Classification/Xception(2017).ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Xception(2017).ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyP3cZjNH799pkoEAUYetmFa",
      "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/Xception(2017).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": "puRY8H7Oa-e1",
        "outputId": "7c07accb-06c1-4735-9feb-50994526c526"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('xception')"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mounted at xception\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "s756eUScbKI0"
      },
      "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": 5,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3q_q1yxpbhhW"
      },
      "source": [
        "# 1. 데이터셋 불러오기\n",
        " 데이터셋은 torchvision 패키지에서 제공하는 STL10 dataset을 이용하겠습니다. STL10 dataset은 10개의 label을 갖으며 train dataset 5000개, test dataset 8000개로 구성됩니다."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-pBqEz_dbl-J",
        "outputId": "19b4d3ec-b3d7-41bd-ebcc-bd42a6490127"
      },
      "source": [
        "# specify path to data\n",
        "path2data = '/content/xception/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": 6,
      "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": "oPldvZYcuwSD"
      },
      "source": [
        "# define transformation\n",
        "transformation = transforms.Compose([\n",
        "                    transforms.ToTensor(),\n",
        "                    transforms.Resize(299)\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": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "Cc11AXcrvPTh",
        "outputId": "503d1922-b0e4-44fe-ac22-8b25693de089"
      },
      "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(11)\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": 8,
      "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": "3ol5obB-bmc0"
      },
      "source": [
        "# 2. 모델 구축하기"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "orljvCtibn3O"
      },
      "source": [
        "# Depthwise Separable Convolution\n",
        "class SeparableConv(nn.Module):\n",
        "    def __init__(self, in_channels, out_channels):\n",
        "        super().__init__()\n",
        "\n",
        "        self.seperable = nn.Sequential(\n",
        "            nn.Conv2d(in_channels, in_channels, 3, stride=1, padding=1, bias=False),\n",
        "            nn.Conv2d(in_channels, out_channels, 1, stride=1, padding=0, bias=False)\n",
        "        )\n",
        "\n",
        "    def forward(self, x):\n",
        "        x = self.seperable(x)\n",
        "        return x\n",
        "\n",
        "\n",
        "# EnrtyFlow\n",
        "class EntryFlow(nn.Module):\n",
        "    def __init__(self):\n",
        "        super().__init__()\n",
        "\n",
        "        self.conv1 = nn.Sequential(\n",
        "            nn.Conv2d(3, 32, 3, stride=2, padding=1, bias=False),\n",
        "            nn.BatchNorm2d(32),\n",
        "            nn.ReLU(),\n",
        "            nn.Conv2d(32, 64, 3, stride=1, padding=0, bias=False),\n",
        "            nn.BatchNorm2d(64),\n",
        "            nn.ReLU()\n",
        "        )\n",
        "\n",
        "        self.conv2_residual = nn.Sequential(\n",
        "            SeparableConv(64, 128),\n",
        "            nn.BatchNorm2d(128),\n",
        "            nn.ReLU(),\n",
        "            SeparableConv(128, 128),\n",
        "            nn.BatchNorm2d(128),\n",
        "            nn.MaxPool2d(3, stride=2, padding=1)\n",
        "        )\n",
        "\n",
        "        self.conv2_shortcut = nn.Sequential(\n",
        "            nn.Conv2d(64, 128, 1, stride=2, padding=0),\n",
        "            nn.BatchNorm2d(128)\n",
        "        )\n",
        "\n",
        "        self.conv3_residual = nn.Sequential(\n",
        "            nn.ReLU(),\n",
        "            SeparableConv(128, 256),\n",
        "            nn.BatchNorm2d(256),\n",
        "            nn.ReLU(),\n",
        "            SeparableConv(256, 256),\n",
        "            nn.BatchNorm2d(256),\n",
        "            nn.MaxPool2d(3, stride=2, padding=1)\n",
        "        )\n",
        "\n",
        "        self.conv3_shortcut = nn.Sequential(\n",
        "            nn.Conv2d(128, 256, 1, stride=2, padding=0),\n",
        "            nn.BatchNorm2d(256)\n",
        "        )\n",
        "\n",
        "        self.conv4_residual = nn.Sequential(\n",
        "            nn.ReLU(),\n",
        "            SeparableConv(256, 728),\n",
        "            nn.BatchNorm2d(728),\n",
        "            nn.ReLU(),\n",
        "            SeparableConv(728, 728),\n",
        "            nn.BatchNorm2d(728),\n",
        "            nn.MaxPool2d(3, stride=2, padding=1)\n",
        "        )\n",
        "\n",
        "        self.conv4_shortcut = nn.Sequential(\n",
        "            nn.Conv2d(256, 728, 1, stride=2, padding=0),\n",
        "            nn.BatchNorm2d(728)\n",
        "        )\n",
        "\n",
        "    def forward(self, x):\n",
        "        x = self.conv1(x)\n",
        "        x = self.conv2_residual(x) + self.conv2_shortcut(x)\n",
        "        x = self.conv3_residual(x) + self.conv3_shortcut(x)\n",
        "        x = self.conv4_residual(x) + self.conv4_shortcut(x)\n",
        "        return x\n",
        "\n",
        "\n",
        "# MiddleFlow\n",
        "class MiddleFlow(nn.Module):\n",
        "    def __init__(self):\n",
        "        super().__init__()\n",
        "\n",
        "        self.conv_residual = nn.Sequential(\n",
        "            nn.ReLU(),\n",
        "            SeparableConv(728, 728),\n",
        "            nn.BatchNorm2d(728),\n",
        "            nn.ReLU(),\n",
        "            SeparableConv(728, 728),\n",
        "            nn.BatchNorm2d(728),\n",
        "            nn.ReLU(),\n",
        "            SeparableConv(728, 728),\n",
        "            nn.BatchNorm2d(728)\n",
        "        )\n",
        "\n",
        "        self.conv_shortcut = nn.Sequential()\n",
        "\n",
        "    def forward(self, x):\n",
        "        return self.conv_shortcut(x) + self.conv_residual(x)\n",
        "\n",
        "\n",
        "# ExitFlow\n",
        "class ExitFlow(nn.Module):\n",
        "    def __init__(self, num_classes=10):\n",
        "        super().__init__()\n",
        "\n",
        "        self.conv1_residual = nn.Sequential(\n",
        "            nn.ReLU(),\n",
        "            SeparableConv(728, 1024),\n",
        "            nn.BatchNorm2d(1024),\n",
        "            nn.ReLU(),\n",
        "            SeparableConv(1024, 1024),\n",
        "            nn.BatchNorm2d(1024),\n",
        "            nn.MaxPool2d(3, stride=2, padding=1)\n",
        "        )\n",
        "\n",
        "        self.conv1_shortcut = nn.Sequential(\n",
        "            nn.Conv2d(728, 1024, 1, stride=2, padding=0),\n",
        "            nn.BatchNorm2d(1024)\n",
        "        )\n",
        "\n",
        "        self.conv2 = nn.Sequential(\n",
        "            SeparableConv(1024, 1536),\n",
        "            nn.BatchNorm2d(1536),\n",
        "            nn.ReLU(),\n",
        "            SeparableConv(1536, 2048),\n",
        "            nn.BatchNorm2d(2048),\n",
        "            nn.ReLU()\n",
        "        )\n",
        "\n",
        "        self.avg_pool = nn.AdaptiveAvgPool2d((1,1))\n",
        "    \n",
        "    def forward(self, x):\n",
        "        x = self.conv1_residual(x) + self.conv1_shortcut(x)\n",
        "        x = self.conv2(x)\n",
        "        x = self.avg_pool(x)\n",
        "        return x\n",
        "\n",
        "\n",
        "# Xception\n",
        "class Xception(nn.Module):\n",
        "    def __init__(self, num_classes=10, init_weights=True):\n",
        "        super().__init__()\n",
        "        self.init_weights = init_weights\n",
        "\n",
        "        self.entry = EntryFlow()\n",
        "        self.middle = self._make_middle_flow()\n",
        "        self.exit = ExitFlow()\n",
        "\n",
        "        self.linear = nn.Linear(2048, num_classes)\n",
        "\n",
        "        # weights initialization\n",
        "        if self.init_weights:\n",
        "            pass\n",
        "\n",
        "\n",
        "    def forward(self, x):\n",
        "        x = self.entry(x)\n",
        "        x = self.middle(x)\n",
        "        x = self.exit(x)\n",
        "        x = x.view(x.size(0), -1)\n",
        "        x = self.linear(x)\n",
        "        return x\n",
        "\n",
        "    def _make_middle_flow(self):\n",
        "        middle = nn.Sequential()\n",
        "        for i in range(8):\n",
        "            middle.add_module('middle_block_{}'.format(i), MiddleFlow())\n",
        "        return middle\n",
        "\n",
        "    def _initialize_weights(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_bias_(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)"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jKDN8cc_sajo",
        "outputId": "b6f9fd16-7ade-4df5-f938-dc849daf6d30"
      },
      "source": [
        "# check model\n",
        "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
        "x = torch.randn(3, 3, 299, 299).to(device)\n",
        "model = Xception().to(device)\n",
        "output = model(x)\n",
        "print('output size:', output.size())"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "output size: torch.Size([3, 10])\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qLPgWyzHuQU0",
        "outputId": "8dcc3a88-229e-4f5a-a4a0-40bb6b415526"
      },
      "source": [
        "# print summary\n",
        "summary(model, (3, 299, 299), device=device.type)"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "----------------------------------------------------------------\n",
            "        Layer (type)               Output Shape         Param #\n",
            "================================================================\n",
            "            Conv2d-1         [-1, 32, 150, 150]             864\n",
            "       BatchNorm2d-2         [-1, 32, 150, 150]              64\n",
            "              ReLU-3         [-1, 32, 150, 150]               0\n",
            "            Conv2d-4         [-1, 64, 148, 148]          18,432\n",
            "       BatchNorm2d-5         [-1, 64, 148, 148]             128\n",
            "              ReLU-6         [-1, 64, 148, 148]               0\n",
            "            Conv2d-7         [-1, 64, 148, 148]          36,864\n",
            "            Conv2d-8        [-1, 128, 148, 148]           8,192\n",
            "     SeparableConv-9        [-1, 128, 148, 148]               0\n",
            "      BatchNorm2d-10        [-1, 128, 148, 148]             256\n",
            "             ReLU-11        [-1, 128, 148, 148]               0\n",
            "           Conv2d-12        [-1, 128, 148, 148]         147,456\n",
            "           Conv2d-13        [-1, 128, 148, 148]          16,384\n",
            "    SeparableConv-14        [-1, 128, 148, 148]               0\n",
            "      BatchNorm2d-15        [-1, 128, 148, 148]             256\n",
            "        MaxPool2d-16          [-1, 128, 74, 74]               0\n",
            "           Conv2d-17          [-1, 128, 74, 74]           8,320\n",
            "      BatchNorm2d-18          [-1, 128, 74, 74]             256\n",
            "             ReLU-19          [-1, 128, 74, 74]               0\n",
            "           Conv2d-20          [-1, 128, 74, 74]         147,456\n",
            "           Conv2d-21          [-1, 256, 74, 74]          32,768\n",
            "    SeparableConv-22          [-1, 256, 74, 74]               0\n",
            "      BatchNorm2d-23          [-1, 256, 74, 74]             512\n",
            "             ReLU-24          [-1, 256, 74, 74]               0\n",
            "           Conv2d-25          [-1, 256, 74, 74]         589,824\n",
            "           Conv2d-26          [-1, 256, 74, 74]          65,536\n",
            "    SeparableConv-27          [-1, 256, 74, 74]               0\n",
            "      BatchNorm2d-28          [-1, 256, 74, 74]             512\n",
            "        MaxPool2d-29          [-1, 256, 37, 37]               0\n",
            "           Conv2d-30          [-1, 256, 37, 37]          33,024\n",
            "      BatchNorm2d-31          [-1, 256, 37, 37]             512\n",
            "             ReLU-32          [-1, 256, 37, 37]               0\n",
            "           Conv2d-33          [-1, 256, 37, 37]         589,824\n",
            "           Conv2d-34          [-1, 728, 37, 37]         186,368\n",
            "    SeparableConv-35          [-1, 728, 37, 37]               0\n",
            "      BatchNorm2d-36          [-1, 728, 37, 37]           1,456\n",
            "             ReLU-37          [-1, 728, 37, 37]               0\n",
            "           Conv2d-38          [-1, 728, 37, 37]       4,769,856\n",
            "           Conv2d-39          [-1, 728, 37, 37]         529,984\n",
            "    SeparableConv-40          [-1, 728, 37, 37]               0\n",
            "      BatchNorm2d-41          [-1, 728, 37, 37]           1,456\n",
            "        MaxPool2d-42          [-1, 728, 19, 19]               0\n",
            "           Conv2d-43          [-1, 728, 19, 19]         187,096\n",
            "      BatchNorm2d-44          [-1, 728, 19, 19]           1,456\n",
            "        EntryFlow-45          [-1, 728, 19, 19]               0\n",
            "             ReLU-46          [-1, 728, 19, 19]               0\n",
            "           Conv2d-47          [-1, 728, 19, 19]       4,769,856\n",
            "           Conv2d-48          [-1, 728, 19, 19]         529,984\n",
            "    SeparableConv-49          [-1, 728, 19, 19]               0\n",
            "      BatchNorm2d-50          [-1, 728, 19, 19]           1,456\n",
            "             ReLU-51          [-1, 728, 19, 19]               0\n",
            "           Conv2d-52          [-1, 728, 19, 19]       4,769,856\n",
            "           Conv2d-53          [-1, 728, 19, 19]         529,984\n",
            "    SeparableConv-54          [-1, 728, 19, 19]               0\n",
            "      BatchNorm2d-55          [-1, 728, 19, 19]           1,456\n",
            "             ReLU-56          [-1, 728, 19, 19]               0\n",
            "           Conv2d-57          [-1, 728, 19, 19]       4,769,856\n",
            "           Conv2d-58          [-1, 728, 19, 19]         529,984\n",
            "    SeparableConv-59          [-1, 728, 19, 19]               0\n",
            "      BatchNorm2d-60          [-1, 728, 19, 19]           1,456\n",
            "       MiddleFlow-61          [-1, 728, 19, 19]               0\n",
            "             ReLU-62          [-1, 728, 19, 19]               0\n",
            "           Conv2d-63          [-1, 728, 19, 19]       4,769,856\n",
            "           Conv2d-64          [-1, 728, 19, 19]         529,984\n",
            "    SeparableConv-65          [-1, 728, 19, 19]               0\n",
            "      BatchNorm2d-66          [-1, 728, 19, 19]           1,456\n",
            "             ReLU-67          [-1, 728, 19, 19]               0\n",
            "           Conv2d-68          [-1, 728, 19, 19]       4,769,856\n",
            "           Conv2d-69          [-1, 728, 19, 19]         529,984\n",
            "    SeparableConv-70          [-1, 728, 19, 19]               0\n",
            "      BatchNorm2d-71          [-1, 728, 19, 19]           1,456\n",
            "             ReLU-72          [-1, 728, 19, 19]               0\n",
            "           Conv2d-73          [-1, 728, 19, 19]       4,769,856\n",
            "           Conv2d-74          [-1, 728, 19, 19]         529,984\n",
            "    SeparableConv-75          [-1, 728, 19, 19]               0\n",
            "      BatchNorm2d-76          [-1, 728, 19, 19]           1,456\n",
            "       MiddleFlow-77          [-1, 728, 19, 19]               0\n",
            "             ReLU-78          [-1, 728, 19, 19]               0\n",
            "           Conv2d-79          [-1, 728, 19, 19]       4,769,856\n",
            "           Conv2d-80          [-1, 728, 19, 19]         529,984\n",
            "    SeparableConv-81          [-1, 728, 19, 19]               0\n",
            "      BatchNorm2d-82          [-1, 728, 19, 19]           1,456\n",
            "             ReLU-83          [-1, 728, 19, 19]               0\n",
            "           Conv2d-84          [-1, 728, 19, 19]       4,769,856\n",
            "           Conv2d-85          [-1, 728, 19, 19]         529,984\n",
            "    SeparableConv-86          [-1, 728, 19, 19]               0\n",
            "      BatchNorm2d-87          [-1, 728, 19, 19]           1,456\n",
            "             ReLU-88          [-1, 728, 19, 19]               0\n",
            "           Conv2d-89          [-1, 728, 19, 19]       4,769,856\n",
            "           Conv2d-90          [-1, 728, 19, 19]         529,984\n",
            "    SeparableConv-91          [-1, 728, 19, 19]               0\n",
            "      BatchNorm2d-92          [-1, 728, 19, 19]           1,456\n",
            "       MiddleFlow-93          [-1, 728, 19, 19]               0\n",
            "             ReLU-94          [-1, 728, 19, 19]               0\n",
            "           Conv2d-95          [-1, 728, 19, 19]       4,769,856\n",
            "           Conv2d-96          [-1, 728, 19, 19]         529,984\n",
            "    SeparableConv-97          [-1, 728, 19, 19]               0\n",
            "      BatchNorm2d-98          [-1, 728, 19, 19]           1,456\n",
            "             ReLU-99          [-1, 728, 19, 19]               0\n",
            "          Conv2d-100          [-1, 728, 19, 19]       4,769,856\n",
            "          Conv2d-101          [-1, 728, 19, 19]         529,984\n",
            "   SeparableConv-102          [-1, 728, 19, 19]               0\n",
            "     BatchNorm2d-103          [-1, 728, 19, 19]           1,456\n",
            "            ReLU-104          [-1, 728, 19, 19]               0\n",
            "          Conv2d-105          [-1, 728, 19, 19]       4,769,856\n",
            "          Conv2d-106          [-1, 728, 19, 19]         529,984\n",
            "   SeparableConv-107          [-1, 728, 19, 19]               0\n",
            "     BatchNorm2d-108          [-1, 728, 19, 19]           1,456\n",
            "      MiddleFlow-109          [-1, 728, 19, 19]               0\n",
            "            ReLU-110          [-1, 728, 19, 19]               0\n",
            "          Conv2d-111          [-1, 728, 19, 19]       4,769,856\n",
            "          Conv2d-112          [-1, 728, 19, 19]         529,984\n",
            "   SeparableConv-113          [-1, 728, 19, 19]               0\n",
            "     BatchNorm2d-114          [-1, 728, 19, 19]           1,456\n",
            "            ReLU-115          [-1, 728, 19, 19]               0\n",
            "          Conv2d-116          [-1, 728, 19, 19]       4,769,856\n",
            "          Conv2d-117          [-1, 728, 19, 19]         529,984\n",
            "   SeparableConv-118          [-1, 728, 19, 19]               0\n",
            "     BatchNorm2d-119          [-1, 728, 19, 19]           1,456\n",
            "            ReLU-120          [-1, 728, 19, 19]               0\n",
            "          Conv2d-121          [-1, 728, 19, 19]       4,769,856\n",
            "          Conv2d-122          [-1, 728, 19, 19]         529,984\n",
            "   SeparableConv-123          [-1, 728, 19, 19]               0\n",
            "     BatchNorm2d-124          [-1, 728, 19, 19]           1,456\n",
            "      MiddleFlow-125          [-1, 728, 19, 19]               0\n",
            "            ReLU-126          [-1, 728, 19, 19]               0\n",
            "          Conv2d-127          [-1, 728, 19, 19]       4,769,856\n",
            "          Conv2d-128          [-1, 728, 19, 19]         529,984\n",
            "   SeparableConv-129          [-1, 728, 19, 19]               0\n",
            "     BatchNorm2d-130          [-1, 728, 19, 19]           1,456\n",
            "            ReLU-131          [-1, 728, 19, 19]               0\n",
            "          Conv2d-132          [-1, 728, 19, 19]       4,769,856\n",
            "          Conv2d-133          [-1, 728, 19, 19]         529,984\n",
            "   SeparableConv-134          [-1, 728, 19, 19]               0\n",
            "     BatchNorm2d-135          [-1, 728, 19, 19]           1,456\n",
            "            ReLU-136          [-1, 728, 19, 19]               0\n",
            "          Conv2d-137          [-1, 728, 19, 19]       4,769,856\n",
            "          Conv2d-138          [-1, 728, 19, 19]         529,984\n",
            "   SeparableConv-139          [-1, 728, 19, 19]               0\n",
            "     BatchNorm2d-140          [-1, 728, 19, 19]           1,456\n",
            "      MiddleFlow-141          [-1, 728, 19, 19]               0\n",
            "            ReLU-142          [-1, 728, 19, 19]               0\n",
            "          Conv2d-143          [-1, 728, 19, 19]       4,769,856\n",
            "          Conv2d-144          [-1, 728, 19, 19]         529,984\n",
            "   SeparableConv-145          [-1, 728, 19, 19]               0\n",
            "     BatchNorm2d-146          [-1, 728, 19, 19]           1,456\n",
            "            ReLU-147          [-1, 728, 19, 19]               0\n",
            "          Conv2d-148          [-1, 728, 19, 19]       4,769,856\n",
            "          Conv2d-149          [-1, 728, 19, 19]         529,984\n",
            "   SeparableConv-150          [-1, 728, 19, 19]               0\n",
            "     BatchNorm2d-151          [-1, 728, 19, 19]           1,456\n",
            "            ReLU-152          [-1, 728, 19, 19]               0\n",
            "          Conv2d-153          [-1, 728, 19, 19]       4,769,856\n",
            "          Conv2d-154          [-1, 728, 19, 19]         529,984\n",
            "   SeparableConv-155          [-1, 728, 19, 19]               0\n",
            "     BatchNorm2d-156          [-1, 728, 19, 19]           1,456\n",
            "      MiddleFlow-157          [-1, 728, 19, 19]               0\n",
            "            ReLU-158          [-1, 728, 19, 19]               0\n",
            "          Conv2d-159          [-1, 728, 19, 19]       4,769,856\n",
            "          Conv2d-160          [-1, 728, 19, 19]         529,984\n",
            "   SeparableConv-161          [-1, 728, 19, 19]               0\n",
            "     BatchNorm2d-162          [-1, 728, 19, 19]           1,456\n",
            "            ReLU-163          [-1, 728, 19, 19]               0\n",
            "          Conv2d-164          [-1, 728, 19, 19]       4,769,856\n",
            "          Conv2d-165          [-1, 728, 19, 19]         529,984\n",
            "   SeparableConv-166          [-1, 728, 19, 19]               0\n",
            "     BatchNorm2d-167          [-1, 728, 19, 19]           1,456\n",
            "            ReLU-168          [-1, 728, 19, 19]               0\n",
            "          Conv2d-169          [-1, 728, 19, 19]       4,769,856\n",
            "          Conv2d-170          [-1, 728, 19, 19]         529,984\n",
            "   SeparableConv-171          [-1, 728, 19, 19]               0\n",
            "     BatchNorm2d-172          [-1, 728, 19, 19]           1,456\n",
            "      MiddleFlow-173          [-1, 728, 19, 19]               0\n",
            "            ReLU-174          [-1, 728, 19, 19]               0\n",
            "          Conv2d-175          [-1, 728, 19, 19]       4,769,856\n",
            "          Conv2d-176         [-1, 1024, 19, 19]         745,472\n",
            "   SeparableConv-177         [-1, 1024, 19, 19]               0\n",
            "     BatchNorm2d-178         [-1, 1024, 19, 19]           2,048\n",
            "            ReLU-179         [-1, 1024, 19, 19]               0\n",
            "          Conv2d-180         [-1, 1024, 19, 19]       9,437,184\n",
            "          Conv2d-181         [-1, 1024, 19, 19]       1,048,576\n",
            "   SeparableConv-182         [-1, 1024, 19, 19]               0\n",
            "     BatchNorm2d-183         [-1, 1024, 19, 19]           2,048\n",
            "       MaxPool2d-184         [-1, 1024, 10, 10]               0\n",
            "          Conv2d-185         [-1, 1024, 10, 10]         746,496\n",
            "     BatchNorm2d-186         [-1, 1024, 10, 10]           2,048\n",
            "          Conv2d-187         [-1, 1024, 10, 10]       9,437,184\n",
            "          Conv2d-188         [-1, 1536, 10, 10]       1,572,864\n",
            "   SeparableConv-189         [-1, 1536, 10, 10]               0\n",
            "     BatchNorm2d-190         [-1, 1536, 10, 10]           3,072\n",
            "            ReLU-191         [-1, 1536, 10, 10]               0\n",
            "          Conv2d-192         [-1, 1536, 10, 10]      21,233,664\n",
            "          Conv2d-193         [-1, 2048, 10, 10]       3,145,728\n",
            "   SeparableConv-194         [-1, 2048, 10, 10]               0\n",
            "     BatchNorm2d-195         [-1, 2048, 10, 10]           4,096\n",
            "            ReLU-196         [-1, 2048, 10, 10]               0\n",
            "AdaptiveAvgPool2d-197           [-1, 2048, 1, 1]               0\n",
            "        ExitFlow-198           [-1, 2048, 1, 1]               0\n",
            "          Linear-199                   [-1, 10]          20,490\n",
            "================================================================\n",
            "Total params: 186,777,042\n",
            "Trainable params: 186,777,042\n",
            "Non-trainable params: 0\n",
            "----------------------------------------------------------------\n",
            "Input size (MB): 1.02\n",
            "Forward/backward pass size (MB): 723.39\n",
            "Params size (MB): 712.50\n",
            "Estimated Total Size (MB): 1436.91\n",
            "----------------------------------------------------------------\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ita4WLSfboNA"
      },
      "source": [
        "# 3. 학습하기"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iXyX1OuKbpPb"
      },
      "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=8)\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=False, 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",
        "    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_loss = float('inf')\n",
        "    best_model_wts = copy.deepcopy(model.state_dict())\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": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sr9GNM-Gvbc3"
      },
      "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": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FXnicN-mvcyg",
        "outputId": "fd6eab57-245c-48f5-ecaf-fb6e95fd95a0"
      },
      "source": [
        "model, loss_hist, metric_hist = train_val(model, params_train)"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 0/29, current lr= 0.01\n",
            "Copied best model weights!\n",
            "train loss: 2.927809, val loss: 5.453811, accuracy: 18.00, time: 4.4177 min\n",
            "----------\n",
            "Epoch 1/29, current lr= 0.01\n",
            "Copied best model weights!\n",
            "train loss: 2.103664, val loss: 2.107721, accuracy: 21.66, time: 8.8368 min\n",
            "----------\n",
            "Epoch 2/29, current lr= 0.01\n",
            "train loss: 1.906034, val loss: 2.290059, accuracy: 23.96, time: 13.2197 min\n",
            "----------\n",
            "Epoch 3/29, current lr= 0.01\n",
            "Copied best model weights!\n",
            "train loss: 1.794080, val loss: 1.774082, accuracy: 27.74, time: 17.6536 min\n",
            "----------\n",
            "Epoch 4/29, current lr= 0.01\n",
            "Copied best model weights!\n",
            "train loss: 1.728006, val loss: 1.652795, accuracy: 32.07, time: 22.0867 min\n",
            "----------\n",
            "Epoch 5/29, current lr= 0.01\n",
            "train loss: 1.674270, val loss: 1.659006, accuracy: 30.86, time: 26.4757 min\n",
            "----------\n",
            "Epoch 6/29, current lr= 0.01\n",
            "train loss: 1.661750, val loss: 1.657596, accuracy: 31.34, time: 30.8582 min\n",
            "----------\n",
            "Epoch 7/29, current lr= 0.01\n",
            "train loss: 1.627006, val loss: 1.698795, accuracy: 33.04, time: 35.2422 min\n",
            "----------\n",
            "Epoch 8/29, current lr= 0.01\n",
            "Copied best model weights!\n",
            "train loss: 1.613597, val loss: 1.555792, accuracy: 37.31, time: 39.6687 min\n",
            "----------\n",
            "Epoch 9/29, current lr= 0.01\n",
            "train loss: 1.598846, val loss: 2.377564, accuracy: 30.86, time: 44.0537 min\n",
            "----------\n",
            "Epoch 10/29, current lr= 0.01\n",
            "train loss: 1.570825, val loss: 1.574502, accuracy: 36.68, time: 48.4409 min\n",
            "----------\n",
            "Epoch 11/29, current lr= 0.01\n",
            "train loss: 1.567407, val loss: 1.584446, accuracy: 36.59, time: 52.8231 min\n",
            "----------\n",
            "Epoch 12/29, current lr= 0.01\n",
            "train loss: 1.544907, val loss: 1.720966, accuracy: 33.00, time: 57.2093 min\n",
            "----------\n",
            "Epoch 13/29, current lr= 0.01\n",
            "train loss: 1.516855, val loss: 1.817993, accuracy: 33.64, time: 61.5961 min\n",
            "----------\n",
            "Epoch 14/29, current lr= 0.01\n",
            "train loss: 1.504551, val loss: 1.610766, accuracy: 37.00, time: 65.9832 min\n",
            "----------\n",
            "Epoch 15/29, current lr= 0.01\n",
            "train loss: 1.488494, val loss: 1.616810, accuracy: 36.56, time: 70.3721 min\n",
            "----------\n",
            "Epoch 16/29, current lr= 0.01\n",
            "train loss: 1.456171, val loss: 1.688971, accuracy: 38.88, time: 74.7606 min\n",
            "----------\n",
            "Epoch 17/29, current lr= 0.01\n",
            "Loading best model weights!\n",
            "train loss: 1.442344, val loss: 1.560692, accuracy: 42.68, time: 79.1493 min\n",
            "----------\n",
            "Epoch 18/29, current lr= 0.001\n",
            "Copied best model weights!\n",
            "train loss: 1.542248, val loss: 1.522606, accuracy: 39.05, time: 83.5882 min\n",
            "----------\n",
            "Epoch 19/29, current lr= 0.001\n",
            "Copied best model weights!\n",
            "train loss: 1.502783, val loss: 1.498424, accuracy: 40.19, time: 88.0264 min\n",
            "----------\n",
            "Epoch 20/29, current lr= 0.001\n",
            "Copied best model weights!\n",
            "train loss: 1.503955, val loss: 1.493501, accuracy: 39.39, time: 92.4644 min\n",
            "----------\n",
            "Epoch 21/29, current lr= 0.001\n",
            "Copied best model weights!\n",
            "train loss: 1.476401, val loss: 1.491829, accuracy: 39.79, time: 96.8993 min\n",
            "----------\n",
            "Epoch 22/29, current lr= 0.001\n",
            "Copied best model weights!\n",
            "train loss: 1.479751, val loss: 1.480063, accuracy: 41.16, time: 101.3336 min\n",
            "----------\n",
            "Epoch 23/29, current lr= 0.001\n",
            "Copied best model weights!\n",
            "train loss: 1.460314, val loss: 1.459419, accuracy: 42.54, time: 105.7674 min\n",
            "----------\n",
            "Epoch 24/29, current lr= 0.001\n",
            "train loss: 1.457395, val loss: 1.472675, accuracy: 42.14, time: 110.1553 min\n",
            "----------\n",
            "Epoch 25/29, current lr= 0.001\n",
            "train loss: 1.440294, val loss: 1.483058, accuracy: 42.29, time: 114.5428 min\n",
            "----------\n",
            "Epoch 26/29, current lr= 0.001\n",
            "Copied best model weights!\n",
            "train loss: 1.414955, val loss: 1.432005, accuracy: 43.33, time: 118.9741 min\n",
            "----------\n",
            "Epoch 27/29, current lr= 0.001\n",
            "train loss: 1.397988, val loss: 1.439973, accuracy: 43.81, time: 123.3624 min\n",
            "----------\n",
            "Epoch 28/29, current lr= 0.001\n",
            "Copied best model weights!\n",
            "train loss: 1.384499, val loss: 1.408282, accuracy: 44.79, time: 127.7893 min\n",
            "----------\n",
            "Epoch 29/29, current lr= 0.001\n",
            "train loss: 1.342067, val loss: 1.470218, accuracy: 43.81, time: 132.1738 min\n",
            "----------\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 573
        },
        "id": "_UheDUSNveX3",
        "outputId": "e9e0d0b3-70a2-41c6-904e-fcabf5ace3f1"
      },
      "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": 15,
      "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": "WccdY46jwBDj"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}