Single_test_inference.ipynb

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      "language": "python",
      "display_name": "Python 3",
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    "language_info": {
      "pygments_lexer": "ipython3",
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        "name": "ipython",
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      "name": "python",
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    "colab": {
      "name": "Single_test_inference.ipynb",
      "provenance": [],
      "include_colab_link": true
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/souravs17031999/Retinal_blindness_detection_Pytorch/blob/master/Single_test_inference.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "_uuid": "d629ff2d2480ee46fbb7e2d37f6b5fab8052498a",
        "_cell_guid": "79c7e3d0-c299-4dcb-8224-4455121ee9b0",
        "trusted": true,
        "id": "aqpNhKZhyQaI",
        "colab_type": "code",
        "colab": {},
        "outputId": "7f810a59-6171-4d42-93b9-6cd82ab55d3b"
      },
      "source": [
        "# Imports here\n",
        "from __future__ import print_function, division\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from torch.utils import data\n",
        "import torch\n",
        "from torch import nn\n",
        "from torch import optim\n",
        "import torchvision\n",
        "import torch.nn.functional as F\n",
        "from torchvision import datasets, transforms, models\n",
        "import torchvision.models as models\n",
        "from torch.utils.data.sampler import SubsetRandomSampler\n",
        "from torch.utils.data import Dataset, DataLoader\n",
        "from skimage import io, transform\n",
        "import torch.utils.data as data_utils\n",
        "from PIL import Image, ImageFile\n",
        "import json\n",
        "from torch.optim import lr_scheduler\n",
        "import time\n",
        "import os\n",
        "import argparse\n",
        "import copy\n",
        "import pandas as pd\n",
        "ImageFile.LOAD_TRUNCATED_IMAGES = True\n",
        "import cv2\n",
        "# Import useful sklearn functions\n",
        "import sklearn\n",
        "from sklearn.metrics import cohen_kappa_score, accuracy_score\n",
        "import time\n",
        "from tqdm import tqdm_notebook\n",
        "\n",
        "import os\n",
        "print(os.listdir(\"../input\"))\n",
        "base_dir = \"../input/aptos2019-blindness-detection/\""
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['aptos2019-blindness-detection', 'kernel4f121f3247']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "8thQ8UPsyQaM",
        "colab_type": "code",
        "colab": {},
        "outputId": "a9812f99-f709-440c-bdfd-b93f0b5ef29e"
      },
      "source": [
        "print(os.listdir(\"../input/kernel4f121f3247\"))\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['__results__.html', 'custom.css', '__notebook__.ipynb', 'classifier.pt', '__results___files', '__output__.json']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "E1WjW_VUyQaa",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "class CreateDataset(Dataset):\n",
        "    def __init__(self, df_data, data_dir = '../input/', transform=None):\n",
        "        super().__init__()\n",
        "        self.df = df_data.values\n",
        "        self.data_dir = data_dir\n",
        "        self.transform = transform\n",
        "\n",
        "    def __len__(self):\n",
        "        return len(self.df)\n",
        "    \n",
        "    def __getitem__(self, index):\n",
        "        img_name,label = self.df[index]\n",
        "        img_path = os.path.join(self.data_dir, img_name+'.png')\n",
        "        image = cv2.imread(img_path)  # if getting error, use PIL to load the image here.\n",
        "        if self.transform is not None:\n",
        "            image = self.transform(image)\n",
        "        return image, label"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "HDJRlQicyQap",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "test_csv = pd.read_csv('../input/aptos2019-blindness-detection/test.csv')\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "gVppvdUfyQau",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "test_path = \"../input/aptos2019-blindness-detection/test_images/\"\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "5uCeyL8CyQa7",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "test_transforms = torchvision.transforms.Compose([\n",
        "    torchvision.transforms.Resize((224, 224)),\n",
        "    #torchvision.transforms.ColorJitter(brightness=2, contrast=2),\n",
        "    torchvision.transforms.RandomHorizontalFlip(p=0.5),\n",
        "    torchvision.transforms.ToTensor(),\n",
        "    torchvision.transforms.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225))\n",
        "])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "w4EMG86xyQa9",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "test_csv['diagnosis'] = -1\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "H9ZYfDaCyQbJ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "test_data = CreateDataset(df_data=test_csv, data_dir=test_path, transform=test_transforms)\n",
        "test_loader = DataLoader(test_data, batch_size=64, shuffle=False)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "6u4PbduZyQbM",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def round_off_preds(preds, coef=[0.5, 1.5, 2.5, 3.5]):\n",
        "    for i, pred in enumerate(preds):\n",
        "            if pred < coef[0]:\n",
        "                preds[i] = 0\n",
        "            elif pred >= coef[0] and pred < coef[1]:\n",
        "                preds[i] = 1\n",
        "            elif pred >= coef[1] and pred < coef[2]:\n",
        "                preds[i] = 2\n",
        "            elif pred >= coef[2] and pred < coef[3]:\n",
        "                preds[i] = 3\n",
        "            else:\n",
        "                preds[i] = 4\n",
        "    return preds"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "RoFP-7rkyQba",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def predict(testloader):\n",
        "    '''Function used to make predictions on the test set'''\n",
        "    model.eval()\n",
        "    preds = []\n",
        "    for batch_i, (data, target) in enumerate(testloader):\n",
        "        data, target = data.cuda(), target.cuda()\n",
        "        output = model(data)\n",
        "        pr = output.detach().cpu().numpy()\n",
        "        for i in pr:\n",
        "            preds.append(i.item())\n",
        "            \n",
        "    return preds"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "8OAK8mGwyQbi",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def load_model(path):\n",
        "  checkpoint = torch.load(path)\n",
        "  model.load_state_dict(checkpoint['model_state_dict'])\n",
        "  optimizer.load_state_dict(checkpoint['optimizer_state_dict'])\n",
        "  return model"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "K7f1z9RKyQbp",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
        "\n",
        "model = models.resnet152(pretrained=False) \n",
        "\n",
        "num_ftrs = model.fc.in_features \n",
        "out_ftrs = 5 \n",
        "  \n",
        "model.fc = nn.Sequential(nn.Linear(num_ftrs, 512),nn.ReLU(),nn.Linear(512,out_ftrs),nn.LogSoftmax(dim=1))\n",
        "\n",
        "criterion = nn.NLLLoss()\n",
        "optimizer = torch.optim.Adam(filter(lambda p:p.requires_grad,model.parameters()) , lr = 0.00001) \n",
        "\n",
        "scheduler = lr_scheduler.StepLR(optimizer, step_size=5, gamma=0.1)\n",
        "model.to(device);"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "pF7lrBGyyQby",
        "colab_type": "code",
        "colab": {},
        "outputId": "38443da0-2db4-4f4c-8ce2-50f9a35507dd"
      },
      "source": [
        "# to unfreeze more layers \n",
        "for name,child in model.named_children():\n",
        "  if name in ['layer2','layer3','layer4','fc']:\n",
        "    print(name + 'is unfrozen')\n",
        "    for param in child.parameters():\n",
        "      param.requires_grad = True\n",
        "  else:\n",
        "    print(name + 'is frozen')\n",
        "    for param in child.parameters():\n",
        "      param.requires_grad = False"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "conv1is frozen\n",
            "bn1is frozen\n",
            "reluis frozen\n",
            "maxpoolis frozen\n",
            "layer1is frozen\n",
            "layer2is unfrozen\n",
            "layer3is unfrozen\n",
            "layer4is unfrozen\n",
            "avgpoolis frozen\n",
            "fcis unfrozen\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "sqaWCqQ6yQb-",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "optimizer = torch.optim.Adam(filter(lambda p:p.requires_grad,model.parameters()) , lr = 0.000001) \n",
        "scheduler = lr_scheduler.StepLR(optimizer, step_size=5, gamma=0.1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "kHPsRfUEyQcK",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model = load_model(\"../input/kernel4f121f3247/classifier.pt\")\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "oToTzW9UyQcP",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "test_dir = \"../input/aptos2019-blindness-detection/test_images/\""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "MmUx-hEryQcT",
        "colab_type": "code",
        "colab": {},
        "outputId": "5c1a0350-f03e-449f-937a-6c99980e9640"
      },
      "source": [
        "with torch.no_grad():\n",
        "    model.eval()\n",
        "    p_labels = []\n",
        "    img_ids = []\n",
        "    i = 0\n",
        "    for inputs, labels in test_loader:\n",
        "        i += 1\n",
        "        if i % 10 == 0:\n",
        "            print(f'{i} pass step')\n",
        "        inputs = inputs.to(device)\n",
        "        labels = labels.to(device)\n",
        "        outputs = model(inputs)\n",
        "        _, preds = torch.max(outputs, 1)\n",
        "        p_labels.append(preds)\n",
        "    # getting ids of file images    "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "10 pass step\n",
            "20 pass step\n",
            "30 pass step\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "error",
          "ename": "NotADirectoryError",
          "evalue": "[Errno 20] Not a directory: '../input/aptos2019-blindness-detection/test_images/42bef0737ac1.png'",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNotADirectoryError\u001b[0m                        Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-20-e2926a7b7054>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     15\u001b[0m     \u001b[0;31m# getting ids of file images\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     16\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0mdir\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlistdir\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtest_dir\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 17\u001b[0;31m         \u001b[0;32mfor\u001b[0m \u001b[0mfile\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlistdir\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtest_dir\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdir\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     18\u001b[0m             \u001b[0mimg_id\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msplitext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfile\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     19\u001b[0m             \u001b[0mimg_ids\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimg_id\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mNotADirectoryError\u001b[0m: [Errno 20] Not a directory: '../input/aptos2019-blindness-detection/test_images/42bef0737ac1.png'"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "n58Bf7aRyQch",
        "colab_type": "code",
        "colab": {},
        "outputId": "5118c232-c777-47de-a789-d7e5b549efb5"
      },
      "source": [
        "p_labels"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              " tensor([2, 2, 0, 0, 2, 1, 2, 2, 2, 3, 2, 2, 2, 2, 2, 4, 0, 2, 0, 2, 2, 2, 2, 0,\n",
              "         2, 4, 2, 2, 2, 0, 2, 2, 2, 0, 2, 2, 2, 0, 2, 0, 0, 0, 0, 2, 2, 2, 0, 2,\n",
              "         2, 2, 2, 2, 2, 2, 2, 0, 3, 2, 0, 0, 2, 3, 2, 1], device='cuda:0'),\n",
              " tensor([2, 1, 2, 2, 2, 1, 0, 2, 2, 3, 2, 2, 2, 0, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2,\n",
              "         0, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 0, 2, 1, 0, 2, 0, 2, 1, 0, 2, 2, 2, 2,\n",
              "         2, 2, 2, 2, 3, 2, 0, 2, 2, 0, 2, 1, 1, 0, 0, 2], device='cuda:0'),\n",
              " tensor([2, 2, 2, 2, 2, 2, 1, 1, 0, 2, 3, 0, 2, 0, 1, 1, 2, 2, 0, 0, 2, 2, 2, 0,\n",
              "         1, 2, 4, 1, 2, 2, 2, 2, 2, 2, 0, 0, 2, 2, 1, 3, 2, 2, 2, 0, 2, 2, 0, 2,\n",
              "         2, 2, 2, 2, 1, 0, 2, 2, 4, 2, 2, 2, 2, 2, 2, 2], device='cuda:0'),\n",
              " tensor([0, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2,\n",
              "         2, 0, 2, 2, 2, 3, 1, 2, 2, 2, 2, 2, 2, 2, 3, 2, 1, 3, 0, 0, 0, 0, 2, 2,\n",
              "         2, 2, 2, 2, 2, 2, 2, 4, 2, 3, 2, 2, 2, 2, 2, 2], device='cuda:0'),\n",
              " tensor([2, 2, 2, 2, 2, 2, 1, 2, 3, 2, 2, 2, 2, 2, 2, 2, 2, 0, 2, 3, 0, 2, 2, 1,\n",
              "         2, 1, 2, 1, 2, 2, 0, 2, 1, 4, 2, 2, 2, 2, 2, 2, 2, 2, 0, 2, 1, 3, 0, 2,\n",
              "         2, 2, 0, 2, 2, 0, 4, 3, 2, 2, 2, 0, 4, 2, 4, 2], device='cuda:0'),\n",
              " tensor([0, 2, 2, 2, 2, 1, 2, 2, 2, 2, 3, 1, 4, 1, 1, 2, 2, 2, 2, 2, 3, 0, 2, 2,\n",
              "         0, 3, 2, 2, 2, 2, 3, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 4, 2, 1, 1, 2, 0, 2,\n",
              "         2, 2, 0, 0, 0, 2, 2, 0, 2, 4, 2, 1, 2, 2, 2, 4], device='cuda:0'),\n",
              " tensor([2, 2, 3, 2, 2, 3, 2, 0, 2, 2, 0, 2, 2, 2, 2, 2, 2, 3, 3, 1, 3, 2, 2, 2,\n",
              "         2, 0, 2, 1, 4, 0, 2, 2, 2, 2, 2, 2, 3, 0, 2, 0, 2, 4, 2, 2, 2, 0, 2, 2,\n",
              "         2, 0, 2, 2, 2, 2, 2, 2, 2, 2, 0, 2, 2, 2, 0, 2], device='cuda:0'),\n",
              " tensor([2, 0, 0, 2, 2, 0, 2, 2, 0, 3, 2, 2, 0, 2, 2, 3, 0, 2, 2, 0, 0, 0, 2, 0,\n",
              "         2, 2, 2, 2, 2, 2, 2, 0, 1, 2, 0, 0, 0, 0, 2, 2, 0, 2, 2, 1, 2, 2, 2, 2,\n",
              "         2, 3, 2, 2, 4, 0, 2, 2, 2, 2, 2, 0, 2, 2, 4, 2], device='cuda:0'),\n",
              " tensor([2, 2, 0, 4, 3, 2, 2, 2, 0, 2, 2, 2, 4, 2, 3, 2, 2, 2, 2, 2, 2, 2, 3, 2,\n",
              "         0, 2, 2, 2, 0, 2, 2, 2, 2, 2, 3, 3, 2, 0, 2, 2, 0, 2, 2, 2, 4, 2, 0, 2,\n",
              "         0, 0, 4, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2], device='cuda:0'),\n",
              " tensor([1, 2, 2, 2, 0, 2, 4, 2, 2, 4, 2, 3, 2, 3, 0, 2, 1, 1, 2, 2, 2, 0, 2, 2,\n",
              "         2, 2, 0, 2, 0, 2, 2, 2, 2, 2, 2, 0, 2, 2, 0, 2, 2, 2, 0, 2, 0, 4, 2, 2,\n",
              "         2, 2, 2, 2, 3, 2, 3, 2, 2, 2, 0, 0, 0, 2, 2, 2], device='cuda:0'),\n",
              " tensor([0, 2, 2, 4, 0, 2, 0, 0, 2, 3, 2, 2, 0, 0, 2, 2, 1, 0, 2, 2, 1, 0, 2, 1,\n",
              "         0, 2, 2, 2, 2, 0, 2, 1, 2, 2, 0, 2, 0, 2, 2, 3, 0, 4, 3, 0, 2, 2, 1, 4,\n",
              "         2, 2, 0, 0, 2, 2, 2, 2, 0, 2, 2, 1, 2, 0, 2, 0], device='cuda:0'),\n",
              " tensor([0, 1, 2, 2, 2, 3, 2, 2, 1, 0, 0, 2, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
              "         2, 0, 2, 0, 2, 2, 2, 3, 2, 2, 2, 0, 2, 0, 2, 0, 2, 2, 2, 4, 2, 2, 2, 0,\n",
              "         0, 0, 1, 2, 0, 2, 0, 2, 2, 2, 2, 2, 2, 2, 2, 0], device='cuda:0'),\n",
              " tensor([2, 2, 3, 2, 2, 2, 4, 4, 2, 2, 2, 0, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
              "         2, 1, 0, 2, 0, 2, 3, 0, 0, 0, 2, 2, 2, 4, 0, 0, 0, 2, 2, 2, 2, 1, 2, 2,\n",
              "         2, 3, 0, 2, 0, 3, 2, 3, 0, 1, 2, 2, 2, 2, 0, 2], device='cuda:0'),\n",
              " tensor([2, 2, 0, 2, 0, 2, 2, 2, 2, 0, 2, 0, 0, 0, 0, 3, 2, 2, 0, 2, 2, 2, 2, 2,\n",
              "         2, 2, 2, 4, 2, 0, 3, 4, 2, 2, 2, 3, 1, 4, 2, 0, 0, 2, 2, 1, 2, 2, 1, 0,\n",
              "         2, 1, 1, 2, 2, 0, 2, 2, 2, 0, 2, 2, 0, 2, 2, 3], device='cuda:0'),\n",
              " tensor([0, 2, 3, 0, 2, 0, 2, 0, 1, 2, 2, 2, 2, 0, 2, 2, 2, 0, 1, 0, 2, 3, 2, 2,\n",
              "         0, 2, 2, 2, 2, 0, 2, 2, 2, 2, 2, 2, 0, 2, 0, 2, 1, 0, 2, 0, 2, 3, 2, 2,\n",
              "         2, 2, 1, 2, 2, 2, 3, 0, 3, 2, 2, 2, 2, 2, 2, 3], device='cuda:0'),\n",
              " tensor([2, 1, 2, 0, 2, 2, 1, 0, 2, 2, 4, 2, 2, 1, 2, 3, 4, 0, 2, 0, 2, 2, 2, 2,\n",
              "         2, 4, 1, 2, 2, 2, 2, 3, 1, 2, 2, 2, 2, 0, 2, 2, 2, 2, 0, 2, 2, 4, 2, 3,\n",
              "         0, 1, 3, 2, 2, 2, 2, 2, 1, 2, 2, 1, 3, 2, 2, 2], device='cuda:0'),\n",
              " tensor([2, 2, 2, 3, 2, 2, 1, 2, 2, 0, 4, 2, 2, 2, 2, 0, 2, 2, 2, 0, 2, 1, 1, 0,\n",
              "         2, 1, 2, 0, 2, 2, 0, 0, 2, 2, 3, 2, 4, 2, 0, 0, 0, 2, 2, 1, 2, 0, 2, 2,\n",
              "         2, 3, 2, 2, 0, 2, 2, 0, 0, 3, 2, 2, 2, 0, 4, 2], device='cuda:0'),\n",
              " tensor([0, 0, 2, 2, 2, 1, 0, 2, 4, 2, 1, 0, 1, 0, 4, 2, 2, 4, 1, 2, 2, 0, 1, 2,\n",
              "         0, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 1, 0, 2, 0, 0, 2, 2, 2, 2, 3, 0, 2, 2,\n",
              "         2, 2, 0, 2, 0, 0, 2, 0, 2, 2, 0, 2, 0, 0, 2, 2], device='cuda:0'),\n",
              " tensor([0, 2, 2, 2, 2, 2, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 2, 2, 2, 1, 2,\n",
              "         2, 2, 0, 3, 2, 2, 2, 3, 2, 2, 3, 2, 2, 2, 2, 2, 2, 2, 1, 2, 0, 2, 0, 0,\n",
              "         2, 2, 1, 0, 2, 4, 0, 0, 2, 2, 2, 2, 2, 2, 2, 2], device='cuda:0'),\n",
              " tensor([3, 3, 2, 0, 0, 2, 4, 3, 2, 2, 2, 0, 3, 1, 2, 2, 0, 2, 3, 1, 2, 0, 0, 0,\n",
              "         4, 2, 0, 2, 2, 3, 2, 2, 2, 2, 0, 0, 2, 2, 2, 0, 2, 2, 3, 2, 2, 4, 2, 2,\n",
              "         2, 1, 2, 2, 0, 1, 2, 2, 2, 2, 2, 0, 0, 0, 0, 2], device='cuda:0'),\n",
              " tensor([0, 1, 0, 2, 0, 2, 2, 3, 1, 2, 3, 2, 1, 2, 1, 2, 2, 2, 2, 3, 2, 2, 2, 2,\n",
              "         4, 0, 2, 2, 4, 2, 1, 2, 0, 0, 0, 2, 2, 2, 4, 2, 2, 2, 0, 0, 0, 2, 2, 2,\n",
              "         1, 2, 4, 0, 2, 2, 0, 3, 2, 2, 2, 2, 2, 1, 2, 2], device='cuda:0'),\n",
              " tensor([2, 2, 2, 0, 2, 2, 3, 0], device='cuda:0')]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "fsWeTc0SyQdA",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "pred_labels = []\n",
        "for l in p_labels:\n",
        "    for l1 in l:\n",
        "        pred_labels.append(l1.item())"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "siCP7zPkyQdm",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "sample_sub = pd.read_csv('../input/aptos2019-blindness-detection/sample_submission.csv')\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "zZ2SmY-NyQd2",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "sample_sub.diagnosis = pred_labels\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "QGFIu9W5yQeD",
        "colab_type": "code",
        "colab": {},
        "outputId": "b27de66f-20ce-4127-f2d6-4e0c2126751e"
      },
      "source": [
        "sample_sub.head"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<bound method NDFrame.head of            id_code  diagnosis\n",
              "0     0005cfc8afb6          2\n",
              "1     003f0afdcd15          2\n",
              "2     006efc72b638          2\n",
              "3     00836aaacf06          2\n",
              "4     009245722fa4          2\n",
              "...            ...        ...\n",
              "1923  ff2fd94448de          0\n",
              "1924  ff4c945d9b17          2\n",
              "1925  ff64897ac0d8          2\n",
              "1926  ffa73465b705          3\n",
              "1927  ffdc2152d455          0\n",
              "\n",
              "[1928 rows x 2 columns]>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "S9YwxsNCyQeP",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "sample_sub.to_csv('submission.csv', index=False)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "RVctDWuOyQeY",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def test_with_single_image(model, file, transform, classes):\n",
        "\n",
        "    file = Image.open(file).convert('RGB')\n",
        "\n",
        "    img = transform(file).unsqueeze(0)\n",
        "\n",
        "    device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
        "\n",
        "    with torch.no_grad():\n",
        "        out = model(img.to(device))\n",
        "        ps = torch.exp(out)\n",
        "        top_p, top_class = ps.topk(1, dim=1)\n",
        "        value = top_class.item()\n",
        "        print(\"Value:\", value)\n",
        "        print(classes[value])\n",
        "        plt.imshow(np.array(file))\n",
        "        plt.show()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "KLWNzaJLyQeo",
        "colab_type": "code",
        "colab": {},
        "outputId": "e80b5a7f-5494-4ad9-b1bd-510415cd6479"
      },
      "source": [
        "import random\n",
        "\n",
        "test_dir = \"../input/aptos2019-blindness-detection\"\n",
        "# take random folder first\n",
        "folders = os.listdir(test_dir)\n",
        "num = 2\n",
        "path = test_dir+\"/\"+folders[num]\n",
        "print(path)\n",
        "# save label\n",
        "label = folders[num]\n",
        "\n",
        "# now take random file\n",
        "files = os.listdir(path)\n",
        "num = random.randint(0, len(files)-1)\n",
        "name = path + \"/\" + files[num]\n",
        "print('Path: ', name)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "../input/aptos2019-blindness-detection/test_images\n",
            "Real Label:  test_images\n",
            "Path:  ../input/aptos2019-blindness-detection/test_images/4df7349f0806.png\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "Nvcd6JNPyQe4",
        "colab_type": "code",
        "colab": {},
        "outputId": "9f743824-3d58-4b9a-d81c-04dffc9d2b36"
      },
      "source": [
        "classes = ['No DR', 'Mild', 'Moderate', 'Severe', 'Proliferative DR']\n",
        "test_with_single_image(model, name, test_transforms, classes)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Value: 2\n",
            "Moderate\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}