Best_Image_Model_EffNetB4.ipynb

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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "thIgTSpyB5xc"
      },
      "source": [
        "#Hardware specs"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wLU8fr3mCBeA"
      },
      "source": [
        "##Disk Info"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YY_WxbrzBnS1"
      },
      "source": [
        "# !df -h"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "US6VsTIyCHd2"
      },
      "source": [
        "##CPU Specs"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_4NM6hnyCLEt"
      },
      "source": [
        "# !cat /proc/cpuinfo"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DUJEyUTlCOnN"
      },
      "source": [
        "##GPU specs"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-O3oBDT7CTaj",
        "outputId": "ce69b1ea-669f-404c-c01d-f774492eeda5"
      },
      "source": [
        "!nvidia-smi"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Tue May 25 17:38:44 2021       \n",
            "+-----------------------------------------------------------------------------+\n",
            "| NVIDIA-SMI 465.19.01    Driver Version: 460.32.03    CUDA Version: 11.2     |\n",
            "|-------------------------------+----------------------+----------------------+\n",
            "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
            "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n",
            "|                               |                      |               MIG M. |\n",
            "|===============================+======================+======================|\n",
            "|   0  Tesla P100-PCIE...  Off  | 00000000:00:04.0 Off |                    0 |\n",
            "| N/A   38C    P0    29W / 250W |      0MiB / 16280MiB |      0%      Default |\n",
            "|                               |                      |                  N/A |\n",
            "+-------------------------------+----------------------+----------------------+\n",
            "                                                                               \n",
            "+-----------------------------------------------------------------------------+\n",
            "| Processes:                                                                  |\n",
            "|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |\n",
            "|        ID   ID                                                   Usage      |\n",
            "|=============================================================================|\n",
            "|  No running processes found                                                 |\n",
            "+-----------------------------------------------------------------------------+\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vn8eFkGZ04EZ"
      },
      "source": [
        "# Preparation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "q6-iE37OLj0D"
      },
      "source": [
        "# set random seed\n",
        "import numpy as np\n",
        "\n",
        "np.random.seed(5329)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DCUyExyX0y8x"
      },
      "source": [
        "## load and process dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HChdqVQ60r6Q",
        "outputId": "fc122ef5-55c3-455e-9e04-246a2bc46fb4"
      },
      "source": [
        "!pip install -U -q PyDrive\n",
        "from pydrive.auth import GoogleAuth\n",
        "from pydrive.drive import GoogleDrive\n",
        "from google.colab import auth\n",
        "from oauth2client.client import GoogleCredentials\n",
        "# Authenticate and create the PyDrive client.\n",
        "auth.authenticate_user()\n",
        "gauth = GoogleAuth()\n",
        "gauth.credentials = GoogleCredentials.get_application_default()\n",
        "drive = GoogleDrive(gauth)\n",
        "\n",
        "link = '1243XACf-GxdGc4GV__MY-BWWV3a7xK5I'  # Restricted shared link\n",
        "\n",
        "downloaded = drive.CreateFile({'id':link}) \n",
        "downloaded.GetContentFile('2021s1comp5329assignment2.zip')\n",
        "\n",
        "!unzip 2021s1comp5329assignment2.zip"
      ],
      "execution_count": null,
      "outputs": [
        {
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          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KBr5VIsP6geK"
      },
      "source": [
        "import re\n",
        "import pandas as pd\n",
        "from io import StringIO\n",
        "FILENAME_TRAIN = '/content/COMP5329S1A2Dataset/train.csv'\n",
        "FILENAME_TEST = '/content/COMP5329S1A2Dataset/test.csv'"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Qpnli1WIqPzx"
      },
      "source": [
        "## deal with lines cannot be read"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9JbGBRFQLS4S"
      },
      "source": [
        "with open(FILENAME_TRAIN) as fp:\n",
        "    # read a list of lines into data\n",
        "    data = fp.readlines()\n",
        "\n",
        "data[4790] = data[4790].replace(\"/\", \"\")\n",
        "data[14716] = data[14716].replace(\"/\", \"\")\n",
        "data[14961] = data[14961].replace(\"/\", \"\")\n",
        "data[29895] = data[29895].replace(\"/\", \"\")\n",
        "\n",
        "# and write everything back\n",
        "with open(FILENAME_TRAIN, 'w') as file:\n",
        "    file.writelines(data)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_fxGlBqbrGCf"
      },
      "source": [
        "## load data to dataframe"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_OFG1p4oKfEb"
      },
      "source": [
        "with open(FILENAME_TRAIN) as file:\n",
        "    lines = [re.sub(r'([^,])\"(\\s*[^\\n])', r'\\1/\"\\2', line) for line in file]\n",
        "    df_train_origin = pd.read_csv(StringIO(''.join(lines)), escapechar=\"/\")\n",
        "with open(FILENAME_TEST) as file:\n",
        "    lines = [re.sub(r'([^,])\"(\\s*[^\\n])', r'\\1/\"\\2', line) for line in file]\n",
        "    df_test = pd.read_csv(StringIO(''.join(lines)), escapechar=\"/\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OdADJxMGqTcG"
      },
      "source": [
        "## remove all \""
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HN1mrZBlqXWF"
      },
      "source": [
        "df_train_origin = df_train_origin.drop(columns = 'Caption').join(df_train_origin['Caption'].str.replace('\\\"', ''))\n",
        "df_test = df_test.drop(columns = 'Caption').join(df_test['Caption'].str.replace('\\\"', ''))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KtExId-mjB51"
      },
      "source": [
        "## split into train set and validation set"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IHHa-j-GMTTU"
      },
      "source": [
        "df_train = df_train_origin\n",
        "df_val = df_train[24000:] # 6000 samples for evaluation\n",
        "df_train = df_train[:24000] # 24000 samples for train"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "id": "5YgQQFFoi5ur",
        "outputId": "faa1309d-e9d9-48b2-edaa-9a3f31a22858"
      },
      "source": [
        "# save splitted train file\n",
        "df_train.to_csv(\"/content/COMP5329S1A2Dataset/train_split.csv\", index=False)\n",
        "df_train"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "      <th></th>\n",
              "      <th>ImageID</th>\n",
              "      <th>Labels</th>\n",
              "      <th>Caption</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.jpg</td>\n",
              "      <td>1</td>\n",
              "      <td>Woman in swim suit holding parasol on sunny day.</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1.jpg</td>\n",
              "      <td>1 19</td>\n",
              "      <td>A couple of men riding horses on top of a gree...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2.jpg</td>\n",
              "      <td>1</td>\n",
              "      <td>They are brave for riding in the jungle on tho...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3.jpg</td>\n",
              "      <td>8 3 13</td>\n",
              "      <td>a black and silver clock tower at an intersect...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4.jpg</td>\n",
              "      <td>8 3 7</td>\n",
              "      <td>A train coming to a stop on the tracks out side.</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23995</th>\n",
              "      <td>23995.jpg</td>\n",
              "      <td>1</td>\n",
              "      <td>A group of people floating on top of waves in ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23996</th>\n",
              "      <td>23996.jpg</td>\n",
              "      <td>1</td>\n",
              "      <td>A man wearing an orange tie and a woman wearin...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23997</th>\n",
              "      <td>23997.jpg</td>\n",
              "      <td>1 10 3 6</td>\n",
              "      <td>A large sign is lit up on a dark city street.</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23998</th>\n",
              "      <td>23998.jpg</td>\n",
              "      <td>1 15</td>\n",
              "      <td>children learning to play tennis on a clay court</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23999</th>\n",
              "      <td>23999.jpg</td>\n",
              "      <td>1 3 6</td>\n",
              "      <td>Cars, cabs and buses driving through a busy to...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>24000 rows × 3 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "         ImageID    Labels                                            Caption\n",
              "0          0.jpg         1   Woman in swim suit holding parasol on sunny day.\n",
              "1          1.jpg      1 19  A couple of men riding horses on top of a gree...\n",
              "2          2.jpg         1  They are brave for riding in the jungle on tho...\n",
              "3          3.jpg    8 3 13  a black and silver clock tower at an intersect...\n",
              "4          4.jpg     8 3 7   A train coming to a stop on the tracks out side.\n",
              "...          ...       ...                                                ...\n",
              "23995  23995.jpg         1  A group of people floating on top of waves in ...\n",
              "23996  23996.jpg         1  A man wearing an orange tie and a woman wearin...\n",
              "23997  23997.jpg  1 10 3 6      A large sign is lit up on a dark city street.\n",
              "23998  23998.jpg      1 15   children learning to play tennis on a clay court\n",
              "23999  23999.jpg     1 3 6  Cars, cabs and buses driving through a busy to...\n",
              "\n",
              "[24000 rows x 3 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "id": "j7_z9NNDi6Xo",
        "outputId": "06ce9ed1-ea76-44e3-f25c-c64928278757"
      },
      "source": [
        "# save splitted val file\n",
        "df_val.to_csv(\"/content/COMP5329S1A2Dataset/val_split.csv\", index=False)\n",
        "df_val"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>ImageID</th>\n",
              "      <th>Labels</th>\n",
              "      <th>Caption</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>24000</th>\n",
              "      <td>24000.jpg</td>\n",
              "      <td>1</td>\n",
              "      <td>A young girl walking on a road carrying an umb...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24001</th>\n",
              "      <td>24001.jpg</td>\n",
              "      <td>1</td>\n",
              "      <td>A woman with a large black purse holding an um...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24002</th>\n",
              "      <td>24002.jpg</td>\n",
              "      <td>1</td>\n",
              "      <td>Two kids play over a soccer ball during a game.</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24003</th>\n",
              "      <td>24003.jpg</td>\n",
              "      <td>1</td>\n",
              "      <td>A man standing and cutting food at a counter.</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24004</th>\n",
              "      <td>24004.jpg</td>\n",
              "      <td>5</td>\n",
              "      <td>A plane is at the airport preparing to take off.</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29995</th>\n",
              "      <td>29995.jpg</td>\n",
              "      <td>8 1 2</td>\n",
              "      <td>A picture of a truck that is in the middle of ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29996</th>\n",
              "      <td>29996.jpg</td>\n",
              "      <td>1</td>\n",
              "      <td>A plate topped with a pizza being cut with a s...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29997</th>\n",
              "      <td>29997.jpg</td>\n",
              "      <td>1</td>\n",
              "      <td>A man riding a snowboard on top of  snow.</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29998</th>\n",
              "      <td>29998.jpg</td>\n",
              "      <td>1</td>\n",
              "      <td>This photo shows people skiing in the mountains.</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29999</th>\n",
              "      <td>29999.jpg</td>\n",
              "      <td>1</td>\n",
              "      <td>Two young men playing soccer and fighting for ...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>6000 rows × 3 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "         ImageID Labels                                            Caption\n",
              "24000  24000.jpg      1  A young girl walking on a road carrying an umb...\n",
              "24001  24001.jpg      1  A woman with a large black purse holding an um...\n",
              "24002  24002.jpg      1    Two kids play over a soccer ball during a game.\n",
              "24003  24003.jpg      1      A man standing and cutting food at a counter.\n",
              "24004  24004.jpg      5   A plane is at the airport preparing to take off.\n",
              "...          ...    ...                                                ...\n",
              "29995  29995.jpg  8 1 2  A picture of a truck that is in the middle of ...\n",
              "29996  29996.jpg      1  A plate topped with a pizza being cut with a s...\n",
              "29997  29997.jpg      1          A man riding a snowboard on top of  snow.\n",
              "29998  29998.jpg      1   This photo shows people skiing in the mountains.\n",
              "29999  29999.jpg      1  Two young men playing soccer and fighting for ...\n",
              "\n",
              "[6000 rows x 3 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "s8E5QSDQk-ml"
      },
      "source": [
        "## get label information"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "tQ4g3_r1A65e",
        "outputId": "b78ec9ef-23f5-4d90-af8f-3fd48c4d3f0e"
      },
      "source": [
        "# Label distribution\n",
        "import matplotlib.pyplot as plt\n",
        "label_list = []\n",
        "\n",
        "for i in df_train_origin['Labels']:\n",
        "    label_list.extend(i.split())\n",
        "label_list = sorted([int(i) for i in label_list])\n",
        "label_list = [str(i) for i in label_list]\n",
        "\n",
        "plt.style.use('ggplot')\n",
        "plt.title(\"Label distribution\")\n",
        "plt.hist(label_list, bins=18)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HJGEr6368rQl",
        "outputId": "3fba1437-438a-430b-a3a1-fed9e7073e63"
      },
      "source": [
        "# create a label set\n",
        "label_set = set(label_list)\n",
        "label_set = sorted([int(i) for i in label_set])\n",
        "label_set"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13, 14, 15, 16, 17, 18, 19]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "L3Md8NQa-fmX",
        "outputId": "835f826a-6644-4455-d8d0-ca2311b8dbc1"
      },
      "source": [
        "# number of class\n",
        "N_CLASS = len(label_set)\n",
        "N_CLASS"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "18"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gXDtwsLx4UTP"
      },
      "source": [
        "# OOP Classes and Functions"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cYa0IViX4Vjj",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f20513af-0a93-4dc7-9cba-99c16633e472"
      },
      "source": [
        "# dependency\n",
        "import os\n",
        "import time\n",
        "import numpy as np\n",
        "from PIL import Image\n",
        "from torch.utils.data.dataset import Dataset\n",
        "from tqdm import tqdm\n",
        "from torchvision import transforms\n",
        "from torchvision import models\n",
        "import torch\n",
        "from torch.utils.tensorboard import SummaryWriter\n",
        "from sklearn.metrics import precision_score, recall_score, f1_score\n",
        "from torch import nn\n",
        "from torch.utils.data.dataloader import DataLoader\n",
        "from matplotlib import pyplot as plt\n",
        "from numpy import printoptions\n",
        "import requests\n",
        "import tarfile\n",
        "import random\n",
        "import json\n",
        "from shutil import copyfile\n",
        "!pip install ipython-autotime\n",
        "%load_ext autotime"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting ipython-autotime\n",
            "  Downloading https://files.pythonhosted.org/packages/b4/c9/b413a24f759641bc27ef98c144b590023c8038dfb8a3f09e713e9dff12c1/ipython_autotime-0.3.1-py2.py3-none-any.whl\n",
            "Requirement already satisfied: ipython in /usr/local/lib/python3.7/dist-packages (from ipython-autotime) (5.5.0)\n",
            "Requirement already satisfied: setuptools>=18.5 in /usr/local/lib/python3.7/dist-packages (from ipython->ipython-autotime) (56.1.0)\n",
            "Requirement already satisfied: prompt-toolkit<2.0.0,>=1.0.4 in /usr/local/lib/python3.7/dist-packages (from ipython->ipython-autotime) (1.0.18)\n",
            "Requirement already satisfied: pexpect; sys_platform != \"win32\" in /usr/local/lib/python3.7/dist-packages (from ipython->ipython-autotime) (4.8.0)\n",
            "Requirement already satisfied: decorator in /usr/local/lib/python3.7/dist-packages (from ipython->ipython-autotime) (4.4.2)\n",
            "Requirement already satisfied: traitlets>=4.2 in /usr/local/lib/python3.7/dist-packages (from ipython->ipython-autotime) (5.0.5)\n",
            "Requirement already satisfied: simplegeneric>0.8 in /usr/local/lib/python3.7/dist-packages (from ipython->ipython-autotime) (0.8.1)\n",
            "Requirement already satisfied: pygments in /usr/local/lib/python3.7/dist-packages (from ipython->ipython-autotime) (2.6.1)\n",
            "Requirement already satisfied: pickleshare in /usr/local/lib/python3.7/dist-packages (from ipython->ipython-autotime) (0.7.5)\n",
            "Requirement already satisfied: six>=1.9.0 in /usr/local/lib/python3.7/dist-packages (from prompt-toolkit<2.0.0,>=1.0.4->ipython->ipython-autotime) (1.15.0)\n",
            "Requirement already satisfied: wcwidth in /usr/local/lib/python3.7/dist-packages (from prompt-toolkit<2.0.0,>=1.0.4->ipython->ipython-autotime) (0.2.5)\n",
            "Requirement already satisfied: ptyprocess>=0.5 in /usr/local/lib/python3.7/dist-packages (from pexpect; sys_platform != \"win32\"->ipython->ipython-autotime) (0.7.0)\n",
            "Requirement already satisfied: ipython-genutils in /usr/local/lib/python3.7/dist-packages (from traitlets>=4.2->ipython->ipython-autotime) (0.2.0)\n",
            "Installing collected packages: ipython-autotime\n",
            "Successfully installed ipython-autotime-0.3.1\n",
            "time: 162 µs (started: 2021-05-25 17:39:52 +00:00)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rEuVjhJzw0e_",
        "outputId": "6b48a587-e058-4737-d5a2-62f556cd353d"
      },
      "source": [
        "# Fix all seeds to make experiments reproducible\n",
        "torch.manual_seed(2020)\n",
        "torch.cuda.manual_seed(2020)\n",
        "np.random.seed(2020)\n",
        "random.seed(2020)\n",
        "torch.backends.cudnn.deterministic = True"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "time: 6.1 ms (started: 2021-05-25 17:39:52 +00:00)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1IR5nxhNjUWP"
      },
      "source": [
        "## EfficientNet"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "y5AxKK-laWhr",
        "outputId": "f33a9c78-3e1d-4b4c-81ba-5b61c3e1ab6d"
      },
      "source": [
        "!pip install efficientnet_pytorch"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting efficientnet_pytorch\n",
            "  Downloading https://files.pythonhosted.org/packages/2e/a0/dd40b50aebf0028054b6b35062948da01123d7be38d08b6b1e5435df6363/efficientnet_pytorch-0.7.1.tar.gz\n",
            "Requirement already satisfied: torch in /usr/local/lib/python3.7/dist-packages (from efficientnet_pytorch) (1.8.1+cu101)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from torch->efficientnet_pytorch) (3.7.4.3)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from torch->efficientnet_pytorch) (1.19.5)\n",
            "Building wheels for collected packages: efficientnet-pytorch\n",
            "  Building wheel for efficientnet-pytorch (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for efficientnet-pytorch: filename=efficientnet_pytorch-0.7.1-cp37-none-any.whl size=16443 sha256=d9e4a2d3977f8c10a96e63e8d92e6c95d3818a5ae9fe861ed6af7ff49fbaa81c\n",
            "  Stored in directory: /root/.cache/pip/wheels/84/27/aa/c46d23c4e8cc72d41283862b1437e0b3ad318417e8ed7d5921\n",
            "Successfully built efficientnet-pytorch\n",
            "Installing collected packages: efficientnet-pytorch\n",
            "Successfully installed efficientnet-pytorch-0.7.1\n",
            "time: 3.77 s (started: 2021-05-25 17:39:52 +00:00)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 137,
          "referenced_widgets": [
            "9b7227e4d69d4aca88084fdc5ef3d11e",
            "4a89d43117bf4a2ca0b972227f363bfc",
            "de9a126f2dd843379098dc4b4ffe56d8",
            "8a456d6169234c6a944c114abf5d885b",
            "68c0266566a14a3981edf0d845042493",
            "1f1f1257cd3e4cae92879d360c472f5c",
            "9846c624fb1c4dbeb8870304b662597e",
            "75d01429548d47a0b58ddb329c507680"
          ]
        },
        "id": "Jf-kf92rb_mB",
        "outputId": "2458c0a0-a5ce-420d-d32d-467538c79658"
      },
      "source": [
        "from efficientnet_pytorch import EfficientNet\n",
        "model = EfficientNet.from_pretrained('efficientnet-b4')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading: \"https://github.com/lukemelas/EfficientNet-PyTorch/releases/download/1.0/efficientnet-b4-6ed6700e.pth\" to /root/.cache/torch/hub/checkpoints/efficientnet-b4-6ed6700e.pth\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "9b7227e4d69d4aca88084fdc5ef3d11e",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=77999237.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Loaded pretrained weights for efficientnet-b4\n",
            "time: 5.43 s (started: 2021-05-25 17:39:56 +00:00)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yDhW4UDDc08e",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "993adc6e-e82d-4e47-d268-b4e3f849a729"
      },
      "source": [
        "model"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "EfficientNet(\n",
              "  (_conv_stem): Conv2dStaticSamePadding(\n",
              "    3, 48, kernel_size=(3, 3), stride=(2, 2), bias=False\n",
              "    (static_padding): ZeroPad2d(padding=(0, 1, 0, 1), value=0.0)\n",
              "  )\n",
              "  (_bn0): BatchNorm2d(48, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
              "  (_blocks): ModuleList(\n",
              "    (0): MBConvBlock(\n",
              "      (_depthwise_conv): Conv2dStaticSamePadding(\n",
              "        48, 48, kernel_size=(3, 3), stride=[1, 1], groups=48, bias=False\n",
              "        (static_padding): ZeroPad2d(padding=(1, 1, 1, 1), value=0.0)\n",
              "      )\n",
              "      (_bn1): BatchNorm2d(48, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
              "      (_se_reduce): Conv2dStaticSamePadding(\n",
              "        48, 12, kernel_size=(1, 1), stride=(1, 1)\n",
              "        (static_padding): Identity()\n",
              "      )\n",
              "      (_se_expand): Conv2dStaticSamePadding(\n",
              "        12, 48, kernel_size=(1, 1), stride=(1, 1)\n",
              "        (static_padding): Identity()\n",
              "      )\n",
              "      (_project_conv): Conv2dStaticSamePadding(\n",
              "        48, 24, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
              "        (static_padding): Identity()\n",
              "      )\n",
              "      (_bn2): BatchNorm2d(24, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
              "      (_swish): MemoryEfficientSwish()\n",
              "    )\n",
              "    (1): MBConvBlock(\n",
              "      (_depthwise_conv): Conv2dStaticSamePadding(\n",
              "        24, 24, kernel_size=(3, 3), stride=(1, 1), groups=24, bias=False\n",
              "        (static_padding): ZeroPad2d(padding=(1, 1, 1, 1), value=0.0)\n",
              "      )\n",
              "      (_bn1): BatchNorm2d(24, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
              "      (_se_reduce): Conv2dStaticSamePadding(\n",
              "        24, 6, kernel_size=(1, 1), stride=(1, 1)\n",
              "        (static_padding): Identity()\n",
              "      )\n",
              "      (_se_expand): Conv2dStaticSamePadding(\n",
              "        6, 24, kernel_size=(1, 1), stride=(1, 1)\n",
              "        (static_padding): Identity()\n",
              "      )\n",
              "      (_project_conv): Conv2dStaticSamePadding(\n",
              "        24, 24, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
              "        (static_padding): Identity()\n",
              "      )\n",
              "      (_bn2): BatchNorm2d(24, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
              "      (_swish): MemoryEfficientSwish()\n",
              "    )\n",
              "    (2): MBConvBlock(\n",
              "      (_expand_conv): Conv2dStaticSamePadding(\n",
              "        24, 144, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
              "        (static_padding): Identity()\n",
              "      )\n",
              "      (_bn0): BatchNorm2d(144, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
              "      (_depthwise_conv): Conv2dStaticSamePadding(\n",
              "        144, 144, kernel_size=(3, 3), stride=[2, 2], groups=144, bias=False\n",
              "        (static_padding): ZeroPad2d(padding=(0, 1, 0, 1), value=0.0)\n",
              "      )\n",
              "      (_bn1): BatchNorm2d(144, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
              "      (_se_reduce): Conv2dStaticSamePadding(\n",
              "        144, 6, kernel_size=(1, 1), stride=(1, 1)\n",
              "        (static_padding): Identity()\n",
              "      )\n",
              "      (_se_expand): Conv2dStaticSamePadding(\n",
              "        6, 144, kernel_size=(1, 1), stride=(1, 1)\n",
              "        (static_padding): Identity()\n",
              "      )\n",
              "      (_project_conv): Conv2dStaticSamePadding(\n",
              "        144, 32, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
              "        (static_padding): Identity()\n",
              "      )\n",
              "      (_bn2): BatchNorm2d(32, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
              "      (_swish): MemoryEfficientSwish()\n",
              "    )\n",
              "    (3): MBConvBlock(\n",
              "      (_expand_conv): Conv2dStaticSamePadding(\n",
              "        32, 192, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
              "        (static_padding): Identity()\n",
              "      )\n",
              "      (_bn0): BatchNorm2d(192, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
              "      (_depthwise_conv): Conv2dStaticSamePadding(\n",
              "        192, 192, kernel_size=(3, 3), stride=(1, 1), groups=192, bias=False\n",
              "        (static_padding): ZeroPad2d(padding=(1, 1, 1, 1), value=0.0)\n",
              "      )\n",
              "      (_bn1): BatchNorm2d(192, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
              "      (_se_reduce): Conv2dStaticSamePadding(\n",
              "        192, 8, kernel_size=(1, 1), stride=(1, 1)\n",
              "        (static_padding): Identity()\n",
              "      )\n",
              "      (_se_expand): Conv2dStaticSamePadding(\n",
              "        8, 192, kernel_size=(1, 1), stride=(1, 1)\n",
              "        (static_padding): Identity()\n",
              "      )\n",
              "      (_project_conv): Conv2dStaticSamePadding(\n",
              "        192, 32, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
              "        (static_padding): Identity()\n",
              "      )\n",
              "      (_bn2): BatchNorm2d(32, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
              "      (_swish): MemoryEfficientSwish()\n",
              "    )\n",
              "    (4): MBConvBlock(\n",
              "      (_expand_conv): Conv2dStaticSamePadding(\n",
              "        32, 192, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
              "        (static_padding): Identity()\n",
              "      )\n",
              "      (_bn0): BatchNorm2d(192, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
              "      (_depthwise_conv): Conv2dStaticSamePadding(\n",
              "        192, 192, kernel_size=(3, 3), stride=(1, 1), groups=192, bias=False\n",
              "        (static_padding): ZeroPad2d(padding=(1, 1, 1, 1), value=0.0)\n",
              "      )\n",
              "      (_bn1): BatchNorm2d(192, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
              "      (_se_reduce): Conv2dStaticSamePadding(\n",
              "        192, 8, kernel_size=(1, 1), stride=(1, 1)\n",
              "        (static_padding): Identity()\n",
              "      )\n",
              "      (_se_expand): Conv2dStaticSamePadding(\n",
              "        8, 192, kernel_size=(1, 1), stride=(1, 1)\n",
              "        (static_padding): Identity()\n",
              "      )\n",
              "      (_project_conv): Conv2dStaticSamePadding(\n",
              "        192, 32, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
              "        (static_padding): Identity()\n",
              "      )\n",
              "      (_bn2): BatchNorm2d(32, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
              "      (_swish): MemoryEfficientSwish()\n",
              "    )\n",
              "    (5): MBConvBlock(\n",
              "      (_expand_conv): Conv2dStaticSamePadding(\n",
              "        32, 192, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
              "        (static_padding): Identity()\n",
              "      )\n",
              "      (_bn0): BatchNorm2d(192, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
              "      (_depthwise_conv): Conv2dStaticSamePadding(\n",
              "        192, 192, kernel_size=(3, 3), stride=(1, 1), groups=192, bias=False\n",
              "        (static_padding): ZeroPad2d(padding=(1, 1, 1, 1), value=0.0)\n",
              "      )\n",
              "      (_bn1): BatchNorm2d(192, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
              "      (_se_reduce): Conv2dStaticSamePadding(\n",
              "        192, 8, kernel_size=(1, 1), stride=(1, 1)\n",
              "        (static_padding): Identity()\n",
              "      )\n",
              "      (_se_expand): Conv2dStaticSamePadding(\n",
              "        8, 192, kernel_size=(1, 1), stride=(1, 1)\n",
              "        (static_padding): Identity()\n",
              "      )\n",
              "      (_project_conv): Conv2dStaticSamePadding(\n",
              "        192, 32, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
              "        (static_padding): Identity()\n",
              "      )\n",
              "      (_bn2): BatchNorm2d(32, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
              "      (_swish): MemoryEfficientSwish()\n",
              "    )\n",
              "    (6): MBConvBlock(\n",
              "      (_expand_conv): Conv2dStaticSamePadding(\n",
              "        32, 192, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
              "        (static_padding): Identity()\n",
              "      )\n",
              "      (_bn0): BatchNorm2d(192, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
              "      (_depthwise_conv): Conv2dStaticSamePadding(\n",
              "        192, 192, kernel_size=(5, 5), stride=[2, 2], groups=192, bias=False\n",
              "        (static_padding): ZeroPad2d(padding=(2, 2, 2, 2), value=0.0)\n",
              "      )\n",
              "      (_bn1): BatchNorm2d(192, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
              "      (_se_reduce): Conv2dStaticSamePadding(\n",
              "        192, 8, kernel_size=(1, 1), stride=(1, 1)\n",
              "        (static_padding): Identity()\n",
              "      )\n",
              "      (_se_expand): Conv2dStaticSamePadding(\n",
              "        8, 192, kernel_size=(1, 1), stride=(1, 1)\n",
              "        (static_padding): Identity()\n",
              "      )\n",
              "      (_project_conv): Conv2dStaticSamePadding(\n",
              "        192, 56, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
              "        (static_padding): Identity()\n",
              "      )\n",
              "      (_bn2): BatchNorm2d(56, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
              "      (_swish): MemoryEfficientSwish()\n",
              "    )\n",
              "    (7): MBConvBlock(\n",
              "      (_expand_conv): Conv2dStaticSamePadding(\n",
              "        56, 336, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
              "        (static_padding): Identity()\n",
              "      )\n",
              "      (_bn0): BatchNorm2d(336, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
              "      (_depthwise_conv): Conv2dStaticSamePadding(\n",
              "        336, 336, kernel_size=(5, 5), stride=(1, 1), groups=336, bias=False\n",
              "        (static_padding): ZeroPad2d(padding=(2, 2, 2, 2), value=0.0)\n",
              "      )\n",
              "      (_bn1): BatchNorm2d(336, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
              "      (_se_reduce): Conv2dStaticSamePadding(\n",
              "        336, 14, kernel_size=(1, 1), stride=(1, 1)\n",
              "        (static_padding): Identity()\n",
              "      )\n",
              "      (_se_expand): Conv2dStaticSamePadding(\n",
              "        14, 336, kernel_size=(1, 1), stride=(1, 1)\n",
              "        (static_padding): Identity()\n",
              "      )\n",
              "      (_project_conv): Conv2dStaticSamePadding(\n",
              "        336, 56, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
              "        (static_padding): Identity()\n",
              "      )\n",
              "      (_bn2): BatchNorm2d(56, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
              "      (_swish): MemoryEfficientSwish()\n",
              "    )\n",
              "    (8): MBConvBlock(\n",
              "      (_expand_conv): Conv2dStaticSamePadding(\n",
              "        56, 336, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
              "        (static_padding): Identity()\n",
              "      )\n",
              "      (_bn0): BatchNorm2d(336, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
              "      (_depthwise_conv): Conv2dStaticSamePadding(\n",
              "        336, 336, kernel_size=(5, 5), stride=(1, 1), groups=336, bias=False\n",
              "        (static_padding): ZeroPad2d(padding=(2, 2, 2, 2), value=0.0)\n",
              "      )\n",
              "      (_bn1): BatchNorm2d(336, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
              "      (_se_reduce): Conv2dStaticSamePadding(\n",
              "        336, 14, kernel_size=(1, 1), stride=(1, 1)\n",
              "        (static_padding): Identity()\n",
              "      )\n",
              "      (_se_expand): Conv2dStaticSamePadding(\n",
              "        14, 336, kernel_size=(1, 1), stride=(1, 1)\n",
              "        (static_padding): Identity()\n",
              "      )\n",
              "      (_project_conv): Conv2dStaticSamePadding(\n",
              "        336, 56, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
              "        (static_padding): Identity()\n",
              "      )\n",
              "      (_bn2): BatchNorm2d(56, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
              "      (_swish): MemoryEfficientSwish()\n",
              "    )\n",
              "    (9): MBConvBlock(\n",
              "      (_expand_conv): Conv2dStaticSamePadding(\n",
              "        56, 336, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
              "        (static_padding): Identity()\n",
              "      )\n",
              "      (_bn0): BatchNorm2d(336, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
              "      (_depthwise_conv): Conv2dStaticSamePadding(\n",
              "        336, 336, kernel_size=(5, 5), stride=(1, 1), groups=336, bias=False\n",
              "        (static_padding): ZeroPad2d(padding=(2, 2, 2, 2), value=0.0)\n",
              "      )\n",
              "      (_bn1): BatchNorm2d(336, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
              "      (_se_reduce): Conv2dStaticSamePadding(\n",
              "        336, 14, kernel_size=(1, 1), stride=(1, 1)\n",
              "        (static_padding): Identity()\n",
              "      )\n",
              "      (_se_expand): Conv2dStaticSamePadding(\n",
              "        14, 336, kernel_size=(1, 1), stride=(1, 1)\n",
              "        (static_padding): Identity()\n",
              "      )\n",
              "      (_project_conv): Conv2dStaticSamePadding(\n",
              "        336, 56, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
              "        (static_padding): Identity()\n",
              "      )\n",
              "      (_bn2): BatchNorm2d(56, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
              "      (_swish): MemoryEfficientSwish()\n",
              "    )\n",
              "    (10): MBConvBlock(\n",
              "      (_expand_conv): Conv2dStaticSamePadding(\n",
              "        56, 336, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
              "        (static_padding): Identity()\n",
              "      )\n",
              "      (_bn0): BatchNorm2d(336, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
              "      (_depthwise_conv): Conv2dStaticSamePadding(\n",
              "        336, 336, kernel_size=(3, 3), stride=[2, 2], groups=336, bias=False\n",
              "        (static_padding): ZeroPad2d(padding=(0, 1, 0, 1), value=0.0)\n",
              "      )\n",
              "      (_bn1): BatchNorm2d(336, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
              "      (_se_reduce): Conv2dStaticSamePadding(\n",
              "        336, 14, kernel_size=(1, 1), stride=(1, 1)\n",
              "        (static_padding): Identity()\n",
              "      )\n",
              "      (_se_expand): Conv2dStaticSamePadding(\n",
              "        14, 336, kernel_size=(1, 1), stride=(1, 1)\n",
              "        (static_padding): Identity()\n",
              "      )\n",
              "      (_project_conv): Conv2dStaticSamePadding(\n",
              "        336, 112, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
              "        (static_padding): Identity()\n",
              "      )\n",
              "      (_bn2): BatchNorm2d(112, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
              "      (_swish): MemoryEfficientSwish()\n",
              "    )\n",
              "    (11): MBConvBlock(\n",
              "      (_expand_conv): Conv2dStaticSamePadding(\n",
              "        112, 672, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
              "        (static_padding): Identity()\n",
              "      )\n",
              "      (_bn0): BatchNorm2d(672, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
              "      (_depthwise_conv): Conv2dStaticSamePadding(\n",
              "        672, 672, kernel_size=(3, 3), stride=(1, 1), groups=672, bias=False\n",
              "        (static_padding): ZeroPad2d(padding=(1, 1, 1, 1), value=0.0)\n",
              "      )\n",
              "      (_bn1): BatchNorm2d(672, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
              "      (_se_reduce): Conv2dStaticSamePadding(\n",
              "        672, 28, kernel_size=(1, 1), stride=(1, 1)\n",
              "        (static_padding): Identity()\n",
              "      )\n",
              "      (_se_expand): Conv2dStaticSamePadding(\n",
              "        28, 672, kernel_size=(1, 1), stride=(1, 1)\n",
              "        (static_padding): Identity()\n",
              "      )\n",
              "      (_project_conv): Conv2dStaticSamePadding(\n",
              "        672, 112, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
              "        (static_padding): Identity()\n",
              "      )\n",
              "      (_bn2): BatchNorm2d(112, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
              "      (_swish): MemoryEfficientSwish()\n",
              "    )\n",
              "    (12): MBConvBlock(\n",
              "      (_expand_conv): Conv2dStaticSamePadding(\n",
              "        112, 672, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
              "        (static_padding): Identity()\n",
              "      )\n",
              "      (_bn0): BatchNorm2d(672, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
              "      (_depthwise_conv): Conv2dStaticSamePadding(\n",
              "        672, 672, kernel_size=(3, 3), stride=(1, 1), groups=672, bias=False\n",
              "        (static_padding): ZeroPad2d(padding=(1, 1, 1, 1), value=0.0)\n",
              "      )\n",
              "      (_bn1): BatchNorm2d(672, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
              "      (_se_reduce): Conv2dStaticSamePadding(\n",
              "        672, 28, kernel_size=(1, 1), stride=(1, 1)\n",
              "        (static_padding): Identity()\n",
              "      )\n",
              "      (_se_expand): Conv2dStaticSamePadding(\n",
              "        28, 672, kernel_size=(1, 1), stride=(1, 1)\n",
              "        (static_padding): Identity()\n",
              "      )\n",
              "      (_project_conv): Conv2dStaticSamePadding(\n",
              "        672, 112, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
              "        (static_padding): Identity()\n",
              "      )\n",
              "      (_bn2): BatchNorm2d(112, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
              "      (_swish): MemoryEfficientSwish()\n",
              "    )\n",
              "    (13): MBConvBlock(\n",
              "      (_expand_conv): Conv2dStaticSamePadding(\n",
              "        112, 672, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
              "        (static_padding): Identity()\n",
              "      )\n",
              "      (_bn0): BatchNorm2d(672, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
              "      (_depthwise_conv): Conv2dStaticSamePadding(\n",
              "        672, 672, kernel_size=(3, 3), stride=(1, 1), groups=672, bias=False\n",
              "        (static_padding): ZeroPad2d(padding=(1, 1, 1, 1), value=0.0)\n",
              "      )\n",
              "      (_bn1): BatchNorm2d(672, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
              "      (_se_reduce): Conv2dStaticSamePadding(\n",
              "        672, 28, kernel_size=(1, 1), stride=(1, 1)\n",
              "        (static_padding): Identity()\n",
              "      )\n",
              "      (_se_expand): Conv2dStaticSamePadding(\n",
              "        28, 672, kernel_size=(1, 1), stride=(1, 1)\n",
              "        (static_padding): Identity()\n",
              "      )\n",
              "      (_project_conv): Conv2dStaticSamePadding(\n",
              "        672, 112, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
              "        (static_padding): Identity()\n",
              "      )\n",
              "      (_bn2): BatchNorm2d(112, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
              "      (_swish): MemoryEfficientSwish()\n",
              "    )\n",
              "    (14): MBConvBlock(\n",
              "      (_expand_conv): Conv2dStaticSamePadding(\n",
              "        112, 672, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
              "        (static_padding): Identity()\n",
              "      )\n",
              "      (_bn0): BatchNorm2d(672, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
              "      (_depthwise_conv): Conv2dStaticSamePadding(\n",
              "        672, 672, kernel_size=(3, 3), stride=(1, 1), groups=672, bias=False\n",
              "        (static_padding): ZeroPad2d(padding=(1, 1, 1, 1), value=0.0)\n",
              "      )\n",
              "      (_bn1): BatchNorm2d(672, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
              "      (_se_reduce): Conv2dStaticSamePadding(\n",
              "        672, 28, kernel_size=(1, 1), stride=(1, 1)\n",
              "        (static_padding): Identity()\n",
              "      )\n",
              "      (_se_expand): Conv2dStaticSamePadding(\n",
              "        28, 672, kernel_size=(1, 1), stride=(1, 1)\n",
              "        (static_padding): Identity()\n",
              "      )\n",
              "      (_project_conv): Conv2dStaticSamePadding(\n",
              "        672, 112, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
              "        (static_padding): Identity()\n",
              "      )\n",
              "      (_bn2): BatchNorm2d(112, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
              "      (_swish): MemoryEfficientSwish()\n",
              "    )\n",
              "    (15): MBConvBlock(\n",
              "      (_expand_conv): Conv2dStaticSamePadding(\n",
              "        112, 672, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
              "        (static_padding): Identity()\n",
              "      )\n",
              "      (_bn0): BatchNorm2d(672, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
              "      (_depthwise_conv): Conv2dStaticSamePadding(\n",
              "        672, 672, kernel_size=(3, 3), stride=(1, 1), groups=672, bias=False\n",
              "        (static_padding): ZeroPad2d(padding=(1, 1, 1, 1), value=0.0)\n",
              "      )\n",
              "      (_bn1): BatchNorm2d(672, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
              "      (_se_reduce): Conv2dStaticSamePadding(\n",
              "        672, 28, kernel_size=(1, 1), stride=(1, 1)\n",
              "        (static_padding): Identity()\n",
              "      )\n",
              "      (_se_expand): Conv2dStaticSamePadding(\n",
              "        28, 672, kernel_size=(1, 1), stride=(1, 1)\n",
              "        (static_padding): Identity()\n",
              "      )\n",
              "      (_project_conv): Conv2dStaticSamePadding(\n",
              "        672, 112, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
              "        (static_padding): Identity()\n",
              "      )\n",
              "      (_bn2): BatchNorm2d(112, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
              "      (_swish): MemoryEfficientSwish()\n",
              "    )\n",
              "    (16): MBConvBlock(\n",
              "      (_expand_conv): Conv2dStaticSamePadding(\n",
              "        112, 672, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
              "        (static_padding): Identity()\n",
              "      )\n",
              "      (_bn0): BatchNorm2d(672, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
              "      (_depthwise_conv): Conv2dStaticSamePadding(\n",
              "        672, 672, kernel_size=(5, 5), stride=[1, 1], groups=672, bias=False\n",
              "        (static_padding): ZeroPad2d(padding=(2, 2, 2, 2), value=0.0)\n",
              "      )\n",
              "      (_bn1): BatchNorm2d(672, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
              "      (_se_reduce): Conv2dStaticSamePadding(\n",
              "        672, 28, kernel_size=(1, 1), stride=(1, 1)\n",
              "        (static_padding): Identity()\n",
              "      )\n",
              "      (_se_expand): Conv2dStaticSamePadding(\n",
              "        28, 672, kernel_size=(1, 1), stride=(1, 1)\n",
              "        (static_padding): Identity()\n",
              "      )\n",
              "      (_project_conv): Conv2dStaticSamePadding(\n",
              "        672, 160, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
              "        (static_padding): Identity()\n",
              "      )\n",
              "      (_bn2): BatchNorm2d(160, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
              "      (_swish): MemoryEfficientSwish()\n",
              "    )\n",
              "    (17): MBConvBlock(\n",
              "      (_expand_conv): Conv2dStaticSamePadding(\n",
              "        160, 960, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
              "        (static_padding): Identity()\n",
              "      )\n",
              "      (_bn0): BatchNorm2d(960, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
              "      (_depthwise_conv): Conv2dStaticSamePadding(\n",
              "        960, 960, kernel_size=(5, 5), stride=(1, 1), groups=960, bias=False\n",
              "        (static_padding): ZeroPad2d(padding=(2, 2, 2, 2), value=0.0)\n",
              "      )\n",
              "      (_bn1): BatchNorm2d(960, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
              "      (_se_reduce): Conv2dStaticSamePadding(\n",
              "        960, 40, kernel_size=(1, 1), stride=(1, 1)\n",
              "        (static_padding): Identity()\n",
              "      )\n",
              "      (_se_expand): Conv2dStaticSamePadding(\n",
              "        40, 960, kernel_size=(1, 1), stride=(1, 1)\n",
              "        (static_padding): Identity()\n",
              "      )\n",
              "      (_project_conv): Conv2dStaticSamePadding(\n",
              "        960, 160, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
              "        (static_padding): Identity()\n",
              "      )\n",
              "      (_bn2): BatchNorm2d(160, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
              "      (_swish): MemoryEfficientSwish()\n",
              "    )\n",
              "    (18): MBConvBlock(\n",
              "      (_expand_conv): Conv2dStaticSamePadding(\n",
              "        160, 960, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
              "        (static_padding): Identity()\n",
              "      )\n",
              "      (_bn0): BatchNorm2d(960, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
              "      (_depthwise_conv): Conv2dStaticSamePadding(\n",
              "        960, 960, kernel_size=(5, 5), stride=(1, 1), groups=960, bias=False\n",
              "        (static_padding): ZeroPad2d(padding=(2, 2, 2, 2), value=0.0)\n",
              "      )\n",
              "      (_bn1): BatchNorm2d(960, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
              "      (_se_reduce): Conv2dStaticSamePadding(\n",
              "        960, 40, kernel_size=(1, 1), stride=(1, 1)\n",
              "        (static_padding): Identity()\n",
              "      )\n",
              "      (_se_expand): Conv2dStaticSamePadding(\n",
              "        40, 960, kernel_size=(1, 1), stride=(1, 1)\n",
              "        (static_padding): Identity()\n",
              "      )\n",
              "      (_project_conv): Conv2dStaticSamePadding(\n",
              "        960, 160, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
              "        (static_padding): Identity()\n",
              "      )\n",
              "      (_bn2): BatchNorm2d(160, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
              "      (_swish): MemoryEfficientSwish()\n",
              "    )\n",
              "    (19): MBConvBlock(\n",
              "      (_expand_conv): Conv2dStaticSamePadding(\n",
              "        160, 960, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
              "        (static_padding): Identity()\n",
              "      )\n",
              "      (_bn0): BatchNorm2d(960, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
              "      (_depthwise_conv): Conv2dStaticSamePadding(\n",
              "        960, 960, kernel_size=(5, 5), stride=(1, 1), groups=960, bias=False\n",
              "        (static_padding): ZeroPad2d(padding=(2, 2, 2, 2), value=0.0)\n",
              "      )\n",
              "      (_bn1): BatchNorm2d(960, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
              "      (_se_reduce): Conv2dStaticSamePadding(\n",
              "        960, 40, kernel_size=(1, 1), stride=(1, 1)\n",
              "        (static_padding): Identity()\n",
              "      )\n",
              "      (_se_expand): Conv2dStaticSamePadding(\n",
              "        40, 960, kernel_size=(1, 1), stride=(1, 1)\n",
              "        (static_padding): Identity()\n",
              "      )\n",
              "      (_project_conv): Conv2dStaticSamePadding(\n",
              "        960, 160, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
              "        (static_padding): Identity()\n",
              "      )\n",
              "      (_bn2): BatchNorm2d(160, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
              "      (_swish): MemoryEfficientSwish()\n",
              "    )\n",
              "    (20): MBConvBlock(\n",
              "      (_expand_conv): Conv2dStaticSamePadding(\n",
              "        160, 960, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
              "        (static_padding): Identity()\n",
              "      )\n",
              "      (_bn0): BatchNorm2d(960, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
              "      (_depthwise_conv): Conv2dStaticSamePadding(\n",
              "        960, 960, kernel_size=(5, 5), stride=(1, 1), groups=960, bias=False\n",
              "        (static_padding): ZeroPad2d(padding=(2, 2, 2, 2), value=0.0)\n",
              "      )\n",
              "      (_bn1): BatchNorm2d(960, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
              "      (_se_reduce): Conv2dStaticSamePadding(\n",
              "        960, 40, kernel_size=(1, 1), stride=(1, 1)\n",
              "        (static_padding): Identity()\n",
              "      )\n",
              "      (_se_expand): Conv2dStaticSamePadding(\n",
              "        40, 960, kernel_size=(1, 1), stride=(1, 1)\n",
              "        (static_padding): Identity()\n",
              "      )\n",
              "      (_project_conv): Conv2dStaticSamePadding(\n",
              "        960, 160, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
              "        (static_padding): Identity()\n",
              "      )\n",
              "      (_bn2): BatchNorm2d(160, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
              "      (_swish): MemoryEfficientSwish()\n",
              "    )\n",
              "    (21): MBConvBlock(\n",
              "      (_expand_conv): Conv2dStaticSamePadding(\n",
              "        160, 960, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
              "        (static_padding): Identity()\n",
              "      )\n",
              "      (_bn0): BatchNorm2d(960, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
              "      (_depthwise_conv): Conv2dStaticSamePadding(\n",
              "        960, 960, kernel_size=(5, 5), stride=(1, 1), groups=960, bias=False\n",
              "        (static_padding): ZeroPad2d(padding=(2, 2, 2, 2), value=0.0)\n",
              "      )\n",
              "      (_bn1): BatchNorm2d(960, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
              "      (_se_reduce): Conv2dStaticSamePadding(\n",
              "        960, 40, kernel_size=(1, 1), stride=(1, 1)\n",
              "        (static_padding): Identity()\n",
              "      )\n",
              "      (_se_expand): Conv2dStaticSamePadding(\n",
              "        40, 960, kernel_size=(1, 1), stride=(1, 1)\n",
              "        (static_padding): Identity()\n",
              "      )\n",
              "      (_project_conv): Conv2dStaticSamePadding(\n",
              "        960, 160, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
              "        (static_padding): Identity()\n",
              "      )\n",
              "      (_bn2): BatchNorm2d(160, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
              "      (_swish): MemoryEfficientSwish()\n",
              "    )\n",
              "    (22): MBConvBlock(\n",
              "      (_expand_conv): Conv2dStaticSamePadding(\n",
              "        160, 960, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
              "        (static_padding): Identity()\n",
              "      )\n",
              "      (_bn0): BatchNorm2d(960, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
              "      (_depthwise_conv): Conv2dStaticSamePadding(\n",
              "        960, 960, kernel_size=(5, 5), stride=[2, 2], groups=960, bias=False\n",
              "        (static_padding): ZeroPad2d(padding=(1, 2, 1, 2), value=0.0)\n",
              "      )\n",
              "      (_bn1): BatchNorm2d(960, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
              "      (_se_reduce): Conv2dStaticSamePadding(\n",
              "        960, 40, kernel_size=(1, 1), stride=(1, 1)\n",
              "        (static_padding): Identity()\n",
              "      )\n",
              "      (_se_expand): Conv2dStaticSamePadding(\n",
              "        40, 960, kernel_size=(1, 1), stride=(1, 1)\n",
              "        (static_padding): Identity()\n",
              "      )\n",
              "      (_project_conv): Conv2dStaticSamePadding(\n",
              "        960, 272, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
              "        (static_padding): Identity()\n",
              "      )\n",
              "      (_bn2): BatchNorm2d(272, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
              "      (_swish): MemoryEfficientSwish()\n",
              "    )\n",
              "    (23): MBConvBlock(\n",
              "      (_expand_conv): Conv2dStaticSamePadding(\n",
              "        272, 1632, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
              "        (static_padding): Identity()\n",
              "      )\n",
              "      (_bn0): BatchNorm2d(1632, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
              "      (_depthwise_conv): Conv2dStaticSamePadding(\n",
              "        1632, 1632, kernel_size=(5, 5), stride=(1, 1), groups=1632, bias=False\n",
              "        (static_padding): ZeroPad2d(padding=(2, 2, 2, 2), value=0.0)\n",
              "      )\n",
              "      (_bn1): BatchNorm2d(1632, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
              "      (_se_reduce): Conv2dStaticSamePadding(\n",
              "        1632, 68, kernel_size=(1, 1), stride=(1, 1)\n",
              "        (static_padding): Identity()\n",
              "      )\n",
              "      (_se_expand): Conv2dStaticSamePadding(\n",
              "        68, 1632, kernel_size=(1, 1), stride=(1, 1)\n",
              "        (static_padding): Identity()\n",
              "      )\n",
              "      (_project_conv): Conv2dStaticSamePadding(\n",
              "        1632, 272, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
              "        (static_padding): Identity()\n",
              "      )\n",
              "      (_bn2): BatchNorm2d(272, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
              "      (_swish): MemoryEfficientSwish()\n",
              "    )\n",
              "    (24): MBConvBlock(\n",
              "      (_expand_conv): Conv2dStaticSamePadding(\n",
              "        272, 1632, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
              "        (static_padding): Identity()\n",
              "      )\n",
              "      (_bn0): BatchNorm2d(1632, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
              "      (_depthwise_conv): Conv2dStaticSamePadding(\n",
              "        1632, 1632, kernel_size=(5, 5), stride=(1, 1), groups=1632, bias=False\n",
              "        (static_padding): ZeroPad2d(padding=(2, 2, 2, 2), value=0.0)\n",
              "      )\n",
              "      (_bn1): BatchNorm2d(1632, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
              "      (_se_reduce): Conv2dStaticSamePadding(\n",
              "        1632, 68, kernel_size=(1, 1), stride=(1, 1)\n",
              "        (static_padding): Identity()\n",
              "      )\n",
              "      (_se_expand): Conv2dStaticSamePadding(\n",
              "        68, 1632, kernel_size=(1, 1), stride=(1, 1)\n",
              "        (static_padding): Identity()\n",
              "      )\n",
              "      (_project_conv): Conv2dStaticSamePadding(\n",
              "        1632, 272, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
              "        (static_padding): Identity()\n",
              "      )\n",
              "      (_bn2): BatchNorm2d(272, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
              "      (_swish): MemoryEfficientSwish()\n",
              "    )\n",
              "    (25): MBConvBlock(\n",
              "      (_expand_conv): Conv2dStaticSamePadding(\n",
              "        272, 1632, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
              "        (static_padding): Identity()\n",
              "      )\n",
              "      (_bn0): BatchNorm2d(1632, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
              "      (_depthwise_conv): Conv2dStaticSamePadding(\n",
              "        1632, 1632, kernel_size=(5, 5), stride=(1, 1), groups=1632, bias=False\n",
              "        (static_padding): ZeroPad2d(padding=(2, 2, 2, 2), value=0.0)\n",
              "      )\n",
              "      (_bn1): BatchNorm2d(1632, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
              "      (_se_reduce): Conv2dStaticSamePadding(\n",
              "        1632, 68, kernel_size=(1, 1), stride=(1, 1)\n",
              "        (static_padding): Identity()\n",
              "      )\n",
              "      (_se_expand): Conv2dStaticSamePadding(\n",
              "        68, 1632, kernel_size=(1, 1), stride=(1, 1)\n",
              "        (static_padding): Identity()\n",
              "      )\n",
              "      (_project_conv): Conv2dStaticSamePadding(\n",
              "        1632, 272, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
              "        (static_padding): Identity()\n",
              "      )\n",
              "      (_bn2): BatchNorm2d(272, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
              "      (_swish): MemoryEfficientSwish()\n",
              "    )\n",
              "    (26): MBConvBlock(\n",
              "      (_expand_conv): Conv2dStaticSamePadding(\n",
              "        272, 1632, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
              "        (static_padding): Identity()\n",
              "      )\n",
              "      (_bn0): BatchNorm2d(1632, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
              "      (_depthwise_conv): Conv2dStaticSamePadding(\n",
              "        1632, 1632, kernel_size=(5, 5), stride=(1, 1), groups=1632, bias=False\n",
              "        (static_padding): ZeroPad2d(padding=(2, 2, 2, 2), value=0.0)\n",
              "      )\n",
              "      (_bn1): BatchNorm2d(1632, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
              "      (_se_reduce): Conv2dStaticSamePadding(\n",
              "        1632, 68, kernel_size=(1, 1), stride=(1, 1)\n",
              "        (static_padding): Identity()\n",
              "      )\n",
              "      (_se_expand): Conv2dStaticSamePadding(\n",
              "        68, 1632, kernel_size=(1, 1), stride=(1, 1)\n",
              "        (static_padding): Identity()\n",
              "      )\n",
              "      (_project_conv): Conv2dStaticSamePadding(\n",
              "        1632, 272, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
              "        (static_padding): Identity()\n",
              "      )\n",
              "      (_bn2): BatchNorm2d(272, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
              "      (_swish): MemoryEfficientSwish()\n",
              "    )\n",
              "    (27): MBConvBlock(\n",
              "      (_expand_conv): Conv2dStaticSamePadding(\n",
              "        272, 1632, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
              "        (static_padding): Identity()\n",
              "      )\n",
              "      (_bn0): BatchNorm2d(1632, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
              "      (_depthwise_conv): Conv2dStaticSamePadding(\n",
              "        1632, 1632, kernel_size=(5, 5), stride=(1, 1), groups=1632, bias=False\n",
              "        (static_padding): ZeroPad2d(padding=(2, 2, 2, 2), value=0.0)\n",
              "      )\n",
              "      (_bn1): BatchNorm2d(1632, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
              "      (_se_reduce): Conv2dStaticSamePadding(\n",
              "        1632, 68, kernel_size=(1, 1), stride=(1, 1)\n",
              "        (static_padding): Identity()\n",
              "      )\n",
              "      (_se_expand): Conv2dStaticSamePadding(\n",
              "        68, 1632, kernel_size=(1, 1), stride=(1, 1)\n",
              "        (static_padding): Identity()\n",
              "      )\n",
              "      (_project_conv): Conv2dStaticSamePadding(\n",
              "        1632, 272, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
              "        (static_padding): Identity()\n",
              "      )\n",
              "      (_bn2): BatchNorm2d(272, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
              "      (_swish): MemoryEfficientSwish()\n",
              "    )\n",
              "    (28): MBConvBlock(\n",
              "      (_expand_conv): Conv2dStaticSamePadding(\n",
              "        272, 1632, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
              "        (static_padding): Identity()\n",
              "      )\n",
              "      (_bn0): BatchNorm2d(1632, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
              "      (_depthwise_conv): Conv2dStaticSamePadding(\n",
              "        1632, 1632, kernel_size=(5, 5), stride=(1, 1), groups=1632, bias=False\n",
              "        (static_padding): ZeroPad2d(padding=(2, 2, 2, 2), value=0.0)\n",
              "      )\n",
              "      (_bn1): BatchNorm2d(1632, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
              "      (_se_reduce): Conv2dStaticSamePadding(\n",
              "        1632, 68, kernel_size=(1, 1), stride=(1, 1)\n",
              "        (static_padding): Identity()\n",
              "      )\n",
              "      (_se_expand): Conv2dStaticSamePadding(\n",
              "        68, 1632, kernel_size=(1, 1), stride=(1, 1)\n",
              "        (static_padding): Identity()\n",
              "      )\n",
              "      (_project_conv): Conv2dStaticSamePadding(\n",
              "        1632, 272, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
              "        (static_padding): Identity()\n",
              "      )\n",
              "      (_bn2): BatchNorm2d(272, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
              "      (_swish): MemoryEfficientSwish()\n",
              "    )\n",
              "    (29): MBConvBlock(\n",
              "      (_expand_conv): Conv2dStaticSamePadding(\n",
              "        272, 1632, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
              "        (static_padding): Identity()\n",
              "      )\n",
              "      (_bn0): BatchNorm2d(1632, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
              "      (_depthwise_conv): Conv2dStaticSamePadding(\n",
              "        1632, 1632, kernel_size=(5, 5), stride=(1, 1), groups=1632, bias=False\n",
              "        (static_padding): ZeroPad2d(padding=(2, 2, 2, 2), value=0.0)\n",
              "      )\n",
              "      (_bn1): BatchNorm2d(1632, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
              "      (_se_reduce): Conv2dStaticSamePadding(\n",
              "        1632, 68, kernel_size=(1, 1), stride=(1, 1)\n",
              "        (static_padding): Identity()\n",
              "      )\n",
              "      (_se_expand): Conv2dStaticSamePadding(\n",
              "        68, 1632, kernel_size=(1, 1), stride=(1, 1)\n",
              "        (static_padding): Identity()\n",
              "      )\n",
              "      (_project_conv): Conv2dStaticSamePadding(\n",
              "        1632, 272, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
              "        (static_padding): Identity()\n",
              "      )\n",
              "      (_bn2): BatchNorm2d(272, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
              "      (_swish): MemoryEfficientSwish()\n",
              "    )\n",
              "    (30): MBConvBlock(\n",
              "      (_expand_conv): Conv2dStaticSamePadding(\n",
              "        272, 1632, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
              "        (static_padding): Identity()\n",
              "      )\n",
              "      (_bn0): BatchNorm2d(1632, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
              "      (_depthwise_conv): Conv2dStaticSamePadding(\n",
              "        1632, 1632, kernel_size=(3, 3), stride=[1, 1], groups=1632, bias=False\n",
              "        (static_padding): ZeroPad2d(padding=(1, 1, 1, 1), value=0.0)\n",
              "      )\n",
              "      (_bn1): BatchNorm2d(1632, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
              "      (_se_reduce): Conv2dStaticSamePadding(\n",
              "        1632, 68, kernel_size=(1, 1), stride=(1, 1)\n",
              "        (static_padding): Identity()\n",
              "      )\n",
              "      (_se_expand): Conv2dStaticSamePadding(\n",
              "        68, 1632, kernel_size=(1, 1), stride=(1, 1)\n",
              "        (static_padding): Identity()\n",
              "      )\n",
              "      (_project_conv): Conv2dStaticSamePadding(\n",
              "        1632, 448, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
              "        (static_padding): Identity()\n",
              "      )\n",
              "      (_bn2): BatchNorm2d(448, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
              "      (_swish): MemoryEfficientSwish()\n",
              "    )\n",
              "    (31): MBConvBlock(\n",
              "      (_expand_conv): Conv2dStaticSamePadding(\n",
              "        448, 2688, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
              "        (static_padding): Identity()\n",
              "      )\n",
              "      (_bn0): BatchNorm2d(2688, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
              "      (_depthwise_conv): Conv2dStaticSamePadding(\n",
              "        2688, 2688, kernel_size=(3, 3), stride=(1, 1), groups=2688, bias=False\n",
              "        (static_padding): ZeroPad2d(padding=(1, 1, 1, 1), value=0.0)\n",
              "      )\n",
              "      (_bn1): BatchNorm2d(2688, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
              "      (_se_reduce): Conv2dStaticSamePadding(\n",
              "        2688, 112, kernel_size=(1, 1), stride=(1, 1)\n",
              "        (static_padding): Identity()\n",
              "      )\n",
              "      (_se_expand): Conv2dStaticSamePadding(\n",
              "        112, 2688, kernel_size=(1, 1), stride=(1, 1)\n",
              "        (static_padding): Identity()\n",
              "      )\n",
              "      (_project_conv): Conv2dStaticSamePadding(\n",
              "        2688, 448, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
              "        (static_padding): Identity()\n",
              "      )\n",
              "      (_bn2): BatchNorm2d(448, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
              "      (_swish): MemoryEfficientSwish()\n",
              "    )\n",
              "  )\n",
              "  (_conv_head): Conv2dStaticSamePadding(\n",
              "    448, 1792, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
              "    (static_padding): Identity()\n",
              "  )\n",
              "  (_bn1): BatchNorm2d(1792, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
              "  (_avg_pooling): AdaptiveAvgPool2d(output_size=1)\n",
              "  (_dropout): Dropout(p=0.4, inplace=False)\n",
              "  (_fc): Linear(in_features=1792, out_features=1000, bias=True)\n",
              "  (_swish): MemoryEfficientSwish()\n",
              ")"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        },
        {
          "output_type": "stream",
          "text": [
            "time: 7 ms (started: 2021-05-25 17:40:02 +00:00)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bshtkPDv5r38",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1e5fff5a-2dad-44a7-e9b4-74eb0f9c1232"
      },
      "source": [
        "# Use the torchvision's implementation of ResNeXt, but add FC layer for a different number of classes (27) and a Sigmoid instead of a default Softmax.\n",
        "class EffNet(nn.Module):\n",
        "    def __init__(self, n_classes):\n",
        "        super().__init__()\n",
        "        EN = EfficientNet.from_pretrained('efficientnet-b4')\n",
        "        EN._fc = nn.Sequential(\n",
        "            nn.Dropout(p=0.5),\n",
        "            nn.Linear(in_features = EN._fc.in_features, out_features = n_classes)\n",
        "        )\n",
        "        self.base_model = EN\n",
        "        self.sigm = nn.Sigmoid()\n",
        "\n",
        "    def forward(self, x):\n",
        "        return self.sigm(self.base_model(x))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "time: 5.98 ms (started: 2021-05-25 17:40:02 +00:00)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UmEKmNpUjoS2"
      },
      "source": [
        "## A2Dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Rz9AMkK4_BN-",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c3dd285e-f254-4358-f58a-4e87142b7d7f"
      },
      "source": [
        "# Simple dataloader and label binarization, that is converting test labels into binary arrays of length 18 (number of classes) with 1 in places of applicable labels).\n",
        "class A2Dataset(Dataset):\n",
        "    def __init__(self, data_path, type, transforms=None):\n",
        "        # data_path: the root path of dataset\n",
        "        # type: dataset type, one of ['train', 'test', 'val', 'whole'], where 'whole' means the whole training set without splitting\n",
        "        # transforms: img tranfroms\n",
        "        self.transforms = transforms\n",
        "        self.type = type\n",
        "        self.data_path = data_path\n",
        "        if self.type == 'train':\n",
        "            file_path = os.path.join(data_path, 'train_split.csv')\n",
        "        elif self.type == 'test':\n",
        "            file_path = os.path.join(data_path, 'test.csv')\n",
        "        elif self.type == 'val':\n",
        "            file_path = os.path.join(data_path, 'val_split.csv')\n",
        "        elif self.type == 'whole':\n",
        "            file_path = os.path.join(data_path, 'train.csv')\n",
        "        with open(file_path) as file:\n",
        "            lines = [re.sub(r'([^,])\"(\\s*[^\\n])', r'\\1/\"\\2', line) for line in file]\n",
        "            df = pd.read_csv(StringIO(''.join(lines)), escapechar=\"/\")\n",
        "        self.samples = df\n",
        "        \n",
        "        if self.type != \"test\":\n",
        "            label_set = []\n",
        "            for i in self.samples[\"Labels\"]:\n",
        "                label_set.extend(i.split())\n",
        "            label_set = set(label_set)\n",
        "            label_set = sorted([int(i) for i in label_set])\n",
        "\n",
        "            self.imgs = []\n",
        "            self.labels = []\n",
        "            for index, row in self.samples.iterrows():\n",
        "                self.imgs.append(row['ImageID'])\n",
        "                self.labels.append(row['Labels'])\n",
        "\n",
        "            self.classes = label_set\n",
        "            self.labels = self.samples['Labels']\n",
        "            self.caption = self.samples['Caption']\n",
        "            self.img_id = self.samples['ImageID']\n",
        "\n",
        "\n",
        "            for item_id in range(len(self.labels)):\n",
        "                item = self.labels[item_id].split()\n",
        "                vector = [1 if str(cls) in item else 0 for cls in self.classes ]\n",
        "                self.labels[item_id] = np.array(vector, dtype=float)\n",
        "\n",
        "        else:\n",
        "            self.imgs = []\n",
        "            for index, row in self.samples.iterrows():\n",
        "                self.imgs.append(row['ImageID'])\n",
        "\n",
        "            self.caption = self.samples['Caption']\n",
        "            self.img_id = self.samples['ImageID']\n",
        "            self.caption = self.samples['Caption']\n",
        "\n",
        "        print(\"Loading {} set with {} samples\".format(self.type, len(self.imgs)))\n",
        "\n",
        "    def __getitem__(self, item):\n",
        "        if self.type != \"test\":\n",
        "            label = self.labels[item]\n",
        "            img_path = os.path.join(self.data_path, 'data', self.samples['ImageID'][item])\n",
        "            caption = self.caption[item]\n",
        "            img = Image.open(img_path)\n",
        "            if self.transforms is not None:\n",
        "                img = self.transforms(img)\n",
        "            return img, caption, label\n",
        "        else:\n",
        "            img_path = os.path.join(self.data_path, 'data', self.samples['ImageID'][item])\n",
        "            caption = self.caption[item]\n",
        "            img = Image.open(img_path)\n",
        "            if self.transforms is not None:\n",
        "                img = self.transforms(img)\n",
        "            return img, caption\n",
        "\n",
        "    def __len__(self):\n",
        "        return len(self.imgs)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "time: 65.1 ms (started: 2021-05-25 17:40:02 +00:00)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "GbfvKO5N_Fk7",
        "outputId": "8ee7f884-dbb6-42ed-d6e3-512636f5f695"
      },
      "source": [
        "# Let's take a look at the data we have. To do it we need to load the dataset without augmentations.\n",
        "dataset_train = A2Dataset(\"/content/COMP5329S1A2Dataset\", \"train\")\n",
        "\n",
        "# A simple function for visualization.\n",
        "def show_sample(img, binary_img_labels):\n",
        "    # Convert the binary labels back to the text representation.    \n",
        "    try:\n",
        "        plt.imshow(img)\n",
        "    except:\n",
        "        plt.imshow(img.reshape(256,256,-1))\n",
        "    plt.axis('off')\n",
        "    plt.show()\n",
        "\n",
        "show_sample(dataset_train[2][0],dataset_train[2][1])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Loading train set with 24000 samples\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "time: 5.4 s (started: 2021-05-25 17:40:02 +00:00)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ki_VU2XCjcX_"
      },
      "source": [
        "## Metrics Function"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "v4Kglqww70qR",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "81369fc0-b403-446b-f750-85d92711aac2"
      },
      "source": [
        "# Use threshold to define predicted labels and invoke sklearn's metrics with different averaging strategies.\n",
        "def calculate_metrics(pred, target, threshold=0.5):\n",
        "    pred = np.array(pred > threshold, dtype=float)\n",
        "    return {'micro/precision': precision_score(y_true=target, y_pred=pred, average='micro'),\n",
        "            'micro/recall': recall_score(y_true=target, y_pred=pred, average='micro'),\n",
        "            'micro/f1': f1_score(y_true=target, y_pred=pred, average='micro'),\n",
        "            'macro/precision': precision_score(y_true=target, y_pred=pred, average='macro'),\n",
        "            'macro/recall': recall_score(y_true=target, y_pred=pred, average='macro'),\n",
        "            'macro/f1': f1_score(y_true=target, y_pred=pred, average='macro'),\n",
        "            'samples/precision': precision_score(y_true=target, y_pred=pred, average='samples'),\n",
        "            'samples/recall': recall_score(y_true=target, y_pred=pred, average='samples'),\n",
        "            'samples/f1': f1_score(y_true=target, y_pred=pred, average='samples'),\n",
        "            }"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "time: 6.9 ms (started: 2021-05-25 17:40:07 +00:00)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KJuPGmu25CVx"
      },
      "source": [
        "## Checkpoint"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "n5ARL8SR5Eu2",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e5ffbca5-6aab-4575-dd43-1a4c5f3fb6c9"
      },
      "source": [
        "# Here is an auxiliary function for checkpoint saving.\n",
        "def checkpoint_save(model, save_path, epoch):\n",
        "    f = os.path.join(save_path, 'checkpoint-{:06d}.pth'.format(epoch))\n",
        "    if 'module' in dir(model):\n",
        "        torch.save(model.module.state_dict(), f)\n",
        "    else:\n",
        "        torch.save(model.state_dict(), f)\n",
        "    print('saved checkpoint:', f)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "time: 3.18 ms (started: 2021-05-25 17:40:07 +00:00)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OFZibqEFjzr0"
      },
      "source": [
        "# Main Process"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JfDuloOxkbJU"
      },
      "source": [
        "## Define Transform"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Vvufbni9_fNt",
        "outputId": "8c76d6d0-bd6d-414e-e3a7-d00e323a6c7d"
      },
      "source": [
        "# define a transform\n",
        "mean = [0.485, 0.456, 0.406]\n",
        "std = [0.229, 0.224, 0.225]\n",
        "\n",
        "# Test preprocessing\n",
        "val_transform = transforms.Compose([\n",
        "    transforms.Resize((256, 256)),\n",
        "    transforms.ToTensor(),\n",
        "    transforms.Normalize(mean, std)\n",
        "])\n",
        "print(tuple(np.array(np.array(mean)*255).tolist()))\n",
        "\n",
        "# Train preprocessing\n",
        "train_transform = transforms.Compose([\n",
        "    transforms.Resize((256, 256)),\n",
        "    transforms.RandomHorizontalFlip(),\n",
        "    transforms.ColorJitter(),\n",
        "    transforms.RandomAffine(degrees=20, translate=(0.2, 0.2), scale=(0.5, 1.5),\n",
        "                            shear=None, resample=False, \n",
        "                            fillcolor=tuple(np.array(np.array(mean)*255).astype(int).tolist())),\n",
        "    transforms.ToTensor(),\n",
        "    transforms.Normalize(mean, std)\n",
        "])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(123.675, 116.28, 103.53)\n",
            "time: 10.3 ms (started: 2021-05-25 17:40:07 +00:00)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/torchvision/transforms/transforms.py:1315: UserWarning: Argument resample is deprecated and will be removed since v0.10.0. Please, use interpolation instead\n",
            "  \"Argument resample is deprecated and will be removed since v0.10.0. Please, use interpolation instead\"\n",
            "/usr/local/lib/python3.7/dist-packages/torchvision/transforms/transforms.py:1329: UserWarning: Argument fillcolor is deprecated and will be removed since v0.10.0. Please, use fill instead\n",
            "  \"Argument fillcolor is deprecated and will be removed since v0.10.0. Please, use fill instead\"\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "s8yQkMAokmKk"
      },
      "source": [
        "## Define Hyperparameters"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mg8FSXcf7dA0",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "01adafb0-838c-4e46-a173-69f1e18ead29"
      },
      "source": [
        "# Initialize the training parameters.\n",
        "num_workers =4 # Number of CPU processes for data preprocessing\n",
        "lr = 1e-4 # Learning rate\n",
        "batch_size = 32\n",
        "save_freq = 1 # Save checkpoint frequency (epochs)\n",
        "test_freq = 200 # Test model frequency (iterations)\n",
        "max_epoch_number = 30#5 # Number of epochs for training \n",
        "# Note: on the small subset of data overfitting happens after 30-35 epochs\n",
        "\n",
        "mean = [0.485, 0.456, 0.406]\n",
        "std = [0.229, 0.224, 0.225]\n",
        "\n",
        "device = torch.device('cuda')\n",
        "# Save path for checkpoints\n",
        "save_path = 'chekpoints/'\n",
        "# Save path for logs\n",
        "logdir = 'logs/'"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "time: 3.44 ms (started: 2021-05-25 17:40:07 +00:00)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IoV5wcgQ1rAy"
      },
      "source": [
        "## Train"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "r1YCX0ku-0Od",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "40bde344-8ce4-42e2-df9e-97ebd2c1c869"
      },
      "source": [
        "# Initialize the dataloaders for training.\n",
        "val_dataset = A2Dataset(\"/content/COMP5329S1A2Dataset\", \"val\", val_transform)\n",
        "train_dataset = A2Dataset(\"/content/COMP5329S1A2Dataset\", \"train\", train_transform)\n",
        "\n",
        "train_dataloader = DataLoader(train_dataset, batch_size=batch_size, num_workers=num_workers, shuffle=True,\n",
        "                              drop_last=True)\n",
        "val_dataloader = DataLoader(val_dataset, batch_size=batch_size, num_workers=num_workers)\n",
        "\n",
        "num_train_batches = int(np.ceil(len(train_dataset) / batch_size))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Loading val set with 6000 samples\n",
            "Loading train set with 24000 samples\n",
            "time: 6.21 s (started: 2021-05-25 17:40:07 +00:00)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "w3fREBgt8O7n",
        "outputId": "53ecc276-11c1-445b-eee6-e1bb77f68465"
      },
      "source": [
        "# Initialize the model\n",
        "model = EffNet(len(train_dataset.classes))\n",
        "# Switch model to the training mode and move it to GPU.\n",
        "model.train()\n",
        "model = model.to(device)\n",
        "\n",
        "optimizer = torch.optim.Adam(model.parameters(), lr=lr)\n",
        "\n",
        "os.makedirs(save_path, exist_ok=True)\n",
        "\n",
        "# Loss function\n",
        "criterion = nn.BCELoss()\n",
        "# Tensoboard logger\n",
        "logger = SummaryWriter(logdir)\n",
        "\n",
        "# Run training\n",
        "epoch = 0\n",
        "iteration = 0\n",
        "while True:\n",
        "    batch_losses = []\n",
        "    for imgs, _, targets in train_dataloader:\n",
        "        imgs = imgs.to(device)\n",
        "        targets = targets.to(device)\n",
        "\n",
        "        optimizer.zero_grad()\n",
        "\n",
        "        model_result = model(imgs)\n",
        "        loss = criterion(model_result, targets.type(torch.float))\n",
        "\n",
        "        batch_loss_value = loss.item()\n",
        "        loss.backward()\n",
        "        optimizer.step()\n",
        "\n",
        "        logger.add_scalar('train_loss', batch_loss_value, iteration)\n",
        "        batch_losses.append(batch_loss_value)\n",
        "        with torch.no_grad():\n",
        "            result = calculate_metrics(model_result.cpu().numpy(), targets.cpu().numpy())\n",
        "            for metric in result:\n",
        "                logger.add_scalar('train/' + metric, result[metric], iteration)\n",
        "\n",
        "        if iteration % test_freq == 0:\n",
        "            model.eval()\n",
        "            with torch.no_grad():\n",
        "                model_result = []\n",
        "                targets = []\n",
        "                for imgs, _, batch_targets in val_dataloader:\n",
        "                    imgs = imgs.to(device)\n",
        "                    model_batch_result = model(imgs)\n",
        "                    model_result.extend(model_batch_result.cpu().numpy())\n",
        "                    targets.extend(batch_targets.cpu().numpy())\n",
        "\n",
        "            result = calculate_metrics(np.array(model_result), np.array(targets))\n",
        "            for metric in result:\n",
        "                logger.add_scalar('val/' + metric, result[metric], iteration)\n",
        "            print(\"epoch:{:2d} iter:{:3d} val: \"\n",
        "                  \"micro f1: {:.3f} \"\n",
        "                  \"macro f1: {:.3f} \"\n",
        "                  \"samples f1: {:.3f}\".format(epoch, iteration,\n",
        "                                              result['micro/f1'],\n",
        "                                              result['macro/f1'],\n",
        "                                              result['samples/f1']))\n",
        "\n",
        "            model.train()\n",
        "        iteration += 1\n",
        "\n",
        "    loss_value = np.mean(batch_losses)\n",
        "    print(\"epoch:{:2d} iter:{:3d} train: loss:{:.3f}\".format(epoch, iteration, loss_value))\n",
        "    if epoch % save_freq == 0:\n",
        "        checkpoint_save(model, save_path, epoch)\n",
        "    epoch += 1\n",
        "    if max_epoch_number < epoch:\n",
        "        break"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Loaded pretrained weights for efficientnet-b4\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Recall is ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "epoch: 0 iter:  0 val: micro f1: 0.149 macro f1: 0.118 samples f1: 0.139\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in samples with no predicted labels. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1515: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no true nor predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  average, \"true nor predicted\", 'F-score is', len(true_sum)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "epoch: 0 iter:200 val: micro f1: 0.605 macro f1: 0.073 samples f1: 0.640\n",
            "epoch: 0 iter:400 val: micro f1: 0.698 macro f1: 0.303 samples f1: 0.705\n",
            "epoch: 0 iter:600 val: micro f1: 0.738 macro f1: 0.440 samples f1: 0.751\n",
            "epoch: 0 iter:750 train: loss:0.202\n",
            "saved checkpoint: chekpoints/checkpoint-000000.pth\n",
            "epoch: 1 iter:800 val: micro f1: 0.771 macro f1: 0.536 samples f1: 0.789\n",
            "epoch: 1 iter:1000 val: micro f1: 0.791 macro f1: 0.623 samples f1: 0.818\n",
            "epoch: 1 iter:1200 val: micro f1: 0.801 macro f1: 0.656 samples f1: 0.833\n",
            "epoch: 1 iter:1400 val: micro f1: 0.812 macro f1: 0.666 samples f1: 0.839\n",
            "epoch: 1 iter:1500 train: loss:0.109\n",
            "saved checkpoint: chekpoints/checkpoint-000001.pth\n",
            "epoch: 2 iter:1600 val: micro f1: 0.811 macro f1: 0.670 samples f1: 0.841\n",
            "epoch: 2 iter:1800 val: micro f1: 0.823 macro f1: 0.707 samples f1: 0.852\n",
            "epoch: 2 iter:2000 val: micro f1: 0.824 macro f1: 0.706 samples f1: 0.851\n",
            "epoch: 2 iter:2200 val: micro f1: 0.825 macro f1: 0.721 samples f1: 0.851\n",
            "epoch: 2 iter:2250 train: loss:0.093\n",
            "saved checkpoint: chekpoints/checkpoint-000002.pth\n",
            "epoch: 3 iter:2400 val: micro f1: 0.829 macro f1: 0.722 samples f1: 0.857\n",
            "epoch: 3 iter:2600 val: micro f1: 0.829 macro f1: 0.730 samples f1: 0.856\n",
            "epoch: 3 iter:2800 val: micro f1: 0.830 macro f1: 0.723 samples f1: 0.858\n",
            "epoch: 3 iter:3000 train: loss:0.084\n",
            "saved checkpoint: chekpoints/checkpoint-000003.pth\n",
            "epoch: 4 iter:3000 val: micro f1: 0.833 macro f1: 0.725 samples f1: 0.861\n",
            "epoch: 4 iter:3200 val: micro f1: 0.832 macro f1: 0.731 samples f1: 0.859\n",
            "epoch: 4 iter:3400 val: micro f1: 0.834 macro f1: 0.736 samples f1: 0.862\n",
            "epoch: 4 iter:3600 val: micro f1: 0.837 macro f1: 0.735 samples f1: 0.864\n",
            "epoch: 4 iter:3750 train: loss:0.078\n",
            "saved checkpoint: chekpoints/checkpoint-000004.pth\n",
            "epoch: 5 iter:3800 val: micro f1: 0.831 macro f1: 0.741 samples f1: 0.857\n",
            "epoch: 5 iter:4000 val: micro f1: 0.837 macro f1: 0.740 samples f1: 0.862\n",
            "epoch: 5 iter:4200 val: micro f1: 0.836 macro f1: 0.742 samples f1: 0.864\n",
            "epoch: 5 iter:4400 val: micro f1: 0.836 macro f1: 0.731 samples f1: 0.865\n",
            "epoch: 5 iter:4500 train: loss:0.072\n",
            "saved checkpoint: chekpoints/checkpoint-000005.pth\n",
            "epoch: 6 iter:4600 val: micro f1: 0.839 macro f1: 0.740 samples f1: 0.868\n",
            "epoch: 6 iter:4800 val: micro f1: 0.836 macro f1: 0.732 samples f1: 0.864\n",
            "epoch: 6 iter:5000 val: micro f1: 0.839 macro f1: 0.739 samples f1: 0.865\n",
            "epoch: 6 iter:5200 val: micro f1: 0.839 macro f1: 0.735 samples f1: 0.870\n",
            "epoch: 6 iter:5250 train: loss:0.067\n",
            "saved checkpoint: chekpoints/checkpoint-000006.pth\n",
            "epoch: 7 iter:5400 val: micro f1: 0.835 macro f1: 0.739 samples f1: 0.863\n",
            "epoch: 7 iter:5600 val: micro f1: 0.835 macro f1: 0.733 samples f1: 0.864\n",
            "epoch: 7 iter:5800 val: micro f1: 0.838 macro f1: 0.741 samples f1: 0.867\n",
            "epoch: 7 iter:6000 train: loss:0.063\n",
            "saved checkpoint: chekpoints/checkpoint-000007.pth\n",
            "epoch: 8 iter:6000 val: micro f1: 0.837 macro f1: 0.745 samples f1: 0.865\n",
            "epoch: 8 iter:6200 val: micro f1: 0.835 macro f1: 0.738 samples f1: 0.862\n",
            "epoch: 8 iter:6400 val: micro f1: 0.834 macro f1: 0.737 samples f1: 0.863\n",
            "epoch: 8 iter:6600 val: micro f1: 0.840 macro f1: 0.742 samples f1: 0.868\n",
            "epoch: 8 iter:6750 train: loss:0.058\n",
            "saved checkpoint: chekpoints/checkpoint-000008.pth\n",
            "epoch: 9 iter:6800 val: micro f1: 0.834 macro f1: 0.739 samples f1: 0.862\n",
            "epoch: 9 iter:7000 val: micro f1: 0.835 macro f1: 0.743 samples f1: 0.861\n",
            "epoch: 9 iter:7200 val: micro f1: 0.834 macro f1: 0.742 samples f1: 0.862\n",
            "epoch: 9 iter:7400 val: micro f1: 0.833 macro f1: 0.732 samples f1: 0.860\n",
            "epoch: 9 iter:7500 train: loss:0.055\n",
            "saved checkpoint: chekpoints/checkpoint-000009.pth\n",
            "epoch:10 iter:7600 val: micro f1: 0.832 macro f1: 0.738 samples f1: 0.859\n",
            "epoch:10 iter:7800 val: micro f1: 0.832 macro f1: 0.735 samples f1: 0.859\n",
            "epoch:10 iter:8000 val: micro f1: 0.834 macro f1: 0.736 samples f1: 0.862\n",
            "epoch:10 iter:8200 val: micro f1: 0.832 macro f1: 0.735 samples f1: 0.860\n",
            "epoch:10 iter:8250 train: loss:0.050\n",
            "saved checkpoint: chekpoints/checkpoint-000010.pth\n",
            "epoch:11 iter:8400 val: micro f1: 0.829 macro f1: 0.731 samples f1: 0.859\n",
            "epoch:11 iter:8600 val: micro f1: 0.833 macro f1: 0.737 samples f1: 0.859\n",
            "epoch:11 iter:8800 val: micro f1: 0.829 macro f1: 0.730 samples f1: 0.858\n",
            "epoch:11 iter:9000 train: loss:0.047\n",
            "saved checkpoint: chekpoints/checkpoint-000011.pth\n",
            "epoch:12 iter:9000 val: micro f1: 0.834 macro f1: 0.731 samples f1: 0.864\n",
            "epoch:12 iter:9200 val: micro f1: 0.829 macro f1: 0.732 samples f1: 0.859\n",
            "epoch:12 iter:9400 val: micro f1: 0.832 macro f1: 0.732 samples f1: 0.861\n",
            "epoch:12 iter:9600 val: micro f1: 0.827 macro f1: 0.728 samples f1: 0.857\n",
            "epoch:12 iter:9750 train: loss:0.044\n",
            "saved checkpoint: chekpoints/checkpoint-000012.pth\n",
            "epoch:13 iter:9800 val: micro f1: 0.830 macro f1: 0.733 samples f1: 0.858\n",
            "epoch:13 iter:10000 val: micro f1: 0.829 macro f1: 0.728 samples f1: 0.860\n",
            "epoch:13 iter:10200 val: micro f1: 0.831 macro f1: 0.733 samples f1: 0.860\n",
            "epoch:13 iter:10400 val: micro f1: 0.829 macro f1: 0.727 samples f1: 0.858\n",
            "epoch:13 iter:10500 train: loss:0.041\n",
            "saved checkpoint: chekpoints/checkpoint-000013.pth\n",
            "epoch:14 iter:10600 val: micro f1: 0.829 macro f1: 0.731 samples f1: 0.860\n",
            "epoch:14 iter:10800 val: micro f1: 0.827 macro f1: 0.727 samples f1: 0.859\n",
            "epoch:14 iter:11000 val: micro f1: 0.827 macro f1: 0.729 samples f1: 0.857\n",
            "epoch:14 iter:11200 val: micro f1: 0.825 macro f1: 0.730 samples f1: 0.856\n",
            "epoch:14 iter:11250 train: loss:0.038\n",
            "saved checkpoint: chekpoints/checkpoint-000014.pth\n",
            "epoch:15 iter:11400 val: micro f1: 0.829 macro f1: 0.729 samples f1: 0.858\n",
            "epoch:15 iter:11600 val: micro f1: 0.823 macro f1: 0.724 samples f1: 0.853\n",
            "epoch:15 iter:11800 val: micro f1: 0.828 macro f1: 0.729 samples f1: 0.857\n",
            "epoch:15 iter:12000 train: loss:0.037\n",
            "saved checkpoint: chekpoints/checkpoint-000015.pth\n",
            "epoch:16 iter:12000 val: micro f1: 0.823 macro f1: 0.722 samples f1: 0.853\n",
            "epoch:16 iter:12200 val: micro f1: 0.823 macro f1: 0.725 samples f1: 0.853\n",
            "epoch:16 iter:12400 val: micro f1: 0.826 macro f1: 0.727 samples f1: 0.856\n",
            "epoch:16 iter:12600 val: micro f1: 0.828 macro f1: 0.729 samples f1: 0.857\n",
            "epoch:16 iter:12750 train: loss:0.033\n",
            "saved checkpoint: chekpoints/checkpoint-000016.pth\n",
            "epoch:17 iter:12800 val: micro f1: 0.824 macro f1: 0.730 samples f1: 0.853\n",
            "epoch:17 iter:13000 val: micro f1: 0.825 macro f1: 0.723 samples f1: 0.856\n",
            "epoch:17 iter:13200 val: micro f1: 0.826 macro f1: 0.725 samples f1: 0.856\n",
            "epoch:17 iter:13400 val: micro f1: 0.824 macro f1: 0.730 samples f1: 0.854\n",
            "epoch:17 iter:13500 train: loss:0.031\n",
            "saved checkpoint: chekpoints/checkpoint-000017.pth\n",
            "epoch:18 iter:13600 val: micro f1: 0.823 macro f1: 0.725 samples f1: 0.852\n",
            "epoch:18 iter:13800 val: micro f1: 0.822 macro f1: 0.722 samples f1: 0.855\n",
            "epoch:18 iter:14000 val: micro f1: 0.822 macro f1: 0.720 samples f1: 0.853\n",
            "epoch:18 iter:14200 val: micro f1: 0.818 macro f1: 0.715 samples f1: 0.853\n",
            "epoch:18 iter:14250 train: loss:0.030\n",
            "saved checkpoint: chekpoints/checkpoint-000018.pth\n",
            "epoch:19 iter:14400 val: micro f1: 0.819 macro f1: 0.719 samples f1: 0.850\n",
            "epoch:19 iter:14600 val: micro f1: 0.823 macro f1: 0.723 samples f1: 0.854\n",
            "epoch:19 iter:14800 val: micro f1: 0.821 macro f1: 0.722 samples f1: 0.853\n",
            "epoch:19 iter:15000 train: loss:0.028\n",
            "saved checkpoint: chekpoints/checkpoint-000019.pth\n",
            "epoch:20 iter:15000 val: micro f1: 0.821 macro f1: 0.722 samples f1: 0.852\n",
            "epoch:20 iter:15200 val: micro f1: 0.825 macro f1: 0.724 samples f1: 0.856\n",
            "epoch:20 iter:15400 val: micro f1: 0.822 macro f1: 0.717 samples f1: 0.853\n",
            "epoch:20 iter:15600 val: micro f1: 0.821 macro f1: 0.720 samples f1: 0.854\n",
            "epoch:20 iter:15750 train: loss:0.026\n",
            "saved checkpoint: chekpoints/checkpoint-000020.pth\n",
            "epoch:21 iter:15800 val: micro f1: 0.822 macro f1: 0.722 samples f1: 0.853\n",
            "epoch:21 iter:16000 val: micro f1: 0.822 macro f1: 0.722 samples f1: 0.852\n",
            "epoch:21 iter:16200 val: micro f1: 0.820 macro f1: 0.721 samples f1: 0.851\n",
            "epoch:21 iter:16400 val: micro f1: 0.822 macro f1: 0.720 samples f1: 0.853\n",
            "epoch:21 iter:16500 train: loss:0.025\n",
            "saved checkpoint: chekpoints/checkpoint-000021.pth\n",
            "epoch:22 iter:16600 val: micro f1: 0.819 macro f1: 0.718 samples f1: 0.852\n",
            "epoch:22 iter:16800 val: micro f1: 0.821 macro f1: 0.717 samples f1: 0.853\n",
            "epoch:22 iter:17000 val: micro f1: 0.819 macro f1: 0.718 samples f1: 0.854\n",
            "epoch:22 iter:17200 val: micro f1: 0.817 macro f1: 0.711 samples f1: 0.852\n",
            "epoch:22 iter:17250 train: loss:0.023\n",
            "saved checkpoint: chekpoints/checkpoint-000022.pth\n",
            "epoch:23 iter:17400 val: micro f1: 0.820 macro f1: 0.719 samples f1: 0.851\n",
            "epoch:23 iter:17600 val: micro f1: 0.822 macro f1: 0.719 samples f1: 0.853\n",
            "epoch:23 iter:17800 val: micro f1: 0.815 macro f1: 0.713 samples f1: 0.848\n",
            "epoch:23 iter:18000 train: loss:0.022\n",
            "saved checkpoint: chekpoints/checkpoint-000023.pth\n",
            "epoch:24 iter:18000 val: micro f1: 0.819 macro f1: 0.714 samples f1: 0.850\n",
            "epoch:24 iter:18200 val: micro f1: 0.821 macro f1: 0.716 samples f1: 0.853\n",
            "epoch:24 iter:18400 val: micro f1: 0.822 macro f1: 0.717 samples f1: 0.853\n",
            "epoch:24 iter:18600 val: micro f1: 0.819 macro f1: 0.716 samples f1: 0.850\n",
            "epoch:24 iter:18750 train: loss:0.022\n",
            "saved checkpoint: chekpoints/checkpoint-000024.pth\n",
            "epoch:25 iter:18800 val: micro f1: 0.819 macro f1: 0.712 samples f1: 0.851\n",
            "epoch:25 iter:19000 val: micro f1: 0.819 macro f1: 0.713 samples f1: 0.851\n",
            "epoch:25 iter:19200 val: micro f1: 0.822 macro f1: 0.714 samples f1: 0.851\n",
            "epoch:25 iter:19400 val: micro f1: 0.818 macro f1: 0.716 samples f1: 0.851\n",
            "epoch:25 iter:19500 train: loss:0.020\n",
            "saved checkpoint: chekpoints/checkpoint-000025.pth\n",
            "epoch:26 iter:19600 val: micro f1: 0.819 macro f1: 0.712 samples f1: 0.852\n",
            "epoch:26 iter:19800 val: micro f1: 0.820 macro f1: 0.713 samples f1: 0.849\n",
            "epoch:26 iter:20000 val: micro f1: 0.819 macro f1: 0.713 samples f1: 0.852\n",
            "epoch:26 iter:20200 val: micro f1: 0.821 macro f1: 0.715 samples f1: 0.853\n",
            "epoch:26 iter:20250 train: loss:0.019\n",
            "saved checkpoint: chekpoints/checkpoint-000026.pth\n",
            "epoch:27 iter:20400 val: micro f1: 0.820 macro f1: 0.716 samples f1: 0.850\n",
            "epoch:27 iter:20600 val: micro f1: 0.820 macro f1: 0.718 samples f1: 0.850\n",
            "epoch:27 iter:20800 val: micro f1: 0.820 macro f1: 0.714 samples f1: 0.851\n",
            "epoch:27 iter:21000 train: loss:0.018\n",
            "saved checkpoint: chekpoints/checkpoint-000027.pth\n",
            "epoch:28 iter:21000 val: micro f1: 0.816 macro f1: 0.712 samples f1: 0.848\n",
            "epoch:28 iter:21200 val: micro f1: 0.817 macro f1: 0.707 samples f1: 0.847\n",
            "epoch:28 iter:21400 val: micro f1: 0.819 macro f1: 0.713 samples f1: 0.852\n",
            "epoch:28 iter:21600 val: micro f1: 0.820 macro f1: 0.718 samples f1: 0.851\n",
            "epoch:28 iter:21750 train: loss:0.018\n",
            "saved checkpoint: chekpoints/checkpoint-000028.pth\n",
            "epoch:29 iter:21800 val: micro f1: 0.819 macro f1: 0.711 samples f1: 0.851\n",
            "epoch:29 iter:22000 val: micro f1: 0.818 macro f1: 0.715 samples f1: 0.850\n",
            "epoch:29 iter:22200 val: micro f1: 0.816 macro f1: 0.711 samples f1: 0.848\n",
            "epoch:29 iter:22400 val: micro f1: 0.816 macro f1: 0.708 samples f1: 0.849\n",
            "epoch:29 iter:22500 train: loss:0.017\n",
            "saved checkpoint: chekpoints/checkpoint-000029.pth\n",
            "epoch:30 iter:22600 val: micro f1: 0.818 macro f1: 0.709 samples f1: 0.852\n",
            "epoch:30 iter:22800 val: micro f1: 0.817 macro f1: 0.714 samples f1: 0.850\n",
            "epoch:30 iter:23000 val: micro f1: 0.817 macro f1: 0.711 samples f1: 0.850\n",
            "epoch:30 iter:23200 val: micro f1: 0.817 macro f1: 0.711 samples f1: 0.851\n",
            "epoch:30 iter:23250 train: loss:0.017\n",
            "saved checkpoint: chekpoints/checkpoint-000030.pth\n",
            "time: 4h 13min 10s (started: 2021-05-25 17:40:13 +00:00)\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}