kagglecode.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "kagglecode.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyMDAkjyZ9lz60eN0reqwmEp",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/andrew-miao/CS480-680-Project/blob/main/kagglecode.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Q9FXIBjBGI8S"
      },
      "source": [
        "All code can run on the colab pro. In this Kaggle competition, I propose a new model NewTransformer to solve this task. Additionally, two other ImageText models (use image and text simutaneous), VGGText and MobileRCNN are combined with four interesting models. More details I will update in my github. [link text](https://github.com/andrew-miao/cs680-kaggle).\n",
        "\n",
        "The directory should be as in the folllowing:\n",
        "\n",
        "-kagglecode.ipynb\n",
        "\n",
        "-drive (I mean google drive)\n",
        "\n",
        "-drive/MyDrive/uw-cs480-fall20.zip"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mYcbjsZyIqtf"
      },
      "source": [
        "Dependencies:\n",
        "\n",
        "NLTK\n",
        "\n",
        "AdaBelief (the install command is in the notebook)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6nnhSoV3I4aM"
      },
      "source": [
        "GPU information: NIVIA P100"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JiRBpCLagT2C",
        "outputId": "d056a6d2-4f7e-4e39-a75a-f89a094c6408"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5ed4s_Y7ggq1",
        "outputId": "b964522c-ad6e-4d94-e035-d34a8c209c1d"
      },
      "source": [
        "!unzip 'drive/MyDrive/uw-cs480-fall20.zip'"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n",
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          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mgXy0lHggjln",
        "outputId": "4a20f30f-9574-45bd-8283-d9f4438482c4"
      },
      "source": [
        "pip install adabelief-pytorch==0.1.0"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting adabelief-pytorch==0.1.0\n",
            "  Downloading https://files.pythonhosted.org/packages/e2/dd/4c09c94513bcef38de13d82e9c05478e3f6856c97c753693990d29808c63/adabelief_pytorch-0.1.0-py3-none-any.whl\n",
            "Collecting colorama>=0.4.0\n",
            "  Downloading https://files.pythonhosted.org/packages/44/98/5b86278fbbf250d239ae0ecb724f8572af1c91f4a11edf4d36a206189440/colorama-0.4.4-py2.py3-none-any.whl\n",
            "Requirement already satisfied: torch>=0.4.0 in /usr/local/lib/python3.6/dist-packages (from adabelief-pytorch==0.1.0) (1.7.0+cu101)\n",
            "Requirement already satisfied: tabulate>=0.7 in /usr/local/lib/python3.6/dist-packages (from adabelief-pytorch==0.1.0) (0.8.7)\n",
            "Requirement already satisfied: future in /usr/local/lib/python3.6/dist-packages (from torch>=0.4.0->adabelief-pytorch==0.1.0) (0.16.0)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.6/dist-packages (from torch>=0.4.0->adabelief-pytorch==0.1.0) (1.18.5)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.6/dist-packages (from torch>=0.4.0->adabelief-pytorch==0.1.0) (3.7.4.3)\n",
            "Requirement already satisfied: dataclasses in /usr/local/lib/python3.6/dist-packages (from torch>=0.4.0->adabelief-pytorch==0.1.0) (0.8)\n",
            "Installing collected packages: colorama, adabelief-pytorch\n",
            "Successfully installed adabelief-pytorch-0.1.0 colorama-0.4.4\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VfzIeQQXgpDc",
        "outputId": "c5c23874-6db1-47ef-c70d-48ac0cfc0cc9"
      },
      "source": [
        "%matplotlib inline\n",
        "import os\n",
        "import torch\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from torch.utils.data import Dataset, DataLoader, random_split\n",
        "from torchvision import transforms, utils\n",
        "from matplotlib.pyplot import imshow\n",
        "import matplotlib.pyplot as plt\n",
        "from PIL import Image\n",
        "import spacy\n",
        "import re\n",
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "import torch.optim as optim\n",
        "from adabelief_pytorch import AdaBelief\n",
        "import nltk\n",
        "nltk.download('punkt')\n",
        "from nltk.tokenize import word_tokenize\n",
        "import math\n",
        "import torchvision.models as models"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]   Package punkt is already up-to-date!\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SUr5b3TNgsyx"
      },
      "source": [
        "train_csv = pd.read_csv('./uw-cs480-fall20/train.csv')\n",
        "test_csv = pd.read_csv('./uw-cs480-fall20/test.csv')\n",
        "train_csv['text'] = train_csv['season'] + ' ' + train_csv['usage'] + ' ' + train_csv['noisyTextDescription']\n",
        "test_csv['text'] = test_csv['season'] + ' ' + test_csv['usage'] + ' ' + test_csv['noisyTextDescription']\n",
        "# Convert category to number\n",
        "cate2num = {}\n",
        "num2cate = {}\n",
        "count = 0\n",
        "for category in train_csv['category']:\n",
        "    if category not in cate2num:\n",
        "        cate2num[category] = count\n",
        "        num2cate[count] = category\n",
        "        count += 1\n",
        "\n",
        "train_size = int(0.8 * len(train_csv))\n",
        "val_size = int(0.1 * len(train_csv))\n",
        "\n",
        "np.random.seed(42)\n",
        "random_idx = np.random.permutation(len(train_csv))\n",
        "tmp_train_csv = train_csv.iloc[random_idx[:train_size]]\n",
        "tmp_val_csv = train_csv.iloc[random_idx[train_size:train_size + val_size]]\n",
        "tmp_test_csv = train_csv.iloc[random_idx[train_size + val_size:]]\n",
        "tmp_final_train_csv = train_csv.iloc[random_idx[:train_size + val_size]]\n",
        "\n",
        "tmp_train_csv.to_csv('tmp_train.csv', index=False)\n",
        "tmp_val_csv.to_csv('tmp_val.csv', index=False)\n",
        "tmp_test_csv.to_csv('tmp_test.csv', index=False)\n",
        "tmp_final_train_csv.to_csv('tmp_final_train.csv', index=False)"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e4p3QtzNoM-c"
      },
      "source": [
        "stopwords = ['nike', 'adidas', 'puma', 'playboy', 'fila', 'french', 'femella', \n",
        "             'police', 'q', 'casio', 'doodle', 'tokyo', 'rasasi', 'batman', 'ap',\n",
        "             'lino', 'perros', 'wrangler', 'rocia', 'baggit', 'paris', 'hilton',\n",
        "             'elle', 'vii']"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mWJTlFMToM8J"
      },
      "source": [
        "from nltk.stem import PorterStemmer\n",
        "\n",
        "def denoiseTokenize(sentence):\n",
        "    sentence = re.sub(r\"[\\*\\\"“”\\n\\\\…\\+\\/\\=\\(\\)‘•:\\[\\]\\|’\\!;><]\", \" \", str(sentence))\n",
        "    sentence = re.sub(r\"[ ]+\", \" \", sentence)\n",
        "    sentence = re.sub(r\"\\!+\", \"!\", sentence)\n",
        "    sentence = re.sub(r\"\\,+\", \",\", sentence)\n",
        "    sentence = re.sub(r\"\\?+\", \"?\", sentence)\n",
        "    sentence = re.sub(r\"\\&+\", \" \", sentence)\n",
        "    sentence = re.sub(r\"\\'s\", \" \", sentence)\n",
        "    sentence = sentence.lower()\n",
        "    \n",
        "    ps = PorterStemmer()\n",
        "    tokens = []\n",
        "    for token in word_tokenize(sentence):\n",
        "        no_add = False\n",
        "        if token != ' ':\n",
        "            for i in range(10):\n",
        "                if str(i) in token:\n",
        "                    no_add = True\n",
        "            if not no_add and token not in stopwords:\n",
        "                token = ps.stem(token)\n",
        "                tokens.append(token)\n",
        "    return tokens"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "voA756efoM6Q"
      },
      "source": [
        "def statsCorpus(csv_file, train=True):\n",
        "    corpus = [None] * len(csv_file)\n",
        "    stats = {}\n",
        "    for i in range(len(csv_file)):\n",
        "        sentence = csv_file['text'].iloc[i]\n",
        "        corpus[i] = denoiseTokenize(sentence)\n",
        "        for token in corpus[i]:\n",
        "            if token in stats:\n",
        "                stats[token] += 1\n",
        "            else:\n",
        "                stats[token] = 1\n",
        "    \n",
        "    if not train:\n",
        "        return corpus\n",
        "    \n",
        "    return corpus, stats"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UKC4uRPMoM3Z"
      },
      "source": [
        "def reconstructCorpus(csv_file, train=True, stats=None):\n",
        "    if not train and stats is None:\n",
        "        raise ValueError('val/test file should has stats')\n",
        "\n",
        "    if stats is None:\n",
        "        corpus, stats = statsCorpus(csv_file)\n",
        "    else:\n",
        "        corpus = statsCorpus(csv_file, train=False)\n",
        "\n",
        "    for i in range(len(csv_file)):\n",
        "        sentence = ''\n",
        "        for token in corpus[i]:\n",
        "            if token in stats:\n",
        "                if stats[token] > 2:\n",
        "                    sentence += token + ' '\n",
        "            \n",
        "        csv_file['text'].iloc[i] = sentence\n",
        "    \n",
        "    if not train:\n",
        "        return csv_file\n",
        "        \n",
        "    return csv_file, stats"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oEFusV2BoM0l",
        "outputId": "13244faa-0967-4fd3-f7cd-facae280a6a0"
      },
      "source": [
        "tmp_train_csv, train_stats = reconstructCorpus(tmp_train_csv)\n",
        "tmp_val_csv = reconstructCorpus(tmp_val_csv, train=False, stats=train_stats)\n",
        "tmp_test_csv = reconstructCorpus(tmp_test_csv, train=False, stats=train_stats)\n",
        "test_csv = reconstructCorpus(test_csv, train=False, stats=train_stats)"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/pandas/core/indexing.py:670: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  iloc._setitem_with_indexer(indexer, value)\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:17: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "id": "9fb54Iu6oMxy",
        "outputId": "9879a8c5-d257-4982-d70f-996cf80e5088"
      },
      "source": [
        "tmp_train_csv"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th>category</th>\n",
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              "    <tr>\n",
              "      <th>383</th>\n",
              "      <td>44183</td>\n",
              "      <td>Topwear</td>\n",
              "      <td>Women</td>\n",
              "      <td>Navy Blue</td>\n",
              "      <td>Fall</td>\n",
              "      <td>Casual</td>\n",
              "      <td>Arrow Woman Elephant Baroque Navy Vomero-5 Swe...</td>\n",
              "      <td>fall casual arrow woman eleph baroqu navi swea...</td>\n",
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              "    <tr>\n",
              "      <th>6186</th>\n",
              "      <td>19264</td>\n",
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              "      <td>Lino Perros LP226-C3 VII Handbag</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>8877</th>\n",
              "      <td>10613</td>\n",
              "      <td>Jewellery</td>\n",
              "      <td>Women</td>\n",
              "      <td>Gold</td>\n",
              "      <td>Summer</td>\n",
              "      <td>Casual</td>\n",
              "      <td>Lucera Gold Plated Earrings</td>\n",
              "      <td>summer casual lucera gold plate ear</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>843</th>\n",
              "      <td>6068</td>\n",
              "      <td>Flip Flops</td>\n",
              "      <td>Men</td>\n",
              "      <td>Black</td>\n",
              "      <td>Summer</td>\n",
              "      <td>Casual</td>\n",
              "      <td>Puma Men Glam Art Black Flip Flops</td>\n",
              "      <td>summer casual men glam art black flip flop</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9619</th>\n",
              "      <td>34484</td>\n",
              "      <td>Topwear</td>\n",
              "      <td>Men</td>\n",
              "      <td>Navy Blue</td>\n",
              "      <td>Fall</td>\n",
              "      <td>Casual</td>\n",
              "      <td>Flame Delight Solid Navy Blue Sweatshirts</td>\n",
              "      <td>fall casual flame solid navi blue sweatshirt</td>\n",
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              "    <tr>\n",
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              "      <td>5974</td>\n",
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              "      <td>Black</td>\n",
              "      <td>Summer</td>\n",
              "      <td>Formal</td>\n",
              "      <td>SHE-3801L-7ADR Men Black Leather Formal Shoes</td>\n",
              "      <td>summer formal men black leather formal shoe</td>\n",
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              "      <th>4763</th>\n",
              "      <td>7655</td>\n",
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              "      <td>Black</td>\n",
              "      <td>Spring</td>\n",
              "      <td>Casual</td>\n",
              "      <td>Paris Hilton Women Heiress Thrill</td>\n",
              "      <td>spring casual women heiress thrill</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9062</th>\n",
              "      <td>16251</td>\n",
              "      <td>Watches</td>\n",
              "      <td>Men</td>\n",
              "      <td>Black</td>\n",
              "      <td>Winter</td>\n",
              "      <td>Casual</td>\n",
              "      <td>SKAGEN Strike Banks Black Dial Watch 861XLSXB</td>\n",
              "      <td>winter casual skagen strike bank black dial wa...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1874</th>\n",
              "      <td>15681</td>\n",
              "      <td>Bags</td>\n",
              "      <td>Women</td>\n",
              "      <td>Black</td>\n",
              "      <td>Summer</td>\n",
              "      <td>Casual</td>\n",
              "      <td>French Connection Women Black Beaded Clutch</td>\n",
              "      <td>summer casual connect women black bead clutch</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17616</th>\n",
              "      <td>49705</td>\n",
              "      <td>Belts</td>\n",
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              "      <td>Black</td>\n",
              "      <td>Summer</td>\n",
              "      <td>Formal</td>\n",
              "      <td>Arrow Men Black &amp; Brown BTS Belt</td>\n",
              "      <td>summer formal arrow men black brown bt belt</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>17301 rows × 8 columns</p>\n",
              "</div>"
            ],
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              "          id  ...                                               text\n",
              "383    44183  ...  fall casual arrow woman eleph baroqu navi swea...\n",
              "6186   19264  ...                             summer casual handbag \n",
              "8877   10613  ...               summer casual lucera gold plate ear \n",
              "843     6068  ...        summer casual men glam art black flip flop \n",
              "9619   34484  ...      fall casual flame solid navi blue sweatshirt \n",
              "...      ...  ...                                                ...\n",
              "10234   5974  ...       summer formal men black leather formal shoe \n",
              "4763    7655  ...                spring casual women heiress thrill \n",
              "9062   16251  ...  winter casual skagen strike bank black dial wa...\n",
              "1874   15681  ...     summer casual connect women black bead clutch \n",
              "17616  49705  ...       summer formal arrow men black brown bt belt \n",
              "\n",
              "[17301 rows x 8 columns]"
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      "cell_type": "code",
      "metadata": {
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          "base_uri": "https://localhost:8080/",
          "height": 419
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        "id": "CCGPbj56q5xM",
        "outputId": "0f99d222-13d5-4701-a5ba-07ea98a35228"
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        {
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              "      <td>Casual</td>\n",
              "      <td>Fogg Fatale Marco Mini</td>\n",
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              "      <td>Casual</td>\n",
              "      <td>BGSW02 Metro to Five Flawless Creme Shell Compact</td>\n",
              "      <td>spring casual to five flawless creme shell com...</td>\n",
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              "      <td>750 Women SD Taj Purple Wallet</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>6548</th>\n",
              "      <td>3955</td>\n",
              "      <td>Belts</td>\n",
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              "      <td>Summer</td>\n",
              "      <td>Casual</td>\n",
              "      <td>Fossil Pin Multi Coloured Quilted Squares Belt</td>\n",
              "      <td>summer casual fossil pin multi colour quilt sq...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4481</th>\n",
              "      <td>2661</td>\n",
              "      <td>Shoes</td>\n",
              "      <td>Women</td>\n",
              "      <td>Turquoise Blue</td>\n",
              "      <td>Winter</td>\n",
              "      <td>Casual</td>\n",
              "      <td>Catwalk Women Ballerina Citi Casual Shoe</td>\n",
              "      <td>winter casual catwalk women ballerina citi cas...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3716</th>\n",
              "      <td>43364</td>\n",
              "      <td>Sandal</td>\n",
              "      <td>Men</td>\n",
              "      <td>Black</td>\n",
              "      <td>Fall</td>\n",
              "      <td>Casual</td>\n",
              "      <td>Lee Cooper Men N9827PP02 Black Sandals</td>\n",
              "      <td>fall casual lee cooper men black sandal</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2162 rows × 8 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "          id  ...                                               text\n",
              "13682  46596  ...                    summer casual men strip kolleg \n",
              "20854  17563  ...     summer casual domyo flurosc her black t-shirt \n",
              "5831   15335  ...  summer casual gini textur joni kiss pretti blo...\n",
              "19530  28321  ...                           spring casual fogg mini \n",
              "191    29129  ...  spring casual to five flawless creme shell com...\n",
              "...      ...  ...                                                ...\n",
              "4837   32487  ...    summer ethnic finish dicolor black white kurta \n",
              "18677  59555  ...           winter casual women sd taj purpl wallet \n",
              "6548    3955  ...  summer casual fossil pin multi colour quilt sq...\n",
              "4481    2661  ...  winter casual catwalk women ballerina citi cas...\n",
              "3716   43364  ...           fall casual lee cooper men black sandal \n",
              "\n",
              "[2162 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
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        "id": "xM0JRUVtq5pY",
        "outputId": "b6449e7b-beab-4bd5-e9fe-2958f099a2d5"
      },
      "source": [
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      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <td>Colorbar Os Sweep Mulberry Lipstick Kapteren</td>\n",
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              "      <td>Mont Blanc Shine Zephyral Boss</td>\n",
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              "      <th>9956</th>\n",
              "      <td>49857</td>\n",
              "      <td>Shoes</td>\n",
              "      <td>Men</td>\n",
              "      <td>Grey</td>\n",
              "      <td>Fall</td>\n",
              "      <td>Casual</td>\n",
              "      <td>Flying Machine Men Grey Casual Shoes</td>\n",
              "      <td>fall casual fli machin men grey casual shoe</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1549</th>\n",
              "      <td>55845</td>\n",
              "      <td>Bottomwear</td>\n",
              "      <td>Girls</td>\n",
              "      <td>Blue</td>\n",
              "      <td>Summer</td>\n",
              "      <td>Casual</td>\n",
              "      <td>Gini and A555 Girls Blue Shorts</td>\n",
              "      <td>summer casual gini and girl blue short</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14818</th>\n",
              "      <td>46890</td>\n",
              "      <td>Topwear</td>\n",
              "      <td>Men</td>\n",
              "      <td>White</td>\n",
              "      <td>Summer</td>\n",
              "      <td>Casual</td>\n",
              "      <td>UCB Men Ben Logo Chest White Shirt</td>\n",
              "      <td>summer casual ucb men ben logo chest white shirt</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11964</th>\n",
              "      <td>16939</td>\n",
              "      <td>Wallets</td>\n",
              "      <td>Men</td>\n",
              "      <td>Brown</td>\n",
              "      <td>Summer</td>\n",
              "      <td>Casual</td>\n",
              "      <td>Hanawi Men Brown Wanigan</td>\n",
              "      <td>summer casual hanawi men brown wanigan</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21575</th>\n",
              "      <td>50422</td>\n",
              "      <td>Bottomwear</td>\n",
              "      <td>Women</td>\n",
              "      <td>Pink</td>\n",
              "      <td>Fall</td>\n",
              "      <td>Casual</td>\n",
              "      <td>Forever New Women Blossom M075J002Y Bad Skirt</td>\n",
              "      <td>fall casual forev new women blossom skirt</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5390</th>\n",
              "      <td>8967</td>\n",
              "      <td>Topwear</td>\n",
              "      <td>Women</td>\n",
              "      <td>Blue</td>\n",
              "      <td>Summer</td>\n",
              "      <td>Casual</td>\n",
              "      <td>Tokyo Talkies Women Blue Mousie</td>\n",
              "      <td>summer casual talki women blue</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>860</th>\n",
              "      <td>44917</td>\n",
              "      <td>Topwear</td>\n",
              "      <td>Women</td>\n",
              "      <td>Multi</td>\n",
              "      <td>Summer</td>\n",
              "      <td>Ethnic</td>\n",
              "      <td>Diva Women Multi Bandana Kurta</td>\n",
              "      <td>summer ethnic diva women multi kurta</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15795</th>\n",
              "      <td>6274</td>\n",
              "      <td>Topwear</td>\n",
              "      <td>Men</td>\n",
              "      <td>Blue</td>\n",
              "      <td>Summer</td>\n",
              "      <td>Casual</td>\n",
              "      <td>Scullers Turbo Blue Cboar Supermantra</td>\n",
              "      <td>summer casual sculler turbo blue</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2164 rows × 8 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "          id  ...                                               text\n",
              "10837  46086  ...       spring casual colorbar os lipstick kapteren \n",
              "6140   12322  ...               spring ethnic mont blanc shine boss \n",
              "9956   49857  ...       fall casual fli machin men grey casual shoe \n",
              "1549   55845  ...            summer casual gini and girl blue short \n",
              "14818  46890  ...  summer casual ucb men ben logo chest white shirt \n",
              "...      ...  ...                                                ...\n",
              "11964  16939  ...            summer casual hanawi men brown wanigan \n",
              "21575  50422  ...         fall casual forev new women blossom skirt \n",
              "5390    8967  ...                    summer casual talki women blue \n",
              "860    44917  ...              summer ethnic diva women multi kurta \n",
              "15795   6274  ...                  summer casual sculler turbo blue \n",
              "\n",
              "[2164 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "id": "XjzA-OC7oMuy",
        "outputId": "7abbda8e-3b0a-422c-c201-d2be775a824e"
      },
      "source": [
        "test_csv"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "    }\n",
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              "        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>gender</th>\n",
              "      <th>baseColour</th>\n",
              "      <th>season</th>\n",
              "      <th>usage</th>\n",
              "      <th>noisyTextDescription</th>\n",
              "      <th>text</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>26266</td>\n",
              "      <td>Men</td>\n",
              "      <td>Black</td>\n",
              "      <td>Summer</td>\n",
              "      <td>Casual</td>\n",
              "      <td>Chromozome Men Black Fashion Vest</td>\n",
              "      <td>summer casual chromozom men black fashion vest</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>22134</td>\n",
              "      <td>Women</td>\n",
              "      <td>Green</td>\n",
              "      <td>Summer</td>\n",
              "      <td>Casual</td>\n",
              "      <td>Elle Women Green Color Clash Top</td>\n",
              "      <td>summer casual women green color clash top</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>28358</td>\n",
              "      <td>Women</td>\n",
              "      <td>Black</td>\n",
              "      <td>Winter</td>\n",
              "      <td>Casual</td>\n",
              "      <td>Baggit Women Chotu Mayur Black Palms</td>\n",
              "      <td>winter casual women chotu mayur black palm</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>15554</td>\n",
              "      <td>Men</td>\n",
              "      <td>Black</td>\n",
              "      <td>Fall</td>\n",
              "      <td>Casual</td>\n",
              "      <td>Greensboro Colors Of Ap Men Latnam Black Casua...</td>\n",
              "      <td>fall casual color of men latnam black casual s...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>53408</td>\n",
              "      <td>Women</td>\n",
              "      <td>Purple</td>\n",
              "      <td>Summer</td>\n",
              "      <td>Casual</td>\n",
              "      <td>Fish White Smooth daddy (SH100)</td>\n",
              "      <td>summer casual white smooth daddi</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21623</th>\n",
              "      <td>39737</td>\n",
              "      <td>Men</td>\n",
              "      <td>Brown</td>\n",
              "      <td>Winter</td>\n",
              "      <td>Casual</td>\n",
              "      <td>Park ClubLife 602 Helium Sunglasses</td>\n",
              "      <td>winter casual park sunglass</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21624</th>\n",
              "      <td>57477</td>\n",
              "      <td>Women</td>\n",
              "      <td>Black</td>\n",
              "      <td>Summer</td>\n",
              "      <td>Casual</td>\n",
              "      <td>Vero Moda Women Black Rey Shift Dress</td>\n",
              "      <td>summer casual vero moda women black rey shift ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21625</th>\n",
              "      <td>22312</td>\n",
              "      <td>Men</td>\n",
              "      <td>White</td>\n",
              "      <td>Fall</td>\n",
              "      <td>Sports</td>\n",
              "      <td>Nike Men Hell With White Tshirts</td>\n",
              "      <td>fall sport men hell with white tshirt</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21626</th>\n",
              "      <td>54105</td>\n",
              "      <td>Women</td>\n",
              "      <td>Red</td>\n",
              "      <td>Spring</td>\n",
              "      <td>Casual</td>\n",
              "      <td>Lakme Holi to Five Day Apricot Nectar Lip K9</td>\n",
              "      <td>spring casual lakm to five day lip</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21627</th>\n",
              "      <td>14080</td>\n",
              "      <td>Unisex</td>\n",
              "      <td>Red</td>\n",
              "      <td>Summer</td>\n",
              "      <td>Casual</td>\n",
              "      <td>Percorso Unisex Red Grit Backpack</td>\n",
              "      <td>summer casual unisex red backpack</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>21628 rows × 7 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "          id  ...                                               text\n",
              "0      26266  ...    summer casual chromozom men black fashion vest \n",
              "1      22134  ...         summer casual women green color clash top \n",
              "2      28358  ...        winter casual women chotu mayur black palm \n",
              "3      15554  ...  fall casual color of men latnam black casual s...\n",
              "4      53408  ...                  summer casual white smooth daddi \n",
              "...      ...  ...                                                ...\n",
              "21623  39737  ...                       winter casual park sunglass \n",
              "21624  57477  ...  summer casual vero moda women black rey shift ...\n",
              "21625  22312  ...             fall sport men hell with white tshirt \n",
              "21626  54105  ...                spring casual lakm to five day lip \n",
              "21627  14080  ...                 summer casual unisex red backpack \n",
              "\n",
              "[21628 rows x 7 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qqwzmocEgunC"
      },
      "source": [
        "class Tokenize(object):\n",
        "    def __init__(object):\n",
        "        pass\n",
        "    \n",
        "    def tokenizer(self, sentence):\n",
        "        tokens = []\n",
        "        for tok in word_tokenize(sentence):\n",
        "            if tok != ' ' and tok != ',' and tok != '.':\n",
        "                tokens.append(tok)\n",
        "        # return list(nltk.bigrams(tokens))\n",
        "        return tokens"
      ],
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LGRkzBSOgwp4"
      },
      "source": [
        "def stats_sentence(sentence, vocab2num, count, max_seq):\n",
        "    for i in range(len(sentence)):\n",
        "        if sentence[i] not in vocab2num:\n",
        "            vocab2num[sentence[i]] = count\n",
        "            count += 1\n",
        "\n",
        "    max_seq = max(len(sentence), max_seq)\n",
        "    return vocab2num, count, max_seq"
      ],
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "L3r5k8LyhCAH"
      },
      "source": [
        "def stats_data(data, train=True):\n",
        "    raw_src_data = [None] * len(data)\n",
        "    src_tokenize = Tokenize()\n",
        "    src_vocab2num = {'<unk>': 0, '<pad>': 1}\n",
        "    src_count = 2\n",
        "    max_src_seq = 0\n",
        "    for i in range(len(data)):\n",
        "        raw_src_data[i] = src_tokenize.tokenizer(data['text'].iloc[i])\n",
        "        if train:\n",
        "            src_vocab2num, src_count, max_src_seq = stats_sentence(raw_src_data[i], src_vocab2num, src_count, max_src_seq)\n",
        "\n",
        "    if not train:\n",
        "        return raw_src_data\n",
        "\n",
        "    return raw_src_data, src_vocab2num, max_src_seq"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wD3zMtDyhETK"
      },
      "source": [
        "class Products(Dataset):\n",
        "    def __init__(self, csv_file, raw_src, src_vocab2num, max_seq,\n",
        "                 cate2num=cate2num, root_dir='./uw-cs480-fall20/suffled-images/shuffled-images/', \n",
        "                 transform=None, real_test=False):\n",
        "        self.product_csv = pd.read_csv(csv_file)\n",
        "        self.raw_src = raw_src\n",
        "        self.src_vocab2num = src_vocab2num\n",
        "        self.max_seq = max_seq\n",
        "        self.cate2num = cate2num\n",
        "        self.root_dir = root_dir\n",
        "        self.transform = transform\n",
        "        self.real_test = real_test\n",
        "        self.src_data = torch.ones(len(self.raw_src), self.max_seq, dtype=torch.long)\n",
        "        self.build_text_tensor()\n",
        "        \n",
        "    def  build_text_tensor(self):\n",
        "        for i in range(len(self.raw_src)):\n",
        "            for j in range(min(len(self.raw_src[i]), max_seq)):\n",
        "                word = self.raw_src[i][j]\n",
        "                if word in self.src_vocab2num:\n",
        "                    self.src_data[i][j] = self.src_vocab2num[word]\n",
        "                else:\n",
        "                    self.src_data[i][j] = self.src_vocab2num['<unk>']\n",
        "\n",
        "    def __len__(self):\n",
        "        return len(self.product_csv)\n",
        "    \n",
        "    def __getitem__(self, idx):\n",
        "        if torch.is_tensor(idx):\n",
        "            idx = idx.tolist()\n",
        "        \n",
        "        img_path = str(self.product_csv['id'].iloc[idx]) + '.jpg'\n",
        "        img_path = os.path.join(self.root_dir, img_path)\n",
        "        image = Image.open(img_path).convert('RGB')\n",
        "        src_text = self.src_data[idx]\n",
        "        if not self.real_test:\n",
        "            category = self.product_csv['category'].iloc[idx]\n",
        "            label = self.cate2num[category]\n",
        "        if self.transform:\n",
        "            image = self.transform(image)\n",
        "        if self.real_test:\n",
        "            return image, src_text\n",
        "\n",
        "        return image, src_text, label"
      ],
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eoPKVbe5hGvN"
      },
      "source": [
        "raw_train_data, train_vocab2num, max_seq= stats_data(tmp_train_csv)\n",
        "raw_val_data = stats_data(tmp_val_csv, train=False)\n",
        "raw_test_data = stats_data(tmp_test_csv, train=False)\n",
        "raw_real_test_data = stats_data(test_csv, train=False)"
      ],
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DMI6WtFuhIj2"
      },
      "source": [
        "normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])\n",
        "train_transform = transforms.Compose([transforms.Resize(256),\n",
        "                                      transforms.RandomResizedCrop(224), \n",
        "                                      transforms.RandomHorizontalFlip(), \n",
        "                                      transforms.ToTensor(),\n",
        "                                      normalize])\n",
        "test_transform = transforms.Compose([transforms.Resize(256),\n",
        "                                     transforms.CenterCrop(224),\n",
        "                                     transforms.ToTensor(),\n",
        "                                     normalize])"
      ],
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FmMahwRChKtY"
      },
      "source": [
        "train = Products('tmp_train.csv', raw_train_data, train_vocab2num, max_seq, transform=train_transform)\n",
        "val = Products('tmp_val.csv', raw_val_data, train_vocab2num, max_seq, transform=test_transform)\n",
        "test = Products('tmp_test.csv', raw_test_data, train_vocab2num, max_seq, transform=test_transform)\n",
        "real_test = Products('./uw-cs480-fall20/test.csv', raw_real_test_data, train_vocab2num, max_seq, transform=test_transform, real_test=True)"
      ],
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Opto5BEaok4A",
        "outputId": "5f3bb666-e533-4090-f666-2aace9e2557c"
      },
      "source": [
        "max_seq"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "14"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dt8iqkixhMte"
      },
      "source": [
        "batch_size = 50\n",
        "train_loader = DataLoader(train, batch_size=batch_size, shuffle=True)\n",
        "val_loader = DataLoader(val, batch_size=10, shuffle=False)\n",
        "test_loader = DataLoader(test, batch_size=10, shuffle=False)\n",
        "real_test_loader = DataLoader(real_test, batch_size=10, shuffle=False)"
      ],
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "reDD4QyNhPcj"
      },
      "source": [
        "def conv3x3(in_planes, out_planes, stride=1, padding=1, dilation=1):\n",
        "    return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, padding=padding, dilation=dilation, bias=False)\n",
        "\n",
        "def conv1x1(in_planes, out_planes, stride=1):\n",
        "    return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False)"
      ],
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e1AIKhTUhRxy"
      },
      "source": [
        "class ResBasicBlock(nn.Module):    \n",
        "    def __init__(self, in_planes, out_planes, stride=1, downsample=None, norm_layer=None):\n",
        "        super(ResBasicBlock, self).__init__()\n",
        "\n",
        "        if norm_layer is None:\n",
        "            norm_layer = nn.BatchNorm2d\n",
        "        self.conv1 = conv3x3(in_planes, out_planes, stride)\n",
        "        self.bn1 = norm_layer(out_planes)\n",
        "        self.conv2 = conv3x3(out_planes, out_planes)\n",
        "        self.bn2 = norm_layer(out_planes)\n",
        "        self.downsample = downsample\n",
        "        self.relu = nn.ReLU(inplace=True)\n",
        "    \n",
        "    def forward(self, x):\n",
        "        identity = x\n",
        "        out = self.conv1(x)\n",
        "        out = self.bn1(out)\n",
        "        out = self.relu(out)\n",
        "        out = self.conv2(out)\n",
        "        out = self.bn2(out)\n",
        "        if self.downsample is not None:\n",
        "            identity = self.downsample(x)\n",
        "        out += identity\n",
        "        out = self.relu(out)\n",
        "        return out"
      ],
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jjvfVC7KhTqd"
      },
      "source": [
        "class ResNet(nn.Module):\n",
        "    def __init__(self, block, layers, norm_layer=None, expansion=1):\n",
        "        super(ResNet, self).__init__()\n",
        "        if norm_layer is None:\n",
        "            self.norm_layer = nn.BatchNorm2d\n",
        "        else:\n",
        "            self.norm_layer = norm_layer\n",
        "        self.expansion = expansion\n",
        "        self.in_planes = 64\n",
        "        self.conv1 = nn.Conv2d(3, self.in_planes, 7, stride=2, padding=3, bias=False)\n",
        "        self.bn1 = self.norm_layer(self.in_planes)\n",
        "        self.relu = nn.ReLU(inplace=True)\n",
        "        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)\n",
        "        self.layer1 = self.makelayer(block, 64, layers[0], stride=1)\n",
        "        self.layer2 = self.makelayer(block, 128, layers[1], stride=2)\n",
        "        self.layer3 = self.makelayer(block, 256, layers[2], stride=2)\n",
        "        self.layer4 = self.makelayer(block, 512, layers[3], stride=2)\n",
        "        self.globalAvg = nn.AdaptiveAvgPool2d((1, 1))\n",
        "    \n",
        "    def makelayer(self, block, planes, num_blocks, stride=1):\n",
        "        downsample = None\n",
        "        if stride != 1 or self.in_planes != planes * self.expansion:\n",
        "            downsample = nn.Sequential(conv1x1(self.in_planes, planes * self.expansion, stride),\n",
        "                                       self.norm_layer(planes * self.expansion))\n",
        "        layer = [block(self.in_planes, planes, stride=stride, downsample=downsample, norm_layer=self.norm_layer)]\n",
        "        self.in_planes = planes * self.expansion\n",
        "        for i in range(1, num_blocks):\n",
        "            layer.append(block(self.in_planes, planes, norm_layer=self.norm_layer))\n",
        "        return nn.Sequential(*layer)\n",
        "    \n",
        "    def forward(self, x):\n",
        "        out = self.conv1(x)\n",
        "        out = self.bn1(out)\n",
        "        out = self.relu(out)\n",
        "        out = self.maxpool(out)\n",
        "        out = self.layer1(out)\n",
        "        out = self.layer2(out)\n",
        "        out = self.layer3(out)\n",
        "        out = self.layer4(out)\n",
        "        out = self.globalAvg(out)\n",
        "        out = torch.flatten(out, 1)\n",
        "        return out"
      ],
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EkIrKO4ehVuR"
      },
      "source": [
        "import time\n",
        "\n",
        "def timeSince(since):\n",
        "    now = time.time()\n",
        "    s = now - since\n",
        "    m = s // 60\n",
        "    s -= m * 60\n",
        "    return '%dm %ds' % (m, s)"
      ],
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "j5fEo9z1EJKF"
      },
      "source": [
        "class ImageModel(nn.Module):\n",
        "    def __init__(self, pretrain_model, output_size=27):\n",
        "        super(ImageModel, self).__init__()\n",
        "\n",
        "        self.pretrain_model = pretrain_model\n",
        "        self.fc1 = nn.Linear(1000, 256)\n",
        "        self.fc2 = nn.Linear(256, output_size)\n",
        "        self.relu = nn.ReLU()\n",
        "\n",
        "    def forward(self, image):\n",
        "        output = self.pretrain_model(image)\n",
        "        output = self.relu(self.fc1(output))\n",
        "        return self.fc2(output)"
      ],
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TWHqfQoeYPKp"
      },
      "source": [
        "def evaluateImageModel(model, data, criterion, device):\n",
        "    model.eval()\n",
        "    total_loss = 0.0\n",
        "    correct = 0\n",
        "    total = 0\n",
        "    with torch.no_grad():\n",
        "        for image, _, trg in data:\n",
        "            image, trg = image.to(device), trg.to(device)\n",
        "            output = model(image)\n",
        "            pred = torch.argmax(output, dim=1)\n",
        "            correct += (pred == trg).sum().item()\n",
        "            total += len(trg)\n",
        "            loss = criterion(output, trg)\n",
        "            total_loss += loss.item()\n",
        "\n",
        "    return total_loss / len(data), correct / total"
      ],
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "s5UcC4fZwkRs"
      },
      "source": [
        "def trainImageModel(model, train_data, val_data, n_epochs, criterion, optimizer, device, path, data_aug=0.5):\n",
        "    model.train()\n",
        "    train_acc_list = []\n",
        "    val_acc_list = []\n",
        "    max_acc = None\n",
        "    step = 0\n",
        "    print_every = len(train_data)\n",
        "    image1 = None\n",
        "    noise_data = False\n",
        "    start = time.time()\n",
        "    for epoch in range(n_epochs):\n",
        "        running_loss = 0.0\n",
        "        running_correct = 0\n",
        "        running_total = 0\n",
        "        for image, _, trg in train_data:\n",
        "            optimizer.zero_grad()\n",
        "            step += 1\n",
        "            image, trg = image.to(device),  trg.to(device)\n",
        "            output = None\n",
        "            tmp = np.random.uniform(0, 1, 1)\n",
        "            if tmp < data_aug:\n",
        "                nosie_data = True\n",
        "            else:\n",
        "                noise_data = False\n",
        "            if noise_data:\n",
        "                if image1 is None:\n",
        "                    image1 = image\n",
        "                else:\n",
        "                    if image1.size(0) == image.size(0):\n",
        "                        new_image = 0.05 * image1 + 0.95 * image\n",
        "                    else:\n",
        "                        new_image = 0.05 * image1[:image.size(0)] + 0.95 * image\n",
        "                    image1 = image\n",
        "                    image = torch.cat((image, new_image), dim=0)\n",
        "                    trg = torch.cat((trg, trg), dim=0)\n",
        "            output = model(image)\n",
        "            pred = torch.argmax(output, dim=1)\n",
        "            running_correct += (pred == trg).sum().item()\n",
        "            running_total += len(trg)\n",
        "            loss = criterion(output, trg)\n",
        "            running_loss += loss.item()\n",
        "            loss.backward()\n",
        "            optimizer.step()\n",
        "\n",
        "            if step % print_every == 0:\n",
        "                val_loss, val_acc = evaluateImageModel(model, val_data, criterion, device)\n",
        "                print(('%d/%d (%s) train loss: %.3f, train acc: %.2f%%, val loss: %.3f, val acc: %.2f%%') %\n",
        "                      (epoch + 1, n_epochs, timeSince(start), running_loss / len(train_data), \n",
        "                       100 * running_correct / running_total, val_loss, 100 * val_acc))\n",
        "                train_acc_list.append(running_correct / running_total)\n",
        "                val_acc_list.append(val_acc)\n",
        "                if max_acc is None or max_acc < val_acc:\n",
        "                    if max_acc is None:\n",
        "                        print(('Validation accuracy in first epoch: %.2f%%') % (100 * val_acc))\n",
        "                    else:\n",
        "                        print(('Validation accuracy is increasing: %.2f%% --> %.2f%%') % \n",
        "                              (100 * max_acc, 100 * val_acc))\n",
        "                    max_acc = val_acc\n",
        "                    print('Saving model...')\n",
        "                    torch.save(model, path)\n",
        "                \n",
        "                model.train()\n",
        "                running_loss = 0.0\n",
        "                running_correct = 0\n",
        "                running_total = 0\n",
        "    \n",
        "    return train_acc_list, val_acc_list"
      ],
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "L10VaJF_wmm9",
        "outputId": "872b0efa-0192-4567-eab8-e091e693cabd"
      },
      "source": [
        "import torchvision.models as models\n",
        "\n",
        "model_list = [ImageModel(models.googlenet(pretrained=True)), ImageModel(models.shufflenet_v2_x1_0(pretrained=True))]\n",
        "model_path = ['googlenet.pt', 'shufflenet.pt']\n",
        "model_name = ['GoogleNet', 'ShuffleNet']\n",
        "criterion = nn.CrossEntropyLoss()\n",
        "n_epochs = 50\n",
        "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
        "for i, model in enumerate(model_list):\n",
        "    model.to(device)\n",
        "    path = model_path[i]\n",
        "    optimizer = AdaBelief(model.parameters())\n",
        "    train_acc_list, val_acc_list = trainImageModel(model, train_loader, val_loader,\n",
        "                                                   n_epochs, criterion, optimizer,\n",
        "                                                   device, path, data_aug=0.7)\n",
        "    model = torch.load(path)\n",
        "    _, test_acc = evaluateImageModel(model, test_loader, criterion, device)\n",
        "    print('%s accuracy in test set: %.2f%%' % (model_name[i], 100 * test_acc))\n",
        "\n",
        "model_list = [torch.load('googlenet.pt'), torch.load('shufflenet.pt')]\n",
        "n_epochs = 30\n",
        "for i, model in enumerate(model_list):\n",
        "    model.to(device)\n",
        "    path = model_path[i]\n",
        "    optimizer = optim.SGD(model.parameters(), lr=3e-04, momentum=0.9)\n",
        "    train_acc_list, val_acc_list = trainImageModel(model, train_loader, val_loader,\n",
        "                                                   n_epochs, criterion, optimizer,\n",
        "                                                   device, path, data_aug=0)\n",
        "    model = torch.load(path)\n",
        "    _, test_acc = evaluateImageModel(model, test_loader, criterion, device)\n",
        "    print('%s accuracy in test set: %.2f%%' % (model_name[i], 100 * test_acc))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\u001b[31mPlease check your arguments if you have upgraded adabelief-pytorch from version 0.0.5.\n",
            "\u001b[31mModifications to default arguments:\n",
            "\u001b[31m                           eps  weight_decouple    rectify\n",
            "-----------------------  -----  -----------------  ---------\n",
            "adabelief-pytorch=0.0.5  1e-08  False              False\n",
            "Current version (0.1.0)  1e-16  True               True\n",
            "\u001b[31mFor a complete table of recommended hyperparameters, see\n",
            "\u001b[31mhttps://github.com/juntang-zhuang/Adabelief-Optimizer\n",
            "\u001b[0m\n",
            "Weight decoupling enabled in AdaBelief\n",
            "Rectification enabled in AdaBelief\n",
            "1/50 (1m 38s) train loss: 1.117, train acc: 69.63%, val loss: 0.531, val acc: 83.95%\n",
            "Validation accuracy in first epoch: 83.95%\n",
            "Saving model...\n",
            "2/50 (3m 17s) train loss: 0.757, train acc: 78.75%, val loss: 0.465, val acc: 87.14%\n",
            "Validation accuracy is increasing: 83.95% --> 87.14%\n",
            "Saving model...\n",
            "3/50 (4m 56s) train loss: 0.698, train acc: 79.97%, val loss: 0.470, val acc: 86.68%\n",
            "4/50 (6m 35s) train loss: 0.688, train acc: 80.54%, val loss: 0.537, val acc: 86.82%\n",
            "5/50 (8m 13s) train loss: 0.670, train acc: 80.93%, val loss: 0.515, val acc: 85.89%\n",
            "6/50 (9m 52s) train loss: 0.658, train acc: 81.84%, val loss: 0.422, val acc: 88.39%\n",
            "Validation accuracy is increasing: 87.14% --> 88.39%\n",
            "Saving model...\n",
            "7/50 (11m 30s) train loss: 0.633, train acc: 81.98%, val loss: 0.389, val acc: 89.59%\n",
            "Validation accuracy is increasing: 88.39% --> 89.59%\n",
            "Saving model...\n",
            "8/50 (13m 8s) train loss: 0.603, train acc: 82.69%, val loss: 0.381, val acc: 90.06%\n",
            "Validation accuracy is increasing: 89.59% --> 90.06%\n",
            "Saving model...\n",
            "9/50 (14m 47s) train loss: 0.608, train acc: 82.96%, val loss: 0.420, val acc: 89.13%\n",
            "10/50 (16m 25s) train loss: 0.606, train acc: 82.52%, val loss: 0.377, val acc: 89.45%\n",
            "11/50 (18m 3s) train loss: 0.566, train acc: 83.86%, val loss: 0.435, val acc: 88.25%\n",
            "12/50 (19m 42s) train loss: 0.574, train acc: 83.86%, val loss: 0.503, val acc: 87.14%\n",
            "13/50 (21m 21s) train loss: 0.563, train acc: 84.08%, val loss: 0.339, val acc: 90.89%\n",
            "Validation accuracy is increasing: 90.06% --> 90.89%\n",
            "Saving model...\n",
            "14/50 (23m 0s) train loss: 0.556, train acc: 84.23%, val loss: 0.366, val acc: 90.47%\n",
            "15/50 (24m 39s) train loss: 0.538, train acc: 84.54%, val loss: 0.355, val acc: 91.12%\n",
            "Validation accuracy is increasing: 90.89% --> 91.12%\n",
            "Saving model...\n",
            "16/50 (26m 18s) train loss: 0.528, train acc: 85.08%, val loss: 0.350, val acc: 91.12%\n",
            "17/50 (27m 57s) train loss: 0.516, train acc: 85.94%, val loss: 0.343, val acc: 91.26%\n",
            "Validation accuracy is increasing: 91.12% --> 91.26%\n",
            "Saving model...\n",
            "18/50 (29m 37s) train loss: 0.569, train acc: 83.84%, val loss: 0.342, val acc: 91.40%\n",
            "Validation accuracy is increasing: 91.26% --> 91.40%\n",
            "Saving model...\n",
            "19/50 (31m 16s) train loss: 0.511, train acc: 85.38%, val loss: 0.303, val acc: 92.23%\n",
            "Validation accuracy is increasing: 91.40% --> 92.23%\n",
            "Saving model...\n",
            "20/50 (32m 56s) train loss: 0.539, train acc: 84.78%, val loss: 0.341, val acc: 90.66%\n",
            "21/50 (34m 36s) train loss: 0.492, train acc: 85.97%, val loss: 0.336, val acc: 92.00%\n",
            "22/50 (36m 15s) train loss: 0.479, train acc: 86.78%, val loss: 0.358, val acc: 90.66%\n",
            "23/50 (37m 54s) train loss: 0.477, train acc: 86.01%, val loss: 0.282, val acc: 92.92%\n",
            "Validation accuracy is increasing: 92.23% --> 92.92%\n",
            "Saving model...\n",
            "24/50 (39m 34s) train loss: 0.462, train acc: 86.50%, val loss: 0.314, val acc: 92.32%\n",
            "25/50 (41m 14s) train loss: 0.452, train acc: 86.98%, val loss: 0.384, val acc: 90.89%\n",
            "26/50 (42m 53s) train loss: 0.448, train acc: 86.84%, val loss: 0.279, val acc: 93.15%\n",
            "Validation accuracy is increasing: 92.92% --> 93.15%\n",
            "Saving model...\n",
            "27/50 (44m 32s) train loss: 0.449, train acc: 87.41%, val loss: 0.317, val acc: 92.18%\n",
            "28/50 (46m 12s) train loss: 0.457, train acc: 86.65%, val loss: 0.291, val acc: 92.32%\n",
            "29/50 (47m 51s) train loss: 0.431, train acc: 87.65%, val loss: 0.272, val acc: 92.51%\n",
            "30/50 (49m 30s) train loss: 0.441, train acc: 87.53%, val loss: 0.375, val acc: 89.96%\n",
            "31/50 (51m 10s) train loss: 0.453, train acc: 87.20%, val loss: 0.304, val acc: 92.55%\n",
            "32/50 (52m 49s) train loss: 0.440, train acc: 87.30%, val loss: 0.287, val acc: 92.51%\n",
            "33/50 (54m 30s) train loss: 0.437, train acc: 87.35%, val loss: 0.309, val acc: 92.04%\n",
            "34/50 (56m 9s) train loss: 0.442, train acc: 87.79%, val loss: 0.329, val acc: 92.65%\n",
            "35/50 (57m 49s) train loss: 0.410, train acc: 88.13%, val loss: 0.301, val acc: 92.97%\n",
            "36/50 (59m 28s) train loss: 0.411, train acc: 88.56%, val loss: 0.282, val acc: 92.92%\n",
            "37/50 (61m 8s) train loss: 0.409, train acc: 88.30%, val loss: 0.314, val acc: 92.37%\n",
            "38/50 (62m 47s) train loss: 0.392, train acc: 88.50%, val loss: 0.316, val acc: 92.97%\n",
            "39/50 (64m 26s) train loss: 0.371, train acc: 89.36%, val loss: 0.336, val acc: 92.14%\n",
            "40/50 (66m 5s) train loss: 0.372, train acc: 89.05%, val loss: 0.284, val acc: 93.06%\n",
            "41/50 (67m 44s) train loss: 0.376, train acc: 89.30%, val loss: 0.301, val acc: 92.60%\n",
            "42/50 (69m 23s) train loss: 0.369, train acc: 89.29%, val loss: 0.350, val acc: 91.44%\n",
            "43/50 (71m 2s) train loss: 0.401, train acc: 88.67%, val loss: 0.308, val acc: 93.06%\n",
            "44/50 (72m 40s) train loss: 0.363, train acc: 88.96%, val loss: 0.295, val acc: 93.02%\n",
            "45/50 (74m 18s) train loss: 0.340, train acc: 89.68%, val loss: 0.284, val acc: 92.92%\n",
            "46/50 (75m 57s) train loss: 0.349, train acc: 89.84%, val loss: 0.295, val acc: 92.65%\n",
            "47/50 (77m 36s) train loss: 0.369, train acc: 89.21%, val loss: 0.307, val acc: 92.46%\n",
            "48/50 (79m 14s) train loss: 0.359, train acc: 89.67%, val loss: 0.295, val acc: 92.88%\n",
            "49/50 (80m 53s) train loss: 0.349, train acc: 89.92%, val loss: 0.307, val acc: 92.37%\n",
            "50/50 (82m 32s) train loss: 0.357, train acc: 89.50%, val loss: 0.297, val acc: 92.97%\n",
            "GoogleNet accuracy in test set: 92.33%\n",
            "\u001b[31mPlease check your arguments if you have upgraded adabelief-pytorch from version 0.0.5.\n",
            "\u001b[31mModifications to default arguments:\n",
            "\u001b[31m                           eps  weight_decouple    rectify\n",
            "-----------------------  -----  -----------------  ---------\n",
            "adabelief-pytorch=0.0.5  1e-08  False              False\n",
            "Current version (0.1.0)  1e-16  True               True\n",
            "\u001b[31mFor a complete table of recommended hyperparameters, see\n",
            "\u001b[31mhttps://github.com/juntang-zhuang/Adabelief-Optimizer\n",
            "\u001b[0m\n",
            "Weight decoupling enabled in AdaBelief\n",
            "Rectification enabled in AdaBelief\n",
            "1/50 (1m 31s) train loss: 1.195, train acc: 68.11%, val loss: 0.501, val acc: 85.99%\n",
            "Validation accuracy in first epoch: 85.99%\n",
            "Saving model...\n",
            "2/50 (3m 3s) train loss: 0.692, train acc: 79.79%, val loss: 0.440, val acc: 87.79%\n",
            "Validation accuracy is increasing: 85.99% --> 87.79%\n",
            "Saving model...\n",
            "3/50 (4m 35s) train loss: 0.634, train acc: 81.96%, val loss: 0.421, val acc: 88.58%\n",
            "Validation accuracy is increasing: 87.79% --> 88.58%\n",
            "Saving model...\n",
            "4/50 (6m 7s) train loss: 0.590, train acc: 82.76%, val loss: 0.422, val acc: 87.70%\n",
            "5/50 (7m 39s) train loss: 0.560, train acc: 83.72%, val loss: 0.401, val acc: 89.45%\n",
            "Validation accuracy is increasing: 88.58% --> 89.45%\n",
            "Saving model...\n",
            "6/50 (9m 12s) train loss: 0.542, train acc: 83.91%, val loss: 0.394, val acc: 90.29%\n",
            "Validation accuracy is increasing: 89.45% --> 90.29%\n",
            "Saving model...\n",
            "7/50 (10m 44s) train loss: 0.528, train acc: 84.80%, val loss: 0.356, val acc: 90.84%\n",
            "Validation accuracy is increasing: 90.29% --> 90.84%\n",
            "Saving model...\n",
            "8/50 (12m 17s) train loss: 0.538, train acc: 84.27%, val loss: 0.419, val acc: 88.99%\n",
            "9/50 (13m 49s) train loss: 0.514, train acc: 85.24%, val loss: 0.379, val acc: 88.81%\n",
            "10/50 (15m 21s) train loss: 0.494, train acc: 85.73%, val loss: 0.371, val acc: 89.78%\n",
            "11/50 (16m 53s) train loss: 0.496, train acc: 85.64%, val loss: 0.346, val acc: 90.52%\n",
            "12/50 (18m 26s) train loss: 0.475, train acc: 86.23%, val loss: 0.392, val acc: 89.82%\n",
            "13/50 (19m 58s) train loss: 0.484, train acc: 85.72%, val loss: 0.323, val acc: 91.95%\n",
            "Validation accuracy is increasing: 90.84% --> 91.95%\n",
            "Saving model...\n",
            "14/50 (21m 30s) train loss: 0.465, train acc: 85.99%, val loss: 0.362, val acc: 90.66%\n",
            "15/50 (23m 2s) train loss: 0.449, train acc: 86.52%, val loss: 0.405, val acc: 91.17%\n",
            "16/50 (24m 34s) train loss: 0.455, train acc: 86.23%, val loss: 0.362, val acc: 90.89%\n",
            "17/50 (26m 7s) train loss: 0.433, train acc: 87.17%, val loss: 0.339, val acc: 91.58%\n",
            "18/50 (27m 39s) train loss: 0.410, train acc: 87.68%, val loss: 0.355, val acc: 91.44%\n",
            "19/50 (29m 12s) train loss: 0.431, train acc: 87.62%, val loss: 0.331, val acc: 91.44%\n",
            "20/50 (30m 44s) train loss: 0.428, train acc: 87.33%, val loss: 0.354, val acc: 91.67%\n",
            "21/50 (32m 16s) train loss: 0.417, train acc: 87.49%, val loss: 0.346, val acc: 91.40%\n",
            "22/50 (33m 49s) train loss: 0.408, train acc: 87.99%, val loss: 0.291, val acc: 92.00%\n",
            "Validation accuracy is increasing: 91.95% --> 92.00%\n",
            "Saving model...\n",
            "23/50 (35m 22s) train loss: 0.411, train acc: 87.39%, val loss: 0.376, val acc: 91.40%\n",
            "24/50 (36m 55s) train loss: 0.401, train acc: 88.07%, val loss: 0.312, val acc: 92.28%\n",
            "Validation accuracy is increasing: 92.00% --> 92.28%\n",
            "Saving model...\n",
            "25/50 (38m 27s) train loss: 0.393, train acc: 88.27%, val loss: 0.345, val acc: 91.17%\n",
            "26/50 (40m 0s) train loss: 0.391, train acc: 88.55%, val loss: 0.311, val acc: 92.00%\n",
            "27/50 (41m 33s) train loss: 0.391, train acc: 88.42%, val loss: 0.351, val acc: 91.86%\n",
            "28/50 (43m 5s) train loss: 0.394, train acc: 88.19%, val loss: 0.308, val acc: 91.86%\n",
            "29/50 (44m 37s) train loss: 0.388, train acc: 88.67%, val loss: 0.316, val acc: 91.67%\n",
            "30/50 (46m 10s) train loss: 0.377, train acc: 88.62%, val loss: 0.343, val acc: 91.81%\n",
            "31/50 (47m 42s) train loss: 0.361, train acc: 88.94%, val loss: 0.330, val acc: 92.55%\n",
            "Validation accuracy is increasing: 92.28% --> 92.55%\n",
            "Saving model...\n",
            "32/50 (49m 15s) train loss: 0.370, train acc: 89.12%, val loss: 0.322, val acc: 92.65%\n",
            "Validation accuracy is increasing: 92.55% --> 92.65%\n",
            "Saving model...\n",
            "33/50 (50m 47s) train loss: 0.371, train acc: 88.93%, val loss: 0.339, val acc: 92.32%\n",
            "34/50 (52m 19s) train loss: 0.359, train acc: 89.17%, val loss: 0.341, val acc: 91.77%\n",
            "35/50 (53m 50s) train loss: 0.370, train acc: 88.72%, val loss: 0.338, val acc: 92.28%\n",
            "36/50 (55m 23s) train loss: 0.350, train acc: 89.41%, val loss: 0.363, val acc: 91.54%\n",
            "37/50 (56m 55s) train loss: 0.353, train acc: 89.21%, val loss: 0.343, val acc: 92.14%\n",
            "38/50 (58m 27s) train loss: 0.321, train acc: 90.11%, val loss: 0.384, val acc: 91.44%\n",
            "39/50 (59m 58s) train loss: 0.334, train acc: 89.75%, val loss: 0.301, val acc: 92.41%\n",
            "40/50 (61m 29s) train loss: 0.328, train acc: 89.84%, val loss: 0.340, val acc: 92.41%\n",
            "41/50 (62m 59s) train loss: 0.343, train acc: 89.65%, val loss: 0.357, val acc: 92.46%\n",
            "42/50 (64m 30s) train loss: 0.326, train acc: 90.06%, val loss: 0.354, val acc: 92.23%\n",
            "43/50 (66m 2s) train loss: 0.326, train acc: 89.95%, val loss: 0.328, val acc: 92.65%\n",
            "44/50 (67m 33s) train loss: 0.323, train acc: 90.28%, val loss: 0.310, val acc: 93.02%\n",
            "Validation accuracy is increasing: 92.65% --> 93.02%\n",
            "Saving model...\n",
            "45/50 (69m 5s) train loss: 0.317, train acc: 90.30%, val loss: 0.337, val acc: 92.65%\n",
            "46/50 (70m 36s) train loss: 0.328, train acc: 90.25%, val loss: 0.364, val acc: 92.04%\n",
            "47/50 (72m 9s) train loss: 0.321, train acc: 90.03%, val loss: 0.329, val acc: 93.02%\n",
            "48/50 (73m 41s) train loss: 0.320, train acc: 90.39%, val loss: 0.347, val acc: 92.18%\n",
            "49/50 (75m 12s) train loss: 0.339, train acc: 89.87%, val loss: 0.347, val acc: 92.55%\n",
            "50/50 (76m 44s) train loss: 0.329, train acc: 89.79%, val loss: 0.314, val acc: 92.97%\n",
            "ShuffleNet accuracy in test set: 93.39%\n",
            "1/30 (1m 25s) train loss: 0.429, train acc: 88.38%, val loss: 0.265, val acc: 93.85%\n",
            "Validation accuracy in first epoch: 93.85%\n",
            "Saving model...\n",
            "2/30 (2m 50s) train loss: 0.391, train acc: 88.87%, val loss: 0.257, val acc: 93.76%\n",
            "3/30 (4m 16s) train loss: 0.380, train acc: 89.02%, val loss: 0.257, val acc: 93.99%\n",
            "Validation accuracy is increasing: 93.85% --> 93.99%\n",
            "Saving model...\n",
            "4/30 (5m 42s) train loss: 0.372, train acc: 88.98%, val loss: 0.264, val acc: 93.80%\n",
            "5/30 (7m 7s) train loss: 0.373, train acc: 88.98%, val loss: 0.254, val acc: 93.89%\n",
            "6/30 (8m 33s) train loss: 0.364, train acc: 89.64%, val loss: 0.256, val acc: 94.08%\n",
            "Validation accuracy is increasing: 93.99% --> 94.08%\n",
            "Saving model...\n",
            "7/30 (9m 59s) train loss: 0.355, train acc: 89.27%, val loss: 0.250, val acc: 94.08%\n",
            "8/30 (11m 25s) train loss: 0.377, train acc: 89.24%, val loss: 0.266, val acc: 93.62%\n",
            "9/30 (12m 51s) train loss: 0.363, train acc: 89.34%, val loss: 0.264, val acc: 93.85%\n",
            "10/30 (14m 16s) train loss: 0.345, train acc: 89.76%, val loss: 0.249, val acc: 94.13%\n",
            "Validation accuracy is increasing: 94.08% --> 94.13%\n",
            "Saving model...\n",
            "11/30 (15m 41s) train loss: 0.348, train acc: 89.51%, val loss: 0.251, val acc: 93.48%\n",
            "12/30 (17m 7s) train loss: 0.340, train acc: 89.88%, val loss: 0.248, val acc: 93.85%\n",
            "13/30 (18m 32s) train loss: 0.337, train acc: 89.99%, val loss: 0.247, val acc: 94.03%\n",
            "14/30 (19m 57s) train loss: 0.353, train acc: 89.81%, val loss: 0.263, val acc: 93.80%\n",
            "15/30 (21m 22s) train loss: 0.339, train acc: 89.72%, val loss: 0.247, val acc: 94.03%\n",
            "16/30 (22m 47s) train loss: 0.330, train acc: 90.12%, val loss: 0.247, val acc: 94.22%\n",
            "Validation accuracy is increasing: 94.13% --> 94.22%\n",
            "Saving model...\n",
            "17/30 (24m 12s) train loss: 0.348, train acc: 89.80%, val loss: 0.261, val acc: 93.80%\n",
            "18/30 (25m 37s) train loss: 0.336, train acc: 89.98%, val loss: 0.248, val acc: 93.71%\n",
            "19/30 (27m 2s) train loss: 0.352, train acc: 89.80%, val loss: 0.249, val acc: 93.85%\n",
            "20/30 (28m 26s) train loss: 0.337, train acc: 89.94%, val loss: 0.259, val acc: 93.66%\n",
            "21/30 (29m 51s) train loss: 0.336, train acc: 90.25%, val loss: 0.255, val acc: 93.71%\n",
            "22/30 (31m 16s) train loss: 0.339, train acc: 90.27%, val loss: 0.248, val acc: 93.85%\n",
            "23/30 (32m 42s) train loss: 0.325, train acc: 90.22%, val loss: 0.244, val acc: 93.66%\n",
            "24/30 (34m 7s) train loss: 0.327, train acc: 89.84%, val loss: 0.243, val acc: 93.80%\n",
            "25/30 (35m 32s) train loss: 0.339, train acc: 89.86%, val loss: 0.261, val acc: 93.94%\n",
            "26/30 (36m 56s) train loss: 0.323, train acc: 90.34%, val loss: 0.251, val acc: 93.80%\n",
            "27/30 (38m 21s) train loss: 0.321, train acc: 90.49%, val loss: 0.248, val acc: 93.80%\n",
            "28/30 (39m 46s) train loss: 0.329, train acc: 90.31%, val loss: 0.254, val acc: 93.85%\n",
            "29/30 (41m 11s) train loss: 0.326, train acc: 90.34%, val loss: 0.251, val acc: 93.66%\n",
            "30/30 (42m 35s) train loss: 0.334, train acc: 90.29%, val loss: 0.252, val acc: 94.03%\n",
            "GoogleNet accuracy in test set: 93.76%\n",
            "1/30 (1m 22s) train loss: 0.280, train acc: 91.52%, val loss: 0.301, val acc: 92.92%\n",
            "Validation accuracy in first epoch: 92.92%\n",
            "Saving model...\n",
            "2/30 (2m 44s) train loss: 0.265, train acc: 92.16%, val loss: 0.313, val acc: 93.34%\n",
            "Validation accuracy is increasing: 92.92% --> 93.34%\n",
            "Saving model...\n",
            "3/30 (4m 6s) train loss: 0.270, train acc: 91.98%, val loss: 0.304, val acc: 93.62%\n",
            "Validation accuracy is increasing: 93.34% --> 93.62%\n",
            "Saving model...\n",
            "4/30 (5m 28s) train loss: 0.272, train acc: 91.93%, val loss: 0.308, val acc: 93.62%\n",
            "5/30 (6m 49s) train loss: 0.242, train acc: 92.29%, val loss: 0.300, val acc: 93.52%\n",
            "6/30 (8m 11s) train loss: 0.260, train acc: 92.15%, val loss: 0.301, val acc: 93.57%\n",
            "7/30 (9m 32s) train loss: 0.232, train acc: 92.71%, val loss: 0.288, val acc: 94.08%\n",
            "Validation accuracy is increasing: 93.62% --> 94.08%\n",
            "Saving model...\n",
            "8/30 (10m 54s) train loss: 0.236, train acc: 92.68%, val loss: 0.287, val acc: 93.71%\n",
            "9/30 (12m 15s) train loss: 0.239, train acc: 92.57%, val loss: 0.296, val acc: 93.76%\n",
            "10/30 (13m 36s) train loss: 0.234, train acc: 92.53%, val loss: 0.297, val acc: 93.76%\n",
            "11/30 (14m 58s) train loss: 0.234, train acc: 92.47%, val loss: 0.293, val acc: 94.17%\n",
            "Validation accuracy is increasing: 94.08% --> 94.17%\n",
            "Saving model...\n",
            "12/30 (16m 21s) train loss: 0.237, train acc: 92.83%, val loss: 0.299, val acc: 93.57%\n",
            "13/30 (17m 43s) train loss: 0.233, train acc: 93.07%, val loss: 0.293, val acc: 93.57%\n",
            "14/30 (19m 5s) train loss: 0.229, train acc: 92.94%, val loss: 0.290, val acc: 93.29%\n",
            "15/30 (20m 27s) train loss: 0.233, train acc: 93.01%, val loss: 0.299, val acc: 93.71%\n",
            "16/30 (21m 50s) train loss: 0.234, train acc: 92.39%, val loss: 0.302, val acc: 93.76%\n",
            "17/30 (23m 12s) train loss: 0.225, train acc: 92.99%, val loss: 0.299, val acc: 93.57%\n",
            "18/30 (24m 36s) train loss: 0.225, train acc: 93.19%, val loss: 0.302, val acc: 93.62%\n",
            "19/30 (25m 59s) train loss: 0.224, train acc: 92.99%, val loss: 0.291, val acc: 93.76%\n",
            "20/30 (27m 22s) train loss: 0.224, train acc: 93.16%, val loss: 0.297, val acc: 93.48%\n",
            "21/30 (28m 45s) train loss: 0.231, train acc: 93.06%, val loss: 0.294, val acc: 93.76%\n",
            "22/30 (30m 8s) train loss: 0.230, train acc: 92.64%, val loss: 0.295, val acc: 93.71%\n",
            "23/30 (31m 31s) train loss: 0.209, train acc: 93.34%, val loss: 0.287, val acc: 94.03%\n",
            "24/30 (32m 53s) train loss: 0.211, train acc: 93.29%, val loss: 0.299, val acc: 93.89%\n",
            "25/30 (34m 16s) train loss: 0.218, train acc: 93.47%, val loss: 0.300, val acc: 94.22%\n",
            "Validation accuracy is increasing: 94.17% --> 94.22%\n",
            "Saving model...\n",
            "26/30 (35m 38s) train loss: 0.220, train acc: 93.35%, val loss: 0.301, val acc: 94.13%\n",
            "27/30 (37m 0s) train loss: 0.213, train acc: 93.14%, val loss: 0.311, val acc: 93.89%\n",
            "28/30 (38m 22s) train loss: 0.216, train acc: 93.19%, val loss: 0.283, val acc: 93.94%\n",
            "29/30 (39m 44s) train loss: 0.217, train acc: 93.30%, val loss: 0.296, val acc: 93.99%\n",
            "30/30 (41m 6s) train loss: 0.207, train acc: 93.47%, val loss: 0.306, val acc: 93.66%\n",
            "ShuffleNet accuracy in test set: 93.85%\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bMpFBxDPhXul"
      },
      "source": [
        "def evaluateImageText(model, data, criterion, device):\n",
        "    model.eval()\n",
        "    total_loss = 0.0\n",
        "    correct = 0\n",
        "    total = 0\n",
        "    with torch.no_grad():\n",
        "        for image, src, trg in data:\n",
        "            image, src, trg = image.to(device), src.to(device), trg.to(device)\n",
        "            output = model(image, src)\n",
        "            pred = torch.argmax(output, dim=1)\n",
        "            correct += (pred == trg).sum().item()\n",
        "            total += len(trg)\n",
        "            loss = criterion(output, trg)\n",
        "            total_loss += loss.item()\n",
        "\n",
        "    return total_loss / len(data), correct / total"
      ],
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UJC25F18hf9P"
      },
      "source": [
        "def trainImageText(model, train_data, val_data, n_epochs, criterion, optimizer, device, path, data_aug=0.5, fusion_ratio=0.25):\n",
        "    model.train()\n",
        "    train_acc_list = []\n",
        "    val_acc_list = []\n",
        "    max_acc = None\n",
        "    step = 1\n",
        "    print_every = len(train_data)\n",
        "    aug_flag = False\n",
        "    last_image = None\n",
        "    last_src = None\n",
        "    start = time.time()\n",
        "    for epoch in range(n_epochs):\n",
        "        running_loss = 0.0\n",
        "        running_correct = 0\n",
        "        running_total = 0\n",
        "        for image, src, trg in train_data:\n",
        "            optimizer.zero_grad()\n",
        "            step += 1\n",
        "            image, src, trg = image.to(device), src.to(device), trg.to(device)\n",
        "            tmp = np.random.uniform(0, 1, 1)\n",
        "            if tmp < data_aug:\n",
        "                aug_flag = True\n",
        "            else:\n",
        "                aug_flag = False\n",
        "            if aug_flag:\n",
        "                if last_image is None:\n",
        "                    last_image = image\n",
        "                    last_src = src\n",
        "                else:\n",
        "                    if last_image.size(0) == image.size(0):\n",
        "                        new_image = fusion_ratio * last_image + (1 - fusion_ratio) * image\n",
        "                    else:\n",
        "                        new_image = fusion_ratio * last_image[:image.size(0)] + (1 - fusion_ratio) * image\n",
        "                    last_image = image\n",
        "                    image = torch.cat((image, new_image), dim=0)\n",
        "                    src = torch.cat((src, src), dim=0)\n",
        "                    trg = torch.cat((trg, trg), dim=0)\n",
        "            output = model(image, src)\n",
        "            pred = torch.argmax(output, dim=1)\n",
        "            running_correct += (pred == trg).sum().item()\n",
        "            running_total += len(trg)\n",
        "            loss = criterion(output, trg)\n",
        "            running_loss += loss.item()\n",
        "            loss.backward()\n",
        "            optimizer.step()\n",
        "            if step % print_every == 0:\n",
        "                val_loss, val_acc = evaluateImageText(model, val_data, criterion, device)\n",
        "                print(('%d/%d (%s) train loss: %.3f, train acc: %.2f%%, val loss: %.3f, val acc: %.2f%%') %\n",
        "                      (epoch + 1, n_epochs, timeSince(start), running_loss / len(train_data), \n",
        "                       100 * running_correct / running_total, val_loss, 100 * val_acc))\n",
        "                train_acc_list.append(running_correct / running_total)\n",
        "                val_acc_list.append(val_acc)\n",
        "                if max_acc is None or max_acc < val_acc:\n",
        "                    if max_acc is None:\n",
        "                        print(('Validation accuracy in first epoch: %.2f%%') % (100 * val_acc))\n",
        "                    else:\n",
        "                        print(('Validation accuracy is increasing: %.2f%% --> %.2f%%') % \n",
        "                              (100 * max_acc, 100 * val_acc))\n",
        "                    max_acc = val_acc\n",
        "                    print('Saving model...')\n",
        "                    torch.save(model, path)\n",
        "                \n",
        "                model.train()\n",
        "                running_loss = 0.0\n",
        "                running_correct = 0\n",
        "                running_total = 0\n",
        "    \n",
        "    return train_acc_list, val_acc_list"
      ],
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "P7u7jc40hh6M"
      },
      "source": [
        "class PositionalEncoding(nn.Module):\n",
        "\n",
        "    def __init__(self, d_model=512, dropout=0.1, max_len=15):\n",
        "        super(PositionalEncoding, self).__init__()\n",
        "        self.dropout = nn.Dropout(p=dropout)\n",
        "\n",
        "        pe = torch.zeros(max_len, d_model)\n",
        "        position = torch.arange(0, max_len, dtype=torch.float).unsqueeze(1)\n",
        "        div_term = torch.exp(torch.arange(0, d_model, 2).float() * (-math.log(10000.0) / d_model))\n",
        "        pe[:, 0::2] = torch.sin(position * div_term)\n",
        "        pe[:, 1::2] = torch.cos(position * div_term)\n",
        "        pe = pe.unsqueeze(0)\n",
        "        self.register_buffer('pe', pe)\n",
        "\n",
        "    def forward(self, x):\n",
        "        x = x + self.pe[:x.size(0), :]\n",
        "        return self.dropout(x)"
      ],
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XqxvWZY9pkeG"
      },
      "source": [
        "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')"
      ],
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qvOCSMAF5CQQ"
      },
      "source": [
        "class EncoderLayer(nn.Module):\n",
        "    def __init__(self, d_model=512):\n",
        "        super(EncoderLayer, self).__init__()\n",
        "\n",
        "        self.d_model = d_model\n",
        "        self.softmax = nn.Softmax(dim=2)\n",
        "\n",
        "    def attention(self, query, key, value):\n",
        "        attn_weights = self.softmax(torch.bmm(query, key.permute(0, 2, 1)) / self.d_model)\n",
        "        return torch.bmm(attn_weights, value)\n",
        "\n",
        "    def forward(self, x):\n",
        "        residual = x\n",
        "        x = self.attention(x, x, x)\n",
        "        return x + residual"
      ],
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AmINYtEI5LSa"
      },
      "source": [
        "class NewTransformer(nn.Module):\n",
        "    def __init__(self, n_vocab, block, layers, n_encoders=1, output_size=27, max_seq=15, d_model=512, dropout=0.1):\n",
        "        super(NewTransformer, self).__init__()\n",
        "        \n",
        "        self.d_model = d_model\n",
        "        self.resnet = ResNet(block, layers)\n",
        "        self.position_encoder = PositionalEncoding(d_model, max_len=max_seq, dropout=dropout)\n",
        "        self.embedding = nn.Embedding(n_vocab + 1, d_model)\n",
        "        self.softmax = nn.Softmax(dim=2)\n",
        "        self.fc1 = nn.Linear(d_model, 128)\n",
        "        self.fc2 = nn.Linear(128, output_size)\n",
        "        self.relu = nn.ReLU()\n",
        "        self.dropout = nn.Dropout(dropout)\n",
        "        self.encoder_blocks = nn.ModuleList([EncoderLayer(d_model=d_model) for _ in range(n_encoders)])\n",
        "\n",
        "    def attention(self, query, key, value):\n",
        "        attn_weights = self.softmax(torch.bmm(query, key.permute(0, 2, 1)) / self.d_model)\n",
        "        return torch.bmm(attn_weights, value)\n",
        "\n",
        "    def encoder(self, image, src):\n",
        "        image = self.resnet(image)\n",
        "        image = torch.unsqueeze(image, dim=1)\n",
        "        src = self.embedding(src)\n",
        "        encoder_output = self.position_encoder(src) + image\n",
        "        for block in self.encoder_blocks:\n",
        "            encoder_output = block(encoder_output)\n",
        "        return image, encoder_output\n",
        "\n",
        "    def decoder(self, image, encoder_out):\n",
        "        target = 27 * torch.ones(encoder_out.size(0), 1, dtype=torch.long).to(device)\n",
        "        target = self.embedding(target)\n",
        "        output = self.attention(target, encoder_out, encoder_out)\n",
        "        output.add_(image)\n",
        "        output = self.relu(self.fc1(output))\n",
        "        output = self.dropout(output)\n",
        "        output = self.fc2(output)\n",
        "        return torch.squeeze(output, dim=1)\n",
        "    \n",
        "    def forward(self, image, src):\n",
        "        image, encoder_out = self.encoder(image, src)\n",
        "        return self.decoder(image, encoder_out)"
      ],
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QcRCNr8D5SgN",
        "outputId": "a9648ca6-e35d-4970-f027-1a6da49d9b8f"
      },
      "source": [
        "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
        "model = NewTransformer(len(train_vocab2num), ResBasicBlock, [3, 4, 6, 3], n_encoders=1, max_seq=max_seq).to(device)\n",
        "criterion = nn.CrossEntropyLoss()\n",
        "optimizer = AdaBelief(model.parameters(), lr=3e-04)\n",
        "path = 'new_transformer.pt'\n",
        "n_epochs = 50\n",
        "train_acc_list, val_acc_list = trainImageText(model, train_loader, val_loader, n_epochs, criterion, optimizer, device, path, data_aug=0.6, fusion_ratio=0.05)\n",
        "model = torch.load(path)\n",
        "_, test_acc = evaluateImageText(model, test_loader, criterion, device)\n",
        "print('Accuracy in test set: %.2f%%' % (100 * test_acc))\n",
        "n_epochs = 20\n",
        "model = torch.load(path)\n",
        "optimizer = AdaBelief(model.parameters(), lr=1e-04)\n",
        "train_acc_list, val_acc_list = trainImageText(model, train_loader, val_loader, n_epochs, criterion, optimizer, device, path, data_aug=0)\n",
        "model = torch.load(path)\n",
        "_, test_acc = evaluateImageText(model, test_loader, criterion, device)"
      ],
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\u001b[31mPlease check your arguments if you have upgraded adabelief-pytorch from version 0.0.5.\n",
            "\u001b[31mModifications to default arguments:\n",
            "\u001b[31m                           eps  weight_decouple    rectify\n",
            "-----------------------  -----  -----------------  ---------\n",
            "adabelief-pytorch=0.0.5  1e-08  False              False\n",
            "Current version (0.1.0)  1e-16  True               True\n",
            "\u001b[31mFor a complete table of recommended hyperparameters, see\n",
            "\u001b[31mhttps://github.com/juntang-zhuang/Adabelief-Optimizer\n",
            "\u001b[0m\n",
            "Weight decoupling enabled in AdaBelief\n",
            "Rectification enabled in AdaBelief\n",
            "1/50 (1m 55s) train loss: 1.744, train acc: 53.23%, val loss: 1.250, val acc: 63.23%\n",
            "Validation accuracy in first epoch: 63.23%\n",
            "Saving model...\n",
            "2/50 (3m 50s) train loss: 1.183, train acc: 67.06%, val loss: 0.790, val acc: 77.06%\n",
            "Validation accuracy is increasing: 63.23% --> 77.06%\n",
            "Saving model...\n",
            "3/50 (5m 44s) train loss: 1.016, train acc: 71.44%, val loss: 0.692, val acc: 79.93%\n",
            "Validation accuracy is increasing: 77.06% --> 79.93%\n",
            "Saving model...\n",
            "4/50 (7m 38s) train loss: 0.898, train acc: 74.28%, val loss: 0.590, val acc: 82.47%\n",
            "Validation accuracy is increasing: 79.93% --> 82.47%\n",
            "Saving model...\n",
            "5/50 (9m 32s) train loss: 0.777, train acc: 77.46%, val loss: 0.522, val acc: 84.51%\n",
            "Validation accuracy is increasing: 82.47% --> 84.51%\n",
            "Saving model...\n",
            "6/50 (11m 26s) train loss: 0.704, train acc: 79.55%, val loss: 0.463, val acc: 87.10%\n",
            "Validation accuracy is increasing: 84.51% --> 87.10%\n",
            "Saving model...\n",
            "7/50 (13m 21s) train loss: 0.598, train acc: 82.46%, val loss: 0.377, val acc: 88.90%\n",
            "Validation accuracy is increasing: 87.10% --> 88.90%\n",
            "Saving model...\n",
            "8/50 (15m 16s) train loss: 0.513, train acc: 84.77%, val loss: 0.420, val acc: 87.37%\n",
            "9/50 (17m 10s) train loss: 0.459, train acc: 86.61%, val loss: 0.269, val acc: 92.46%\n",
            "Validation accuracy is increasing: 88.90% --> 92.46%\n",
            "Saving model...\n",
            "10/50 (19m 5s) train loss: 0.377, train acc: 89.11%, val loss: 0.252, val acc: 92.88%\n",
            "Validation accuracy is increasing: 92.46% --> 92.88%\n",
            "Saving model...\n",
            "11/50 (21m 0s) train loss: 0.352, train acc: 89.60%, val loss: 0.221, val acc: 94.03%\n",
            "Validation accuracy is increasing: 92.88% --> 94.03%\n",
            "Saving model...\n",
            "12/50 (22m 55s) train loss: 0.290, train acc: 91.80%, val loss: 0.182, val acc: 95.56%\n",
            "Validation accuracy is increasing: 94.03% --> 95.56%\n",
            "Saving model...\n",
            "13/50 (24m 50s) train loss: 0.261, train acc: 92.71%, val loss: 0.193, val acc: 94.68%\n",
            "14/50 (26m 46s) train loss: 0.294, train acc: 92.04%, val loss: 0.169, val acc: 95.33%\n",
            "15/50 (28m 40s) train loss: 0.219, train acc: 94.03%, val loss: 0.168, val acc: 95.65%\n",
            "Validation accuracy is increasing: 95.56% --> 95.65%\n",
            "Saving model...\n",
            "16/50 (30m 36s) train loss: 0.203, train acc: 94.26%, val loss: 0.163, val acc: 95.79%\n",
            "Validation accuracy is increasing: 95.65% --> 95.79%\n",
            "Saving model...\n",
            "17/50 (32m 31s) train loss: 0.191, train acc: 94.78%, val loss: 0.130, val acc: 96.11%\n",
            "Validation accuracy is increasing: 95.79% --> 96.11%\n",
            "Saving model...\n",
            "18/50 (34m 25s) train loss: 0.208, train acc: 94.00%, val loss: 0.125, val acc: 96.48%\n",
            "Validation accuracy is increasing: 96.11% --> 96.48%\n",
            "Saving model...\n",
            "19/50 (36m 18s) train loss: 0.166, train acc: 95.35%, val loss: 0.176, val acc: 95.28%\n",
            "20/50 (38m 12s) train loss: 0.159, train acc: 95.45%, val loss: 0.138, val acc: 96.25%\n",
            "21/50 (40m 4s) train loss: 0.142, train acc: 95.92%, val loss: 0.149, val acc: 95.79%\n",
            "22/50 (41m 58s) train loss: 0.184, train acc: 94.98%, val loss: 0.116, val acc: 96.95%\n",
            "Validation accuracy is increasing: 96.48% --> 96.95%\n",
            "Saving model...\n",
            "23/50 (43m 51s) train loss: 0.141, train acc: 96.08%, val loss: 0.127, val acc: 96.62%\n",
            "24/50 (45m 46s) train loss: 0.123, train acc: 96.57%, val loss: 0.119, val acc: 96.62%\n",
            "25/50 (47m 39s) train loss: 0.128, train acc: 96.52%, val loss: 0.122, val acc: 96.58%\n",
            "26/50 (49m 33s) train loss: 0.114, train acc: 96.81%, val loss: 0.113, val acc: 96.81%\n",
            "27/50 (51m 26s) train loss: 0.144, train acc: 95.87%, val loss: 0.111, val acc: 97.13%\n",
            "Validation accuracy is increasing: 96.95% --> 97.13%\n",
            "Saving model...\n",
            "28/50 (53m 21s) train loss: 0.108, train acc: 96.81%, val loss: 0.103, val acc: 96.99%\n",
            "29/50 (55m 15s) train loss: 0.100, train acc: 96.99%, val loss: 0.110, val acc: 96.76%\n",
            "30/50 (57m 10s) train loss: 0.096, train acc: 97.32%, val loss: 0.109, val acc: 97.18%\n",
            "Validation accuracy is increasing: 97.13% --> 97.18%\n",
            "Saving model...\n",
            "31/50 (59m 3s) train loss: 0.095, train acc: 97.41%, val loss: 0.113, val acc: 96.81%\n",
            "32/50 (60m 57s) train loss: 0.087, train acc: 97.41%, val loss: 0.125, val acc: 96.85%\n",
            "33/50 (62m 51s) train loss: 0.127, train acc: 96.36%, val loss: 0.119, val acc: 96.72%\n",
            "34/50 (64m 46s) train loss: 0.086, train acc: 97.57%, val loss: 0.104, val acc: 97.18%\n",
            "35/50 (66m 41s) train loss: 0.076, train acc: 97.98%, val loss: 0.101, val acc: 97.27%\n",
            "Validation accuracy is increasing: 97.18% --> 97.27%\n",
            "Saving model...\n",
            "36/50 (68m 37s) train loss: 0.078, train acc: 97.78%, val loss: 0.111, val acc: 97.04%\n",
            "37/50 (70m 33s) train loss: 0.070, train acc: 98.00%, val loss: 0.101, val acc: 97.09%\n",
            "38/50 (72m 27s) train loss: 0.070, train acc: 98.04%, val loss: 0.095, val acc: 97.18%\n",
            "39/50 (74m 22s) train loss: 0.067, train acc: 98.04%, val loss: 0.115, val acc: 96.72%\n",
            "40/50 (76m 15s) train loss: 0.104, train acc: 96.94%, val loss: 0.112, val acc: 96.53%\n",
            "41/50 (78m 10s) train loss: 0.067, train acc: 98.09%, val loss: 0.104, val acc: 97.22%\n",
            "42/50 (80m 4s) train loss: 0.066, train acc: 98.10%, val loss: 0.110, val acc: 97.13%\n",
            "43/50 (81m 59s) train loss: 0.054, train acc: 98.49%, val loss: 0.103, val acc: 96.95%\n",
            "44/50 (83m 53s) train loss: 0.052, train acc: 98.51%, val loss: 0.123, val acc: 96.99%\n",
            "45/50 (85m 49s) train loss: 0.049, train acc: 98.63%, val loss: 0.099, val acc: 97.22%\n",
            "46/50 (87m 44s) train loss: 0.053, train acc: 98.39%, val loss: 0.092, val acc: 97.36%\n",
            "Validation accuracy is increasing: 97.27% --> 97.36%\n",
            "Saving model...\n",
            "47/50 (89m 40s) train loss: 0.051, train acc: 98.40%, val loss: 0.108, val acc: 96.81%\n",
            "48/50 (91m 34s) train loss: 0.043, train acc: 98.76%, val loss: 0.113, val acc: 97.36%\n",
            "49/50 (93m 27s) train loss: 0.047, train acc: 98.62%, val loss: 0.112, val acc: 97.36%\n",
            "50/50 (95m 20s) train loss: 0.045, train acc: 98.64%, val loss: 0.109, val acc: 97.36%\n",
            "Accuracy in test set: 96.72%\n",
            "\u001b[31mPlease check your arguments if you have upgraded adabelief-pytorch from version 0.0.5.\n",
            "\u001b[31mModifications to default arguments:\n",
            "\u001b[31m                           eps  weight_decouple    rectify\n",
            "-----------------------  -----  -----------------  ---------\n",
            "adabelief-pytorch=0.0.5  1e-08  False              False\n",
            "Current version (0.1.0)  1e-16  True               True\n",
            "\u001b[31mFor a complete table of recommended hyperparameters, see\n",
            "\u001b[31mhttps://github.com/juntang-zhuang/Adabelief-Optimizer\n",
            "\u001b[0m\n",
            "Weight decoupling enabled in AdaBelief\n",
            "Rectification enabled in AdaBelief\n",
            "1/20 (1m 42s) train loss: 0.039, train acc: 98.80%, val loss: 0.092, val acc: 97.64%\n",
            "Validation accuracy in first epoch: 97.64%\n",
            "Saving model...\n",
            "2/20 (3m 24s) train loss: 0.034, train acc: 99.00%, val loss: 0.092, val acc: 97.64%\n",
            "3/20 (5m 7s) train loss: 0.033, train acc: 99.01%, val loss: 0.095, val acc: 97.50%\n",
            "4/20 (6m 48s) train loss: 0.032, train acc: 99.14%, val loss: 0.094, val acc: 97.73%\n",
            "Validation accuracy is increasing: 97.64% --> 97.73%\n",
            "Saving model...\n",
            "5/20 (8m 30s) train loss: 0.034, train acc: 98.99%, val loss: 0.093, val acc: 97.50%\n",
            "6/20 (10m 12s) train loss: 0.030, train acc: 99.08%, val loss: 0.101, val acc: 97.13%\n",
            "7/20 (11m 53s) train loss: 0.031, train acc: 99.13%, val loss: 0.088, val acc: 97.64%\n",
            "8/20 (13m 35s) train loss: 0.031, train acc: 99.10%, val loss: 0.087, val acc: 97.92%\n",
            "Validation accuracy is increasing: 97.73% --> 97.92%\n",
            "Saving model...\n",
            "9/20 (15m 18s) train loss: 0.030, train acc: 99.14%, val loss: 0.096, val acc: 97.87%\n",
            "10/20 (17m 0s) train loss: 0.029, train acc: 99.09%, val loss: 0.091, val acc: 97.50%\n",
            "11/20 (18m 43s) train loss: 0.028, train acc: 99.22%, val loss: 0.107, val acc: 97.50%\n",
            "12/20 (20m 25s) train loss: 0.036, train acc: 98.92%, val loss: 0.093, val acc: 97.64%\n",
            "13/20 (22m 7s) train loss: 0.029, train acc: 99.15%, val loss: 0.095, val acc: 97.73%\n",
            "14/20 (23m 49s) train loss: 0.030, train acc: 99.07%, val loss: 0.094, val acc: 97.73%\n",
            "15/20 (25m 31s) train loss: 0.028, train acc: 99.16%, val loss: 0.091, val acc: 97.59%\n",
            "16/20 (27m 14s) train loss: 0.025, train acc: 99.29%, val loss: 0.091, val acc: 97.50%\n",
            "17/20 (28m 56s) train loss: 0.024, train acc: 99.27%, val loss: 0.094, val acc: 97.69%\n",
            "18/20 (30m 38s) train loss: 0.021, train acc: 99.39%, val loss: 0.092, val acc: 97.69%\n",
            "19/20 (32m 19s) train loss: 0.029, train acc: 99.15%, val loss: 0.100, val acc: 97.50%\n",
            "20/20 (34m 3s) train loss: 0.031, train acc: 99.01%, val loss: 0.087, val acc: 98.01%\n",
            "Validation accuracy is increasing: 97.92% --> 98.01%\n",
            "Saving model...\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eJXMopMsPzl5",
        "outputId": "df7a0052-9fd8-4fe9-e3c7-dac1c5bee208"
      },
      "source": [
        "path = 'new_transformer.pt'\n",
        "model = torch.load(path)\n",
        "_, test_acc = evaluateImageText(model, test_loader, criterion, device)\n",
        "print('Accuracy in test set: %.2f%%' % (100 * test_acc))"
      ],
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accuracy in test set: 97.18%\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Fxa28ztOQB_X",
        "outputId": "cd8a9de6-cb67-40f3-affb-5df01ba2f640"
      },
      "source": [
        "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
        "model = NewTransformer(len(train_vocab2num), ResBasicBlock, [3, 4, 6, 3], n_encoders=1, max_seq=max_seq).to(device)\n",
        "criterion = nn.CrossEntropyLoss()\n",
        "optimizer = AdaBelief(model.parameters(), lr=3e-04)\n",
        "path = 'brandnew_transformer.pt'\n",
        "n_epochs = 50\n",
        "train_acc_list, val_acc_list = trainImageText(model, train_loader, val_loader, n_epochs, criterion, optimizer, device, path, data_aug=0.6)\n",
        "model = torch.load(path)\n",
        "_, test_acc = evaluateImageText(model, test_loader, criterion, device)\n",
        "print('Accuracy in test set: %.2f%%' % (100 * test_acc))\n",
        "n_epochs = 30\n",
        "model = torch.load(path)\n",
        "optimizer = AdaBelief(model.parameters(), lr=1e-04)\n",
        "train_acc_list, val_acc_list = trainImageText(model, train_loader, val_loader, n_epochs, criterion, optimizer, device, path, data_aug=0)\n",
        "model = torch.load(path)\n",
        "_, test_acc = evaluateImageText(model, test_loader, criterion, device)\n",
        "print('Accuracy in test set: %.2f%%' % (100 * test_acc))"
      ],
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\u001b[31mPlease check your arguments if you have upgraded adabelief-pytorch from version 0.0.5.\n",
            "\u001b[31mModifications to default arguments:\n",
            "\u001b[31m                           eps  weight_decouple    rectify\n",
            "-----------------------  -----  -----------------  ---------\n",
            "adabelief-pytorch=0.0.5  1e-08  False              False\n",
            "Current version (0.1.0)  1e-16  True               True\n",
            "\u001b[31mFor a complete table of recommended hyperparameters, see\n",
            "\u001b[31mhttps://github.com/juntang-zhuang/Adabelief-Optimizer\n",
            "\u001b[0m\n",
            "Weight decoupling enabled in AdaBelief\n",
            "Rectification enabled in AdaBelief\n",
            "1/50 (1m 55s) train loss: 1.756, train acc: 53.03%, val loss: 1.166, val acc: 67.53%\n",
            "Validation accuracy in first epoch: 67.53%\n",
            "Saving model...\n",
            "2/50 (3m 53s) train loss: 1.188, train acc: 67.07%, val loss: 0.799, val acc: 78.49%\n",
            "Validation accuracy is increasing: 67.53% --> 78.49%\n",
            "Saving model...\n",
            "3/50 (5m 48s) train loss: 1.023, train acc: 70.97%, val loss: 0.652, val acc: 81.68%\n",
            "Validation accuracy is increasing: 78.49% --> 81.68%\n",
            "Saving model...\n",
            "4/50 (7m 43s) train loss: 0.902, train acc: 73.84%, val loss: 0.567, val acc: 83.90%\n",
            "Validation accuracy is increasing: 81.68% --> 83.90%\n",
            "Saving model...\n",
            "5/50 (9m 38s) train loss: 0.779, train acc: 77.51%, val loss: 0.547, val acc: 84.64%\n",
            "Validation accuracy is increasing: 83.90% --> 84.64%\n",
            "Saving model...\n",
            "6/50 (11m 34s) train loss: 0.697, train acc: 79.68%, val loss: 0.402, val acc: 88.34%\n",
            "Validation accuracy is increasing: 84.64% --> 88.34%\n",
            "Saving model...\n",
            "7/50 (13m 27s) train loss: 0.566, train acc: 83.46%, val loss: 0.390, val acc: 88.16%\n",
            "8/50 (15m 22s) train loss: 0.511, train acc: 84.99%, val loss: 0.300, val acc: 91.54%\n",
            "Validation accuracy is increasing: 88.34% --> 91.54%\n",
            "Saving model...\n",
            "9/50 (17m 18s) train loss: 0.416, train acc: 87.61%, val loss: 0.304, val acc: 92.14%\n",
            "Validation accuracy is increasing: 91.54% --> 92.14%\n",
            "Saving model...\n",
            "10/50 (19m 14s) train loss: 0.417, train acc: 88.13%, val loss: 0.267, val acc: 92.78%\n",
            "Validation accuracy is increasing: 92.14% --> 92.78%\n",
            "Saving model...\n",
            "11/50 (21m 9s) train loss: 0.324, train acc: 90.66%, val loss: 0.236, val acc: 93.66%\n",
            "Validation accuracy is increasing: 92.78% --> 93.66%\n",
            "Saving model...\n",
            "12/50 (23m 4s) train loss: 0.287, train acc: 91.75%, val loss: 0.192, val acc: 94.68%\n",
            "Validation accuracy is increasing: 93.66% --> 94.68%\n",
            "Saving model...\n",
            "13/50 (25m 0s) train loss: 0.251, train acc: 93.03%, val loss: 0.170, val acc: 95.70%\n",
            "Validation accuracy is increasing: 94.68% --> 95.70%\n",
            "Saving model...\n",
            "14/50 (26m 56s) train loss: 0.236, train acc: 93.47%, val loss: 0.202, val acc: 94.40%\n",
            "15/50 (28m 51s) train loss: 0.300, train acc: 91.54%, val loss: 0.168, val acc: 95.74%\n",
            "Validation accuracy is increasing: 95.70% --> 95.74%\n",
            "Saving model...\n",
            "16/50 (30m 45s) train loss: 0.250, train acc: 92.86%, val loss: 0.154, val acc: 96.11%\n",
            "Validation accuracy is increasing: 95.74% --> 96.11%\n",
            "Saving model...\n",
            "17/50 (32m 40s) train loss: 0.192, train acc: 94.55%, val loss: 0.135, val acc: 96.53%\n",
            "Validation accuracy is increasing: 96.11% --> 96.53%\n",
            "Saving model...\n",
            "18/50 (34m 33s) train loss: 0.183, train acc: 94.89%, val loss: 0.128, val acc: 96.95%\n",
            "Validation accuracy is increasing: 96.53% --> 96.95%\n",
            "Saving model...\n",
            "19/50 (36m 28s) train loss: 0.195, train acc: 94.63%, val loss: 0.128, val acc: 96.76%\n",
            "20/50 (38m 23s) train loss: 0.157, train acc: 95.63%, val loss: 0.119, val acc: 96.62%\n",
            "21/50 (40m 16s) train loss: 0.143, train acc: 96.00%, val loss: 0.134, val acc: 96.25%\n",
            "22/50 (42m 11s) train loss: 0.198, train acc: 94.39%, val loss: 0.129, val acc: 96.58%\n",
            "23/50 (44m 5s) train loss: 0.138, train acc: 96.08%, val loss: 0.110, val acc: 97.09%\n",
            "Validation accuracy is increasing: 96.95% --> 97.09%\n",
            "Saving model...\n",
            "24/50 (45m 59s) train loss: 0.122, train acc: 96.58%, val loss: 0.125, val acc: 96.76%\n",
            "25/50 (47m 53s) train loss: 0.128, train acc: 96.50%, val loss: 0.122, val acc: 96.76%\n",
            "26/50 (49m 46s) train loss: 0.112, train acc: 96.74%, val loss: 0.117, val acc: 96.81%\n",
            "27/50 (51m 39s) train loss: 0.110, train acc: 96.75%, val loss: 0.136, val acc: 96.02%\n",
            "28/50 (53m 34s) train loss: 0.144, train acc: 96.03%, val loss: 0.135, val acc: 96.25%\n",
            "29/50 (55m 28s) train loss: 0.105, train acc: 96.88%, val loss: 0.107, val acc: 96.90%\n",
            "30/50 (57m 23s) train loss: 0.161, train acc: 95.79%, val loss: 0.112, val acc: 96.72%\n",
            "31/50 (59m 18s) train loss: 0.095, train acc: 97.26%, val loss: 0.116, val acc: 96.62%\n",
            "32/50 (61m 12s) train loss: 0.086, train acc: 97.54%, val loss: 0.119, val acc: 96.67%\n",
            "33/50 (63m 7s) train loss: 0.087, train acc: 97.61%, val loss: 0.108, val acc: 97.13%\n",
            "Validation accuracy is increasing: 97.09% --> 97.13%\n",
            "Saving model...\n",
            "34/50 (65m 1s) train loss: 0.134, train acc: 96.17%, val loss: 0.110, val acc: 97.09%\n",
            "35/50 (66m 56s) train loss: 0.090, train acc: 97.35%, val loss: 0.103, val acc: 96.85%\n",
            "36/50 (68m 51s) train loss: 0.078, train acc: 97.77%, val loss: 0.106, val acc: 97.32%\n",
            "Validation accuracy is increasing: 97.13% --> 97.32%\n",
            "Saving model...\n",
            "37/50 (70m 45s) train loss: 0.075, train acc: 97.80%, val loss: 0.114, val acc: 97.22%\n",
            "38/50 (72m 40s) train loss: 0.069, train acc: 97.99%, val loss: 0.126, val acc: 96.76%\n",
            "39/50 (74m 35s) train loss: 0.073, train acc: 98.01%, val loss: 0.117, val acc: 97.13%\n",
            "40/50 (76m 29s) train loss: 0.066, train acc: 98.10%, val loss: 0.112, val acc: 97.22%\n",
            "41/50 (78m 25s) train loss: 0.081, train acc: 97.71%, val loss: 0.112, val acc: 97.32%\n",
            "42/50 (80m 20s) train loss: 0.056, train acc: 98.38%, val loss: 0.122, val acc: 97.13%\n",
            "43/50 (82m 17s) train loss: 0.062, train acc: 98.15%, val loss: 0.108, val acc: 97.36%\n",
            "Validation accuracy is increasing: 97.32% --> 97.36%\n",
            "Saving model...\n",
            "44/50 (84m 13s) train loss: 0.056, train acc: 98.37%, val loss: 0.139, val acc: 96.67%\n",
            "45/50 (86m 9s) train loss: 0.052, train acc: 98.50%, val loss: 0.120, val acc: 97.36%\n",
            "46/50 (88m 4s) train loss: 0.056, train acc: 98.41%, val loss: 0.158, val acc: 95.79%\n",
            "47/50 (90m 0s) train loss: 0.047, train acc: 98.68%, val loss: 0.106, val acc: 97.22%\n",
            "48/50 (91m 56s) train loss: 0.046, train acc: 98.54%, val loss: 0.116, val acc: 97.22%\n",
            "49/50 (93m 51s) train loss: 0.052, train acc: 98.49%, val loss: 0.112, val acc: 97.59%\n",
            "Validation accuracy is increasing: 97.36% --> 97.59%\n",
            "Saving model...\n",
            "50/50 (95m 46s) train loss: 0.060, train acc: 98.31%, val loss: 0.229, val acc: 95.24%\n",
            "Accuracy in test set: 97.00%\n",
            "\u001b[31mPlease check your arguments if you have upgraded adabelief-pytorch from version 0.0.5.\n",
            "\u001b[31mModifications to default arguments:\n",
            "\u001b[31m                           eps  weight_decouple    rectify\n",
            "-----------------------  -----  -----------------  ---------\n",
            "adabelief-pytorch=0.0.5  1e-08  False              False\n",
            "Current version (0.1.0)  1e-16  True               True\n",
            "\u001b[31mFor a complete table of recommended hyperparameters, see\n",
            "\u001b[31mhttps://github.com/juntang-zhuang/Adabelief-Optimizer\n",
            "\u001b[0m\n",
            "Weight decoupling enabled in AdaBelief\n",
            "Rectification enabled in AdaBelief\n",
            "1/30 (1m 42s) train loss: 0.034, train acc: 99.05%, val loss: 0.102, val acc: 97.46%\n",
            "Validation accuracy in first epoch: 97.46%\n",
            "Saving model...\n",
            "2/30 (3m 25s) train loss: 0.030, train acc: 99.17%, val loss: 0.098, val acc: 97.78%\n",
            "Validation accuracy is increasing: 97.46% --> 97.78%\n",
            "Saving model...\n",
            "3/30 (5m 7s) train loss: 0.030, train acc: 99.17%, val loss: 0.099, val acc: 97.46%\n",
            "4/30 (6m 49s) train loss: 0.027, train acc: 99.23%, val loss: 0.100, val acc: 97.59%\n",
            "5/30 (8m 33s) train loss: 0.029, train acc: 99.05%, val loss: 0.100, val acc: 97.59%\n",
            "6/30 (10m 14s) train loss: 0.027, train acc: 99.20%, val loss: 0.107, val acc: 97.32%\n",
            "7/30 (11m 56s) train loss: 0.028, train acc: 99.17%, val loss: 0.103, val acc: 97.55%\n",
            "8/30 (13m 37s) train loss: 0.026, train acc: 99.24%, val loss: 0.099, val acc: 97.64%\n",
            "9/30 (15m 18s) train loss: 0.026, train acc: 99.18%, val loss: 0.098, val acc: 97.78%\n",
            "10/30 (17m 0s) train loss: 0.027, train acc: 99.13%, val loss: 0.101, val acc: 97.46%\n",
            "11/30 (18m 41s) train loss: 0.026, train acc: 99.29%, val loss: 0.105, val acc: 97.73%\n",
            "12/30 (20m 22s) train loss: 0.026, train acc: 99.13%, val loss: 0.105, val acc: 97.41%\n",
            "13/30 (22m 3s) train loss: 0.026, train acc: 99.27%, val loss: 0.104, val acc: 97.46%\n",
            "14/30 (23m 45s) train loss: 0.025, train acc: 99.26%, val loss: 0.111, val acc: 97.41%\n",
            "15/30 (25m 26s) train loss: 0.025, train acc: 99.22%, val loss: 0.101, val acc: 97.64%\n",
            "16/30 (27m 8s) train loss: 0.022, train acc: 99.31%, val loss: 0.100, val acc: 97.50%\n",
            "17/30 (28m 50s) train loss: 0.025, train acc: 99.29%, val loss: 0.102, val acc: 97.73%\n",
            "18/30 (30m 31s) train loss: 0.020, train acc: 99.35%, val loss: 0.100, val acc: 97.69%\n",
            "19/30 (32m 13s) train loss: 0.021, train acc: 99.32%, val loss: 0.098, val acc: 97.69%\n",
            "20/30 (33m 55s) train loss: 0.021, train acc: 99.40%, val loss: 0.100, val acc: 97.50%\n",
            "21/30 (35m 36s) train loss: 0.020, train acc: 99.47%, val loss: 0.101, val acc: 97.50%\n",
            "22/30 (37m 18s) train loss: 0.019, train acc: 99.43%, val loss: 0.106, val acc: 97.55%\n",
            "23/30 (38m 59s) train loss: 0.017, train acc: 99.46%, val loss: 0.097, val acc: 97.64%\n",
            "24/30 (40m 40s) train loss: 0.026, train acc: 99.17%, val loss: 0.101, val acc: 97.64%\n",
            "25/30 (42m 21s) train loss: 0.023, train acc: 99.36%, val loss: 0.114, val acc: 97.41%\n",
            "26/30 (44m 2s) train loss: 0.018, train acc: 99.44%, val loss: 0.106, val acc: 97.83%\n",
            "Validation accuracy is increasing: 97.78% --> 97.83%\n",
            "Saving model...\n",
            "27/30 (45m 44s) train loss: 0.018, train acc: 99.53%, val loss: 0.114, val acc: 97.59%\n",
            "28/30 (47m 25s) train loss: 0.018, train acc: 99.46%, val loss: 0.108, val acc: 97.69%\n",
            "29/30 (49m 7s) train loss: 0.015, train acc: 99.56%, val loss: 0.113, val acc: 97.32%\n",
            "30/30 (50m 50s) train loss: 0.016, train acc: 99.52%, val loss: 0.109, val acc: 97.69%\n",
            "Accuracy in test set: 97.09%\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "k5AUTFmRWydW"
      },
      "source": [
        "class MobileRCNN(nn.Module):\n",
        "    def __init__(self, n_vocab, output_size=27, embedding_size=512, n_layers=1, hidden_size=128, dropout=0.1):\n",
        "        super(MobileRCNN, self).__init__()\n",
        "\n",
        "        self.imagemodel = models.mobilenet_v2(pretrained=True)\n",
        "        self.fc0 = nn.Linear(1000, embedding_size)\n",
        "        self.embedding = nn.Embedding(n_vocab, embedding_size, padding_idx=1)\n",
        "        self.lstm = nn.LSTM(input_size=embedding_size, hidden_size=hidden_size, num_layers=n_layers,\n",
        "                            dropout=dropout, bidirectional=True, batch_first=True)\n",
        "        self.dropout = nn.Dropout(dropout)\n",
        "        self.W = nn.Linear(embedding_size + 2 * hidden_size, hidden_size)\n",
        "        self.tanh = nn.Tanh()\n",
        "        self.fc = nn.Linear(hidden_size, output_size)\n",
        "        self.softmax = nn.LogSoftmax(dim=1)\n",
        "\n",
        "    def forward(self, image, x):\n",
        "        image = self.imagemodel(image)\n",
        "        image = self.fc0(image)\n",
        "        image = torch.unsqueeze(image, dim=1)\n",
        "        x = self.embedding(x)\n",
        "        x = x + image\n",
        "        lstm_out, _ = self.lstm(x)  # lstm_out = (batch_size, seq_len, 2 * hidden_size)\n",
        "        x = torch.cat([lstm_out, x], 2)\n",
        "        linear_output = self.tanh(self.W(x))\n",
        "        linear_output = linear_output.permute(0, 2, 1)  # linear_output = (batch_size, seq_len, hidden_size)\n",
        "        max_out_features = F.max_pool1d(linear_output, linear_output.size()[2]).squeeze(2)\n",
        "        max_out_features = self.dropout(max_out_features)\n",
        "        out = self.fc(max_out_features)\n",
        "        return out"
      ],
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Hs3J0enpXspO",
        "outputId": "a9aeffcc-5e98-44c3-9363-286c7e968f37"
      },
      "source": [
        "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
        "model = MobileRCNN(len(train_vocab2num), dropout=0.3).to(device)\n",
        "criterion = nn.CrossEntropyLoss()\n",
        "optimizer = AdaBelief(model.parameters(), lr=3e-04)\n",
        "path = 'mobilercnn.pt'\n",
        "n_epochs = 20\n",
        "train_acc_list, val_acc_list = trainImageText(model, train_loader, val_loader, n_epochs, criterion, optimizer, device, path, data_aug=0.6, fusion_ratio=0.05)\n",
        "model = torch.load(path)\n",
        "_, test_acc = evaluateImageText(model, test_loader, criterion, device)\n",
        "print('Accuracy in test set: %.2f%%' % (100 * test_acc))"
      ],
      "execution_count": 78,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/torch/nn/modules/rnn.py:61: UserWarning: dropout option adds dropout after all but last recurrent layer, so non-zero dropout expects num_layers greater than 1, but got dropout=0.3 and num_layers=1\n",
            "  \"num_layers={}\".format(dropout, num_layers))\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\u001b[31mPlease check your arguments if you have upgraded adabelief-pytorch from version 0.0.5.\n",
            "\u001b[31mModifications to default arguments:\n",
            "\u001b[31m                           eps  weight_decouple    rectify\n",
            "-----------------------  -----  -----------------  ---------\n",
            "adabelief-pytorch=0.0.5  1e-08  False              False\n",
            "Current version (0.1.0)  1e-16  True               True\n",
            "\u001b[31mFor a complete table of recommended hyperparameters, see\n",
            "\u001b[31mhttps://github.com/juntang-zhuang/Adabelief-Optimizer\n",
            "\u001b[0m\n",
            "Weight decoupling enabled in AdaBelief\n",
            "Rectification enabled in AdaBelief\n",
            "1/20 (1m 57s) train loss: 1.376, train acc: 65.30%, val loss: 0.666, val acc: 83.21%\n",
            "Validation accuracy in first epoch: 83.21%\n",
            "Saving model...\n",
            "2/20 (3m 54s) train loss: 0.682, train acc: 82.12%, val loss: 0.406, val acc: 89.27%\n",
            "Validation accuracy is increasing: 83.21% --> 89.27%\n",
            "Saving model...\n",
            "3/20 (5m 51s) train loss: 0.488, train acc: 87.34%, val loss: 0.295, val acc: 92.28%\n",
            "Validation accuracy is increasing: 89.27% --> 92.28%\n",
            "Saving model...\n",
            "4/20 (7m 49s) train loss: 0.363, train acc: 90.69%, val loss: 0.222, val acc: 95.19%\n",
            "Validation accuracy is increasing: 92.28% --> 95.19%\n",
            "Saving model...\n",
            "5/20 (9m 45s) train loss: 0.275, train acc: 93.30%, val loss: 0.174, val acc: 95.84%\n",
            "Validation accuracy is increasing: 95.19% --> 95.84%\n",
            "Saving model...\n",
            "6/20 (11m 42s) train loss: 0.218, train acc: 94.88%, val loss: 0.149, val acc: 96.39%\n",
            "Validation accuracy is increasing: 95.84% --> 96.39%\n",
            "Saving model...\n",
            "7/20 (13m 40s) train loss: 0.180, train acc: 95.58%, val loss: 0.126, val acc: 96.58%\n",
            "Validation accuracy is increasing: 96.39% --> 96.58%\n",
            "Saving model...\n",
            "8/20 (15m 37s) train loss: 0.186, train acc: 95.54%, val loss: 0.136, val acc: 96.62%\n",
            "Validation accuracy is increasing: 96.58% --> 96.62%\n",
            "Saving model...\n",
            "9/20 (17m 35s) train loss: 0.156, train acc: 96.17%, val loss: 0.107, val acc: 97.04%\n",
            "Validation accuracy is increasing: 96.62% --> 97.04%\n",
            "Saving model...\n",
            "10/20 (19m 33s) train loss: 0.153, train acc: 96.35%, val loss: 0.103, val acc: 97.18%\n",
            "Validation accuracy is increasing: 97.04% --> 97.18%\n",
            "Saving model...\n",
            "11/20 (21m 30s) train loss: 0.101, train acc: 97.66%, val loss: 0.112, val acc: 97.22%\n",
            "Validation accuracy is increasing: 97.18% --> 97.22%\n",
            "Saving model...\n",
            "12/20 (23m 28s) train loss: 0.092, train acc: 97.98%, val loss: 0.088, val acc: 97.64%\n",
            "Validation accuracy is increasing: 97.22% --> 97.64%\n",
            "Saving model...\n",
            "13/20 (25m 24s) train loss: 0.072, train acc: 98.39%, val loss: 0.104, val acc: 97.27%\n",
            "14/20 (27m 22s) train loss: 0.085, train acc: 98.10%, val loss: 0.100, val acc: 97.55%\n",
            "15/20 (29m 19s) train loss: 0.081, train acc: 98.25%, val loss: 0.092, val acc: 97.59%\n",
            "16/20 (31m 15s) train loss: 0.065, train acc: 98.67%, val loss: 0.130, val acc: 96.30%\n",
            "17/20 (33m 13s) train loss: 0.066, train acc: 98.55%, val loss: 0.110, val acc: 97.22%\n",
            "18/20 (35m 9s) train loss: 0.064, train acc: 98.62%, val loss: 0.119, val acc: 96.85%\n",
            "19/20 (37m 6s) train loss: 0.152, train acc: 96.22%, val loss: 0.174, val acc: 95.14%\n",
            "20/20 (39m 3s) train loss: 0.133, train acc: 96.93%, val loss: 0.163, val acc: 95.37%\n",
            "Accuracy in test set: 97.18%\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mSMMqITiqPBI",
        "outputId": "7b97cb8f-269b-46fa-a622-8335b9a2afad"
      },
      "source": [
        "n_epochs = 10\n",
        "path = 'mobilercnn.pt'\n",
        "model = torch.load(path)\n",
        "optimizer = AdaBelief(model.parameters(), lr=1e-04)\n",
        "train_acc_list, val_acc_list = trainImageText(model, train_loader, val_loader, n_epochs, criterion, optimizer, device, path, data_aug=0)\n",
        "model = torch.load(path)\n",
        "_, test_acc = evaluateImageText(model, test_loader, criterion, device)\n",
        "print('Accuracy in test set: %.2f%%' % (100 * test_acc))"
      ],
      "execution_count": 79,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\u001b[31mPlease check your arguments if you have upgraded adabelief-pytorch from version 0.0.5.\n",
            "\u001b[31mModifications to default arguments:\n",
            "\u001b[31m                           eps  weight_decouple    rectify\n",
            "-----------------------  -----  -----------------  ---------\n",
            "adabelief-pytorch=0.0.5  1e-08  False              False\n",
            "Current version (0.1.0)  1e-16  True               True\n",
            "\u001b[31mFor a complete table of recommended hyperparameters, see\n",
            "\u001b[31mhttps://github.com/juntang-zhuang/Adabelief-Optimizer\n",
            "\u001b[0m\n",
            "Weight decoupling enabled in AdaBelief\n",
            "Rectification enabled in AdaBelief\n",
            "1/10 (1m 46s) train loss: 0.058, train acc: 98.88%, val loss: 0.086, val acc: 97.59%\n",
            "Validation accuracy in first epoch: 97.59%\n",
            "Saving model...\n",
            "2/10 (3m 32s) train loss: 0.051, train acc: 99.02%, val loss: 0.078, val acc: 97.87%\n",
            "Validation accuracy is increasing: 97.59% --> 97.87%\n",
            "Saving model...\n",
            "3/10 (5m 18s) train loss: 0.053, train acc: 98.87%, val loss: 0.076, val acc: 97.87%\n",
            "4/10 (7m 4s) train loss: 0.051, train acc: 98.97%, val loss: 0.073, val acc: 98.06%\n",
            "Validation accuracy is increasing: 97.87% --> 98.06%\n",
            "Saving model...\n",
            "5/10 (8m 50s) train loss: 0.048, train acc: 99.04%, val loss: 0.074, val acc: 97.92%\n",
            "6/10 (10m 35s) train loss: 0.051, train acc: 98.97%, val loss: 0.075, val acc: 97.87%\n",
            "7/10 (12m 21s) train loss: 0.037, train acc: 99.27%, val loss: 0.074, val acc: 98.10%\n",
            "Validation accuracy is increasing: 98.06% --> 98.10%\n",
            "Saving model...\n",
            "8/10 (14m 6s) train loss: 0.037, train acc: 99.20%, val loss: 0.076, val acc: 98.06%\n",
            "9/10 (15m 52s) train loss: 0.046, train acc: 98.99%, val loss: 0.093, val acc: 97.22%\n",
            "10/10 (17m 37s) train loss: 0.035, train acc: 99.30%, val loss: 0.077, val acc: 97.69%\n",
            "Accuracy in test set: 97.50%\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4pI0xNtIN3Eo"
      },
      "source": [
        "import torchvision.models as models\n",
        "\n",
        "class VGGTextCNN(nn.Module):\n",
        "    def __init__(self, n_vocab, embedding_size=512, output_size=27, kernel_num=100, kernel_size=[3, 4, 5], dropout=0.1):\n",
        "        super(VGGTextCNN, self).__init__()\n",
        "\n",
        "        self.imagemodel = models.vgg11_bn(pretrained=True)\n",
        "        self.fc0 = nn.Linear(1000, embedding_size)\n",
        "        V = n_vocab\n",
        "        D = embedding_size\n",
        "        C = output_size\n",
        "        Ci = 1\n",
        "        Co = kernel_num\n",
        "        Ks = kernel_size\n",
        "\n",
        "        self.embed = nn.Embedding(V, D, padding_idx=1)\n",
        "        self.convs = nn.ModuleList([nn.Conv2d(Ci, Co, (K, D)) for K in Ks])\n",
        "        self.dropout = nn.Dropout(dropout)\n",
        "        self.fc1 = nn.Linear(len(Ks) * Co, 128)\n",
        "        self.fc2 = nn.Linear(128, C)\n",
        "        self.relu = nn.ReLU()\n",
        "\n",
        "    def forward(self, image, x):\n",
        "        image = self.imagemodel(image)\n",
        "        image = self.fc0(image)\n",
        "        image = torch.unsqueeze(image, dim=1)\n",
        "        x = self.embed(x)  # (N, W, D)\n",
        "\n",
        "        x = x + image\n",
        "        x = x.unsqueeze(1)  # (N, Ci, W, D)\n",
        "\n",
        "        x = [self.relu(conv(x)).squeeze(3) for conv in self.convs]  # [(N, Co, W), ...]*len(Ks)\n",
        "\n",
        "        x = [F.max_pool1d(i, i.size(2)).squeeze(2) for i in x]  # [(N, Co), ...]*len(Ks)\n",
        "\n",
        "        x = torch.cat(x, 1)\n",
        "\n",
        "        x = self.dropout(x)  # (N, len(Ks)*Co)\n",
        "        x = self.relu(self.fc1(x))\n",
        "        return self.dropout(self.fc2(x))"
      ],
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pHWdzwd0PI4B",
        "outputId": "0307f9c5-39d7-4a07-fda0-793a0617659f"
      },
      "source": [
        "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
        "model = VGGTextCNN(len(train_vocab2num), dropout=0.3).to(device)\n",
        "criterion = nn.CrossEntropyLoss()\n",
        "optimizer = AdaBelief(model.parameters(), lr=3e-04)\n",
        "path = 'vggtext.pt'\n",
        "n_epochs = 20\n",
        "train_acc_list, val_acc_list = trainImageText(model, train_loader, val_loader, n_epochs, criterion, optimizer, device, path, data_aug=0.6, fusion_ratio=0.05)\n",
        "model = torch.load(path)\n",
        "_, test_acc = evaluateImageText(model, test_loader, criterion, device)\n",
        "print('Accuracy in test set: %.2f%%' % (100 * test_acc))\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\u001b[31mPlease check your arguments if you have upgraded adabelief-pytorch from version 0.0.5.\n",
            "\u001b[31mModifications to default arguments:\n",
            "\u001b[31m                           eps  weight_decouple    rectify\n",
            "-----------------------  -----  -----------------  ---------\n",
            "adabelief-pytorch=0.0.5  1e-08  False              False\n",
            "Current version (0.1.0)  1e-16  True               True\n",
            "\u001b[31mFor a complete table of recommended hyperparameters, see\n",
            "\u001b[31mhttps://github.com/juntang-zhuang/Adabelief-Optimizer\n",
            "\u001b[0m\n",
            "Weight decoupling enabled in AdaBelief\n",
            "Rectification enabled in AdaBelief\n",
            "1/20 (2m 28s) train loss: 1.835, train acc: 50.23%, val loss: 0.668, val acc: 83.67%\n",
            "Validation accuracy in first epoch: 83.67%\n",
            "Saving model...\n",
            "2/20 (5m 0s) train loss: 1.232, train acc: 64.98%, val loss: 0.362, val acc: 91.07%\n",
            "Validation accuracy is increasing: 83.67% --> 91.07%\n",
            "Saving model...\n",
            "3/20 (7m 27s) train loss: 1.002, train acc: 70.24%, val loss: 0.229, val acc: 94.63%\n",
            "Validation accuracy is increasing: 91.07% --> 94.63%\n",
            "Saving model...\n",
            "4/20 (9m 56s) train loss: 0.901, train acc: 72.18%, val loss: 0.187, val acc: 95.42%\n",
            "Validation accuracy is increasing: 94.63% --> 95.42%\n",
            "Saving model...\n",
            "5/20 (12m 26s) train loss: 0.867, train acc: 72.85%, val loss: 0.158, val acc: 95.70%\n",
            "Validation accuracy is increasing: 95.42% --> 95.70%\n",
            "Saving model...\n",
            "6/20 (14m 57s) train loss: 0.828, train acc: 73.57%, val loss: 0.165, val acc: 95.70%\n",
            "7/20 (17m 28s) train loss: 0.793, train acc: 74.32%, val loss: 0.133, val acc: 96.76%\n",
            "Validation accuracy is increasing: 95.70% --> 96.76%\n",
            "Saving model...\n",
            "8/20 (20m 4s) train loss: 0.757, train acc: 75.05%, val loss: 0.177, val acc: 95.33%\n",
            "9/20 (22m 32s) train loss: 0.740, train acc: 75.55%, val loss: 0.139, val acc: 96.62%\n",
            "10/20 (25m 3s) train loss: 0.734, train acc: 75.86%, val loss: 0.163, val acc: 95.61%\n",
            "11/20 (27m 32s) train loss: 0.721, train acc: 75.70%, val loss: 0.161, val acc: 95.84%\n",
            "12/20 (30m 0s) train loss: 0.717, train acc: 75.75%, val loss: 0.169, val acc: 95.79%\n",
            "13/20 (32m 29s) train loss: 0.693, train acc: 76.43%, val loss: 0.182, val acc: 95.61%\n",
            "14/20 (34m 55s) train loss: 0.712, train acc: 75.71%, val loss: 0.173, val acc: 96.11%\n",
            "15/20 (37m 23s) train loss: 0.702, train acc: 76.20%, val loss: 0.178, val acc: 95.88%\n",
            "16/20 (39m 51s) train loss: 0.689, train acc: 76.70%, val loss: 0.253, val acc: 94.59%\n",
            "17/20 (42m 19s) train loss: 0.693, train acc: 76.30%, val loss: 0.224, val acc: 94.77%\n",
            "18/20 (44m 47s) train loss: 0.675, train acc: 76.76%, val loss: 0.228, val acc: 94.40%\n",
            "19/20 (47m 16s) train loss: 0.672, train acc: 76.75%, val loss: 0.198, val acc: 95.56%\n",
            "20/20 (49m 45s) train loss: 0.664, train acc: 77.10%, val loss: 0.203, val acc: 95.65%\n",
            "Accuracy in test set: 96.58%\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yuurUG2tPUtX",
        "outputId": "fccb9321-b74d-4229-801e-37b10bab5356"
      },
      "source": [
        "n_epochs = 10\n",
        "model = torch.load(path)\n",
        "optimizer = AdaBelief(model.parameters(), lr=1e-04)\n",
        "train_acc_list, val_acc_list = trainImageText(model, train_loader, val_loader, n_epochs, criterion, optimizer, device, path, data_aug=0)\n",
        "model = torch.load(path)\n",
        "_, test_acc = evaluateImageText(model, test_loader, criterion, device)\n",
        "print('Accuracy in test set: %.2f%%' % (100 * test_acc))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\u001b[31mPlease check your arguments if you have upgraded adabelief-pytorch from version 0.0.5.\n",
            "\u001b[31mModifications to default arguments:\n",
            "\u001b[31m                           eps  weight_decouple    rectify\n",
            "-----------------------  -----  -----------------  ---------\n",
            "adabelief-pytorch=0.0.5  1e-08  False              False\n",
            "Current version (0.1.0)  1e-16  True               True\n",
            "\u001b[31mFor a complete table of recommended hyperparameters, see\n",
            "\u001b[31mhttps://github.com/juntang-zhuang/Adabelief-Optimizer\n",
            "\u001b[0m\n",
            "Weight decoupling enabled in AdaBelief\n",
            "Rectification enabled in AdaBelief\n",
            "1/10 (1m 43s) train loss: 0.749, train acc: 75.08%, val loss: 0.112, val acc: 97.32%\n",
            "Validation accuracy in first epoch: 97.32%\n",
            "Saving model...\n",
            "2/10 (3m 27s) train loss: 0.717, train acc: 76.18%, val loss: 0.107, val acc: 97.41%\n",
            "Validation accuracy is increasing: 97.32% --> 97.41%\n",
            "Saving model...\n",
            "3/10 (5m 12s) train loss: 0.713, train acc: 76.25%, val loss: 0.111, val acc: 97.64%\n",
            "Validation accuracy is increasing: 97.41% --> 97.64%\n",
            "Saving model...\n",
            "4/10 (6m 57s) train loss: 0.706, train acc: 75.97%, val loss: 0.120, val acc: 97.32%\n",
            "5/10 (8m 40s) train loss: 0.700, train acc: 76.20%, val loss: 0.104, val acc: 97.87%\n",
            "Validation accuracy is increasing: 97.64% --> 97.87%\n",
            "Saving model...\n",
            "6/10 (10m 25s) train loss: 0.696, train acc: 76.53%, val loss: 0.124, val acc: 97.22%\n",
            "7/10 (12m 8s) train loss: 0.708, train acc: 75.66%, val loss: 0.116, val acc: 97.46%\n",
            "8/10 (13m 51s) train loss: 0.696, train acc: 76.15%, val loss: 0.116, val acc: 97.04%\n",
            "9/10 (15m 34s) train loss: 0.692, train acc: 76.57%, val loss: 0.113, val acc: 97.41%\n",
            "10/10 (17m 18s) train loss: 0.688, train acc: 76.64%, val loss: 0.126, val acc: 97.04%\n",
            "Accuracy in test set: 97.55%\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NHlgk1st9ujl"
      },
      "source": [
        "from scipy.stats import mode\n",
        "\n",
        "def predictByMode(model_list, model_usage, image, src, device):    \n",
        "    pred = torch.zeros(image.size(0), len(model_list))\n",
        "    for i, model in enumerate(model_list):\n",
        "        model.eval()\n",
        "        model = model.to(device)\n",
        "        if model_usage[i]['img'] and model_usage[i]['src']:\n",
        "            image = image.to(device)\n",
        "            src = src.to(device)\n",
        "            pred[:, i] = torch.argmax(model(image, src), dim=1)\n",
        "        elif model_usage[i]['img']:\n",
        "            image = image.to(device)\n",
        "            pred[:, i] = torch.argmax(model(image), dim=1)\n",
        "        else:\n",
        "            src = src.to(device)\n",
        "            pred[:, i] = torch.argmax(model(src), dim=1)\n",
        "    \n",
        "    pred = torch.from_numpy(mode(pred, axis=1)[0])\n",
        "    return torch.squeeze(pred, dim=1).to(device)"
      ],
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jsrHNlEILQZ7"
      },
      "source": [
        "def predictByWeight(model_list, model_usage, image, src, device, weights, num_classes=27):\n",
        "    pred = torch.zeros(image.size(0), num_classes).to(device)\n",
        "    for i in range(len(model_list)):\n",
        "        model = model_list[i].to(device)\n",
        "        model.eval()\n",
        "        output = None\n",
        "        if model_usage[i]['img'] and model_usage[i]['src']:\n",
        "            image = image.to(device)\n",
        "            src = src.to(device)\n",
        "            output = model(image, src)\n",
        "        elif model_usage[i]['img']:\n",
        "            image = image.to(device)\n",
        "            output = model(image)\n",
        "        else:\n",
        "            src = src.to(device)\n",
        "            output = model(src)\n",
        "        \n",
        "        output = weights[i] * output\n",
        "        pred.add_(output)\n",
        "    \n",
        "    return torch.argmax(pred, dim=1)"
      ],
      "execution_count": 37,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vNdyCS2RLSgx"
      },
      "source": [
        "def evaluateEnsemble(model_list, model_usage, data, device, weights=None, num_classes=27):\n",
        "    correct = 0\n",
        "    total = 0\n",
        "    for image, src, labels in data:\n",
        "        pred = None\n",
        "        labels = labels.to(device)\n",
        "        if weights is None:\n",
        "            pred = predictByMode(model_list, model_usage, image, src, device)\n",
        "        else:\n",
        "            pred = predictByWeight(model_list, model_usage, image, src, device, weights, num_classes)\n",
        "\n",
        "        correct += (pred == labels).sum().item()\n",
        "        total += len(labels)\n",
        "\n",
        "    return correct / total"
      ],
      "execution_count": 38,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fD8CrMHZv4Ya"
      },
      "source": [
        "# Test\n",
        "new_transformer = torch.load('new_transformer.pt')\n",
        "googlenet = torch.load('googlenet.pt')\n",
        "shufflenet = torch.load('shufflenet.pt')\n",
        "mobilercnn = torch.load('mobilercnn.pt')\n",
        "vggtext = torch.load('vggtext.pt')\n",
        "\n",
        "model_list = [new_transformer, googlenet, shufflenet, mobilercnn, vggtext]\n",
        "model_usage = [{'img': True, 'src': True},\n",
        "               {'img': True, 'src': False},\n",
        "               {'img': True, 'src': False},\n",
        "               {'img': True, 'src': True},\n",
        "               {'img': True, 'src': True}]"
      ],
      "execution_count": 39,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iP90HBzxz7CK",
        "outputId": "8144b731-3b03-409a-8c56-6b582afa4490"
      },
      "source": [
        "# Test\n",
        "val_acc = evaluateEnsemble(model_list, model_usage, val_loader, device)\n",
        "print('Ensemble accuracy in validation set: %.2f%%' % (100 * val_acc))\n",
        "test_acc = evaluateEnsemble(model_list, model_usage, test_loader, device)\n",
        "print('Ensemble accuracy in final test set: %.2f%%' % (100 * test_acc))"
      ],
      "execution_count": 90,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Ensemble accuracy in validation set: 98.47%\n",
            "Ensemble accuracy in final test set: 97.83%\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zKpMUkP00sSO"
      },
      "source": [
        "def generateRandomWeights(d, start=0.0, end=1.0):\n",
        "    tmp = np.random.uniform(start, end, size=d)\n",
        "    weights = [None] * len(tmp)\n",
        "    for i in range(len(weights)):\n",
        "        weights[i] = tmp[i] / np.sum(tmp)\n",
        "\n",
        "    return weights\n",
        "\n",
        "def findWeights(model_list, model_usage, train_data, val_data, device, k=1000, path='weights.pt'):\n",
        "    weights = generateRandomWeights(len(model_list))\n",
        "    tmp = weights.copy()\n",
        "    val_acc = 0\n",
        "    max_val_acc = 0\n",
        "    train_acc = 0\n",
        "    max_train_acc = 0\n",
        "    for i in range(k):\n",
        "        torch.cuda.empty_cache()\n",
        "        train_acc = evaluateEnsemble(model_list, model_usage, train_data, device, tmp)\n",
        "        print('Now %d/%d train acc: %.2f%%' % (i + 1, k, 100 * train_acc))\n",
        "        if max_train_acc < train_acc:\n",
        "            val_acc = evaluateEnsemble(model_list, model_usage, val_data, device, tmp)\n",
        "            if max_val_acc < val_acc:\n",
        "                max_train_acc = train_acc\n",
        "                print('Now %d/%d max val acc increasing: %.2f%% -> %.2f%%' % (i + 1, k, 100 * max_val_acc, 100 * val_acc))\n",
        "                max_val_acc = val_acc\n",
        "                weights = tmp.copy()\n",
        "                torch.save(weights, path)\n",
        "\n",
        "        tmp = generateRandomWeights(len(model_list))\n",
        "\n",
        "    return weights"
      ],
      "execution_count": 91,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bgz_4KR3z6eV",
        "outputId": "18aa60a0-060b-4b49-a187-25625063e2a7"
      },
      "source": [
        "# Test\n",
        "start = time.time()\n",
        "weights = findWeights(model_list, model_usage,  val_loader, test_loader, device, k=20)\n",
        "print(weights)\n",
        "print(timeSince(start))"
      ],
      "execution_count": 114,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Now 1/20 train acc: 98.01%\n",
            "Now 1/20 max val acc increasing: 0.00% -> 97.83%\n",
            "Now 2/20 train acc: 97.55%\n",
            "Now 3/20 train acc: 98.52%\n",
            "Now 4/20 train acc: 97.46%\n",
            "Now 5/20 train acc: 98.20%\n",
            "Now 5/20 max val acc increasing: 97.83% -> 97.97%\n",
            "Now 6/20 train acc: 97.78%\n",
            "Now 7/20 train acc: 98.29%\n",
            "Now 7/20 max val acc increasing: 97.97% -> 98.01%\n",
            "Now 8/20 train acc: 97.69%\n",
            "Now 9/20 train acc: 98.43%\n",
            "Now 10/20 train acc: 98.47%\n",
            "Now 11/20 train acc: 98.24%\n",
            "Now 12/20 train acc: 97.32%\n",
            "Now 13/20 train acc: 98.33%\n",
            "Now 14/20 train acc: 98.38%\n",
            "Now 15/20 train acc: 97.46%\n",
            "Now 16/20 train acc: 98.52%\n",
            "Now 17/20 train acc: 98.15%\n",
            "Now 18/20 train acc: 98.24%\n",
            "Now 19/20 train acc: 98.52%\n",
            "Now 20/20 train acc: 96.99%\n",
            "[0.22262474308644636, 0.26835218907530883, 0.14642288040955073, 0.20878930780357, 0.153810879625124]\n",
            "10m 3s\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qgdT0etn2y7z",
        "outputId": "b9f13bf8-0526-4d7a-8ef0-4acad7bf945d"
      },
      "source": [
        "val_acc = evaluateEnsemble(model_list, model_usage, val_loader, device, weights)\n",
        "print('Ensemble accuracy in validation set: %.2f%%' % (100 * val_acc))\n",
        "test_acc = evaluateEnsemble(model_list, model_usage, test_loader, device, weights)\n",
        "print('Ensemble accuracy in final test set: %.2f%%' % (100 * test_acc))"
      ],
      "execution_count": 95,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Ensemble accuracy in validation set: 98.47%\n",
            "Ensemble accuracy in final test set: 97.92%\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ri4cM_GKO34J"
      },
      "source": [
        "def makePredictions(model_list, model_usage, device, data=real_test_loader, weights=None):\n",
        "    predictions = None\n",
        "    for i, (image, src) in enumerate(data):\n",
        "        if weights is None:\n",
        "            pred = predictByMode(model_list, model_usage, image, src, device)\n",
        "        else:\n",
        "            pred = predictByWeight(model_list, model_usage, image, src, device, weights)\n",
        "        \n",
        "        if predictions is None:\n",
        "            predictions = pred\n",
        "        else:\n",
        "            predictions = torch.cat((predictions, pred), dim=0)\n",
        "\n",
        "    return predictions"
      ],
      "execution_count": 41,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 437
        },
        "id": "2EA0tdki5IoY",
        "outputId": "05d113f4-d6cf-4152-eb8a-d801d936e348"
      },
      "source": [
        "start = time.time()\n",
        "weights = torch.load('weights.pt')\n",
        "predictions = makePredictions(model_list, model_usage, device, real_test_loader, weights)\n",
        "print('%s' % timeSince(start))\n",
        "pred = [None] * len(predictions)\n",
        "for i in range(len(predictions)):\n",
        "    pred[i] = num2cate[predictions[i].item()]\n",
        "\n",
        "id_data = test_csv['id']\n",
        "predict = pd.DataFrame(data=id_data)\n",
        "predict['category'] = pred\n",
        "predict"
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "3m 10s\n"
          ],
          "name": "stdout"
        },
        {
          "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>category</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>26266</td>\n",
              "      <td>Innerwear</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>22134</td>\n",
              "      <td>Topwear</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>28358</td>\n",
              "      <td>Belts</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>15554</td>\n",
              "      <td>Shoes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>53408</td>\n",
              "      <td>Innerwear</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21623</th>\n",
              "      <td>39737</td>\n",
              "      <td>Eyewear</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21624</th>\n",
              "      <td>57477</td>\n",
              "      <td>Topwear</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21625</th>\n",
              "      <td>22312</td>\n",
              "      <td>Topwear</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21626</th>\n",
              "      <td>54105</td>\n",
              "      <td>Lips</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21627</th>\n",
              "      <td>14080</td>\n",
              "      <td>Bags</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>21628 rows × 2 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "          id   category\n",
              "0      26266  Innerwear\n",
              "1      22134    Topwear\n",
              "2      28358      Belts\n",
              "3      15554      Shoes\n",
              "4      53408  Innerwear\n",
              "...      ...        ...\n",
              "21623  39737    Eyewear\n",
              "21624  57477    Topwear\n",
              "21625  22312    Topwear\n",
              "21626  54105       Lips\n",
              "21627  14080       Bags\n",
              "\n",
              "[21628 rows x 2 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7CHZazfU5If9"
      },
      "source": [
        "predict.to_csv('predict.csv', index=False)"
      ],
      "execution_count": 43,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "id": "Tp8fzOip5IXD",
        "outputId": "fe0a37a6-e2ea-444a-a6fc-05b786593f5a"
      },
      "source": [
        "from google.colab import files\n",
        "\n",
        "files.download('predict.csv')"
      ],
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "download(\"download_bace3075-e7f7-46b2-9708-7784ca617f74\", \"predict.csv\", 296654)"
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nbeJPP_iCQfI"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}