inference.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Untitled6-test.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "machine_shape": "hm"
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "TPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NhsMSpWhX2ZU",
        "outputId": "736aaa68-6d0d-49b4-876b-46fb92da6106"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5FThMAYTX6vF",
        "outputId": "4f46ae1e-f464-465b-928a-fff6b7c8362e"
      },
      "source": [
        "!curl https://raw.githubusercontent.com/pytorch/xla/master/contrib/scripts/env-setup.py -o pytorch-xla-env-setup.py\n",
        "!python pytorch-xla-env-setup.py --version nightly --apt-packages libomp5 libopenblas-dev"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current\n",
            "                                 Dload  Upload   Total   Spent    Left  Speed\n",
            "\r  0     0    0     0    0     0      0      0 --:--:-- --:--:-- --:--:--     0\r100  5116  100  5116    0     0  27358      0 --:--:-- --:--:-- --:--:-- 27358\n",
            "Updating... This may take around 2 minutes.\n",
            "Updating TPU runtime to pytorch-nightly ...\n",
            "Collecting cloud-tpu-client\n",
            "  Downloading https://files.pythonhosted.org/packages/56/9f/7b1958c2886db06feb5de5b2c191096f9e619914b6c31fdf93999fdbbd8b/cloud_tpu_client-0.10-py3-none-any.whl\n",
            "Requirement already satisfied: oauth2client in /usr/local/lib/python3.7/dist-packages (from cloud-tpu-client) (4.1.3)\n",
            "Collecting google-api-python-client==1.8.0\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/9a/b4/a955f393b838bc47cbb6ae4643b9d0f90333d3b4db4dc1e819f36aad18cc/google_api_python_client-1.8.0-py3-none-any.whl (57kB)\n",
            "\u001b[K     |████████████████████████████████| 61kB 3.2MB/s \n",
            "\u001b[?25hRequirement already satisfied: rsa>=3.1.4 in /usr/local/lib/python3.7/dist-packages (from oauth2client->cloud-tpu-client) (4.7.1)\n",
            "Requirement already satisfied: six>=1.6.1 in /usr/local/lib/python3.7/dist-packages (from oauth2client->cloud-tpu-client) (1.15.0)\n",
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            "Requirement already satisfied: google-auth>=1.4.1 in /usr/local/lib/python3.7/dist-packages (from google-api-python-client==1.8.0->cloud-tpu-client) (1.27.0)\n",
            "Requirement already satisfied: google-auth-httplib2>=0.0.3 in /usr/local/lib/python3.7/dist-packages (from google-api-python-client==1.8.0->cloud-tpu-client) (0.0.4)\n",
            "Requirement already satisfied: uritemplate<4dev,>=3.0.0 in /usr/local/lib/python3.7/dist-packages (from google-api-python-client==1.8.0->cloud-tpu-client) (3.0.1)\n",
            "Requirement already satisfied: google-api-core<2dev,>=1.13.0 in /usr/local/lib/python3.7/dist-packages (from google-api-python-client==1.8.0->cloud-tpu-client) (1.16.0)\n",
            "Uninstalling torch-1.7.0+cu101:\n",
            "Requirement already satisfied: setuptools>=40.3.0 in /usr/local/lib/python3.7/dist-packages (from google-auth>=1.4.1->google-api-python-client==1.8.0->cloud-tpu-client) (53.0.0)\n",
            "Requirement already satisfied: cachetools<5.0,>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from google-auth>=1.4.1->google-api-python-client==1.8.0->cloud-tpu-client) (4.2.1)\n",
            "Requirement already satisfied: googleapis-common-protos<2.0dev,>=1.6.0 in /usr/local/lib/python3.7/dist-packages (from google-api-core<2dev,>=1.13.0->google-api-python-client==1.8.0->cloud-tpu-client) (1.52.0)\n",
            "Requirement already satisfied: protobuf>=3.4.0 in /usr/local/lib/python3.7/dist-packages (from google-api-core<2dev,>=1.13.0->google-api-python-client==1.8.0->cloud-tpu-client) (3.12.4)\n",
            "Requirement already satisfied: pytz in /usr/local/lib/python3.7/dist-packages (from google-api-core<2dev,>=1.13.0->google-api-python-client==1.8.0->cloud-tpu-client) (2018.9)\n",
            "Requirement already satisfied: requests<3.0.0dev,>=2.18.0 in /usr/local/lib/python3.7/dist-packages (from google-api-core<2dev,>=1.13.0->google-api-python-client==1.8.0->cloud-tpu-client) (2.23.0)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests<3.0.0dev,>=2.18.0->google-api-core<2dev,>=1.13.0->google-api-python-client==1.8.0->cloud-tpu-client) (2020.12.5)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests<3.0.0dev,>=2.18.0->google-api-core<2dev,>=1.13.0->google-api-python-client==1.8.0->cloud-tpu-client) (2.10)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests<3.0.0dev,>=2.18.0->google-api-core<2dev,>=1.13.0->google-api-python-client==1.8.0->cloud-tpu-client) (3.0.4)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests<3.0.0dev,>=2.18.0->google-api-core<2dev,>=1.13.0->google-api-python-client==1.8.0->cloud-tpu-client) (1.24.3)\n",
            "Installing collected packages: google-api-python-client, cloud-tpu-client\n",
            "  Found existing installation: google-api-python-client 1.7.12\n",
            "    Uninstalling google-api-python-client-1.7.12:\n",
            "      Successfully uninstalled google-api-python-client-1.7.12\n",
            "Successfully installed cloud-tpu-client-0.10 google-api-python-client-1.8.0\n",
            "  Successfully uninstalled torch-1.7.0+cu101\n",
            "Uninstalling torchvision-0.8.1+cu101:\n",
            "  Successfully uninstalled torchvision-0.8.1+cu101\n",
            "Done updating TPU runtime\n",
            "Copying gs://tpu-pytorch/wheels/torch-nightly-cp37-cp37m-linux_x86_64.whl...\n",
            "|\n",
            "Operation completed over 1 objects/122.6 MiB.                                    \n",
            "Copying gs://tpu-pytorch/wheels/torch_xla-nightly-cp37-cp37m-linux_x86_64.whl...\n",
            "\\ [1 files][132.6 MiB/132.6 MiB]                                                \n",
            "Operation completed over 1 objects/132.6 MiB.                                    \n",
            "Copying gs://tpu-pytorch/wheels/torchvision-nightly-cp37-cp37m-linux_x86_64.whl...\n",
            "/ [1 files][  4.6 MiB/  4.6 MiB]                                                \n",
            "Operation completed over 1 objects/4.6 MiB.                                      \n",
            "Processing ./torch-nightly-cp37-cp37m-linux_x86_64.whl\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from torch==nightly) (3.7.4.3)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from torch==nightly) (1.19.5)\n",
            "\u001b[31mERROR: fastai 1.0.61 requires torchvision, which is not installed.\u001b[0m\n",
            "Installing collected packages: torch\n",
            "Successfully installed torch-1.9.0a0+92a4ee1\n",
            "Processing ./torch_xla-nightly-cp37-cp37m-linux_x86_64.whl\n",
            "Installing collected packages: torch-xla\n",
            "Successfully installed torch-xla-1.9+7671584\n",
            "Processing ./torchvision-nightly-cp37-cp37m-linux_x86_64.whl\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from torchvision==nightly) (1.19.5)\n",
            "Requirement already satisfied: pillow>=4.1.1 in /usr/local/lib/python3.7/dist-packages (from torchvision==nightly) (7.0.0)\n",
            "Requirement already satisfied: torch in /usr/local/lib/python3.7/dist-packages (from torchvision==nightly) (1.9.0a0+92a4ee1)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from torch->torchvision==nightly) (3.7.4.3)\n",
            "Installing collected packages: torchvision\n",
            "Successfully installed torchvision-0.9.0a0+870edfd\n",
            "Reading package lists... Done\n",
            "Building dependency tree       \n",
            "Reading state information... Done\n",
            "libopenblas-dev is already the newest version (0.2.20+ds-4).\n",
            "The following NEW packages will be installed:\n",
            "  libomp5\n",
            "0 upgraded, 1 newly installed, 0 to remove and 13 not upgraded.\n",
            "Need to get 234 kB of archives.\n",
            "After this operation, 774 kB of additional disk space will be used.\n",
            "Get:1 http://archive.ubuntu.com/ubuntu bionic/universe amd64 libomp5 amd64 5.0.1-1 [234 kB]\n",
            "Fetched 234 kB in 1s (292 kB/s)\n",
            "Selecting previously unselected package libomp5:amd64.\n",
            "(Reading database ... 149406 files and directories currently installed.)\n",
            "Preparing to unpack .../libomp5_5.0.1-1_amd64.deb ...\n",
            "Unpacking libomp5:amd64 (5.0.1-1) ...\n",
            "Setting up libomp5:amd64 (5.0.1-1) ...\n",
            "Processing triggers for libc-bin (2.27-3ubuntu1.4) ...\n",
            "/sbin/ldconfig.real: /usr/local/lib/python3.7/dist-packages/ideep4py/lib/libmkldnn.so.0 is not a symbolic link\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PWIcUh0dX_fm",
        "outputId": "8b364505-75ee-42ce-c3e2-673684202869"
      },
      "source": [
        "!unzip drive/MyDrive/highly-probable/imgs-300w.zip "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
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          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nyegWSgfYCir",
        "outputId": "098a52da-49aa-4645-c262-98b272cc5cfa"
      },
      "source": [
        "!pip install efficientnet_pytorch"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting efficientnet_pytorch\n",
            "  Downloading https://files.pythonhosted.org/packages/4e/83/f9c5f44060f996279e474185ebcbd8dbd91179593bffb9abe3afa55d085b/efficientnet_pytorch-0.7.0.tar.gz\n",
            "Requirement already satisfied: torch in /usr/local/lib/python3.7/dist-packages (from efficientnet_pytorch) (1.9.0a0+92a4ee1)\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-0.7.0-cp37-none-any.whl size=16031 sha256=7bafc94f0fde16ed5ad81aa1cf8266e1b5c60e164504fa858451f1481341b55d\n",
            "  Stored in directory: /root/.cache/pip/wheels/e9/c6/e1/7a808b26406239712cfce4b5ceeb67d9513ae32aa4b31445c6\n",
            "Successfully built efficientnet-pytorch\n",
            "Installing collected packages: efficientnet-pytorch\n",
            "Successfully installed efficientnet-pytorch-0.7.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fPjOE30oYOmz"
      },
      "source": [
        "import gc\n",
        "import os\n",
        "import torch\n",
        "import albumentations\n",
        "\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "\n",
        "import torch.nn as nn\n",
        "from sklearn import metrics\n",
        "from sklearn import model_selection\n",
        "from torch.nn import functional as F\n",
        "\n",
        "# from wtfml.engine import Engine\n",
        "# from wtfml.utils import EarlyStopping\n",
        "# from wtfml.data_loaders.image import ClassificationDataLoader\n",
        "\n",
        "\n",
        "import torch_xla.core.xla_model as xm\n",
        "import torch_xla.distributed.parallel_loader as pl\n",
        "import torch_xla.distributed.xla_multiprocessing as xmp\n",
        "\n",
        "import efficientnet_pytorch"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MfLHqzlURden"
      },
      "source": [
        "NUM_EPOCHS = 15"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WS5VBvTtYXcT"
      },
      "source": [
        "class EfficientNet(nn.Module):\n",
        "    def __init__(self):\n",
        "        super(EfficientNet, self).__init__()\n",
        "        self.base_model = efficientnet_pytorch.EfficientNet.from_pretrained(\n",
        "            'efficientnet-b0'\n",
        "        )\n",
        "        self.base_model._fc = nn.Linear(\n",
        "            in_features=1280, \n",
        "            out_features=23, \n",
        "            bias=True\n",
        "        )\n",
        "        \n",
        "    def forward(self, image, targets):\n",
        "        out = self.base_model(image)\n",
        "        #loss = nn.BCEWithLogitsLoss()(out, targets.view(-1, 1).type_as(out))\n",
        "        loss = nn.CrossEntropyLoss()(out, targets.type_as(out))\n",
        "\n",
        "        accuracy = metrics.accuracy_score(targets.cpu(), torch.argmax(out.cpu(), dim=1))\n",
        "\n",
        "        return out, loss, accuracy"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qa1Al1_SY1oM"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 221
        },
        "id": "rmu0TGufYqwH",
        "outputId": "fde265ae-0c08-41e6-cf6d-9bb9fb4618e0"
      },
      "source": [
        "DATA_DIR = 'imgs-300w'\n",
        "\n",
        "def load_df():\n",
        "    df = pd.read_csv(\"drive/MyDrive/highly-probable/womens-clothing-imageid-5fold.csv\")\n",
        "    # label encoding\n",
        "    df['target'] = df['mid_category'].astype('category').cat.codes\n",
        "\n",
        "    # remove rows with missing images (9 images)\n",
        "    df['image_exists'] = df['image_name'].apply(lambda x: os.path.exists(f'{DATA_DIR}/{x}'))\n",
        "    df = df[df['image_exists']]\n",
        "\n",
        "    df = df[['image_name', 'target', 'fold']]\n",
        "\n",
        "    df = df.rename(columns={'fold':'kfold'})\n",
        "\n",
        "    \n",
        "    print(df.shape)\n",
        "\n",
        "    return df\n",
        "\n",
        "df = load_df()\n",
        "df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(277620, 3)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>image_name</th>\n",
              "      <th>target</th>\n",
              "      <th>kfold</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>16222141_1.jpg</td>\n",
              "      <td>6</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>16222141_2.jpg</td>\n",
              "      <td>6</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
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              "      <td>6</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>16222141_4.jpg</td>\n",
              "      <td>6</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>16222141_5.jpg</td>\n",
              "      <td>6</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       image_name  target  kfold\n",
              "0  16222141_1.jpg       6    0.0\n",
              "1  16222141_2.jpg       6    0.0\n",
              "2  16222141_3.jpg       6    0.0\n",
              "3  16222141_4.jpg       6    0.0\n",
              "4  16222141_5.jpg       6    0.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 84
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MXON-CuvZn5p",
        "outputId": "5be9837b-83b3-4e18-b5bf-da9c3d65a752"
      },
      "source": [
        "MX = EfficientNet()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Loaded pretrained weights for efficientnet-b0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VtAcadjbeZq0"
      },
      "source": [
        "class AverageMeter:\n",
        "    \"\"\"\n",
        "    Computes and stores the average and current value\n",
        "    \"\"\"\n",
        "\n",
        "    def __init__(self):\n",
        "        self.val = 0\n",
        "        self.avg = 0\n",
        "        self.sum = 0\n",
        "        self.count = 0\n",
        "\n",
        "    def reset(self):\n",
        "        self.val = 0\n",
        "        self.avg = 0\n",
        "        self.sum = 0\n",
        "        self.count = 0\n",
        "\n",
        "    def update(self, val, n=1):\n",
        "        self.val = val\n",
        "        self.sum += val * n\n",
        "        self.count += n\n",
        "        self.avg = self.sum / self.count\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZTARvW08b3rj"
      },
      "source": [
        "try:\n",
        "    import torch_xla.core.xla_model as xm\n",
        "\n",
        "    _xla_available = True\n",
        "except ImportError:\n",
        "    _xla_available = False\n",
        "\n",
        "\n",
        "class EarlyStopping:\n",
        "    def __init__(self, patience=7, mode=\"max\", delta=0.0001, tpu=False):\n",
        "        self.patience = patience\n",
        "        self.counter = 0\n",
        "        self.mode = mode\n",
        "        self.best_score = None\n",
        "        self.early_stop = False\n",
        "        self.tpu = tpu\n",
        "        self.delta = delta\n",
        "        if self.mode == \"min\":\n",
        "            self.val_score = np.Inf\n",
        "        else:\n",
        "            self.val_score = -np.Inf\n",
        "\n",
        "        if self.tpu and not _xla_available:\n",
        "            raise Exception(\n",
        "                \"You want to use TPUs but you dont have pytorch_xla installed\"\n",
        "            )\n",
        "\n",
        "    def __call__(self, epoch_score, model, model_path):\n",
        "        if self.mode == \"min\":\n",
        "            score = -1.0 * epoch_score\n",
        "        else:\n",
        "            score = np.copy(epoch_score)\n",
        "\n",
        "        if self.best_score is None:\n",
        "            self.best_score = score\n",
        "            self.save_checkpoint(epoch_score, model, model_path)\n",
        "        elif score < self.best_score + self.delta:\n",
        "            self.counter += 1\n",
        "            if self.tpu:\n",
        "                xm.master_print(\n",
        "                    \"EarlyStopping counter: {} out of {}\".format(\n",
        "                        self.counter, self.patience\n",
        "                    )\n",
        "                )\n",
        "            else:\n",
        "                print(\n",
        "                    \"EarlyStopping counter: {} out of {}\".format(\n",
        "                        self.counter, self.patience\n",
        "                    )\n",
        "                )\n",
        "            if self.counter >= self.patience:\n",
        "                self.early_stop = True\n",
        "        else:\n",
        "            self.best_score = score\n",
        "            self.save_checkpoint(epoch_score, model, model_path)\n",
        "            self.counter = 0\n",
        "\n",
        "    def save_checkpoint(self, epoch_score, model, model_path):\n",
        "        if epoch_score not in [-np.inf, np.inf, -np.nan, np.nan]:\n",
        "            if self.tpu:\n",
        "                xm.master_print(\n",
        "                    \"Validation score improved ({} --> {}). Saving model!\".format(\n",
        "                        self.val_score, epoch_score\n",
        "                    )\n",
        "                )\n",
        "            else:\n",
        "                print(\n",
        "                    \"Validation score improved ({} --> {}). Saving model!\".format(\n",
        "                        self.val_score, epoch_score\n",
        "                    )\n",
        "                )\n",
        "            if self.tpu:\n",
        "                xm.save(model.state_dict(), model_path)\n",
        "            else:\n",
        "                torch.save(model.state_dict(), model_path)\n",
        "        self.val_score = epoch_score"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dYWeeGCFbCLB"
      },
      "source": [
        "import cv2\n",
        "import torch\n",
        "\n",
        "import numpy as np\n",
        "\n",
        "from PIL import Image\n",
        "from PIL import ImageFile\n",
        "\n",
        "try:\n",
        "    import torch_xla.core.xla_model as xm\n",
        "\n",
        "    _xla_available = True\n",
        "except ImportError:\n",
        "    _xla_available = False\n",
        "\n",
        "ImageFile.LOAD_TRUNCATED_IMAGES = True\n",
        "\n",
        "\n",
        "class ClassificationDataset:\n",
        "    def __init__(\n",
        "        self,\n",
        "        image_paths,\n",
        "        targets,\n",
        "        resize,\n",
        "        augmentations=None,\n",
        "        backend=\"pil\",\n",
        "        channel_first=True,\n",
        "    ):\n",
        "        \"\"\"\n",
        "        :param image_paths: list of paths to images\n",
        "        :param targets: numpy array\n",
        "        :param resize: tuple or None\n",
        "        :param augmentations: albumentations augmentations\n",
        "        \"\"\"\n",
        "        self.image_paths = image_paths\n",
        "        self.targets = targets\n",
        "        self.resize = resize\n",
        "        self.augmentations = augmentations\n",
        "        self.backend = backend\n",
        "        self.channel_first = channel_first\n",
        "\n",
        "    def __len__(self):\n",
        "        return len(self.image_paths)\n",
        "\n",
        "    def __getitem__(self, item):\n",
        "        targets = self.targets[item]\n",
        "        if self.backend == \"pil\":\n",
        "            image = Image.open(self.image_paths[item])\n",
        "            if self.resize is not None:\n",
        "                image = image.resize(\n",
        "                    (self.resize[1], self.resize[0]), resample=Image.BILINEAR\n",
        "                )\n",
        "            image = np.array(image)\n",
        "            if self.augmentations is not None:\n",
        "                augmented = self.augmentations(image=image)\n",
        "                image = augmented[\"image\"]\n",
        "        elif self.backend == \"cv2\":\n",
        "            image = cv2.imread(self.image_paths[item])\n",
        "            image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n",
        "            if self.resize is not None:\n",
        "                image = cv2.resize(\n",
        "                    image,\n",
        "                    (self.resize[1], self.resize[0]),\n",
        "                    interpolation=cv2.INTER_CUBIC,\n",
        "                )\n",
        "            if self.augmentations is not None:\n",
        "                augmented = self.augmentations(image=image)\n",
        "            image = augmented[\"image\"]\n",
        "        else:\n",
        "            raise Exception(\"Backend not implemented\")\n",
        "        if self.channel_first:\n",
        "            image = np.transpose(image, (2, 0, 1)).astype(np.float32)\n",
        "        return {\n",
        "            \"image\": torch.tensor(image),\n",
        "            \"targets\": torch.tensor(targets),\n",
        "        }\n",
        "\n",
        "\n",
        "class ClassificationDataLoader:\n",
        "    def __init__(\n",
        "        self,\n",
        "        image_paths,\n",
        "        targets,\n",
        "        resize,\n",
        "        augmentations=None,\n",
        "        backend=\"pil\",\n",
        "        channel_first=True,\n",
        "    ):\n",
        "        \"\"\"\n",
        "        :param image_paths: list of paths to images\n",
        "        :param targets: numpy array\n",
        "        :param resize: tuple or None\n",
        "        :param augmentations: albumentations augmentations\n",
        "        \"\"\"\n",
        "        self.image_paths = image_paths\n",
        "        self.targets = targets\n",
        "        self.resize = resize\n",
        "        self.augmentations = augmentations\n",
        "        self.backend = backend\n",
        "        self.channel_first = channel_first\n",
        "        self.dataset = ClassificationDataset(\n",
        "            image_paths=self.image_paths,\n",
        "            targets=self.targets,\n",
        "            resize=self.resize,\n",
        "            augmentations=self.augmentations,\n",
        "            backend=self.backend,\n",
        "            channel_first=self.channel_first,\n",
        "        )\n",
        "\n",
        "    def fetch(\n",
        "        self,\n",
        "        batch_size,\n",
        "        num_workers,\n",
        "        drop_last=False,\n",
        "        shuffle=True,\n",
        "        tpu=False,\n",
        "        sampler=None,\n",
        "    ):\n",
        "        \"\"\"\n",
        "        :param batch_size: batch size\n",
        "        :param num_workers: number of processes to use\n",
        "        :param drop_last: drop the last batch?\n",
        "        :param shuffle: True/False\n",
        "        :param tpu: True/False, to use tpu or not\n",
        "        \"\"\"\n",
        "        if tpu:\n",
        "            sampler = torch.utils.data.distributed.DistributedSampler(\n",
        "                self.dataset,\n",
        "                num_replicas=xm.xrt_world_size(),\n",
        "                rank=xm.get_ordinal(),\n",
        "                shuffle=shuffle,\n",
        "            )\n",
        "\n",
        "        data_loader = torch.utils.data.DataLoader(\n",
        "            self.dataset,\n",
        "            batch_size=batch_size,\n",
        "            sampler=sampler,\n",
        "            drop_last=drop_last,\n",
        "            num_workers=num_workers,\n",
        "        )\n",
        "        return data_loader"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hcWODhJIcAcW"
      },
      "source": [
        "import datetime\n",
        "import torch\n",
        "from tqdm import tqdm\n",
        " \n",
        "try:\n",
        "    from torch.cuda import amp\n",
        "    _amp_available = True\n",
        "except ImportError:\n",
        "    _amp_available = False\n",
        " \n",
        "try:\n",
        "    import torch_xla.core.xla_model as xm\n",
        "    import torch_xla.distributed.parallel_loader as pl\n",
        " \n",
        "    _xla_available = True\n",
        "except ImportError:\n",
        "    _xla_available = False\n",
        " \n",
        " \n",
        "def reduce_fn(vals):\n",
        "    return sum(vals) / len(vals)\n",
        " \n",
        " \n",
        "class Engine:\n",
        "    def __init__(\n",
        "        self,\n",
        "        model,\n",
        "        optimizer,\n",
        "        device,\n",
        "        scheduler=None,\n",
        "        accumulation_steps=1,\n",
        "        use_tpu=False,\n",
        "        tpu_print=10,\n",
        "        fp16=False,\n",
        "        model_fn=None,\n",
        "        use_mean_loss=False,\n",
        "    ):\n",
        "        \"\"\"\n",
        "        model_fn should take batch of data, device and model and return loss\n",
        "        for example:\n",
        "            def model_fn(data, device, model):\n",
        "                images, targets = data\n",
        "                images = list(image.to(device) for image in images)\n",
        "                targets = [{k: v.to(device) for k, v in t.items()} for t in targets]\n",
        "                _, loss = model(images, targets)\n",
        "                return loss\n",
        "        \"\"\"\n",
        "        self.model = model\n",
        "        self.optimizer = optimizer\n",
        "        self.device = device\n",
        "        self.scheduler = scheduler\n",
        "        self.accumulation_steps = accumulation_steps\n",
        "        self.use_tpu = use_tpu\n",
        "        self.tpu_print = tpu_print\n",
        "        self.model_fn = model_fn\n",
        "        self.fp16 = fp16\n",
        "        if self.fp16 and not _amp_available:\n",
        "            raise Exception(\n",
        "                \"You want to use fp16 but dont have amp installed\"\n",
        "            )\n",
        "        self.use_mean_loss = use_mean_loss\n",
        "        self.scaler = None\n",
        " \n",
        "        if self.use_tpu and not _xla_available:\n",
        "            raise Exception(\n",
        "                \"You want to use TPUs but you dont have pytorch_xla installed\"\n",
        "            )\n",
        "        if self.fp16 and use_tpu:\n",
        "            raise Exception(\"Apex fp16 is not available when using TPUs\")\n",
        "        if self.fp16:\n",
        "            self.scaler = amp.GradScaler()\n",
        " \n",
        "    def train(self, data_loader):\n",
        "        losses = AverageMeter()\n",
        "        self.model.train()\n",
        "        print_idx = int(len(data_loader) * self.tpu_print / 100)\n",
        "        if self.accumulation_steps > 1:\n",
        "            self.optimizer.zero_grad()\n",
        "        if self.use_tpu:\n",
        "            para_loader = pl.ParallelLoader(data_loader, [self.device])\n",
        "            tk0 = para_loader.per_device_loader(self.device)\n",
        "        else:\n",
        "            tk0 = tqdm(data_loader, total=len(data_loader))\n",
        " \n",
        "        for b_idx, data in enumerate(tk0):\n",
        "            if self.accumulation_steps == 1 and b_idx == 0:\n",
        "                self.optimizer.zero_grad()\n",
        " \n",
        "            if self.model_fn is None:\n",
        "                for key, value in data.items():\n",
        "                    data[key] = value.to(self.device)\n",
        "                _, loss = self.model(**data)\n",
        "            else:\n",
        "                if self.fp16:\n",
        "                    with amp.autocast():\n",
        "                        loss = self.model_fn(data, self.device, self.model)\n",
        "                else:\n",
        "                    loss = self.model_fn(data, self.device, self.model)\n",
        " \n",
        "            if not self.use_tpu:\n",
        "                with torch.set_grad_enabled(True):\n",
        "                    if self.use_mean_loss:\n",
        "                        loss = loss.mean()\n",
        " \n",
        "                    if self.fp16:\n",
        "                        self.scaler.scale(loss).backward()\n",
        "                    else:\n",
        "                        loss.backward()\n",
        " \n",
        "                    if (b_idx + 1) % self.accumulation_steps == 0:\n",
        "                        if self.fp16:\n",
        "                            self.scaler.step(self.optimizer)\n",
        "                        else:\n",
        "                            self.optimizer.step()\n",
        " \n",
        "                        if self.scheduler is not None:\n",
        "                            self.scheduler.step()\n",
        " \n",
        "                        if self.fp16:\n",
        "                            self.scaler.update()\n",
        " \n",
        "                        if b_idx > 0:\n",
        "                            self.optimizer.zero_grad()\n",
        "            else:\n",
        "                loss.backward()\n",
        "                xm.optimizer_step(self.optimizer)\n",
        "                if self.scheduler is not None:\n",
        "                    self.scheduler.step()\n",
        "                if b_idx > 0:\n",
        "                    self.optimizer.zero_grad()\n",
        "            if self.use_tpu:\n",
        "                reduced_loss = xm.mesh_reduce(\"loss_reduce\", loss, reduce_fn)\n",
        "                losses.update(reduced_loss.item(), data_loader.batch_size)\n",
        "                \n",
        "\n",
        "            else:\n",
        "                losses.update(loss.item(), data_loader.batch_size)\n",
        " \n",
        "            if not self.use_tpu:\n",
        "                tk0.set_postfix(loss=losses.avg)\n",
        "            else:\n",
        "                # if b_idx % print_idx == 0 or b_idx == len(data_loader):\n",
        "                if b_idx % 10 == 0 or b_idx == len(data_loader):\n",
        "                    xm.master_print(\n",
        "                        f\"{datetime.datetime.now()}: Batch {b_idx} / {len(data_loader)}, loss={losses.avg}\"\n",
        "                    )\n",
        "        if not self.use_tpu:\n",
        "            tk0.close()\n",
        "        return losses.avg\n",
        " \n",
        "    def evaluate(self, data_loader, return_predictions=False):\n",
        "        losses = AverageMeter()\n",
        "        accs = AverageMeter()\n",
        "        print_idx = int(len(data_loader) * self.tpu_print / 100)\n",
        "        self.model.eval()\n",
        "        final_predictions = []\n",
        "        with torch.no_grad():\n",
        "            if self.use_tpu:\n",
        "                para_loader = pl.ParallelLoader(data_loader, [self.device])\n",
        "                tk0 = para_loader.per_device_loader(self.device)\n",
        "            else:\n",
        "                tk0 = tqdm(data_loader, total=len(data_loader))\n",
        "            for b_idx, data in enumerate(tk0):\n",
        "                for key, value in data.items():\n",
        "                    data[key] = value.to(self.device)\n",
        "                if self.fp16:\n",
        "                    with amp.autocast():\n",
        "                        batch_preds, loss, accuracy = self.model(**data)\n",
        "                else:\n",
        "                    batch_preds, loss, accuracy = self.model(**data)\n",
        "                if return_predictions:\n",
        "                    final_predictions.append(batch_preds)\n",
        "                if self.use_tpu:\n",
        "                    reduced_loss = xm.mesh_reduce(\"loss_reduce\", loss, reduce_fn)\n",
        "                    losses.update(reduced_loss.item(), data_loader.batch_size)\n",
        "                    \n",
        "                    reduced_accuracy = xm.mesh_reduce(\"acc_reduce\", accuracy, reduce_fn)\n",
        "                    accs.update(reduced_accuracy.item(), data_loader.batch_size)\n",
        "                else:\n",
        "                    if self.use_mean_loss:\n",
        "                        loss = loss.mean()\n",
        "                    losses.update(loss.item(), data_loader.batch_size)\n",
        "                if not self.use_tpu:\n",
        "                    tk0.set_postfix(loss=losses.avg)\n",
        "                else:\n",
        "                    #if b_idx % print_idx == 0 or b_idx == len(data_loader):\n",
        "                    if b_idx % 10 == 0 or b_idx == len(data_loader):\n",
        "                        xm.master_print(\n",
        "                            f\"{datetime.datetime.now()}: Batch {b_idx} / {len(data_loader)}, loss={losses.avg}\"\n",
        "                        )\n",
        "            if not self.use_tpu:\n",
        "                tk0.close()\n",
        "        return accs.avg, losses.avg, final_predictions\n",
        " \n",
        "    def predict(self, data_loader):\n",
        "        self.model.eval()\n",
        "        final_predictions = []\n",
        "        if self.use_tpu:\n",
        "            raise Exception(\"TPU not available for predict yet!\")\n",
        "        with torch.no_grad():\n",
        "            tk0 = tqdm(data_loader, total=len(data_loader))\n",
        "            for data in tk0:\n",
        "                for key, value in data.items():\n",
        "                    data[key] = value.to(self.device)\n",
        "                predictions, _ = self.model(**data)\n",
        "                predictions = predictions.cpu()\n",
        "                final_predictions.append(predictions)\n",
        "        return final_predictions"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rjJVBYJ6Zn32"
      },
      "source": [
        "def train():\n",
        "    training_data_path = \"imgs-300w/\"\n",
        "    df = load_df()\n",
        "    device = xm.xla_device()\n",
        "    epochs = NUM_EPOCHS\n",
        "    train_bs = 32\n",
        "    valid_bs = 16\n",
        "    fold = 0\n",
        " \n",
        "    df_train = df[df.kfold != fold].reset_index(drop=True)\n",
        "    df_valid = df[df.kfold == fold].reset_index(drop=True)\n",
        " \n",
        "    model = MX.to(device)\n",
        " \n",
        "    mean = (0.485, 0.456, 0.406)\n",
        "    std = (0.229, 0.224, 0.225)\n",
        "    train_aug = albumentations.Compose(\n",
        "        [   albumentations.CenterCrop(\n",
        "                height=300,\n",
        "                width=300,\n",
        "            ),\n",
        "         \n",
        "            albumentations.Normalize(\n",
        "                mean, \n",
        "                std, \n",
        "                max_pixel_value=255.0, \n",
        "                always_apply=True\n",
        "            ),\n",
        "            albumentations.ShiftScaleRotate(\n",
        "                shift_limit=0.0625, \n",
        "                scale_limit=0.1, \n",
        "                rotate_limit=15\n",
        "            ),\n",
        "            albumentations.Flip(p=0.5)\n",
        "        ]\n",
        "    )\n",
        " \n",
        "    valid_aug = albumentations.Compose(\n",
        "        [\n",
        "            albumentations.CenterCrop(\n",
        "                height=300,\n",
        "                width=300,\n",
        "            ),\n",
        "            albumentations.Normalize(\n",
        "                mean, \n",
        "                std, \n",
        "                max_pixel_value=255.0,\n",
        "                always_apply=True\n",
        "            )\n",
        "        ]\n",
        "    )\n",
        "\n",
        "    # df_train = df_train.iloc[:256]\n",
        "    # df_valid = df_valid.iloc[:128]\n",
        " \n",
        "    train_images = df_train.image_name.values.tolist()\n",
        "    train_images = [\n",
        "        os.path.join(training_data_path, i) for i in train_images\n",
        "    ]\n",
        "    train_targets = df_train.target.values\n",
        " \n",
        "    valid_images = df_valid.image_name.values.tolist()\n",
        "    valid_images = [\n",
        "        os.path.join(training_data_path, i) for i in valid_images\n",
        "    ]\n",
        "    valid_targets = df_valid.target.values\n",
        " \n",
        "    train_loader = ClassificationDataLoader(\n",
        "        image_paths=train_images,\n",
        "        targets=train_targets,\n",
        "        resize=None,\n",
        "        augmentations=train_aug,\n",
        "    ).fetch(\n",
        "        batch_size=train_bs, \n",
        "        drop_last=True, \n",
        "        num_workers=4, \n",
        "        shuffle=True, \n",
        "        tpu=True\n",
        "    )\n",
        " \n",
        "    valid_loader = ClassificationDataLoader(\n",
        "        image_paths=valid_images,\n",
        "        targets=valid_targets,\n",
        "        resize=None,\n",
        "        augmentations=valid_aug,\n",
        "    ).fetch(\n",
        "        batch_size=valid_bs, \n",
        "        drop_last=True,\n",
        "        num_workers=2, \n",
        "        shuffle=False, \n",
        "        tpu=True\n",
        "    )\n",
        " \n",
        "        \n",
        "    optimizer = torch.optim.Adam(model.parameters(), lr=1e-3)\n",
        "\n",
        "    # scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(\n",
        "    #     optimizer,\n",
        "    #     patience=3,\n",
        "    #     threshold=0.001,\n",
        "    #     mode=\"min\"\n",
        "    # )\n",
        "\n",
        "    scheduler = {'scheduler': torch.optim.lr_scheduler.OneCycleLR(\n",
        "                                        optimizer,\n",
        "                                        max_lr=1e-3,\n",
        "                                        steps_per_epoch=int(len(train_loader)),\n",
        "                                        epochs=NUM_EPOCHS,\n",
        "                                        anneal_strategy=\"linear\",\n",
        "                                        final_div_factor = 30,\n",
        "                                    ),\n",
        "                        'name': 'learning_rate',\n",
        "                        'interval':'step',\n",
        "                        'frequency': 1}\n",
        " \n",
        "    es = EarlyStopping(patience=15, mode=\"min\", tpu=True)\n",
        "    eng = Engine(model, optimizer, device=device, use_tpu=True, tpu_print=25)\n",
        " \n",
        " \n",
        " \n",
        "    for epoch in range(epochs):\n",
        "        train_loss = eng.train(train_loader)\n",
        "        valid_loss, predictions = eng.evaluate(valid_loader, return_predictions=True)\n",
        "        xm.master_print(f\"Epoch = {epoch}, LOSS = {valid_loss}\")\n",
        "\n",
        "        #xm.master_print(valid_loss)\n",
        " \n",
        "        \n",
        "        es(valid_loss, model, model_path=f\"drive/MyDrive/highly-probable/results/tp1/model_fold_{fold}.bin\")\n",
        "        if es.early_stop:\n",
        "            xm.master_print(\"Early stopping\")\n",
        "            break\n",
        "        gc.collect()\n",
        "\n",
        "def validate():\n",
        "    training_data_path = \"imgs-300w/\"\n",
        "    df = load_df()\n",
        "    device = xm.xla_device()\n",
        "    epochs = NUM_EPOCHS\n",
        "    train_bs = 32\n",
        "    valid_bs = 16\n",
        "    fold = 0\n",
        " \n",
        "    # df_train = df[df.kfold != fold].reset_index(drop=True)\n",
        "    df_valid = df[df.kfold == fold].reset_index(drop=True)\n",
        " \n",
        "    model = MX.to(device)\n",
        "    model.load_state_dict(torch.load('drive/MyDrive/highly-probable/results/tp1/model_fold_0.bin'))\n",
        "\n",
        " \n",
        "    mean = (0.485, 0.456, 0.406)\n",
        "    std = (0.229, 0.224, 0.225)\n",
        "    # train_aug = albumentations.Compose(\n",
        "    #     [   albumentations.CenterCrop(\n",
        "    #             height=300,\n",
        "    #             width=300,\n",
        "    #         ),\n",
        "         \n",
        "    #         albumentations.Normalize(\n",
        "    #             mean, \n",
        "    #             std, \n",
        "    #             max_pixel_value=255.0, \n",
        "    #             always_apply=True\n",
        "    #         ),\n",
        "    #         albumentations.ShiftScaleRotate(\n",
        "    #             shift_limit=0.0625, \n",
        "    #             scale_limit=0.1, \n",
        "    #             rotate_limit=15\n",
        "    #         ),\n",
        "    #         albumentations.Flip(p=0.5)\n",
        "    #     ]\n",
        "    # )\n",
        " \n",
        "    valid_aug = albumentations.Compose(\n",
        "        [\n",
        "            albumentations.CenterCrop(\n",
        "                height=300,\n",
        "                width=300,\n",
        "            ),\n",
        "            albumentations.Normalize(\n",
        "                mean, \n",
        "                std, \n",
        "                max_pixel_value=255.0,\n",
        "                always_apply=True\n",
        "            )\n",
        "        ]\n",
        "    )\n",
        "\n",
        "    # df_train = df_train.iloc[:256]\n",
        "    # df_valid = df_valid.iloc[:128]\n",
        " \n",
        "    # train_images = df_train.image_name.values.tolist()\n",
        "    # train_images = [\n",
        "    #     os.path.join(training_data_path, i) for i in train_images\n",
        "    # ]\n",
        "    # train_targets = df_train.target.values\n",
        " \n",
        "    valid_images = df_valid.image_name.values.tolist()\n",
        "    valid_images = [\n",
        "        os.path.join(training_data_path, i) for i in valid_images\n",
        "    ]\n",
        "    valid_targets = df_valid.target.values\n",
        " \n",
        "    # train_loader = ClassificationDataLoader(\n",
        "    #     image_paths=train_images,\n",
        "    #     targets=train_targets,\n",
        "    #     resize=None,\n",
        "    #     augmentations=train_aug,\n",
        "    # ).fetch(\n",
        "    #     batch_size=train_bs, \n",
        "    #     drop_last=True, \n",
        "    #     num_workers=4, \n",
        "    #     shuffle=True, \n",
        "    #     tpu=True\n",
        "    # )\n",
        " \n",
        "    valid_loader = ClassificationDataLoader(\n",
        "        image_paths=valid_images,\n",
        "        targets=valid_targets,\n",
        "        resize=None,\n",
        "        augmentations=valid_aug,\n",
        "    ).fetch(\n",
        "        batch_size=valid_bs, \n",
        "        drop_last=True,\n",
        "        num_workers=2, \n",
        "        shuffle=False, \n",
        "        tpu=True\n",
        "    )\n",
        " \n",
        "        \n",
        "    optimizer = torch.optim.Adam(model.parameters(), lr=1e-3)\n",
        "    # scheduler = {'scheduler': torch.optim.lr_scheduler.OneCycleLR(\n",
        "    #                                     optimizer,\n",
        "    #                                     max_lr=1e-3,\n",
        "    #                                     steps_per_epoch=int(len(train_loader)),\n",
        "    #                                     epochs=NUM_EPOCHS,\n",
        "    #                                     anneal_strategy=\"linear\",\n",
        "    #                                     final_div_factor = 30,\n",
        "    #                                 ),\n",
        "    #                     'name': 'learning_rate',\n",
        "    #                     'interval':'step',\n",
        "    #                     'frequency': 1}\n",
        " \n",
        "    eng = Engine(model, optimizer, device=device, use_tpu=True, tpu_print=25)\n",
        "    valid_accuracy, valid_loss, logits = eng.evaluate(valid_loader, return_predictions=True)\n",
        "    # predictions = torch.argmax(torch.cat(logits), dim=1)\n",
        "    # xm.master_print(logits)\n",
        "    # xm.master_print(predictions)\n",
        "    # xm.master_print(valid_targets)\n",
        "    # valid_accuracy = metrics.accuracy_score(valid_targets, predictions)\n",
        "    xm.master_print(f\"LOSS = {valid_loss}, Accuracy = {valid_accuracy}\")\n",
        "    gc.collect()\n",
        "\n",
        "        "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KcTpJ6OCrCQ0"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UNu8LjazZn18"
      },
      "source": [
        "def _mp_fn(rank, flags):\n",
        "    torch.set_default_tensor_type('torch.FloatTensor')\n",
        "    a = validate()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bNsN5EyjYl6j",
        "outputId": "9c180378-9a8f-49cd-ac16-6078d7c3a6d8"
      },
      "source": [
        "FLAGS={}\n",
        "xmp.spawn(_mp_fn, args=(FLAGS,), nprocs=8, start_method='fork')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(277620, 3)\n",
            "(277620, 3)\n",
            "(277620, 3)\n",
            "(277620, 3)\n",
            "(277620, 3)\n",
            "(277620, 3)\n",
            "(277620, 3)\n",
            "(277620, 3)\n",
            "2021-02-26 08:37:32.495190: Batch 0 / 433, loss=1.5629571676254272\n",
            "2021-02-26 08:37:41.672980: Batch 10 / 433, loss=0.7743805132128976\n",
            "2021-02-26 08:37:55.196431: Batch 20 / 433, loss=0.7498890175705865\n",
            "2021-02-26 08:38:05.578711: Batch 30 / 433, loss=0.7611670369102109\n",
            "2021-02-26 08:38:19.842978: Batch 40 / 433, loss=0.7398029238712497\n",
            "2021-02-26 08:38:29.539491: Batch 50 / 433, loss=0.7528594013522653\n",
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            "2021-02-26 08:45:11.409445: Batch 390 / 433, loss=0.70278796774652\n",
            "2021-02-26 08:45:25.112705: Batch 400 / 433, loss=0.7032562686023569\n",
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            "2021-02-26 08:45:48.563233: Batch 420 / 433, loss=0.7028826463817134\n",
            "2021-02-26 08:45:55.854167: Batch 430 / 433, loss=0.7021670250217765\n",
            "LOSS = 0.7032498677388075, Accuracy = 0.7579387990762124\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6b8CcS3MYl4k",
        "outputId": "9c8af1ce-86d5-4379-a4dc-78c3dcb0735a"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<function len>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 105
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QYp9af9fQWUO"
      },
      "source": [
        "\n",
        "t1 = torch.Tensor([[ -4.0578,  -8.9004,  -8.1468,   0.7182,  -4.6377,  -1.4946,   9.7441,\n",
        "          -1.7964,  -7.6763,  -9.0211,  -7.1281,  -1.6963,  -7.3100,  -8.5007,\n",
        "          -0.4693,  -5.7752,  -2.2465,  -1.8670,  -8.1119,  -8.8775,  -8.6117,\n",
        "           4.3674,   0.0494],\n",
        "        [ -1.0636,  -4.8603,  -5.4210,  -0.1171,  -0.6040,   0.0831,   1.3921,\n",
        "          -0.2309,  -4.1246,  -5.1452,  -5.6408,   1.3084,  -4.0589,  -5.8564,\n",
        "           0.0450,  -2.5080,  -3.0302,  -2.4206,  -4.1956,  -5.3382,  -5.2597,\n",
        "          -0.2480,   3.4566],\n",
        "        [ -5.1050,  -6.6847,  -8.5644,  -4.0545,  -2.2365,  -1.1563,   1.3982,\n",
        "           3.2515,  -8.2708,  -8.2989,  -8.3606,   0.7099,  -8.4915,  -7.7682,\n",
        "          -4.2133,  -5.1981,  -0.4873,   3.9077,  -5.2250,  -8.8272,  -8.3565,\n",
        "          -5.8237,   4.0996],\n",
        "        [ -7.5464,  -6.5540,  -9.3766,  -0.8305,  -1.3218,  -6.1765,   4.2763,\n",
        "           5.6337,  -9.6570,  -9.8515,  -9.7481,  -2.0424,  -9.0626,  -9.4960,\n",
        "          -5.0519,  -4.9890,  -1.7645,  -2.7084,  -4.5378,  -9.8645, -10.0249,\n",
        "          -4.2825,   6.8422],\n",
        "        [ -4.2155,  -4.2437,  -7.7727,  -6.5253,  -1.6051,  -0.7649,  -2.6998,\n",
        "           4.2403,  -7.1058,  -7.8559,  -7.7598,   1.6383,  -8.0333,  -5.7151,\n",
        "          -2.3399,  -4.8864,   1.1154,   2.9296,  -4.3717,  -7.7189,  -7.7494,\n",
        "          -6.5524,   5.3776],\n",
        "        [ -5.2851,  -6.5032,  -9.2297,  -0.8373,  -1.7292,   2.8279,   0.8132,\n",
        "           2.8236,  -7.7933,  -8.8084,  -9.1962,  -2.1960,  -8.0978,  -9.4600,\n",
        "          -1.8449,  -5.5163,  -2.0939,  -3.3875,  -6.2630,  -9.4321,  -9.4624,\n",
        "          -2.2305,   6.0902],\n",
        "        [ -7.6281,  -9.3882, -11.0737,  -0.3564,   1.6851,  -3.9320,   2.9248,\n",
        "           3.6319,  -8.9935, -10.3054, -10.8736,   3.7311,  -9.2857, -10.3548,\n",
        "          -3.1580,  -6.6020,  -2.8683,  -1.8021,  -7.1932, -11.2721, -10.7029,\n",
        "          -3.5078,   3.9736],\n",
        "        [ -4.4476,  -6.5810,  -7.4404,  -3.1400,   7.6178,  -4.3544,  -1.0737,\n",
        "           0.2824,  -4.4509,  -7.3714,  -6.9666,   6.4900,  -6.6098,  -4.5342,\n",
        "          -1.6687,  -5.6462,   0.5463,  -1.7210,  -6.0462,  -7.6385,  -7.5721,\n",
        "          -3.3123,  -0.3678],\n",
        "        [ -4.1227,  -3.5180,  -4.2887,   0.8731,   3.6689,  -2.9151,   2.4010,\n",
        "           1.8018,  -4.0304,  -4.1822,  -4.5264,  -0.3725,  -2.8682,  -4.3148,\n",
        "          -2.1020,  -1.8784,  -1.8123,  -2.6236,  -2.6206,  -4.2139,  -4.5768,\n",
        "          -2.0312,   2.8745],\n",
        "        [ -2.0931,  -5.0425,  -5.2670,  -0.9187,   2.6347,  -0.7699,   1.8701,\n",
        "           0.2768,  -6.4155,  -5.0374,  -4.8065,  -1.4019,  -5.5259,  -3.9943,\n",
        "          -2.9691,  -3.6729,   1.4032,   3.0506,  -5.2287,  -6.0594,  -5.2305,\n",
        "          -3.1503,   0.4525],\n",
        "        [ -0.1351,  -6.0615,  -6.5711,  -1.5352,  -0.8255,  -1.4739,   0.6137,\n",
        "          -2.1672,  -6.6945,  -8.0324,  -6.1045,   1.5999,  -6.5050,  -4.4149,\n",
        "           0.0569,  -4.4863,   5.4237,  -1.4023,  -6.7684,  -7.3615,  -7.4003,\n",
        "          -2.5453,   1.7930],\n",
        "        [ -4.9942,  -9.9421,  -9.5870,   5.1570,  -7.3982,   2.6506,   9.5032,\n",
        "          -1.5348,  -8.7071,  -8.7157,  -8.3847,  -3.9595,  -6.7236, -10.3208,\n",
        "           0.5746,  -6.7496,  -5.1827,  -2.7215,  -9.2741, -10.3576,  -9.5665,\n",
        "           3.4658,  -0.5893],\n",
        "        [ -6.2687, -10.6241, -13.1800,  -5.1565,  -0.8236,  -5.0206,   1.1692,\n",
        "           3.1116,  -9.1548, -12.7681, -13.3703,   7.8208, -11.8221, -11.0603,\n",
        "           0.4807,  -7.4006,  -1.1081,  -4.0987,  -8.3184, -13.5922, -13.0280,\n",
        "          -3.7718,   6.0637],\n",
        "        [ -8.2072,  -9.3248, -11.7358,  -0.4060,  -0.0333,  -4.7856,   1.4977,\n",
        "           4.5762, -10.6627, -11.2091, -11.5518,   2.2344, -10.0120, -10.4248,\n",
        "          -5.6325,  -6.7217,  -0.4482,  -0.0416,  -7.1621, -11.7915, -11.2715,\n",
        "          -7.8237,   6.5325],\n",
        "        [ -2.9316,  -6.1250,  -7.9566,  -3.7964,  -1.9796,   1.0969,   0.8802,\n",
        "           2.5595,  -4.4910,  -7.0841,  -8.3894,   1.8045,  -5.6817,  -7.7179,\n",
        "           8.4842,  -4.2021,  -5.0693,  -7.5823,  -5.0515,  -8.0398,  -8.6495,\n",
        "           0.2078,   1.6647],\n",
        "        [ -1.4495,  -9.3525, -11.3788,  -2.0648,  -6.2584,   5.9604,  -1.7271,\n",
        "          -1.4173, -10.4999, -10.2073,  -9.9474,   2.7338, -10.5014, -11.0423,\n",
        "          -1.8098,  -8.3335,  -1.5104,   5.0161, -10.8629, -12.0137, -11.6607,\n",
        "          -6.7809,   3.4897]])\n",
        "\n",
        "t2 = torch.Tensor([[  1.6649,  -2.9340,  -6.6344,  -5.6007,  -4.3817,   3.7547,  -2.1206,\n",
        "           4.2543,  -7.3630,  -7.0425,  -6.7711,  -4.0889,  -6.8821,  -7.2610,\n",
        "           1.5472,  -4.8337,  -2.6937,  -2.4223,  -4.0609,  -6.9169,  -7.2993,\n",
        "          -3.0769,   4.2746],\n",
        "        [  4.4675,  -5.3455,  -8.2182,   1.5456,  -7.0646,   6.9733,   0.4629,\n",
        "           1.1221,  -7.9156,  -7.6971,  -7.3783,  -4.5198,  -6.7829, -10.0985,\n",
        "           2.6376,  -5.7024,  -6.9764,  -4.9502,  -6.6253,  -7.9667,  -7.6628,\n",
        "          -1.3474,   1.4487],\n",
        "        [ -5.6458,  -5.2420,  -7.0010,  -0.1460,   6.9970,  -3.5832,   2.1083,\n",
        "           1.1481,  -6.3399,  -6.6563,  -6.6851,  -0.5752,  -5.5074,  -6.1518,\n",
        "          -2.6929,  -3.8298,  -1.1626,  -1.6428,  -4.8282,  -6.8501,  -7.3331,\n",
        "          -3.4557,   3.6197],\n",
        "        [  0.3431,  -8.1226,  -8.9407,  -4.8710,   4.0835,  -4.1221,  -2.3645,\n",
        "          -0.5717,  -6.4556,  -9.6048,  -8.9850,   8.7007,  -9.2128,  -6.9799,\n",
        "           0.3366,  -6.7988,  -0.9254,  -2.7996,  -8.1522,  -9.7556,  -9.1448,\n",
        "          -1.5034,  -0.1911],\n",
        "        [ -5.8601,  -8.3552, -10.2026,  -2.1538,  -2.5582,   4.1812,   1.9199,\n",
        "           0.7035,  -9.1477,  -9.2734,  -9.6069,  -2.5017,  -8.6097,  -8.4412,\n",
        "           0.0670,  -6.0905,   1.5587,   1.4411,  -8.7537, -10.0778,  -9.9822,\n",
        "          -3.6864,   3.1619],\n",
        "        [ -5.0478,  -6.4017,  -9.6845,  -1.5056,  -4.6436,  -0.7071,   0.1037,\n",
        "           9.3574, -10.7064,  -9.5382, -10.2290,  -3.1548,  -9.0742, -10.7330,\n",
        "          -4.4986,  -5.7290,  -4.6972,  -3.0478,  -3.7863, -10.4164, -10.2749,\n",
        "          -6.4920,   6.1659],\n",
        "        [ -3.3377,  -7.9710,  -8.9440,  -0.8930,  -3.7937,   6.1331,   2.2229,\n",
        "          -1.6892,  -8.0235,  -8.4267,  -8.0341,  -4.0309,  -7.4492,  -7.1280,\n",
        "           2.5376,  -5.7231,   1.3994,   0.5770,  -9.5138,  -9.2044,  -8.9412,\n",
        "          -1.0424,   0.3671],\n",
        "        [ -6.6435,  -5.2929,  -9.1867,  -5.6656,   0.6932,  -3.8457,  -0.6334,\n",
        "           7.3554,  -9.4301,  -8.7491,  -9.5693,  -0.3598,  -9.1305,  -7.3991,\n",
        "          -3.9577,  -5.2902,   0.4465,   0.6632,  -3.4501,  -9.4808,  -9.5346,\n",
        "          -6.8920,   4.7274],\n",
        "        [ -5.4317,  -7.1617,  -8.4973,   1.8781,  -1.6755,   0.0758,   3.5512,\n",
        "           9.0816, -10.0417,  -7.9978,  -8.6244,  -2.9238,  -7.4249,  -9.6715,\n",
        "          -3.6050,  -5.7118,  -4.3633,  -3.0924,  -3.6113,  -9.1415,  -8.7007,\n",
        "          -2.4588,   0.7310],\n",
        "        [ -7.2998, -10.2992, -13.1481,  -0.8470,  -3.6656,   2.6011,   1.7698,\n",
        "          -0.6456, -11.3088, -12.6157, -12.3345,  -0.4328, -10.8053, -12.2262,\n",
        "          -3.5477,  -7.8529,  -1.8669,   2.0673, -10.8284, -13.1431, -12.7209,\n",
        "          -5.7294,   7.6919],\n",
        "        [ -2.2778,  -8.3753,  -9.3191,   0.2765,  -5.3698,   6.1114,   0.6886,\n",
        "           0.9109,  -8.3857,  -8.0115,  -8.8855,  -1.0862,  -7.5735,  -9.4064,\n",
        "          -2.6463,  -6.2093,  -3.4004,   2.2647,  -8.1020,  -9.5405,  -9.0627,\n",
        "          -2.6494,   2.2780],\n",
        "        [ -7.0492, -10.6521, -12.2627,  -1.4711,   3.4300,  -3.1939,   1.6283,\n",
        "           0.6360,  -9.5827, -10.8267, -10.9385,   6.7426, -11.0197,  -8.7608,\n",
        "          -3.2542,  -8.2067,   2.4477,   0.8747,  -9.7176, -12.5008, -12.0116,\n",
        "          -3.6606,  -0.5525],\n",
        "        [ -4.9558,  -7.7313, -10.6454,  -3.3818,  -3.6679,   4.6483,  -1.2995,\n",
        "           1.7993,  -8.8944,  -9.0831,  -9.5370,   1.3152,  -9.2684,  -8.7975,\n",
        "          -1.6107,  -6.7618,  -0.7401,   3.1490,  -8.3737, -10.4842, -10.0152,\n",
        "          -5.7893,   3.2100],\n",
        "        [ -3.0941,  -6.1330,  -7.1539,  -1.7411,   0.1286,   2.5211,   0.0275,\n",
        "          -1.8025,  -6.2302,  -6.7477,  -6.1379,  -0.5557,  -6.6373,  -4.1707,\n",
        "          -0.5339,  -4.5448,   3.8428,   3.1066,  -7.5896,  -7.2921,  -7.1417,\n",
        "          -2.8434,   0.3889],\n",
        "        [ -4.7475,  -9.2243,  -9.3524,   0.6677,  -3.3686,   5.7136,   0.9498,\n",
        "          -1.1142,  -9.0059,  -7.9423,  -8.1285,  -2.9046,  -7.9405,  -7.4894,\n",
        "          -2.4120,  -6.2783,   2.0715,   5.9911,  -9.8596,  -9.5603,  -9.1462,\n",
        "          -3.8669,  -0.3228],\n",
        "        [  0.1315,  -6.2035,  -7.3645,   0.4825,  -3.3153,   2.4951,   1.7093,\n",
        "           0.0315,  -7.2340,  -7.3340,  -6.4412,   2.7079,  -7.6853,  -8.2445,\n",
        "          -0.4751,  -5.2220,  -0.6158,  -0.7399,  -6.6951,  -8.2998,  -7.8399,\n",
        "          -3.1746,   1.8055]])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "niKh7yvjYEW9"
      },
      "source": [
        "t1.shape"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6mjSt9o0bLkN"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "K-a93bstYKYI"
      },
      "source": [
        "asd = [t1, t2]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LNVqBOhvYICU"
      },
      "source": [
        "asd = torch.cat(asd)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fuCXCrRTYV1C"
      },
      "source": [
        "torch.argmax(asd, dim=1).shape"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OSW7I6fWYVqq"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iBZj7HevYFoY"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "22uNYp72XMTo"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RdCg6Z2BZnz7"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RrTVIYqqZnv5"
      },
      "source": [
        "df[df.target=='']"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oBgx2gzwZntz"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}