make the directory\n",
        "if not os.path.exists(path2data):\n",
        "    os.mkdir(path2data)\n",
        "\n",
        "# load dataset\n",
        "train_ds = datasets.STL10(path2data, split='train', download=True, transform=transforms.ToTensor())\n",
        "val_ds = datasets.STL10(path2data, split='test', download=True, transform=transforms.ToTensor())\n",
        "\n",
        "print(len(train_ds))\n",
        "print(len(val_ds))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Files already downloaded and verified\n",
            "Files already downloaded and verified\n",
            "5000\n",
            "8000\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MKBbs2IJU0wY"
      },
      "source": [
        "# define transformation\n",
        "transformation = transforms.Compose([\n",
        "                    transforms.ToTensor(),\n",
        "                    transforms.Resize(64)\n",
        "])\n",
        "\n",
        "# apply transformation to dataset\n",
        "train_ds.transform = transformation\n",
        "val_ds.transform = transformation\n",
        "\n",
        "# make dataloade\n",
        "train_dl = DataLoader(train_ds, batch_size=32, shuffle=True)\n",
        "val_dl = DataLoader(val_ds, batch_size=32, shuffle=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 207
        },
        "id": "lI5j9kCbVrVb",
        "outputId": "3be239bc-12cd-4e32-e037-dc36e8f2e275"
      },
      "source": [
        "# check sample images\n",
        "def show(img, y=None):\n",
        "    npimg = img.numpy()\n",
        "    npimg_tr = np.transpose(npimg, (1, 2, 0))\n",
        "    plt.imshow(npimg_tr)\n",
        "\n",
        "    if y is not None:\n",
        "        plt.title('labels:' + str(y))\n",
        "\n",
        "np.random.seed(5)\n",
        "torch.manual_seed(0)\n",
        "\n",
        "grid_size=4\n",
        "rnd_ind = np.random.randint(0, len(train_ds), grid_size)\n",
        "\n",
        "x_grid = [train_ds[i][0] for i in rnd_ind]\n",
        "y_grid = [val_ds[i][1] for i in rnd_ind]\n",
        "\n",
        "x_grid = utils.make_grid(x_grid, nrow=grid_size, padding=2)\n",
        "plt.figure(figsize=(10,10))\n",
        "show(x_grid, y_grid)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SSy5QVAiDjtj"
      },
      "source": [
        "# 2. 모델 구축하기\n",
        "코드는 https://github.com/weiaicunzai/pytorch-cifar100/blob/master/models/wideresidual.py 를 참고했습니다."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zotzBq7MDn1s"
      },
      "source": [
        "class WiderBlock(nn.Module):\n",
        "    def __init__(self, in_channels, out_channels, stride):\n",
        "        super().__init__()\n",
        "\n",
        "        self.residual = nn.Sequential(\n",
        "            nn.BatchNorm2d(in_channels),\n",
        "            nn.ReLU(),\n",
        "            nn.Conv2d(in_channels, out_channels, 3, stride=stride, padding=1),\n",
        "            nn.BatchNorm2d(out_channels),\n",
        "            nn.ReLU(),\n",
        "            nn.Dropout(),\n",
        "            nn.Conv2d(out_channels, out_channels, 3, stride=1, padding=1)\n",
        "        )\n",
        "\n",
        "        self.shortcut = nn.Sequential()\n",
        "\n",
        "        if stride != 1 or in_channels != out_channels:\n",
        "            self.shortcut = nn.Conv2d(in_channels, out_channels, 1, stride=stride, padding=0)\n",
        "\n",
        "    def forward(self, x):\n",
        "        x_shortcut = self.shortcut(x)\n",
        "        x_res = self.residual(x)\n",
        "        return x_shortcut + x_res\n",
        "\n",
        "\n",
        "class WRN(nn.Module):\n",
        "    def __init__(self, depth=40, k=10, num_classes=10, init_weights=True):\n",
        "        super().__init__()\n",
        "        N = int((depth-4)/6)\n",
        "        self.in_channels = 16\n",
        "\n",
        "        self.conv1 = nn.Conv2d(3, 16, 3, stride=1, padding=1)\n",
        "        self.conv2 = self._make_layer(16*k, N, 1)\n",
        "        self.conv3 = self._make_layer(32*k, N, 2)\n",
        "        self.conv4 = self._make_layer(64*k, N, 2)\n",
        "        self.bn = nn.BatchNorm2d(64*k)\n",
        "        self.relu = nn.ReLU()\n",
        "        self.avg_pool = nn.AdaptiveAvgPool2d((1,1))\n",
        "        self.fc = nn.Linear(64*k, num_classes)\n",
        "\n",
        "        # weight initialization\n",
        "        if init_weights:\n",
        "            self._weights_initialize()\n",
        "\n",
        "    def forward(self, x):\n",
        "        x = self.conv1(x)\n",
        "        x = self.conv2(x)\n",
        "        x = self.conv3(x)\n",
        "        x = self.conv4(x)\n",
        "        x = self.bn(x)\n",
        "        x = self.relu(x)\n",
        "        x = self.avg_pool(x)\n",
        "        x = x.view(x.size(0), -1)\n",
        "        x = self.fc(x)\n",
        "\n",
        "        return x\n",
        "\n",
        "    def _make_layer(self, out_channels, num_blocks, stride):\n",
        "        strides = [stride] + [1] * (num_blocks-1)\n",
        "        layers = []\n",
        "\n",
        "        for stride in strides:\n",
        "            layers.append(WiderBlock(self.in_channels, out_channels, stride))\n",
        "            self.in_channels = out_channels\n",
        "        return nn.Sequential(*layers)\n",
        "\n",
        "    # weight initialization\n",
        "    def _weights_initialize(self):\n",
        "        for m in self.modules():\n",
        "            if isinstance(m, nn.Conv2d):\n",
        "                nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')\n",
        "                if m.bias is not None:\n",
        "                    nn.init.constant_(m.bias, 0)\n",
        "            elif isinstance(m, nn.BatchNorm2d):\n",
        "                nn.init.constant_(m.weight, 1)\n",
        "                nn.init.constant_(m.bias, 0)\n",
        "            elif isinstance(m, nn.Linear):\n",
        "                nn.init.normal_(m.weight, 0, 0.01)\n",
        "                nn.init.constant_(m.bias, 0)\n",
        "\n",
        "def WRN_40_10():\n",
        "    return WRN(40, 10)\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yTKWhk8hIcWP",
        "outputId": "b874503b-755e-4531-fce8-b6fbfdde39b8"
      },
      "source": [
        "# check model\n",
        "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
        "x = torch.randn(3, 3, 64, 64).to(device)\n",
        "model = WRN_40_10().to(device)\n",
        "output = model(x)\n",
        "print(output.size())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "torch.Size([3, 10])\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c1KmPuEqPmQX",
        "outputId": "7677ca00-2218-419f-f543-0a3c998a5e01"
      },
      "source": [
        "# check model summary\n",
        "summary(model, (3, 64, 64), device=device.type)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "----------------------------------------------------------------\n",
            "        Layer (type)               Output Shape         Param #\n",
            "================================================================\n",
            "            Conv2d-1           [-1, 16, 64, 64]             448\n",
            "            Conv2d-2          [-1, 160, 64, 64]           2,720\n",
            "       BatchNorm2d-3           [-1, 16, 64, 64]              32\n",
            "              ReLU-4           [-1, 16, 64, 64]               0\n",
            "            Conv2d-5          [-1, 160, 64, 64]          23,200\n",
            "       BatchNorm2d-6          [-1, 160, 64, 64]             320\n",
            "              ReLU-7          [-1, 160, 64, 64]               0\n",
            "           Dropout-8          [-1, 160, 64, 64]               0\n",
            "            Conv2d-9          [-1, 160, 64, 64]         230,560\n",
            "       WiderBlock-10          [-1, 160, 64, 64]               0\n",
            "      BatchNorm2d-11          [-1, 160, 64, 64]             320\n",
            "             ReLU-12          [-1, 160, 64, 64]               0\n",
            "           Conv2d-13          [-1, 160, 64, 64]         230,560\n",
            "      BatchNorm2d-14          [-1, 160, 64, 64]             320\n",
            "             ReLU-15          [-1, 160, 64, 64]               0\n",
            "          Dropout-16          [-1, 160, 64, 64]               0\n",
            "           Conv2d-17          [-1, 160, 64, 64]         230,560\n",
            "       WiderBlock-18          [-1, 160, 64, 64]               0\n",
            "      BatchNorm2d-19          [-1, 160, 64, 64]             320\n",
            "             ReLU-20          [-1, 160, 64, 64]               0\n",
            "           Conv2d-21          [-1, 160, 64, 64]         230,560\n",
            "      BatchNorm2d-22          [-1, 160, 64, 64]             320\n",
            "             ReLU-23          [-1, 160, 64, 64]               0\n",
            "          Dropout-24          [-1, 160, 64, 64]               0\n",
            "           Conv2d-25          [-1, 160, 64, 64]         230,560\n",
            "       WiderBlock-26          [-1, 160, 64, 64]               0\n",
            "      BatchNorm2d-27          [-1, 160, 64, 64]             320\n",
            "             ReLU-28          [-1, 160, 64, 64]               0\n",
            "           Conv2d-29          [-1, 160, 64, 64]         230,560\n",
            "      BatchNorm2d-30          [-1, 160, 64, 64]             320\n",
            "             ReLU-31          [-1, 160, 64, 64]               0\n",
            "          Dropout-32          [-1, 160, 64, 64]               0\n",
            "           Conv2d-33          [-1, 160, 64, 64]         230,560\n",
            "       WiderBlock-34          [-1, 160, 64, 64]               0\n",
            "      BatchNorm2d-35          [-1, 160, 64, 64]             320\n",
            "             ReLU-36          [-1, 160, 64, 64]               0\n",
            "           Conv2d-37          [-1, 160, 64, 64]         230,560\n",
            "      BatchNorm2d-38          [-1, 160, 64, 64]             320\n",
            "             ReLU-39          [-1, 160, 64, 64]               0\n",
            "          Dropout-40          [-1, 160, 64, 64]               0\n",
            "           Conv2d-41          [-1, 160, 64, 64]         230,560\n",
            "       WiderBlock-42          [-1, 160, 64, 64]               0\n",
            "      BatchNorm2d-43          [-1, 160, 64, 64]             320\n",
            "             ReLU-44          [-1, 160, 64, 64]               0\n",
            "           Conv2d-45          [-1, 160, 64, 64]         230,560\n",
            "      BatchNorm2d-46          [-1, 160, 64, 64]             320\n",
            "             ReLU-47          [-1, 160, 64, 64]               0\n",
            "          Dropout-48          [-1, 160, 64, 64]               0\n",
            "           Conv2d-49          [-1, 160, 64, 64]         230,560\n",
            "       WiderBlock-50          [-1, 160, 64, 64]               0\n",
            "           Conv2d-51          [-1, 320, 32, 32]          51,520\n",
            "      BatchNorm2d-52          [-1, 160, 64, 64]             320\n",
            "             ReLU-53          [-1, 160, 64, 64]               0\n",
            "           Conv2d-54          [-1, 320, 32, 32]         461,120\n",
            "      BatchNorm2d-55          [-1, 320, 32, 32]             640\n",
            "             ReLU-56          [-1, 320, 32, 32]               0\n",
            "          Dropout-57          [-1, 320, 32, 32]               0\n",
            "           Conv2d-58          [-1, 320, 32, 32]         921,920\n",
            "       WiderBlock-59          [-1, 320, 32, 32]               0\n",
            "      BatchNorm2d-60          [-1, 320, 32, 32]             640\n",
            "             ReLU-61          [-1, 320, 32, 32]               0\n",
            "           Conv2d-62          [-1, 320, 32, 32]         921,920\n",
            "      BatchNorm2d-63          [-1, 320, 32, 32]             640\n",
            "             ReLU-64          [-1, 320, 32, 32]               0\n",
            "          Dropout-65          [-1, 320, 32, 32]               0\n",
            "           Conv2d-66          [-1, 320, 32, 32]         921,920\n",
            "       WiderBlock-67          [-1, 320, 32, 32]               0\n",
            "      BatchNorm2d-68          [-1, 320, 32, 32]             640\n",
            "             ReLU-69          [-1, 320, 32, 32]               0\n",
            "           Conv2d-70          [-1, 320, 32, 32]         921,920\n",
            "      BatchNorm2d-71          [-1, 320, 32, 32]             640\n",
            "             ReLU-72          [-1, 320, 32, 32]               0\n",
            "          Dropout-73          [-1, 320, 32, 32]               0\n",
            "           Conv2d-74          [-1, 320, 32, 32]         921,920\n",
            "       WiderBlock-75          [-1, 320, 32, 32]               0\n",
            "      BatchNorm2d-76          [-1, 320, 32, 32]             640\n",
            "             ReLU-77          [-1, 320, 32, 32]               0\n",
            "           Conv2d-78          [-1, 320, 32, 32]         921,920\n",
            "      BatchNorm2d-79          [-1, 320, 32, 32]             640\n",
            "             ReLU-80          [-1, 320, 32, 32]               0\n",
            "          Dropout-81          [-1, 320, 32, 32]               0\n",
            "           Conv2d-82          [-1, 320, 32, 32]         921,920\n",
            "       WiderBlock-83          [-1, 320, 32, 32]               0\n",
            "      BatchNorm2d-84          [-1, 320, 32, 32]             640\n",
            "             ReLU-85          [-1, 320, 32, 32]               0\n",
            "           Conv2d-86          [-1, 320, 32, 32]         921,920\n",
            "      BatchNorm2d-87          [-1, 320, 32, 32]             640\n",
            "             ReLU-88          [-1, 320, 32, 32]               0\n",
            "          Dropout-89          [-1, 320, 32, 32]               0\n",
            "           Conv2d-90          [-1, 320, 32, 32]         921,920\n",
            "       WiderBlock-91          [-1, 320, 32, 32]               0\n",
            "      BatchNorm2d-92          [-1, 320, 32, 32]             640\n",
            "             ReLU-93          [-1, 320, 32, 32]               0\n",
            "           Conv2d-94          [-1, 320, 32, 32]         921,920\n",
            "      BatchNorm2d-95          [-1, 320, 32, 32]             640\n",
            "             ReLU-96          [-1, 320, 32, 32]               0\n",
            "          Dropout-97          [-1, 320, 32, 32]               0\n",
            "           Conv2d-98          [-1, 320, 32, 32]         921,920\n",
            "       WiderBlock-99          [-1, 320, 32, 32]               0\n",
            "          Conv2d-100          [-1, 640, 16, 16]         205,440\n",
            "     BatchNorm2d-101          [-1, 320, 32, 32]             640\n",
            "            ReLU-102          [-1, 320, 32, 32]               0\n",
            "          Conv2d-103          [-1, 640, 16, 16]       1,843,840\n",
            "     BatchNorm2d-104          [-1, 640, 16, 16]           1,280\n",
            "            ReLU-105          [-1, 640, 16, 16]               0\n",
            "         Dropout-106          [-1, 640, 16, 16]               0\n",
            "          Conv2d-107          [-1, 640, 16, 16]       3,687,040\n",
            "      WiderBlock-108          [-1, 640, 16, 16]               0\n",
            "     BatchNorm2d-109          [-1, 640, 16, 16]           1,280\n",
            "            ReLU-110          [-1, 640, 16, 16]               0\n",
            "          Conv2d-111          [-1, 640, 16, 16]       3,687,040\n",
            "     BatchNorm2d-112          [-1, 640, 16, 16]           1,280\n",
            "            ReLU-113          [-1, 640, 16, 16]               0\n",
            "         Dropout-114          [-1, 640, 16, 16]               0\n",
            "          Conv2d-115          [-1, 640, 16, 16]       3,687,040\n",
            "      WiderBlock-116          [-1, 640, 16, 16]               0\n",
            "     BatchNorm2d-117          [-1, 640, 16, 16]           1,280\n",
            "            ReLU-118          [-1, 640, 16, 16]               0\n",
            "          Conv2d-119          [-1, 640, 16, 16]       3,687,040\n",
            "     BatchNorm2d-120          [-1, 640, 16, 16]           1,280\n",
            "            ReLU-121          [-1, 640, 16, 16]               0\n",
            "         Dropout-122          [-1, 640, 16, 16]               0\n",
            "          Conv2d-123          [-1, 640, 16, 16]       3,687,040\n",
            "      WiderBlock-124          [-1, 640, 16, 16]               0\n",
            "     BatchNorm2d-125          [-1, 640, 16, 16]           1,280\n",
            "            ReLU-126          [-1, 640, 16, 16]               0\n",
            "          Conv2d-127          [-1, 640, 16, 16]       3,687,040\n",
            "     BatchNorm2d-128          [-1, 640, 16, 16]           1,280\n",
            "            ReLU-129          [-1, 640, 16, 16]               0\n",
            "         Dropout-130          [-1, 640, 16, 16]               0\n",
            "          Conv2d-131          [-1, 640, 16, 16]       3,687,040\n",
            "      WiderBlock-132          [-1, 640, 16, 16]               0\n",
            "     BatchNorm2d-133          [-1, 640, 16, 16]           1,280\n",
            "            ReLU-134          [-1, 640, 16, 16]               0\n",
            "          Conv2d-135          [-1, 640, 16, 16]       3,687,040\n",
            "     BatchNorm2d-136          [-1, 640, 16, 16]           1,280\n",
            "            ReLU-137          [-1, 640, 16, 16]               0\n",
            "         Dropout-138          [-1, 640, 16, 16]               0\n",
            "          Conv2d-139          [-1, 640, 16, 16]       3,687,040\n",
            "      WiderBlock-140          [-1, 640, 16, 16]               0\n",
            "     BatchNorm2d-141          [-1, 640, 16, 16]           1,280\n",
            "            ReLU-142          [-1, 640, 16, 16]               0\n",
            "          Conv2d-143          [-1, 640, 16, 16]       3,687,040\n",
            "     BatchNorm2d-144          [-1, 640, 16, 16]           1,280\n",
            "            ReLU-145          [-1, 640, 16, 16]               0\n",
            "         Dropout-146          [-1, 640, 16, 16]               0\n",
            "          Conv2d-147          [-1, 640, 16, 16]       3,687,040\n",
            "      WiderBlock-148          [-1, 640, 16, 16]               0\n",
            "     BatchNorm2d-149          [-1, 640, 16, 16]           1,280\n",
            "            ReLU-150          [-1, 640, 16, 16]               0\n",
            "AdaptiveAvgPool2d-151            [-1, 640, 1, 1]               0\n",
            "          Linear-152                   [-1, 10]           6,410\n",
            "================================================================\n",
            "Total params: 55,856,330\n",
            "Trainable params: 55,856,330\n",
            "Non-trainable params: 0\n",
            "----------------------------------------------------------------\n",
            "Input size (MB): 0.05\n",
            "Forward/backward pass size (MB): 430.25\n",
            "Params size (MB): 213.07\n",
            "Estimated Total Size (MB): 643.38\n",
            "----------------------------------------------------------------\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Sdwv1_lERv6F"
      },
      "source": [
        "# 3. 학습하기"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "el5t0GJsRq-B"
      },
      "source": [
        "# define loss function, optimizer, lr_scheduler\n",
        "loss_func = nn.CrossEntropyLoss(reduction='sum')\n",
        "opt = optim.Adam(model.parameters(), lr=0.01)\n",
        "\n",
        "from torch.optim.lr_scheduler import ReduceLROnPlateau\n",
        "lr_scheduler = ReduceLROnPlateau(opt, mode='min', factor=0.1, patience=5)\n",
        "\n",
        "\n",
        "# get current lr\n",
        "def get_lr(opt):\n",
        "    for param_group in opt.param_groups:\n",
        "        return param_group['lr']\n",
        "\n",
        "\n",
        "# calculate the metric per mini-batch\n",
        "def metric_batch(output, target):\n",
        "    pred = output.argmax(1, keepdim=True)\n",
        "    corrects = pred.eq(target.view_as(pred)).sum().item()\n",
        "    return corrects\n",
        "\n",
        "\n",
        "# calculate the loss per mini-batch\n",
        "def loss_batch(loss_func, output, target, opt=None):\n",
        "    loss_b = loss_func(output, target)\n",
        "    metric_b = metric_batch(output, target)\n",
        "\n",
        "    if opt is not None:\n",
        "        opt.zero_grad()\n",
        "        loss_b.backward()\n",
        "        opt.step()\n",
        "    \n",
        "    return loss_b.item(), metric_b\n",
        "\n",
        "\n",
        "# calculate the loss per epochs\n",
        "def loss_epoch(model, loss_func, dataset_dl, sanity_check, opt=None):\n",
        "    running_loss = 0.0\n",
        "    running_metric = 0.0\n",
        "    len_data = len(dataset_dl.dataset)\n",
        "\n",
        "    for xb, yb in dataset_dl:\n",
        "        xb = xb.to(device)\n",
        "        yb = yb.to(device)\n",
        "        output = model(xb)\n",
        "\n",
        "        loss_b, metric_b = loss_batch(loss_func, output, yb, opt)\n",
        "\n",
        "        running_loss += loss_b\n",
        "\n",
        "        if metric_b is not None:\n",
        "            running_metric += metric_b\n",
        "\n",
        "        if sanity_check is True:\n",
        "            break\n",
        "\n",
        "    loss = running_loss / len_data\n",
        "    metric = running_metric / len_data\n",
        "\n",
        "    return loss, metric\n",
        "\n",
        "\n",
        "# function to start training\n",
        "def train_val(model, params):\n",
        "    num_epochs=params['num_epochs']\n",
        "    loss_func=params['loss_func']\n",
        "    opt=params['optimizer']\n",
        "    train_dl=params['train_dl']\n",
        "    val_dl=params['val_dl']\n",
        "    sanity_check=params['sanity_check']\n",
        "    lr_scheduler=params['lr_scheduler']\n",
        "    path2weights=params['path2weights']\n",
        "\n",
        "    loss_history={'train': [], 'val': []}\n",
        "    metric_history={'train': [], 'val': []}\n",
        "\n",
        "    best_model_wts = copy.deepcopy(model.state_dict())\n",
        "    best_loss = float('inf')\n",
        "    start_time = time.time()\n",
        "\n",
        "    for epoch in range(num_epochs):\n",
        "        current_lr = get_lr(opt)\n",
        "        print('Epoch {}/{}, current lr={}'.format(epoch, num_epochs-1, current_lr))\n",
        "\n",
        "        model.train()\n",
        "        train_loss, train_metric = loss_epoch(model, loss_func, train_dl, sanity_check, opt)\n",
        "        loss_history['train'].append(train_loss)\n",
        "        metric_history['train'].append(train_metric)\n",
        "\n",
        "        model.eval()\n",
        "        with torch.no_grad():\n",
        "            val_loss, val_metric = loss_epoch(model, loss_func, val_dl, sanity_check)\n",
        "        loss_history['val'].append(val_loss)\n",
        "        metric_history['val'].append(val_metric)\n",
        "\n",
        "        if val_loss < best_loss:\n",
        "            best_loss = val_loss\n",
        "            best_model_wts = copy.deepcopy(model.state_dict())\n",
        "            torch.save(model.state_dict(), path2weights)\n",
        "            print('Copied best model weights!')\n",
        "\n",
        "        lr_scheduler.step(val_loss)\n",
        "        if current_lr != get_lr(opt):\n",
        "            print('Loading best model weights!')\n",
        "            model.load_state_dict(best_model_wts)\n",
        "\n",
        "        print('train loss: %.6f, val loss: %.6f, accuracy: %.2f, time: %.4f min' %(train_loss, val_loss, 100*val_metric, (time.time()-start_time)/60))\n",
        "        print('-'*10)\n",
        "\n",
        "    model.load_state_dict(best_model_wts)\n",
        "    return model, loss_history, metric_history"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iFoCVYVYciOM"
      },
      "source": [
        "# define the training parameters\n",
        "params_train = {\n",
        "    'num_epochs':30,\n",
        "    'optimizer':opt,\n",
        "    'loss_func':loss_func,\n",
        "    'train_dl':train_dl,\n",
        "    'val_dl':val_dl,\n",
        "    'sanity_check':False,\n",
        "    'lr_scheduler':lr_scheduler,\n",
        "    'path2weights':'./models/weights.pt',\n",
        "}\n",
        "\n",
        "# check the directory to save weights.pt\n",
        "def createFolder(directory):\n",
        "    try:\n",
        "        if not os.path.exists(directory):\n",
        "            os.makedirs(directory)\n",
        "    except OSerror:\n",
        "        print('Error')\n",
        "createFolder('./models')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DG__kfRFhHvy",
        "outputId": "f67348f0-a6cc-4ef0-f6ab-596fb41c9132"
      },
      "source": [
        "model, loss_hist, metric_hist = train_val(model, params_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 0/29, current lr=0.01\n",
            "Copied best model weights!\n",
            "train loss: 2.124016, val loss: 1.885508, accuracy: 25.59, time: 4.2151 min\n",
            "----------\n",
            "Epoch 1/29, current lr=0.01\n",
            "Copied best model weights!\n",
            "train loss: 1.804658, val loss: 1.802408, accuracy: 27.77, time: 8.4277 min\n",
            "----------\n",
            "Epoch 2/29, current lr=0.01\n",
            "Copied best model weights!\n",
            "train loss: 1.715108, val loss: 1.764161, accuracy: 30.40, time: 12.6365 min\n",
            "----------\n",
            "Epoch 3/29, current lr=0.01\n",
            "Copied best model weights!\n",
            "train loss: 1.671609, val loss: 1.678693, accuracy: 33.38, time: 16.8429 min\n",
            "----------\n",
            "Epoch 4/29, current lr=0.01\n",
            "train loss: 1.586184, val loss: 1.833298, accuracy: 28.98, time: 21.0457 min\n",
            "----------\n",
            "Epoch 5/29, current lr=0.01\n",
            "Copied best model weights!\n",
            "train loss: 1.565752, val loss: 1.578054, accuracy: 37.45, time: 25.2567 min\n",
            "----------\n",
            "Epoch 6/29, current lr=0.01\n",
            "train loss: 1.559752, val loss: 1.609315, accuracy: 37.79, time: 29.4570 min\n",
            "----------\n",
            "Epoch 7/29, current lr=0.01\n",
            "Copied best model weights!\n",
            "train loss: 1.525625, val loss: 1.577752, accuracy: 38.39, time: 33.6646 min\n",
            "----------\n",
            "Epoch 8/29, current lr=0.01\n",
            "train loss: 1.483446, val loss: 1.765697, accuracy: 35.39, time: 37.8603 min\n",
            "----------\n",
            "Epoch 9/29, current lr=0.01\n",
            "Copied best model weights!\n",
            "train loss: 1.413568, val loss: 1.542685, accuracy: 42.85, time: 42.0612 min\n",
            "----------\n",
            "Epoch 10/29, current lr=0.01\n",
            "Copied best model weights!\n",
            "train loss: 1.385007, val loss: 1.438979, accuracy: 47.59, time: 46.2584 min\n",
            "----------\n",
            "Epoch 11/29, current lr=0.01\n",
            "Copied best model weights!\n",
            "train loss: 1.318186, val loss: 1.374230, accuracy: 46.48, time: 50.4516 min\n",
            "----------\n",
            "Epoch 12/29, current lr=0.01\n",
            "train loss: 1.264045, val loss: 1.382123, accuracy: 47.52, time: 54.6342 min\n",
            "----------\n",
            "Epoch 13/29, current lr=0.01\n",
            "train loss: 1.227282, val loss: 2.156404, accuracy: 37.43, time: 58.8093 min\n",
            "----------\n",
            "Epoch 14/29, current lr=0.01\n",
            "train loss: 1.185140, val loss: 2.087108, accuracy: 36.54, time: 62.9853 min\n",
            "----------\n",
            "Epoch 15/29, current lr=0.01\n",
            "Copied best model weights!\n",
            "train loss: 1.139327, val loss: 1.222690, accuracy: 54.59, time: 67.1674 min\n",
            "----------\n",
            "Epoch 16/29, current lr=0.01\n",
            "train loss: 1.101338, val loss: 1.348555, accuracy: 51.16, time: 71.3425 min\n",
            "----------\n",
            "Epoch 17/29, current lr=0.01\n",
            "Copied best model weights!\n",
            "train loss: 1.077177, val loss: 1.132793, accuracy: 57.90, time: 75.5209 min\n",
            "----------\n",
            "Epoch 18/29, current lr=0.01\n",
            "train loss: 1.036949, val loss: 1.576509, accuracy: 46.23, time: 79.6906 min\n",
            "----------\n",
            "Epoch 19/29, current lr=0.01\n",
            "Copied best model weights!\n",
            "train loss: 1.011036, val loss: 1.077700, accuracy: 60.95, time: 83.8657 min\n",
            "----------\n",
            "Epoch 20/29, current lr=0.01\n",
            "train loss: 0.957407, val loss: 1.146066, accuracy: 59.69, time: 88.0326 min\n",
            "----------\n",
            "Epoch 21/29, current lr=0.01\n",
            "train loss: 0.931653, val loss: 1.176359, accuracy: 58.94, time: 92.1971 min\n",
            "----------\n",
            "Epoch 22/29, current lr=0.01\n",
            "Copied best model weights!\n",
            "train loss: 0.917273, val loss: 1.030002, accuracy: 63.86, time: 96.3681 min\n",
            "----------\n",
            "Epoch 23/29, current lr=0.01\n",
            "Copied best model weights!\n",
            "train loss: 0.875439, val loss: 1.013416, accuracy: 63.02, time: 100.5393 min\n",
            "----------\n",
            "Epoch 24/29, current lr=0.01\n",
            "Copied best model weights!\n",
            "train loss: 0.843460, val loss: 0.999249, accuracy: 64.16, time: 104.7105 min\n",
            "----------\n",
            "Epoch 25/29, current lr=0.01\n",
            "train loss: 0.801370, val loss: 1.225123, accuracy: 57.75, time: 108.8710 min\n",
            "----------\n",
            "Epoch 26/29, current lr=0.01\n",
            "Copied best model weights!\n",
            "train loss: 0.744701, val loss: 0.971992, accuracy: 65.28, time: 113.0402 min\n",
            "----------\n",
            "Epoch 27/29, current lr=0.01\n",
            "train loss: 0.720608, val loss: 1.100069, accuracy: 62.68, time: 117.1988 min\n",
            "----------\n",
            "Epoch 28/29, current lr=0.01\n",
            "train loss: 0.693345, val loss: 1.278031, accuracy: 60.59, time: 121.3566 min\n",
            "----------\n",
            "Epoch 29/29, current lr=0.01\n",
            "train loss: 0.651336, val loss: 0.982081, accuracy: 65.76, time: 125.5140 min\n",
            "----------\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 573
        },
        "id": "tZCYEwd9hNJZ",
        "outputId": "1513fab6-3d85-4db3-d0ee-72ed18a3d2ba"
      },
      "source": [
        "# Train-Validation progress\n",
        "num_epochs = params_train['num_epochs']\n",
        "\n",
        "# plot loss progress\n",
        "plt.title('Train-Val Loss')\n",
        "plt.plot(range(1, num_epochs+1), loss_hist['train'], label='train')\n",
        "plt.plot(range(1, num_epochs+1), loss_hist['val'], label='val')\n",
        "plt.ylabel('Loss')\n",
        "plt.xlabel('Training Epochs')\n",
        "plt.legend()\n",
        "plt.show()\n",
        "\n",
        "# plot accuracy progress\n",
        "plt.title('Train-Val Accuracy')\n",
        "plt.plot(range(1, num_epochs+1), metric_hist['train'], label='train')\n",
        "plt.plot(range(1, num_epochs+1), metric_hist['val'], label='val')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.xlabel('Training Epochs')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iVVdNdHxi_Ky"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}