Lightning_Custom_Net_Softmax.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "d8da8359",
        "outputId": "486753c7-b4fe-4519-f2f3-8491183b8c74"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive', force_remount=True)"
      ],
      "id": "d8da8359"
    },
    {
      "cell_type": "code",
      "source": [
        "# import zipfile\n",
        "# !unzip /content/drive/MyDrive/Data/All_images.zip -d /content/drive/MyDrive/Data/Final"
      ],
      "metadata": {
        "id": "FGXF_CS7oh3n"
      },
      "id": "FGXF_CS7oh3n",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "8P3ONXDSaUiP"
      },
      "outputs": [],
      "source": [
        "# import os\n",
        "# totalFiles = 0\n",
        "# totalDir = 0\n",
        "# for base, dirs, files in os.walk('/content/drive/MyDrive/Data/Final/All_images/'):\n",
        "#     for directories in dirs:\n",
        "#         totalDir += 1\n",
        "#     for Files in files:\n",
        "#         totalFiles += 1\n",
        "\n",
        "# print(totalFiles)"
      ],
      "id": "8P3ONXDSaUiP"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "5834035d-bd11-417c-b109-8a263c658be5",
        "outputId": "012104e3-56f6-4bac-d979-e4effdc29be3"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "  Downloading pytorch_lightning-1.5.10-py3-none-any.whl (527 kB)\n",
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            "Collecting aiohttp\n",
            "  Downloading aiohttp-3.8.1-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl (1.1 MB)\n",
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            "Requirement already satisfied: importlib-metadata>=4.4 in /usr/local/lib/python3.7/dist-packages (from markdown>=2.6.8->tensorboard>=2.2.0->pytorch-lightning) (4.10.1)\n",
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            "Requirement already satisfied: oauthlib>=3.0.0 in /usr/local/lib/python3.7/dist-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<0.5,>=0.4.1->tensorboard>=2.2.0->pytorch-lightning) (3.2.0)\n",
            "Collecting yarl<2.0,>=1.0\n",
            "  Downloading yarl-1.7.2-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl (271 kB)\n",
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            "Collecting multidict<7.0,>=4.5\n",
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            "\u001b[?25hCollecting asynctest==0.13.0\n",
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            "Collecting aiosignal>=1.1.2\n",
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            "Building wheels for collected packages: future\n",
            "  Building wheel for future (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for future: filename=future-0.18.2-py3-none-any.whl size=491070 sha256=b1ab633ab5707132857093b435206cf2b6ece475468b1e9a692685db30c70ebe\n",
            "  Stored in directory: /root/.cache/pip/wheels/56/b0/fe/4410d17b32f1f0c3cf54cdfb2bc04d7b4b8f4ae377e2229ba0\n",
            "Successfully built future\n",
            "Installing collected packages: setuptools, multidict, frozenlist, yarl, asynctest, async-timeout, aiosignal, pyDeprecate, fsspec, aiohttp, torchmetrics, PyYAML, future, pytorch-lightning\n",
            "  Attempting uninstall: setuptools\n",
            "    Found existing installation: setuptools 57.4.0\n",
            "    Uninstalling setuptools-57.4.0:\n",
            "      Successfully uninstalled setuptools-57.4.0\n",
            "  Attempting uninstall: PyYAML\n",
            "    Found existing installation: PyYAML 3.13\n",
            "    Uninstalling PyYAML-3.13:\n",
            "      Successfully uninstalled PyYAML-3.13\n",
            "  Attempting uninstall: future\n",
            "    Found existing installation: future 0.16.0\n",
            "    Uninstalling future-0.16.0:\n",
            "      Successfully uninstalled future-0.16.0\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "datascience 0.10.6 requires folium==0.2.1, but you have folium 0.8.3 which is incompatible.\u001b[0m\n",
            "Successfully installed PyYAML-6.0 aiohttp-3.8.1 aiosignal-1.2.0 async-timeout-4.0.2 asynctest-0.13.0 frozenlist-1.3.0 fsspec-2022.1.0 future-0.18.2 multidict-6.0.2 pyDeprecate-0.3.1 pytorch-lightning-1.5.10 setuptools-59.5.0 torchmetrics-0.7.1 yarl-1.7.2\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.colab-display-data+json": {
              "pip_warning": {
                "packages": [
                  "pkg_resources"
                ]
              }
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "Requirement already satisfied: numpy>=1.11.1 in /usr/local/lib/python3.7/dist-packages (from albumentations) (1.19.5)\n",
            "Collecting imgaug<0.2.7,>=0.2.5\n",
            "  Downloading imgaug-0.2.6.tar.gz (631 kB)\n",
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            "Requirement already satisfied: matplotlib!=3.0.0,>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from scikit-image>=0.11.0->imgaug<0.2.7,>=0.2.5->albumentations) (3.2.2)\n",
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            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib!=3.0.0,>=2.0.0->scikit-image>=0.11.0->imgaug<0.2.7,>=0.2.5->albumentations) (1.3.2)\n",
            "Building wheels for collected packages: imgaug\n",
            "  Building wheel for imgaug (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for imgaug: filename=imgaug-0.2.6-py3-none-any.whl size=654017 sha256=b490d8ca6aa41bd11fec2126f60ea1f76e8d034c2eef268c56b4792a27ff6a9a\n",
            "  Stored in directory: /root/.cache/pip/wheels/89/72/98/3ebfdba1069a9a8eaaa7ae7265cfd67d63ef0197aaee2e5f9c\n",
            "Successfully built imgaug\n",
            "Installing collected packages: imgaug\n",
            "  Attempting uninstall: imgaug\n",
            "    Found existing installation: imgaug 0.2.9\n",
            "    Uninstalling imgaug-0.2.9:\n",
            "      Successfully uninstalled imgaug-0.2.9\n",
            "Successfully installed imgaug-0.2.6\n"
          ]
        }
      ],
      "source": [
        "# !pip install timm\n",
        "!pip install pytorch-lightning\n",
        "!pip install albumentations"
      ],
      "id": "5834035d-bd11-417c-b109-8a263c658be5"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "a4385839",
        "outputId": "d49266a5-fbd6-45ba-dc92-15c54cc47bd4"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "PyTorch Lightning Version : 1.5.10\n"
          ]
        }
      ],
      "source": [
        "# !pip install pytorch_model_summary\n",
        "import torch\n",
        "import sys\n",
        "from PIL import Image, ImageFile\n",
        "from torch.utils.data import TensorDataset, DataLoader,Dataset, random_split\n",
        "from torchvision import transforms\n",
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "import torchvision.models as models\n",
        "from collections import OrderedDict\n",
        "from torch.autograd import Variable\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import time\n",
        "import pandas as pd\n",
        "from typing import Optional # required for \"Optional[type]\"\n",
        "\n",
        "# lightning related imports\n",
        "import pytorch_lightning as pl\n",
        "from pytorch_lightning.callbacks import Callback\n",
        "from pytorch_lightning.callbacks.early_stopping import EarlyStopping\n",
        "from pytorch_lightning.loggers import WandbLogger\n",
        "from pytorch_lightning.callbacks import ModelCheckpoint\n",
        "from torchmetrics import Accuracy\n",
        "\n",
        "import albumentations as alb\n",
        "\n",
        "print(\"PyTorch Lightning Version : {}\".format(pl.__version__))\n",
        "\n",
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\", message=\".*does not have many workers which may be a bottleneck.*\")\n",
        "\n",
        "import os\n",
        "ImageFile.LOAD_TRUNCATED_IMAGES = True\n",
        "\n",
        "# import wandb\n",
        "# wandb.init(reinit=True)\n",
        "\n",
        "# from kaggle_secrets import UserSecretsClient\n",
        "# user_secrets = UserSecretsClient()\n",
        "\n",
        "# personal_key_for_api = user_secrets.get_secret(\"wandb-key\")\n",
        "\n",
        "# !wandb login $personal_key_for_api"
      ],
      "id": "a4385839"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "e512075a"
      },
      "outputs": [],
      "source": [
        "# NOTE: class is inherited from Dataset\n",
        "class ImageLabelDataset(Dataset):\n",
        "    def __init__(self, df_data, prediction, size=0, isTrain=True):\n",
        "        super().__init__()\n",
        "        self.df = df_data\n",
        "        self.prediction = prediction\n",
        "        self.isTrain = isTrain\n",
        "        self.size = size\n",
        "\n",
        "    def __len__(self):\n",
        "        return len(self.df)\n",
        "\n",
        "    @property\n",
        "    def train_labels(self):\n",
        "        # warnings.warn(\"train_labels has been renamed targets\")\n",
        "        return self.prediction\n",
        "\n",
        "    def __getitem__(self, index):\n",
        "        label = self.prediction[index]\n",
        "        label_tensor = int(label)\n",
        "        # label_tensor = torch.tensor(label, dtype=torch.float32)\n",
        "        tensorimage = self.preprocess_image(self.df[index], self.size[index], label_tensor)\n",
        "        return tensorimage, label_tensor\n",
        "        # return {'image': tensorimage, 'labels': label_tensor}\n",
        "\n",
        "    def preprocess_image(self, img_path, i_size, label):\n",
        "\n",
        "        image_transform_train = alb.Compose([\n",
        "            alb.Resize(p=1.0, width=2560, height=1920, interpolation=2),\n",
        "            alb.CenterCrop(p=1.0, width=1100, height=1100),\n",
        "            alb.Resize(p=1.0, width=224, height=224, interpolation=3),\n",
        "            # alb.RandomResizedCrop(299, 299),\n",
        "            # alb.Transpose(p=0.5),\n",
        "            # alb.Rotate(p=0.5, limit=(-198, 242), interpolation=3),\n",
        "            alb.HorizontalFlip(p=0.5),\n",
        "            alb.VerticalFlip(p=0.5),\n",
        "            alb.ShiftScaleRotate(p=0.5),\n",
        "            alb.HueSaturationValue(\n",
        "                hue_shift_limit=0.2,\n",
        "                sat_shift_limit=0.2,\n",
        "                val_shift_limit=0.2,\n",
        "                p=0.5\n",
        "            ),\n",
        "            alb.RandomBrightnessContrast(\n",
        "                brightness_limit=(-0.1,0.1),\n",
        "                contrast_limit=(-0.1, 0.1),\n",
        "                p=0.5\n",
        "            ),\n",
        "            alb.Cutout(p=0.5)\n",
        "        ], p=1.0)\n",
        "\n",
        "        image_transform_test = alb.Compose([\n",
        "            alb.Resize(p=1.0, width=2560, height=1920, interpolation=2),\n",
        "            alb.CenterCrop(p=1.0, width=1100, height=1100),\n",
        "            alb.Resize(p=1.0, width=224, height=224, interpolation=3),\n",
        "        ], p=1.0)\n",
        "\n",
        "        preprocess = transforms.Compose([\n",
        "            transforms.ToTensor(),\n",
        "            transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),\n",
        "        ])\n",
        "\n",
        "        folder = '/content/drive/MyDrive/Data/Final/All_images'\n",
        "        if(self.isTrain):\n",
        "            folder = '/content/drive/MyDrive/Data/Final/All_images'\n",
        "\n",
        "        image = Image.open(\"{}/{}\".format(folder, img_path))\n",
        "\n",
        "        if(self.isTrain):\n",
        "            transform_image = image_transform_train(image=np.array(image))['image']\n",
        "            return preprocess(transform_image)\n",
        "        else:\n",
        "            transform_image = image_transform_test(image=np.array(image))['image']\n",
        "            return preprocess(transform_image)\n",
        "\n",
        "    def get_dummies(self, attribute_id):\n",
        "        label_tensor = torch.zeros((1, 102))\n",
        "        label_tensor[0, int(attribute_id)-1] = 1\n",
        "        return label_tensor"
      ],
      "id": "e512075a"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2ad05641",
        "outputId": "e2861db5-d46e-4af7-8b03-e19ab4ac435c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "label         0\n",
            "image_name    0\n",
            "Size          0\n",
            "dtype: int64\n",
            "label         4743\n",
            "image_name    4743\n",
            "Size          4743\n",
            "dtype: int64\n",
            "label         7886\n",
            "image_name    7886\n",
            "Size          7886\n",
            "dtype: int64\n",
            "label         12629\n",
            "image_name    12629\n",
            "Size          12629\n",
            "dtype: int64\n"
          ]
        }
      ],
      "source": [
        "# Read CSV file into DataFrame df\n",
        "BATCH_SIZE = 32\n",
        "\n",
        "df_train = pd.read_csv('/content/drive/MyDrive/Data/Final_train.csv', encoding='utf-8')\n",
        "df_test = pd.read_csv('/content/drive/MyDrive/Data/Final_test.csv', encoding='utf-8')\n",
        "\n",
        "df_compare = pd.read_csv('/content/drive/MyDrive/Data/OtherCSV/effnet-b7-unfreeze-v1_results.csv', encoding='utf-8')\n",
        "\n",
        "df_infected = df_train[(df_train['label'] == 1) & (df_train['Size'] < 640)][:7500]\n",
        "df_infected_1 = df_train[(df_train['label'] == 1)]\n",
        "df_not_infected = df_train[(df_train['label'] == 0)]\n",
        "\n",
        "print(df_infected.count())\n",
        "print(df_infected_1.count())\n",
        "print(df_not_infected.count())\n",
        "\n",
        "df_infected = df_infected.append(df_infected_1, ignore_index=True)\n",
        "df_append_final = df_infected.append(df_not_infected, ignore_index=True)\n",
        "\n",
        "df_append_final = df_append_final.sample(frac = 1).reset_index()\n",
        "\n",
        "df_infected_test = df_test[(df_test['label'] == 1)][:500]\n",
        "df_not_infected_test = df_test[(df_test['label'] == 0)][:500]\n",
        "\n",
        "df_append_final_test = df_infected_test.append(df_not_infected_test, ignore_index=True)\n",
        "df_append_final_test = df_append_final_test.sample(frac = 1).reset_index()\n",
        "\n",
        "print(df_train.count())\n",
        "\n",
        "# df_append_final = df_train"
      ],
      "id": "2ad05641"
    },
    {
      "cell_type": "code",
      "source": [
        "# from os.path import exists\n",
        "# for index, row in df_train.iterrows():\n",
        "#   file_exists = exists('/content/drive/MyDrive/Data/Final/All_images/'+row['image_name'])\n",
        "#   if(not file_exists):\n",
        "#     print(\"image_name: \", row['image_name'])"
      ],
      "metadata": {
        "id": "pF9z_i4Tr9LB"
      },
      "id": "pF9z_i4Tr9LB",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "a3f903ed"
      },
      "outputs": [],
      "source": [
        "from torch.utils.data import DataLoader\n",
        "\n",
        "train_image = df_train[\"image_name\"].values\n",
        "train_target = df_train[\"label\"].values\n",
        "train_size = df_train[\"Size\"].values\n",
        "\n",
        "test_image = df_test[\"image_name\"].values\n",
        "test_target = df_test[\"label\"].values\n",
        "test_size = df_test[\"Size\"].values\n",
        "\n",
        "val_image = df_append_final_test[\"image_name\"].values\n",
        "val_target = df_append_final_test[\"label\"].values\n",
        "val_size = df_append_final_test[\"Size\"].values\n",
        "\n",
        "train_dataset = ImageLabelDataset(df_data=train_image, prediction=train_target, size=train_size)\n",
        "test_dataset = ImageLabelDataset(df_data=test_image, prediction=test_target, size=test_size, isTrain=False)\n",
        "val_dataset = ImageLabelDataset(df_data=val_image, prediction=val_target, size=val_size, isTrain=False)\n",
        "\n",
        "train_loader = DataLoader(train_dataset, batch_size=BATCH_SIZE, num_workers=4, shuffle=True)\n",
        "val_loader  = DataLoader(val_dataset,  batch_size=BATCH_SIZE, num_workers=4, shuffle=False)\n",
        "test_loader  = DataLoader(test_dataset,  batch_size=BATCH_SIZE, num_workers=4, shuffle=False)"
      ],
      "id": "a3f903ed"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "cd9a9a51"
      },
      "outputs": [],
      "source": [
        "early_stop_callback = EarlyStopping(\n",
        "   monitor='val_loss',\n",
        "   patience=10,\n",
        "   verbose=False,\n",
        "   mode='min'\n",
        ")"
      ],
      "id": "cd9a9a51"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "741315d2"
      },
      "outputs": [],
      "source": [
        "MODEL_CKPT_PATH = '/content/drive/MyDrive/Data/'\n",
        "MODEL_CKPT = 'Custom-softmax-{epoch:02d}-{val_loss:.2f}'\n",
        "\n",
        "checkpoint_callback = ModelCheckpoint(\n",
        "    dirpath=MODEL_CKPT_PATH,\n",
        "    monitor='val_loss',\n",
        "    filename=MODEL_CKPT ,\n",
        "    save_top_k=3,\n",
        "    mode='min')"
      ],
      "id": "741315d2"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "5e67bfa7"
      },
      "outputs": [],
      "source": [
        "# timm.create_model('inception_resnet_v2', pretrained=False)"
      ],
      "id": "5e67bfa7"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "d4464700-055a-4d1b-aba5-9101142ea766"
      },
      "outputs": [],
      "source": [
        "def binary_acc(y_pred, y_test):\n",
        "    y_pred_tag = torch.log_softmax(y_pred, dim = 1)\n",
        "    _, y_pred_tags = torch.max(y_pred_tag, dim = 1)\n",
        "    correct_results_sum = (y_pred_tags == y_test).sum().float()\n",
        "    acc = correct_results_sum/y_test.shape[0]\n",
        "    acc = torch.round(acc * 100)\n",
        "    return acc"
      ],
      "id": "d4464700-055a-4d1b-aba5-9101142ea766"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "36415a67-9e39-4efa-9ea7-a2e461436a3e"
      },
      "outputs": [],
      "source": [
        "class Flatten(torch.nn.Module):\n",
        "    def forward(self, x):\n",
        "        # print(x.shape)\n",
        "        batch_size = x.shape[0]\n",
        "        # print(\"x=\",x.view(batch_size, -1).size())\n",
        "        return x.view(batch_size, -1)\n",
        "\n",
        "def weights_init(m):\n",
        "    classname = m.__class__.__name__\n",
        "    if classname.find('Conv') != -1:\n",
        "        torch.nn.init.normal_(m.weight, 0.0, 0.02)\n",
        "    elif classname.find('BatchNorm') != -1:\n",
        "        torch.nn.init.normal_(m.weight, 1.0, 0.02)\n",
        "        torch.nn.init.zeros_(m.bias)\n",
        "    elif classname.find('Linear') != -1:\n",
        "        # get the number of the inputs\n",
        "        n = m.in_features\n",
        "        y = 1.0 / np.sqrt(n)\n",
        "        m.weight.data.normal_(0.0, y)\n",
        "        m.bias.data.fill_(0)\n",
        "        # torch.nn.init.normal_(m.weight, 1.0, 0.02)\n",
        "        # torch.nn.init.zeros_(m.bias)\n",
        "\n",
        "def create_model():\n",
        "  model = nn.Sequential(\n",
        "    nn.Conv2d(3, 32, 3, padding=0, stride=1),\n",
        "    # nn.BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True),\n",
        "    nn.ReLU(),\n",
        "    nn.MaxPool2d(2, padding=0, stride=2),\n",
        "    nn.Conv2d(32, 32, 3, padding=0, stride=1),\n",
        "    # nn.BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True),\n",
        "    nn.ReLU(),\n",
        "    # nn.MaxPool2d(2, padding=0, stride=2),\n",
        "    nn.AdaptiveAvgPool2d(54),\n",
        "    Flatten(),\n",
        "    nn.Linear(93312, 128),\n",
        "    nn.ReLU(),\n",
        "    nn.Dropout(0.35),\n",
        "    nn.Linear(128, 2),\n",
        "    nn.Softmax(dim=1)\n",
        "  )\n",
        "  # model.apply(weights_init)\n",
        "  model.load_state_dict(torch.load('/content/drive/MyDrive/Data/Saved/Custom_softmax.pt'))\n",
        "  return model"
      ],
      "id": "36415a67-9e39-4efa-9ea7-a2e461436a3e"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "679bc6ce"
      },
      "outputs": [],
      "source": [
        "from torch.optim import Adam, SGD\n",
        "\n",
        "class OtisClassifier(pl.LightningModule):\n",
        "    def __init__(self, learning_rate=0.005):\n",
        "        super().__init__()\n",
        "\n",
        "        self.accuracy = Accuracy()\n",
        "        self.model = create_model()\n",
        "        self.lr = learning_rate\n",
        "        self.loss_fn = nn.CrossEntropyLoss()\n",
        "\n",
        "    def forward(self, X_batch):\n",
        "        return self.model(X_batch)\n",
        "\n",
        "    def training_step(self, batch, batch_idx):\n",
        "        X_batch, Y_batch = batch\n",
        "        logits = self.model(X_batch)\n",
        "\n",
        "        loss_val = self.loss_fn(logits.squeeze(), Y_batch)\n",
        "        acc = binary_acc(logits, Y_batch)\n",
        "        self.log('train_loss', loss_val, prog_bar=True)\n",
        "        self.log('train_acc', acc, prog_bar=True)\n",
        "\n",
        "        return loss_val\n",
        "\n",
        "    def training_epoch_end(self, outputs):\n",
        "        # print(\"Train end ep\")\n",
        "        torch.save(self.model.state_dict(), \"/content/drive/MyDrive/Data/Saved/Custom_softmax.pt\")\n",
        "\n",
        "    def validation_step(self, batch, batch_idx):\n",
        "        X_batch, Y_batch = batch\n",
        "        logits = self.model(X_batch)\n",
        "\n",
        "        loss_val = self.loss_fn(logits.squeeze(), Y_batch)\n",
        "\n",
        "        acc = binary_acc(logits, Y_batch)\n",
        "        self.log('val_loss', loss_val, prog_bar=True)\n",
        "        self.log('val_acc', acc, prog_bar=True)\n",
        "        return loss_val\n",
        "\n",
        "    def test_step(self, batch, batch_idx):\n",
        "        X_batch, Y_batch = batch\n",
        "        logits = self.model(X_batch)\n",
        "\n",
        "        #.unsqueeze(1)\n",
        "        loss_val = self.loss_fn(logits.squeeze(), Y_batch)\n",
        "\n",
        "        acc = binary_acc(logits, Y_batch)\n",
        "        self.log('test_loss', loss_val, prog_bar=True)\n",
        "        self.log('test_acc', acc, prog_bar=True)\n",
        "\n",
        "        return loss_val\n",
        "\n",
        "    def predict_step(self, batch, batch_idx):\n",
        "        X_batch, Y_batch = batch\n",
        "        preds = self.model(X_batch.float())\n",
        "        return preds\n",
        "\n",
        "    def configure_optimizers(self):\n",
        "        print('Learning Rate', self.lr)\n",
        "        # optimizer = Adam(self.model.parameters(), lr=self.lr)\n",
        "        optimizer = SGD(self.model.parameters(), lr=self.lr, momentum=0.9)\n",
        "        return optimizer"
      ],
      "id": "679bc6ce"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 520,
          "referenced_widgets": [
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            "ff9333ebe7e54f6599213269bb7da83f",
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            "7757d43d9ef8477382e3647cfeec46ce",
            "61bbe918a7c34799aef087f5f01db768",
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            "cfa2c385bc034615944bb2e0a4cfac5d"
          ]
        },
        "id": "d054c4df",
        "outputId": "12c9be83-2ca3-4236-c9b3-2e21a0dddaad"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/pytorch_lightning/trainer/connectors/checkpoint_connector.py:46: LightningDeprecationWarning: Setting `Trainer(resume_from_checkpoint=)` is deprecated in v1.5 and will be removed in v1.7. Please pass `Trainer.fit(ckpt_path=)` directly instead.\n",
            "  \"Setting `Trainer(resume_from_checkpoint=)` is deprecated in v1.5 and\"\n",
            "GPU available: True, used: True\n",
            "TPU available: False, using: 0 TPU cores\n",
            "IPU available: False, using: 0 IPUs\n",
            "/usr/local/lib/python3.7/dist-packages/pytorch_lightning/trainer/trainer.py:1906: LightningDeprecationWarning: `trainer.resume_from_checkpoint` is deprecated in v1.5 and will be removed in v1.7. Specify the fit checkpoint path with `trainer.fit(ckpt_path=)` instead.\n",
            "  \"`trainer.resume_from_checkpoint` is deprecated in v1.5 and will be removed in v1.7.\"\n",
            "Restoring states from the checkpoint path at /content/drive/MyDrive/Data/Saved/Custom-softmax-epoch=19-val_loss=0.68.ckpt\n",
            "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n",
            "/usr/local/lib/python3.7/dist-packages/pytorch_lightning/trainer/connectors/checkpoint_connector.py:251: UserWarning: You're resuming from a checkpoint that ended mid-epoch. Training will start from the beginning of the next epoch. This can cause unreliable results if further training is done, consider using an end of epoch checkpoint.\n",
            "  \"You're resuming from a checkpoint that ended mid-epoch.\"\n",
            "Restored all states from the checkpoint file at /content/drive/MyDrive/Data/Saved/Custom-softmax-epoch=19-val_loss=0.68.ckpt\n",
            "\n",
            "  | Name     | Type             | Params\n",
            "----------------------------------------------\n",
            "0 | accuracy | Accuracy         | 0     \n",
            "1 | model    | Sequential       | 12.0 M\n",
            "2 | loss_fn  | CrossEntropyLoss | 0     \n",
            "----------------------------------------------\n",
            "12.0 M    Trainable params\n",
            "0         Non-trainable params\n",
            "12.0 M    Total params\n",
            "47.818    Total estimated model params size (MB)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Learning Rate 1e-05\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/pytorch_lightning/callbacks/model_checkpoint.py:631: UserWarning: Checkpoint directory /content/drive/MyDrive/Data exists and is not empty.\n",
            "  rank_zero_warn(f\"Checkpoint directory {dirpath} exists and is not empty.\")\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "0024a6d1cc794af0a0d78e59d9446cff",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "Validation sanity check: 0it [00:00, ?it/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "9716194627f94036948dbc278264094f",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "Training: 0it [00:00, ?it/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "10dd99bcbf504ad8b64f338ef1d5ad36",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "Validating: 0it [00:00, ?it/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "cc4806b30ce8461e9293831eb4f48c72",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "Validating: 0it [00:00, ?it/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "98c9ad40aff64b5ba8a6ec8a3e428ae3",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "Validating: 0it [00:00, ?it/s]"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "# classifier = OtisClassifier(learning_rate=0.00001)\n",
        "# classifier.unfreeze()\n",
        "# classifier.freeze()\n",
        "classifier = OtisClassifier.load_from_checkpoint(\n",
        "    learning_rate=0.00001,\n",
        "    checkpoint_path='/content/drive/MyDrive/Data/Saved/Custom-softmax-epoch=19-val_loss=0.68.ckpt'\n",
        ")\n",
        "\n",
        "# trainer = pl.Trainer(max_epochs=10, progress_bar_refresh_rate=20,\n",
        "#                      log_every_n_steps=100,\n",
        "#                      gpus=1,\n",
        "#                      callbacks=[early_stop_callback, checkpoint_callback]\n",
        "#                     ) #, deterministic=True)\n",
        "\n",
        "trainer = pl.Trainer(max_epochs=25, gpus=1, callbacks=[early_stop_callback, checkpoint_callback], resume_from_checkpoint=\"/content/drive/MyDrive/Data/Saved/Custom-softmax-epoch=19-val_loss=0.68.ckpt\")\n",
        "\n",
        "trainer.fit(classifier, train_loader, val_loader)"
      ],
      "id": "d054c4df"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "3ad1cc95",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 153,
          "referenced_widgets": [
            "d5e8b585704b468381af35567398516b",
            "a7de91ccbd0f4c10b52e6da88a25b3a7",
            "7521c3b72aa54ed3be201e266638613d",
            "c5bc295e117b45c9bc6f907a33ccf244",
            "0dbd2d36cc9446f486f3066e9b107987",
            "08138ad785fa48348dcc2f91c014326c",
            "31851b198e63488a84d7f48f0a90ce78",
            "28a8fbd8c41347c5be1386822b6edde8",
            "20975042ddf34146ac68767ccdd01a14",
            "b34c2de3b86f45c19c63c06e6fdb5fec",
            "0089732ce4bd4acba30bee955cbb1f04"
          ]
        },
        "outputId": "b519b924-5388-4373-f0e7-b170d84b4de7"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "d5e8b585704b468381af35567398516b",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "Validating: 0it [00:00, ?it/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--------------------------------------------------------------------------------\n",
            "DATALOADER:0 VALIDATE RESULTS\n",
            "{'val_acc': 56.88800048828125, 'val_loss': 0.6922315955162048}\n",
            "--------------------------------------------------------------------------------\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[{'val_acc': 56.88800048828125, 'val_loss': 0.6922315955162048}]"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ],
      "source": [
        "trainer.validate(classifier, val_loader)"
      ],
      "id": "3ad1cc95"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "20419f90",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 153,
          "referenced_widgets": [
            "34bfe37dc1804c20affb3e3deb97192a",
            "8aedea9c60684879b4466389a091794e",
            "f68b159d303c498ba9de3576c59dbf0c",
            "142778fe9a9944f096ae575fc50c24ae",
            "9a9e8e10e27d4fcc902bc94097557e57",
            "d4143a0b611e4f6fb0fc0383f1c6308e",
            "c1062861dd204350857ce8ebf1599dad",
            "8823ba43c2314884885347d7189163ed",
            "fc355ca537934f7ea7f8b94bb21a3225",
            "1b5817ca5f374d9383ad87069169431e",
            "361bf8936c1d44068025dd307024922c"
          ]
        },
        "outputId": "e5f10bb6-7539-400d-d002-375f93872a95"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "34bfe37dc1804c20affb3e3deb97192a",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "Testing: 0it [00:00, ?it/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--------------------------------------------------------------------------------\n",
            "DATALOADER:0 TEST RESULTS\n",
            "{'test_acc': 65.1532211303711, 'test_loss': 0.6376740336418152}\n",
            "--------------------------------------------------------------------------------\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[{'test_acc': 65.1532211303711, 'test_loss': 0.6376740336418152}]"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ],
      "source": [
        "trainer.test(classifier, test_loader)"
      ],
      "id": "20419f90"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "d87ba4a4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 153,
          "referenced_widgets": [
            "a16ff4e5a11b437998324eeefa258116",
            "e5b77d1731c447bfa4c3770fa8bfde92",
            "3541dff633a9442f8159756802dfd756",
            "84d7a0608a454162addb9091040aa7c2",
            "8f06f6f1984b4fe0b714534813c79db6",
            "6e135896689d48a19ed39348900694a1",
            "1f73d3d1a8f544e483102d49f2b45522",
            "864ea373fa3a4d66aff531ce800e7c4e",
            "4054aa23621448b88c5e492f9f3d5d49",
            "4541af17fdcb4bae8d0fc06994c19447",
            "0d7a978bbadc4f52a1703d66e0e4da0a"
          ]
        },
        "outputId": "a3ce930d-7123-46a2-830e-8be60eeebd37"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "a16ff4e5a11b437998324eeefa258116",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "Predicting: 395it [00:00, ?it/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "        1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "        0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0,\n",
              "        0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0,\n",
              "        0, 1, 0, 0])"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ],
      "source": [
        "preds = trainer.predict(classifier, test_loader)\n",
        "# preds = torch.sigmoid(preds)\n",
        "\n",
        "preds = torch.cat(preds)\n",
        "\n",
        "preds = preds.argmax(axis=1)\n",
        "\n",
        "preds[:100]"
      ],
      "id": "d87ba4a4"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "CofmEK97Tu4D",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d1210a07-5972-45f4-aec4-ae33fde1fd46"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([1, 1, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0,\n",
              "        1, 1, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 0, 1, 0, 0, 0,\n",
              "        1, 1, 0, 0, 1, 0, 1, 1, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 0,\n",
              "        1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0,\n",
              "        0, 1, 0, 1])"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ],
      "source": [
        "Y_test = []\n",
        "for x,y in test_loader:\n",
        "  Y_test.append(y)\n",
        "\n",
        "Y_test = torch.cat(Y_test)\n",
        "Y_test[:100]"
      ],
      "id": "CofmEK97Tu4D"
    },
    {
      "cell_type": "code",
      "source": [
        "# Y_pred_e = []\n",
        "# df_final = pd.DataFrame(data=[])\n",
        "# for index, row in df_compare.iterrows():\n",
        "#   # Y_pred_e.append(torch.min(torch.tensor([preds[index], row[\"predicted\"]])))\n",
        "#   # Y_pred_e.append(torch.tensor([row[\"predicted\"]]))\n",
        "#   # Y_pred_e =\n",
        "#   row_1 = dict()\n",
        "#   row_1['image_name'] = row['image_name']\n",
        "#   row_1['label'] = row['label']\n",
        "#   row_1['predicted'] = preds[index].item()\n",
        "\n",
        "#   df_final = df_final.append(row_1, ignore_index=True)\n",
        "\n",
        "# df_final.to_csv('/content/drive/MyDrive/Data/inception_results.csv', index=False)\n",
        "# # Y_pred_e = torch.stack(Y_pred_e)\n",
        "# # Y_pred_e = torch.cat(Y_pred_e)\n",
        "# # Y_pred_e[:100]"
      ],
      "metadata": {
        "id": "ZDE64caTSCmw"
      },
      "id": "ZDE64caTSCmw",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "52115200",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f9fdc4d4-247c-4ac2-8189-cdc21a9aa3b1"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test Accuracy : 0.652\n"
          ]
        }
      ],
      "source": [
        "from sklearn.metrics import accuracy_score\n",
        "\n",
        "print(\"Test Accuracy : {:.3f}\".format(accuracy_score(preds, Y_test)))"
      ],
      "id": "52115200"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "34927489",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d15bc282-16bf-4f17-fd52-803d8dbb1951"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Classification Report : \n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.66      0.92      0.77      7886\n",
            "           1       0.61      0.20      0.30      4743\n",
            "\n",
            "    accuracy                           0.65     12629\n",
            "   macro avg       0.64      0.56      0.53     12629\n",
            "weighted avg       0.64      0.65      0.59     12629\n",
            "\n"
          ]
        }
      ],
      "source": [
        "from sklearn.metrics import classification_report, confusion_matrix\n",
        "\n",
        "print(\"Classification Report : \")\n",
        "print(classification_report(Y_test, preds))"
      ],
      "id": "34927489"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "723eaebd",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8e7c58a0-a7b0-4eca-b061-35e20ae9bbe2"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "ax = sns.heatmap(confusion_matrix(Y_test, preds), annot=True, fmt='.2f', cmap='Blues')\n",
        "\n",
        "ax.set_title('Seaborn Confusion Matrix with labels\\n\\n');\n",
        "ax.set_xlabel('\\nPredicted Values')\n",
        "ax.set_ylabel('Actual Values');\n",
        "\n",
        "## Display the visualization of the Confusion Matrix.\n",
        "plt.show()"
      ],
      "id": "723eaebd"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "c39d5b5b"
      },
      "outputs": [],
      "source": [],
      "id": "c39d5b5b"
    }
  ],
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