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  },
  "cells": [
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      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Dfsa6ZFUWaI7",
        "outputId": "dadbca1e-2b4f-47db-8bed-6207e54a84be"
      },
      "source": [
        "!pip install gdown >/dev/null\n",
        "!pip install pytorch_lightning >/dev/null"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\u001b[31mERROR: tensorflow 1.15.2 has requirement gast==0.2.2, but you'll have gast 0.3.3 which is incompatible.\u001b[0m\n",
            "\u001b[31mERROR: tensorflow 1.15.2 has requirement tensorboard<1.16.0,>=1.15.0, but you'll have tensorboard 2.4.1 which is incompatible.\u001b[0m\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ByFxzfkMSAzu",
        "outputId": "1117e2b3-3c8a-483b-b2d9-7b064ab7e2a2"
      },
      "source": [
        "import os\n",
        "#Connect to Gdrive to store model checkpoints\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive',force_remount=True)\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Lj5bXacRsOhU"
      },
      "source": [
        "import os\n",
        "import pickle\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "from PIL import Image\n",
        "import copy\n",
        "import torch\n",
        "import random\n",
        "import torch.nn as nn\n",
        "from torch.utils.data import Dataset, DataLoader, random_split\n",
        "from torchvision import transforms\n",
        "import torchvision.models as models\n",
        "import pytorch_lightning as pl\n",
        "from pytorch_lightning import loggers as pl_loggers\n",
        "from pytorch_lightning import callbacks, seed_everything"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qxo67l1hj67w",
        "outputId": "57947c9c-705e-4b56-c3cc-e85e72ad0aaa"
      },
      "source": [
        "!wget -O cData.zip https://hmgubox2.helmholtz-muenchen.de/index.php/s/mZCrqQcFxJgMcxD/download"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2021-04-28 23:15:37--  https://hmgubox2.helmholtz-muenchen.de/index.php/s/mZCrqQcFxJgMcxD/download\n",
            "Resolving hmgubox2.helmholtz-muenchen.de (hmgubox2.helmholtz-muenchen.de)... 146.107.5.51\n",
            "Connecting to hmgubox2.helmholtz-muenchen.de (hmgubox2.helmholtz-muenchen.de)|146.107.5.51|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: unspecified [application/zip]\n",
            "Saving to: ‘cData.zip’\n",
            "\n",
            "cData.zip               [              <=>   ]  17.07G  4.73MB/s    in 12m 39s \n",
            "\n",
            "2021-04-28 23:28:38 (23.0 MB/s) - ‘cData.zip’ saved [18328186115]\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3kjfT8xN1w8s"
      },
      "source": [
        "import gdown\n",
        "import zipfile\n",
        "\n",
        "\n",
        "with zipfile.ZipFile(\"./cData.zip\",\"r\") as zip_ref:\n",
        "    zip_ref.extractall(\"./covidData\")\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pk_t9fmK62Ae",
        "outputId": "5a3b39aa-290b-495d-cbf1-fb8c630478ed"
      },
      "source": [
        "!wget -O lung.zip https://tubcloud.tu-berlin.de/s/KFpifMdkJxDyRaK/download"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2021-04-29 09:18:01--  https://tubcloud.tu-berlin.de/s/KFpifMdkJxDyRaK/download\n",
            "Resolving tubcloud.tu-berlin.de (tubcloud.tu-berlin.de)... 141.23.34.165\n",
            "Connecting to tubcloud.tu-berlin.de (tubcloud.tu-berlin.de)|141.23.34.165|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: unspecified [application/zip]\n",
            "Saving to: ‘lung.zip’\n",
            "\n",
            "lung.zip                [                 <=>] 187.25M  4.05MB/s    in 46s     \n",
            "\n",
            "2021-04-29 09:18:49 (4.10 MB/s) - ‘lung.zip’ saved [196350303]\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ELSG1eSy6_5R"
      },
      "source": [
        "import gdown\n",
        "import zipfile\n",
        "\n",
        "\n",
        "with zipfile.ZipFile(\"./lung.zip\",\"r\") as zip_ref:\n",
        "    zip_ref.extractall(\"./lung\")\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uRWdgk_m7xcB"
      },
      "source": [
        "import gdown\n",
        "import zipfile\n",
        "\n",
        "\n",
        "with zipfile.ZipFile(\"lung/COVID_Datathon/masked_xrays_trainSet.zip\",\"r\") as zip_ref:\n",
        "    zip_ref.extractall(\"./lungTrain\")\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GmXY0k_rskL7"
      },
      "source": [
        "np.random.seed(10)\n",
        "class CovidData(Dataset):\n",
        "  def __init__(self,transform,type_):\n",
        "\n",
        "    self.transform = transform\n",
        "      \n",
        "    self.type_=type_\n",
        "    \n",
        "    if self.type_==\"train\" or self.type_==\"valid\" or self.type_==\"test\":\n",
        "      table_dir = \"./covidData/HackathonCovidData/trainSet/trainSet.txt\"\n",
        "      self.image_path = \"./covidData/HackathonCovidData/trainSet/normalizedImg\"\n",
        "      data =  pd.read_csv(table_dir)\n",
        "\n",
        "      #Get indices for train and valid\n",
        "      indices = [i for i in range(len(data))]\n",
        "      v = 100 #100 samples for valid. Change as you want\n",
        "      valid_indices = np.random.choice(indices, size=v,replace=False)\n",
        "      t_d = [i for i in indices if i not in valid_indices]\n",
        "      v_d = [i for i in indices if i in valid_indices]\n",
        "      \n",
        "      if self.type_ ==\"train\":\n",
        "        self.data = data.iloc[t_d]\n",
        "      if self.type_ ==\"valid\" or self.type_==\"test\":\n",
        "        self.data = data.iloc[v_d]\n",
        "\n",
        "\n",
        "    #elif self.type_ ==\"test\":\n",
        "     # table_dir = \"./covidData/HackathonCovidData/testSet/testSet.txt\"\n",
        "     # self.image_path = \"./covidData/HackathonCovidData/testSet/normalizedImg\"\n",
        "     # self.data =  pd.read_csv(table_dir)\n",
        "  \n",
        "  def __getitem__(self, index):\n",
        "    if torch.is_tensor(index):\n",
        "      index = index.tolist()\n",
        "\n",
        "    row = self.data.iloc[index]\n",
        "  \n",
        "    image_file=row['ImageFile']\n",
        "    \n",
        "   \n",
        "    img = Image.open(os.path.join(self.image_path,image_file))\n",
        "    img = img.convert('RGB')\n",
        "  \n",
        "    img = self.transform(img)\n",
        "    \n",
        "    label_str = row[\"Prognosis\"]\n",
        "\n",
        "  \n",
        "      \n",
        "    if label_str == \"MILD\":\n",
        "        label=0\n",
        "    elif label_str ==\"SEVERE\":\n",
        "        label=1\n",
        "    else:\n",
        "        label=-1\n",
        "  \n",
        "\n",
        "    row =  row.fillna(value={\"Age\":64.44,\"Sex\":0.34,\"Temp_C\":37.60,\"Cough\":0.51,\"DifficultyInBreathing\":0.50,\"WBC\":7.06,\"CRP\":40.68,\"Fibrinogen\":602.52,\"LDH\":368.17,\"Ddimer\":2500,\"Ox_percentage\":92.54,\"PaO2\":72.09,\"SaO2\":91.92,\"pH\":7.45,\"CardiovascularDisease\":0.28,\"RespiratoryFailure\":0.02}) \n",
        "    tabular = row[[\"Age\",\"Sex\",\"Temp_C\",\"Cough\",\"DifficultyInBreathing\",\"WBC\",\"CRP\",\"Fibrinogen\",\"LDH\",\"Ddimer\",\"Ox_percentage\",\"PaO2\",\"SaO2\",\"pH\",\"CardiovascularDisease\",\"RespiratoryFailure\"]]\n",
        "    tabular = tabular.tolist()\n",
        "    tabular = torch.FloatTensor(tabular)\n",
        "    tab = tabular.view(1,tabular.size(0),1,1)\n",
        "\n",
        "    tab_transform = transforms.Normalize(mean=(64.44,0.34,37.60,0.51,0.50,7.06,40.68,602.52,368.17,2500,92.54,72.09,91.92,7.45,0.28,0.02),std=(15.05,0.58,0.97,0.71,0.71,3.53,66.93,158.36,235.22,6743.13,7.00,26.11,8.24,0.06,0.53,0.14))\n",
        "    tab_ = tab_transform(tab)\n",
        "    tabl = tabular.view(tab_.size(1))\n",
        "\n",
        "    return img, tabl, label\n",
        "  def __len__(self):\n",
        "    return len(self.data)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "t0vXh9Xt1qq9"
      },
      "source": [
        "class CovidClassifier(pl.LightningModule):\n",
        "  def __init__( self):\n",
        "    super().__init__()\n",
        "   \n",
        "\n",
        "    model = models.densenet121(pretrained=True)\n",
        "\n",
        "    self.densenet = features = nn.Sequential(*(list(model.children())[:-1]))\n",
        "   # num_ftrs = self.densenet.classifier.in_features\n",
        "    #densenet.classifier = nn.Linear(num_ftrs,14)\n",
        "    #namel = []\n",
        "    #for name,layers in model.named_modules():\n",
        "    #  print(name, ' : ',layers)\n",
        "    \n",
        "    #pyt_model = keras_to_pyt(kmodel, densenet)\n",
        "\n",
        "    #print(namel)\n",
        "    #for m in densenet.children():\n",
        "    #  print(m)\n",
        "    \n",
        "    #densenet.load_state_dict(torch.load('brucechou1983_CheXNet_Keras_0.3.0_weights.h5'))\n",
        "   \n",
        "    #self.densenet.classifier = nn.Linear(num_ftrs,2)\n",
        "\n",
        "\n",
        "    self.ln1 = nn.Linear(16, 8)\n",
        "    self.relu = nn.ReLU()\n",
        "    self.batchnorm = nn.BatchNorm1d(16)\n",
        "    self.dropout = nn.Dropout(0.5)\n",
        "    \n",
        "    self.ln2 = nn.Linear(1032, 512)\n",
        "    self.ln3 = nn.Linear(512, 256)\n",
        "    self.ln4 = nn.Linear(256, 64)\n",
        "    self.classifier = nn.Linear(64,1)\n",
        "    self.sigmoid = nn.Sigmoid()\n",
        "  def forward(self, img, tab):\n",
        "  \n",
        "    img = self.densenet(img)\n",
        "    img = img.mean([2,3]) #1024 dim\n",
        "     \n",
        "    tab = self.ln1(tab)\n",
        "    tab = self.relu(tab)\n",
        "\n",
        "    x = torch.cat((img, tab), dim=1) #1032\n",
        "   \n",
        "    x=self.ln2(x)\n",
        "    x = self.relu(x)\n",
        "    x=self.ln3(x)\n",
        "    x = self.relu(x)\n",
        "    x=self.ln4(x)\n",
        "    x = self.relu(x)\n",
        "    x=self.dropout(x)\n",
        "    x=self.classifier(x) \n",
        "    x=self.sigmoid(x) \n",
        "    x=torch.squeeze(x)\n",
        "    return x\n",
        "     "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kgLuHs8jZATl"
      },
      "source": [
        " \n",
        "tr = transforms.Compose(\n",
        "    [transforms.Resize((224,224)),\n",
        "     transforms.ToTensor(),\n",
        "    transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])\n",
        "    ])\n",
        "   \n",
        "train_image_data = CovidData(tr,'train')\n",
        "valid_image_data = CovidData(tr,'valid')\n",
        "test_image_data = CovidData(tr,'test')\n",
        "\n",
        " \n",
        "\n",
        "train_dataloader = DataLoader(train_image_data, batch_size=26,num_workers=4)\n",
        "val_dataloader = DataLoader(valid_image_data, batch_size=26,num_workers=4)\n",
        "test_dataloader = DataLoader(test_image_data, batch_size=26,num_workers=4)\n",
        "\n",
        " "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "T1Uiq8fUp_OE",
        "outputId": "d2d2136a-75c8-4b88-8758-7a4cb9d81386"
      },
      "source": [
        "learning_rate = 0.0001\n",
        "num_epochs = 200\n",
        "\n",
        "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
        "model = CovidClassifier()\n",
        "weight_path= '/content/drive/MyDrive/HIDA/weights3'\n",
        "with open(weight_path, \"rb\") as fp:   # Unpickling\n",
        "    best_model_wts = pickle.load(fp)\n",
        "model.load_state_dict(best_model_wts)\n",
        "model = model.to(device)\n",
        "criterion =nn.MSELoss()\n",
        "#criterion = nn.BCELoss()\n",
        "optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)\n",
        "\n",
        "scheduler = torch.optim.lr_scheduler.OneCycleLR(optimizer, max_lr=learning_rate, \n",
        "                                            steps_per_epoch=int(len(train_dataloader)),\n",
        "                                            epochs=num_epochs,\n",
        "                                            anneal_strategy='linear')\n",
        "\n",
        "\n",
        "model= train_model(model,criterion,optimizer,scheduler,num_epochs=num_epochs,patience=100)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 0/199\n",
            "----------\n",
            "Loss: 0.0425 Acc: 24.9333 | Val Acc: 23.75\n",
            "Saving best weights\n",
            "Epoch 1/199\n",
            "----------\n",
            "Loss: 0.0341 Acc: 25.0000 | Val Acc: 23.75\n",
            "Epoch 2/199\n",
            "----------\n",
            "Loss: 0.0275 Acc: 25.1333 | Val Acc: 23.75\n",
            "Epoch 3/199\n",
            "----------\n",
            "Loss: 0.0258 Acc: 25.1333 | Val Acc: 23.75\n",
            "Epoch 4/199\n",
            "----------\n",
            "Loss: 0.0194 Acc: 25.3000 | Val Acc: 23.75\n",
            "Epoch 5/199\n",
            "----------\n",
            "Loss: 0.0165 Acc: 25.2333 | Val Acc: 23.75\n",
            "Epoch 6/199\n",
            "----------\n",
            "Loss: 0.0132 Acc: 25.3333 | Val Acc: 23.25\n",
            "Epoch 7/199\n",
            "----------\n",
            "Loss: 0.0145 Acc: 25.2333 | Val Acc: 24.0\n",
            "Saving best weights\n",
            "Epoch 8/199\n",
            "----------\n",
            "Loss: 0.0113 Acc: 25.3333 | Val Acc: 23.25\n",
            "Epoch 9/199\n",
            "----------\n",
            "Loss: 0.0117 Acc: 25.2667 | Val Acc: 23.75\n",
            "Epoch 10/199\n",
            "----------\n",
            "Loss: 0.0101 Acc: 25.3000 | Val Acc: 23.5\n",
            "Epoch 11/199\n",
            "----------\n",
            "Loss: 0.0076 Acc: 25.2667 | Val Acc: 23.75\n",
            "Epoch 12/199\n",
            "----------\n",
            "Loss: 0.0107 Acc: 25.2333 | Val Acc: 22.75\n",
            "Epoch 13/199\n",
            "----------\n",
            "Loss: 0.0113 Acc: 25.2667 | Val Acc: 23.5\n",
            "Epoch 14/199\n",
            "----------\n",
            "Loss: 0.0078 Acc: 25.2667 | Val Acc: 23.5\n",
            "Epoch 15/199\n",
            "----------\n",
            "Loss: 0.0109 Acc: 25.1667 | Val Acc: 23.5\n",
            "Epoch 16/199\n",
            "----------\n",
            "Loss: 0.0152 Acc: 25.0333 | Val Acc: 23.0\n",
            "Epoch 17/199\n",
            "----------\n",
            "Loss: 0.0131 Acc: 25.0667 | Val Acc: 23.75\n",
            "Epoch 18/199\n",
            "----------\n",
            "Loss: 0.0088 Acc: 25.2333 | Val Acc: 23.5\n",
            "Epoch 19/199\n",
            "----------\n",
            "Loss: 0.0086 Acc: 25.2000 | Val Acc: 23.5\n",
            "Epoch 20/199\n",
            "----------\n",
            "Loss: 0.0069 Acc: 25.2333 | Val Acc: 23.75\n",
            "Epoch 21/199\n",
            "----------\n",
            "Loss: 0.0115 Acc: 25.0333 | Val Acc: 23.25\n",
            "Epoch 22/199\n",
            "----------\n",
            "Loss: 0.0193 Acc: 24.9667 | Val Acc: 22.75\n",
            "Epoch 23/199\n",
            "----------\n",
            "Loss: 0.0148 Acc: 24.9667 | Val Acc: 23.25\n",
            "Epoch 24/199\n",
            "----------\n",
            "Loss: 0.0091 Acc: 25.1667 | Val Acc: 24.0\n",
            "Epoch 25/199\n",
            "----------\n",
            "Loss: 0.0114 Acc: 25.1000 | Val Acc: 23.5\n",
            "Epoch 26/199\n",
            "----------\n",
            "Loss: 0.0037 Acc: 25.4000 | Val Acc: 24.0\n",
            "Epoch 27/199\n",
            "----------\n",
            "Loss: 0.0034 Acc: 25.3667 | Val Acc: 23.75\n",
            "Epoch 28/199\n",
            "----------\n",
            "Loss: 0.0045 Acc: 25.3333 | Val Acc: 23.75\n",
            "Epoch 29/199\n",
            "----------\n",
            "Loss: 0.0028 Acc: 25.3667 | Val Acc: 24.0\n",
            "Epoch 30/199\n",
            "----------\n",
            "Loss: 0.0044 Acc: 25.3000 | Val Acc: 24.0\n",
            "Epoch 31/199\n",
            "----------\n",
            "Loss: 0.0041 Acc: 25.3333 | Val Acc: 23.5\n",
            "Epoch 32/199\n",
            "----------\n",
            "Loss: 0.0024 Acc: 25.4000 | Val Acc: 24.0\n",
            "Epoch 33/199\n",
            "----------\n",
            "Loss: 0.0070 Acc: 25.1667 | Val Acc: 24.25\n",
            "Saving best weights\n",
            "Epoch 34/199\n",
            "----------\n",
            "Loss: 0.0344 Acc: 24.3667 | Val Acc: 21.5\n",
            "Epoch 35/199\n",
            "----------\n",
            "Loss: 0.0934 Acc: 22.7333 | Val Acc: 21.0\n",
            "Epoch 36/199\n",
            "----------\n",
            "Loss: 0.0820 Acc: 22.8000 | Val Acc: 21.5\n",
            "Epoch 37/199\n",
            "----------\n",
            "Loss: 0.0533 Acc: 23.8333 | Val Acc: 22.5\n",
            "Epoch 38/199\n",
            "----------\n",
            "Loss: 0.0302 Acc: 24.5000 | Val Acc: 22.75\n",
            "Epoch 39/199\n",
            "----------\n",
            "Loss: 0.0173 Acc: 24.9667 | Val Acc: 23.5\n",
            "Epoch 40/199\n",
            "----------\n",
            "Loss: 0.0086 Acc: 25.2000 | Val Acc: 24.0\n",
            "Epoch 41/199\n",
            "----------\n",
            "Loss: 0.0063 Acc: 25.2667 | Val Acc: 23.75\n",
            "Epoch 42/199\n",
            "----------\n",
            "Loss: 0.0062 Acc: 25.2667 | Val Acc: 24.0\n",
            "Epoch 43/199\n",
            "----------\n",
            "Loss: 0.0028 Acc: 25.3667 | Val Acc: 24.0\n",
            "Epoch 44/199\n",
            "----------\n",
            "Loss: 0.0008 Acc: 25.4333 | Val Acc: 24.0\n",
            "Epoch 45/199\n",
            "----------\n",
            "Loss: 0.0003 Acc: 25.4333 | Val Acc: 24.0\n",
            "Epoch 46/199\n",
            "----------\n",
            "Loss: 0.0002 Acc: 25.4333 | Val Acc: 24.0\n",
            "Epoch 47/199\n",
            "----------\n",
            "Loss: 0.0003 Acc: 25.4333 | Val Acc: 24.0\n",
            "Epoch 48/199\n",
            "----------\n",
            "Loss: 0.0019 Acc: 25.3667 | Val Acc: 24.0\n",
            "Epoch 49/199\n",
            "----------\n",
            "Loss: 0.0018 Acc: 25.3667 | Val Acc: 24.0\n",
            "Epoch 50/199\n",
            "----------\n",
            "Loss: 0.0015 Acc: 25.4000 | Val Acc: 24.0\n",
            "Epoch 51/199\n",
            "----------\n",
            "Loss: 0.0015 Acc: 25.4000 | Val Acc: 24.0\n",
            "Epoch 52/199\n",
            "----------\n",
            "Loss: 0.0014 Acc: 25.4000 | Val Acc: 24.0\n",
            "Epoch 53/199\n",
            "----------\n",
            "Loss: 0.0013 Acc: 25.4000 | Val Acc: 24.0\n",
            "Epoch 54/199\n",
            "----------\n",
            "Loss: 0.0014 Acc: 25.4000 | Val Acc: 24.0\n",
            "Epoch 55/199\n",
            "----------\n",
            "Loss: 0.0013 Acc: 25.4000 | Val Acc: 24.0\n",
            "Epoch 56/199\n",
            "----------\n",
            "Loss: 0.0015 Acc: 25.4000 | Val Acc: 24.0\n",
            "Epoch 57/199\n",
            "----------\n",
            "Loss: 0.0014 Acc: 25.4000 | Val Acc: 24.0\n",
            "Epoch 58/199\n",
            "----------\n",
            "Loss: 0.0014 Acc: 25.4000 | Val Acc: 24.0\n",
            "Epoch 59/199\n",
            "----------\n",
            "Loss: 0.0014 Acc: 25.4000 | Val Acc: 24.0\n",
            "Epoch 60/199\n",
            "----------\n",
            "Loss: 0.0016 Acc: 25.4000 | Val Acc: 24.0\n",
            "Epoch 61/199\n",
            "----------\n",
            "Loss: 0.0013 Acc: 25.4000 | Val Acc: 24.0\n",
            "Epoch 62/199\n",
            "----------\n",
            "Loss: 0.0013 Acc: 25.4000 | Val Acc: 24.0\n",
            "Epoch 63/199\n",
            "----------\n",
            "Loss: 0.0013 Acc: 25.4000 | Val Acc: 24.0\n",
            "Epoch 64/199\n",
            "----------\n",
            "Loss: 0.0014 Acc: 25.4000 | Val Acc: 24.0\n",
            "Epoch 65/199\n",
            "----------\n",
            "Loss: 0.0013 Acc: 25.4000 | Val Acc: 24.0\n",
            "Epoch 66/199\n",
            "----------\n",
            "Loss: 0.0013 Acc: 25.4000 | Val Acc: 24.0\n",
            "Epoch 67/199\n",
            "----------\n",
            "Loss: 0.0013 Acc: 25.4000 | Val Acc: 24.0\n",
            "Epoch 68/199\n",
            "----------\n",
            "Loss: 0.0013 Acc: 25.4000 | Val Acc: 24.0\n",
            "Epoch 69/199\n",
            "----------\n",
            "Loss: 0.0013 Acc: 25.4000 | Val Acc: 24.0\n",
            "Epoch 70/199\n",
            "----------\n",
            "Loss: 0.0013 Acc: 25.4000 | Val Acc: 24.0\n",
            "Epoch 71/199\n",
            "----------\n",
            "Loss: 0.0015 Acc: 25.4000 | Val Acc: 24.0\n",
            "Epoch 72/199\n",
            "----------\n",
            "Loss: 0.0013 Acc: 25.4000 | Val Acc: 24.0\n",
            "Epoch 73/199\n",
            "----------\n",
            "Loss: 0.0013 Acc: 25.4000 | Val Acc: 24.0\n",
            "Epoch 74/199\n",
            "----------\n",
            "Loss: 0.0013 Acc: 25.4000 | Val Acc: 24.0\n",
            "Epoch 75/199\n",
            "----------\n",
            "Loss: 0.0013 Acc: 25.4000 | Val Acc: 24.0\n",
            "Epoch 76/199\n",
            "----------\n",
            "Loss: 0.0013 Acc: 25.4000 | Val Acc: 24.0\n",
            "Epoch 77/199\n",
            "----------\n",
            "Loss: 0.0013 Acc: 25.4000 | Val Acc: 24.0\n",
            "Epoch 78/199\n",
            "----------\n",
            "Loss: 0.0013 Acc: 25.4000 | Val Acc: 24.0\n",
            "Epoch 79/199\n",
            "----------\n",
            "Loss: 0.0013 Acc: 25.4000 | Val Acc: 24.0\n",
            "Epoch 80/199\n",
            "----------\n",
            "Loss: 0.0014 Acc: 25.4000 | Val Acc: 24.0\n",
            "Epoch 81/199\n",
            "----------\n",
            "Loss: 0.0013 Acc: 25.4000 | Val Acc: 24.0\n",
            "Epoch 82/199\n",
            "----------\n",
            "Loss: 0.0013 Acc: 25.4000 | Val Acc: 24.0\n",
            "Epoch 83/199\n",
            "----------\n",
            "Loss: 0.0013 Acc: 25.4000 | Val Acc: 24.0\n",
            "Epoch 84/199\n",
            "----------\n",
            "Loss: 0.0013 Acc: 25.4000 | Val Acc: 24.0\n",
            "Epoch 85/199\n",
            "----------\n",
            "Loss: 0.0013 Acc: 25.4000 | Val Acc: 24.0\n",
            "Epoch 86/199\n",
            "----------\n",
            "Loss: 0.0013 Acc: 25.4000 | Val Acc: 24.0\n",
            "Epoch 87/199\n",
            "----------\n",
            "Loss: 0.0013 Acc: 25.4000 | Val Acc: 24.0\n",
            "Epoch 88/199\n",
            "----------\n",
            "Loss: 0.0013 Acc: 25.4000 | Val Acc: 24.0\n",
            "Epoch 89/199\n",
            "----------\n",
            "Loss: 0.0075 Acc: 25.2333 | Val Acc: 24.0\n",
            "Epoch 90/199\n",
            "----------\n",
            "Loss: 0.0088 Acc: 25.2000 | Val Acc: 23.75\n",
            "Epoch 91/199\n",
            "----------\n",
            "Loss: 0.1071 Acc: 22.3000 | Val Acc: 16.75\n",
            "Epoch 92/199\n",
            "----------\n",
            "Loss: 0.1915 Acc: 19.9000 | Val Acc: 20.0\n",
            "Epoch 93/199\n",
            "----------\n",
            "Loss: 0.0933 Acc: 22.2667 | Val Acc: 21.75\n",
            "Epoch 94/199\n",
            "----------\n",
            "Loss: 0.0399 Acc: 24.1667 | Val Acc: 24.25\n",
            "Epoch 95/199\n",
            "----------\n",
            "Loss: 0.0130 Acc: 25.0333 | Val Acc: 24.25\n",
            "Epoch 96/199\n",
            "----------\n",
            "Loss: 0.0137 Acc: 25.0333 | Val Acc: 24.25\n",
            "Epoch 97/199\n",
            "----------\n",
            "Loss: 0.0095 Acc: 25.1000 | Val Acc: 23.0\n",
            "Epoch 98/199\n",
            "----------\n",
            "Loss: 0.0070 Acc: 25.2333 | Val Acc: 23.0\n",
            "Epoch 99/199\n",
            "----------\n",
            "Loss: 0.0055 Acc: 25.3000 | Val Acc: 23.5\n",
            "Epoch 100/199\n",
            "----------\n",
            "Loss: 0.0053 Acc: 25.3000 | Val Acc: 23.5\n",
            "Epoch 101/199\n",
            "----------\n",
            "Loss: 0.0072 Acc: 25.2333 | Val Acc: 23.0\n",
            "Epoch 102/199\n",
            "----------\n",
            "Loss: 0.0081 Acc: 25.2333 | Val Acc: 23.25\n",
            "Epoch 103/199\n",
            "----------\n",
            "Loss: 0.0064 Acc: 25.2333 | Val Acc: 22.75\n",
            "Epoch 104/199\n",
            "----------\n",
            "Loss: 0.0082 Acc: 25.2000 | Val Acc: 23.0\n",
            "Epoch 105/199\n",
            "----------\n",
            "Loss: 0.0076 Acc: 25.2333 | Val Acc: 23.25\n",
            "Epoch 106/199\n",
            "----------\n",
            "Loss: 0.0060 Acc: 25.2667 | Val Acc: 23.25\n",
            "Epoch 107/199\n",
            "----------\n",
            "Loss: 0.0053 Acc: 25.3000 | Val Acc: 23.25\n",
            "Epoch 108/199\n",
            "----------\n",
            "Loss: 0.0040 Acc: 25.3333 | Val Acc: 23.5\n",
            "Epoch 109/199\n",
            "----------\n",
            "Loss: 0.0039 Acc: 25.3333 | Val Acc: 23.5\n",
            "Epoch 110/199\n",
            "----------\n",
            "Loss: 0.0040 Acc: 25.3333 | Val Acc: 23.25\n",
            "Epoch 111/199\n",
            "----------\n",
            "Loss: 0.0041 Acc: 25.3333 | Val Acc: 23.5\n",
            "Epoch 112/199\n",
            "----------\n",
            "Loss: 0.0040 Acc: 25.3333 | Val Acc: 23.5\n",
            "Epoch 113/199\n",
            "----------\n",
            "Loss: 0.0039 Acc: 25.3333 | Val Acc: 23.5\n",
            "Epoch 114/199\n",
            "----------\n",
            "Loss: 0.0041 Acc: 25.3333 | Val Acc: 23.5\n",
            "Epoch 115/199\n",
            "----------\n",
            "Loss: 0.0039 Acc: 25.3333 | Val Acc: 23.5\n",
            "Epoch 116/199\n",
            "----------\n",
            "Loss: 0.0039 Acc: 25.3333 | Val Acc: 23.5\n",
            "Epoch 117/199\n",
            "----------\n",
            "Loss: 0.0039 Acc: 25.3333 | Val Acc: 23.5\n",
            "Epoch 118/199\n",
            "----------\n",
            "Loss: 0.0038 Acc: 25.3333 | Val Acc: 23.5\n",
            "Epoch 119/199\n",
            "----------\n",
            "Loss: 0.0039 Acc: 25.3333 | Val Acc: 23.5\n",
            "Epoch 120/199\n",
            "----------\n",
            "Loss: 0.0039 Acc: 25.3333 | Val Acc: 23.5\n",
            "Epoch 121/199\n",
            "----------\n",
            "Loss: 0.0039 Acc: 25.3333 | Val Acc: 23.5\n",
            "Epoch 122/199\n",
            "----------\n",
            "Loss: 0.0036 Acc: 25.3333 | Val Acc: 23.5\n",
            "Epoch 123/199\n",
            "----------\n",
            "Loss: 0.0028 Acc: 25.3667 | Val Acc: 23.25\n",
            "Epoch 124/199\n",
            "----------\n",
            "Loss: 0.0037 Acc: 25.3333 | Val Acc: 23.25\n",
            "Epoch 125/199\n",
            "----------\n",
            "Loss: 0.0028 Acc: 25.3667 | Val Acc: 23.5\n",
            "Epoch 126/199\n",
            "----------\n",
            "Loss: 0.0027 Acc: 25.3667 | Val Acc: 23.25\n",
            "Epoch 127/199\n",
            "----------\n",
            "Loss: 0.0026 Acc: 25.3667 | Val Acc: 23.25\n",
            "Epoch 128/199\n",
            "----------\n",
            "Loss: 0.0026 Acc: 25.3667 | Val Acc: 23.25\n",
            "Epoch 129/199\n",
            "----------\n",
            "Loss: 0.0026 Acc: 25.3667 | Val Acc: 23.25\n",
            "Epoch 130/199\n",
            "----------\n",
            "Loss: 0.0026 Acc: 25.3667 | Val Acc: 23.25\n",
            "Epoch 131/199\n",
            "----------\n",
            "Loss: 0.0026 Acc: 25.3667 | Val Acc: 23.25\n",
            "Epoch 132/199\n",
            "----------\n",
            "Loss: 0.0027 Acc: 25.3667 | Val Acc: 23.25\n",
            "Epoch 133/199\n",
            "----------\n",
            "Loss: 0.0026 Acc: 25.3667 | Val Acc: 23.5\n",
            "Early stopping!\n",
            "Best val Acc: 24.250000\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 462
        },
        "id": "EP33zB4RKyTp",
        "outputId": "86693bd0-0c34-43b7-a123-70e8bfabfa74"
      },
      "source": [
        "table_dir = \"./covidData/HackathonCovidData/testSet/testSet.txt\"\n",
        "image_path = \"./covidData/HackathonCovidData/testSet/normalizedImg\"\n",
        "data =  pd.read_csv(table_dir)\n",
        "\n",
        "tr = transforms.Compose(\n",
        "    [transforms.Resize((224,224)),\n",
        "     transforms.ToTensor(),\n",
        "    transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])\n",
        "    ])\n",
        "   \n",
        "\n",
        "patient =[]\n",
        "result=[]\n",
        "\n",
        "indices = [i for i in range(len(data))]\n",
        "for ind in indices:\n",
        "  row = data.iloc[ind]\n",
        "  image_file=row['ImageFile']\n",
        "  patient.append(row['PatientID'])\n",
        "  if image_file==image_file:\n",
        "    \n",
        "    img = Image.open(os.path.join(image_path,image_file))\n",
        "    img = img.convert('RGB')\n",
        "\n",
        "    img = tr(img)\n",
        "   \n",
        "\n",
        "\n",
        "    row =  row.fillna(value={\"Age\":64.44,\"Sex\":0.34,\"Temp_C\":37.60,\"Cough\":0.51,\"DifficultyInBreathing\":0.50,\"WBC\":7.06,\"CRP\":40.68,\"Fibrinogen\":602.52,\"LDH\":368.17,\"Ddimer\":2500,\"Ox_percentage\":92.54,\"PaO2\":72.09,\"SaO2\":91.92,\"pH\":7.45,\"CardiovascularDisease\":0.28,\"RespiratoryFailure\":0.02}) \n",
        "    tabular = row[[\"Age\",\"Sex\",\"Temp_C\",\"Cough\",\"DifficultyInBreathing\",\"WBC\",\"CRP\",\"Fibrinogen\",\"LDH\",\"Ddimer\",\"Ox_percentage\",\"PaO2\",\"SaO2\",\"pH\",\"CardiovascularDisease\",\"RespiratoryFailure\"]]\n",
        "    tabular = tabular.tolist()\n",
        "    tabular = torch.FloatTensor(tabular)\n",
        "    tab = tabular.view(1,tabular.size(0),1,1)\n",
        "\n",
        "    tab_transform = transforms.Normalize(mean=(64.44,0.34,37.60,0.51,0.50,7.06,40.68,602.52,368.17,2500,92.54,72.09,91.92,7.45,0.28,0.02),std=(15.05,0.58,0.97,0.71,0.71,3.53,66.93,158.36,235.22,6743.13,7.00,26.11,8.24,0.06,0.53,0.14))\n",
        "    tab_ = tab_transform(tab)\n",
        "    tabl = tabular.view(tab_.size(1))\n",
        "    img = torch.unsqueeze(img, 0)\n",
        "\n",
        "    tabl = torch.unsqueeze(tabl, 0)\n",
        "    img, tabl = img.to(device),tabl.to(device)\n",
        "    pred = model(img,tabl)\n",
        "    #print(pred)\n",
        "\n",
        "    import torchviz\n",
        "    from torchviz import make_dot\n",
        "\n",
        "  \n",
        "\n",
        "    make_dot(pred.mean(), params=dict(model.named_parameters())).render(\"attached\", format=\"png\")\n",
        "    exit(1)\n",
        "    result.append(pred.item())\n",
        "  else:\n",
        "    result.append(np.nan)\n",
        "\n",
        "print(len(patient))\n",
        "print(len(result))\n",
        "DF=pd.DataFrame({\n",
        "    'PatientID':patient,\n",
        "    'result':result\n",
        "})\n",
        "DF.to_csv('results_Chris.csv')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "dot: graph is too large for cairo-renderer bitmaps. Scaling by 0.908705 to fit\n",
            "tcmalloc: large alloc 1840594944 bytes == 0x560233c00000 @  0x7fe207186001 0x7fe20341d1fa 0x7fe20341d2ad 0x7fe2045a86df 0x7fe204a9f261 0x7fe206f1c468 0x7fe206f1ed53 0x56022c94f092 0x7fe2068d1bf7 0x56022c94f12a\n",
            "dot: graph is too large for cairo-renderer bitmaps. Scaling by 0.908705 to fit\n",
            "tcmalloc: large alloc 1840594944 bytes == 0x56553e3b8000 @  0x7f70d9b0a001 0x7f70d5da11fa 0x7f70d5da12ad 0x7f70d6f2c6df 0x7f70d7423261 0x7f70d98a0468 0x7f70d98a2d53 0x565537835092 0x7f70d9255bf7 0x56553783512a\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "error",
          "ename": "KeyboardInterrupt",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-100-f4b45e67014f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     48\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     49\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 50\u001b[0;31m     \u001b[0mmake_dot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpred\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparams\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnamed_parameters\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrender\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"attached\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mformat\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"png\"\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     51\u001b[0m     \u001b[0mexit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     52\u001b[0m     \u001b[0mresult\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpred\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m(\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[0;32m/usr/local/lib/python3.7/dist-packages/graphviz/files.py\u001b[0m in \u001b[0;36mrender\u001b[0;34m(self, filename, directory, view, cleanup, format, renderer, formatter)\u001b[0m\n\u001b[1;32m    186\u001b[0m             \u001b[0mformat\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_format\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    187\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 188\u001b[0;31m         \u001b[0mrendered\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbackend\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrender\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mformat\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfilepath\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mformatter\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    189\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    190\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mcleanup\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/graphviz/backend.py\u001b[0m in \u001b[0;36mrender\u001b[0;34m(engine, format, filepath, renderer, formatter, quiet)\u001b[0m\n\u001b[1;32m    181\u001b[0m     \"\"\"\n\u001b[1;32m    182\u001b[0m     \u001b[0mcmd\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrendered\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcommand\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mengine\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mformat\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfilepath\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mformatter\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 183\u001b[0;31m     \u001b[0mrun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcmd\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcapture_output\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcheck\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mquiet\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mquiet\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    184\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mrendered\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    185\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/graphviz/backend.py\u001b[0m in \u001b[0;36mrun\u001b[0;34m(cmd, input, capture_output, check, quiet, **kwargs)\u001b[0m\n\u001b[1;32m    152\u001b[0m             \u001b[0;32mraise\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    153\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 154\u001b[0;31m     \u001b[0mout\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mproc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcommunicate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput\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    155\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    156\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mquiet\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/lib/python3.7/subprocess.py\u001b[0m in \u001b[0;36mcommunicate\u001b[0;34m(self, input, timeout)\u001b[0m\n\u001b[1;32m    962\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    963\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 964\u001b[0;31m                 \u001b[0mstdout\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstderr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_communicate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mendtime\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtimeout\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    965\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyboardInterrupt\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    966\u001b[0m                 \u001b[0;31m# https://bugs.python.org/issue25942\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/lib/python3.7/subprocess.py\u001b[0m in \u001b[0;36m_communicate\u001b[0;34m(self, input, endtime, orig_timeout)\u001b[0m\n\u001b[1;32m   1713\u001b[0m                             'failed to raise TimeoutExpired.')\n\u001b[1;32m   1714\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1715\u001b[0;31m                     \u001b[0mready\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mselector\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mselect\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimeout\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   1716\u001b[0m                     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_check_timeout\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mendtime\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morig_timeout\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstdout\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstderr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1717\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/lib/python3.7/selectors.py\u001b[0m in \u001b[0;36mselect\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m    413\u001b[0m         \u001b[0mready\u001b[0m \u001b[0;34m=\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[1;32m    414\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 415\u001b[0;31m             \u001b[0mfd_event_list\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_selector\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpoll\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimeout\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    416\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mInterruptedError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    417\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mready\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZY7YdwOrT44P",
        "outputId": "f83b9f3b-bb5a-405a-981c-7eeadf58ef47"
      },
      "source": [
        "!nvidia-smi --gpu-reset"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "GPU 00000000:00:04.0 is currently in use by another process.\n",
            "\n",
            "1 device is currently being used by one or more other processes (e.g., Fabric Manager, CUDA application, graphics application such as an X server, or a monitoring application such as another instance of nvidia-smi). Please first kill all processes using this device and all compute applications running in the system.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mKECenZL2hgk"
      },
      "source": [
        "def get_accuracy(pred,y):\n",
        "  pred[pred>=0.5]=1\n",
        "  pred[pred<0.5] = 0\n",
        "  acc = torch.sum(pred==y)\n",
        "  return acc\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ra0dg5Fnhn2m"
      },
      "source": [
        "def train_model(model, criterion, optimizer, scheduler, num_epochs,patience):\n",
        "   \n",
        "    best_model_wts = copy.deepcopy(model.state_dict())\n",
        "    best_acc = 0.0\n",
        "    pat=0\n",
        "    for epoch in range(num_epochs):\n",
        "        print('Epoch {}/{}'.format(epoch, num_epochs - 1))\n",
        "        print('-' * 10)\n",
        "\n",
        "        running_loss=0\n",
        "        running_corrects=0\n",
        "\n",
        "        # Each epoch has a training and validation phase\n",
        "\n",
        "        model.train()\n",
        "            # Iterate over data.\n",
        "        for image, tabular, y  in train_dataloader:\n",
        "          y = y.float()\n",
        "          image, tabular, y =image.to(device),tabular.to(device), y.to(device)\n",
        "          # zero the parameter gradients\n",
        "       \n",
        "          y_pred = model(image, tabular)\n",
        "          #y_pred = y_pred.double()\n",
        "\n",
        "          loss = criterion(y_pred, y)\n",
        "       \n",
        "\n",
        "\n",
        "          loss.backward()\n",
        "          optimizer.step()\n",
        "          scheduler.step()\n",
        "\n",
        "          optimizer.zero_grad()\n",
        "\n",
        "          acc = get_accuracy(y_pred,y)\n",
        "\n",
        "    \n",
        "            # statistics\n",
        "          running_loss += loss.item()\n",
        "          running_corrects +=acc\n",
        "     \n",
        "\n",
        "        epoch_loss = running_loss / len(train_dataloader)\n",
        "        epoch_acc = running_corrects.double() / len(train_dataloader)\n",
        "\n",
        "       \n",
        "\n",
        "        model.eval()\n",
        "        val_corrects=0\n",
        "      \n",
        "        for image, tabular, y  in val_dataloader:\n",
        "          image, tabular, y =image.to(device),tabular.to(device), y.to(device)\n",
        "          y_pred = model(image, tabular)\n",
        "          y_pred = y_pred.double()\n",
        "\n",
        "       \n",
        "          acc = get_accuracy(y_pred,y)\n",
        "\n",
        "          val_corrects+=acc\n",
        "        \n",
        "        vepoch_acc =val_corrects.double() / len(val_dataloader)\n",
        "\n",
        "        print('Loss: {:.4f} Acc: {:.4f} | Val Acc: {}'.format(epoch_loss, epoch_acc,vepoch_acc))\n",
        "        if vepoch_acc > best_acc:\n",
        "            pat=0\n",
        "            print(\"Saving best weights\")\n",
        "            best_acc = vepoch_acc\n",
        "            best_model_wts = copy.deepcopy(model.state_dict())\n",
        "            with open(weight_path,'wb') as fp:\n",
        "              pickle.dump(best_model_wts,fp)\n",
        "        else:\n",
        "            pat+=1\n",
        "\n",
        "        if pat==patience:\n",
        "          print(\"Early stopping!\")\n",
        "          break\n",
        "    print('Best val Acc: {:4f}'.format(best_acc))\n",
        "\n",
        "    # load best model weights\n",
        "    model.load_state_dict(best_model_wts)\n",
        "    return model"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
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            "45eb86ee1f6e43fe83df774fae951cd6",
            "f4d8f72bcc0c4f6cbef2a75f775d98f6",
            "0d49689db7bb4dac86e367bcfb8dab93",
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            "8b3e9c7492a14258bcc8ddd092c140dc",
            "8c33f3b20aff47078edbdb621448a4cd",
            "25984f992db14206b97374e7185a9f3e",
            "879a05ada92e4a90a948749a1c910f37",
            "8f9731f9f70740bd8f68ff4d0b39498e",
            "eb9b9b8278244574ad72293b8e5e0345"
          ]
        },
        "id": "Hu9bth3EIg4z",
        "outputId": "c52edb0f-a7a3-4ef6-8273-e9995fe42897"
      },
      "source": [
        "seed_everything(42)\n",
        "weight_path= '/content/drive/MyDrive/HIDA/weights2'\n",
        "# sets seeds for numpy, torch, python.random and PYTHONHASHSEED.\n",
        "logger = pl_loggers.TensorBoardLogger(\"lightning_logs\", name=\"multi_input\")\n",
        "early_stop_callback = callbacks.early_stopping.EarlyStopping(monitor=\"val_loss\", min_delta=5000, patience=30, verbose=True, mode=\"min\")\n",
        "model = CovidClassifier()\n",
        "trainer = pl.Trainer(gpus=1, logger=logger, callbacks=early_stop_callback,check_val_every_n_epoch=5,weights_save_path=weight_path,max_epochs=50)\n",
        "lr_finder = trainer.tuner.lr_find(model)\n",
        "fig = lr_finder.plot(suggest=True, show=True)\n",
        "new_lr = lr_finder.suggestion()\n",
        "model.hparams.lr = new_lr\n",
        "\n",
        "\n",
        "trainer.fit(model,train_dataloader=train_dataloader,val_dataloaders=val_dataloader)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Global seed set to 42\n",
            "GPU available: True, used: True\n",
            "TPU available: False, using: 0 TPU cores\n",
            "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n",
            "\n",
            "  | Name       | Type        | Params\n",
            "-------------------------------------------\n",
            "0 | densenet   | Sequential  | 7.0 M \n",
            "1 | ln1        | Linear      | 136   \n",
            "2 | relu       | ReLU        | 0     \n",
            "3 | batchnorm  | BatchNorm1d | 32    \n",
            "4 | dropout    | Dropout     | 0     \n",
            "5 | ln2        | Linear      | 528 K \n",
            "6 | ln3        | Linear      | 131 K \n",
            "7 | ln4        | Linear      | 16.4 K\n",
            "8 | classifier | Linear      | 130   \n",
            "-------------------------------------------\n",
            "7.6 M     Trainable params\n",
            "0         Non-trainable params\n",
            "7.6 M     Total params\n",
            "30.523    Total estimated model params size (MB)\n",
            "/usr/local/lib/python3.7/dist-packages/pytorch_lightning/utilities/distributed.py:68: UserWarning: Your val_dataloader has `shuffle=True`, it is best practice to turn this off for validation and test dataloaders.\n",
            "  warnings.warn(*args, **kwargs)\n",
            "/usr/local/lib/python3.7/dist-packages/pytorch_lightning/utilities/distributed.py:68: UserWarning: The dataloader, val dataloader 0, does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` (try 4 which is the number of cpus on this machine) in the `DataLoader` init to improve performance.\n",
            "  warnings.warn(*args, **kwargs)\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:83: UserWarning: Implicit dimension choice for log_softmax has been deprecated. Change the call to include dim=X as an argument.\n",
            "/usr/local/lib/python3.7/dist-packages/pytorch_lightning/utilities/distributed.py:68: UserWarning: The dataloader, train dataloader, does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` (try 4 which is the number of cpus on this machine) in the `DataLoader` init to improve performance.\n",
            "  warnings.warn(*args, **kwargs)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "44ec545cbace4fa7b7615f08cc175e99",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='Finding best initial lr', style=ProgressStyle(description…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:67: UserWarning: Implicit dimension choice for log_softmax has been deprecated. Change the call to include dim=X as an argument.\n",
            "/usr/local/lib/python3.7/dist-packages/pytorch_lightning/utilities/distributed.py:68: UserWarning: The {log:dict keyword} was deprecated in 0.9.1 and will be removed in 1.0.0\n",
            "Please use self.log(...) inside the lightningModule instead.\n",
            "# log on a step or aggregate epoch metric to the logger and/or progress bar (inside LightningModule)\n",
            "self.log('train_loss', loss, on_step=True, on_epoch=True, prog_bar=True)\n",
            "  warnings.warn(*args, **kwargs)\n",
            "Restored states from the checkpoint file at /content/lr_find_temp_model.ckpt\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n",
            "\n",
            "  | Name       | Type        | Params\n",
            "-------------------------------------------\n",
            "0 | densenet   | Sequential  | 7.0 M \n",
            "1 | ln1        | Linear      | 136   \n",
            "2 | relu       | ReLU        | 0     \n",
            "3 | batchnorm  | BatchNorm1d | 32    \n",
            "4 | dropout    | Dropout     | 0     \n",
            "5 | ln2        | Linear      | 528 K \n",
            "6 | ln3        | Linear      | 131 K \n",
            "7 | ln4        | Linear      | 16.4 K\n",
            "8 | classifier | Linear      | 130   \n",
            "-------------------------------------------\n",
            "7.6 M     Trainable params\n",
            "0         Non-trainable params\n",
            "7.6 M     Total params\n",
            "30.523    Total estimated model params size (MB)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
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          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\r"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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              "model_id": "38f80edd2ba24a2f9e4d62c06b588cc5",
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              "HBox(children=(FloatProgress(value=1.0, bar_style='info', description='Training', layout=Layout(flex='2'), max…"
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        },
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        {
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          }
        },
        {
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        },
        {
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        },
        {
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        },
        {
          "output_type": "display_data",
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        },
        {
          "output_type": "display_data",
          "data": {
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            "text/plain": [
              "HBox(children=(FloatProgress(value=1.0, bar_style='info', description='Validating', layout=Layout(flex='2'), m…"
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          "metadata": {
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        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "0d49689db7bb4dac86e367bcfb8dab93",
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          "metadata": {
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        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 152
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OUpmmDFDQYjq",
        "outputId": "62db6204-0523-49ed-841f-cd2468c22ef6"
      },
      "source": [
        "trainer.test(model)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/pytorch_lightning/utilities/distributed.py:68: UserWarning: you defined a test_step but have no test_dataloader. Skipping test loop\n",
            "  warnings.warn(*args, **kwargs)\n",
            "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 153
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rAWMClfUIFWf",
        "outputId": "fbb60d6d-0880-43e6-8cb7-a68ef070f713"
      },
      "source": [
        "!tensorboard --logdir=lightning_logs/multi_input/version_9"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2021-04-29 12:31:11.235493: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudart.so.11.0\n",
            "Serving TensorBoard on localhost; to expose to the network, use a proxy or pass --bind_all\n",
            "TensorBoard 2.4.1 at http://localhost:6006/ (Press CTRL+C to quit)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UnOtJNC_nn7j",
        "outputId": "a3cacd82-0f10-4de1-fbd6-a4deb93b6b22"
      },
      "source": [
        "!pip install torchviz"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting torchviz\n",
            "  Downloading https://files.pythonhosted.org/packages/79/e7/643808913211d6c1fc96a3a4333bf4c9276858fab00bcafaf98ea58a97be/torchviz-0.0.2.tar.gz\n",
            "Requirement already satisfied: torch in /usr/local/lib/python3.7/dist-packages (from torchviz) (1.8.1+cu101)\n",
            "Requirement already satisfied: graphviz in /usr/local/lib/python3.7/dist-packages (from torchviz) (0.10.1)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from torch->torchviz) (3.7.4.3)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from torch->torchviz) (1.19.5)\n",
            "Building wheels for collected packages: torchviz\n",
            "  Building wheel for torchviz (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for torchviz: filename=torchviz-0.0.2-cp37-none-any.whl size=4152 sha256=931fc9e7df52c5a0338ac1c7e672c6e3e6061664f255642a99a2d6e3f92ef6b6\n",
            "  Stored in directory: /root/.cache/pip/wheels/01/26/58/026ffd533dbe8b3972eb423da9c7949beca68d1c98ed9e8624\n",
            "Successfully built torchviz\n",
            "Installing collected packages: torchviz\n",
            "Successfully installed torchviz-0.0.2\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 231
        },
        "id": "BF2oNCPLneJ7",
        "outputId": "ec040696-7f44-4c85-817a-b02607ae3d39"
      },
      "source": [
        "\n",
        "#make_dot(model).render(\"attached\", format=\"png\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-99-05508c92d396>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mtorchviz\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mmake_dot\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0my_pred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimage\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtabular\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      5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0mmake_dot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_pred\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparams\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnamed_parameters\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrender\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"attached\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mformat\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"png\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mNameError\u001b[0m: name 'image' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UHXh0LFAyyZ1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c372142a-58da-446c-a60b-f588623e361b"
      },
      "source": [
        "%tensorflow_version 1.x\n",
        "from keras.models import load_model\n",
        "from __future__ import print_function\n",
        "from keras.utils import np_utils\n",
        "from keras.preprocessing.image import ImageDataGenerator\n",
        "from keras import applications\n",
        "import cv2 \n",
        "import keras\n",
        "from os import listdir\n",
        "import numpy as np\n",
        "import matplotlib. pyplot as plt\n",
        "from keras import layers\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Dense, Conv2D, MaxPooling2D, Dropout, Flatten, AveragePooling2D,GlobalAveragePooling2D,Activation,BatchNormalization,Dropout,Concatenate\n",
        "from keras.models import Model\n",
        "from keras.applications import DenseNet121\n",
        "from keras.applications.inception_v3 import InceptionV3\n",
        "from keras.optimizers import Adam, Adamax\n",
        "from keras.callbacks import LearningRateScheduler\n",
        "from keras import optimizers\n",
        "import tensorflow as tf\n",
        "#Augmentation\n",
        "from numpy import expand_dims\n",
        "from keras.preprocessing.image import load_img\n",
        "from keras.preprocessing.image import img_to_array\n",
        "from keras.preprocessing.image import ImageDataGenerator\n",
        "from matplotlib import pyplot\n",
        "from sklearn.utils import shuffle\n",
        "from sklearn.utils import class_weight\n",
        "from keras.callbacks import ReduceLROnPlateau, ModelCheckpoint, ReduceLROnPlateau, TensorBoard, EarlyStopping\n",
        "from sklearn.model_selection import KFold,StratifiedKFold\n",
        "from sklearn.model_selection import train_test_split\n",
        "import urllib,math"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "TensorFlow 1.x selected.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Using TensorFlow backend.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "q-9wg-UIO29m"
      },
      "source": [
        "densenet_ = DenseNet121(weights='brucechou1983_CheXNet_Keras_0.3.0_weights.h5',\n",
        "        # weights = None ,\n",
        "        include_top=True,\n",
        "        input_shape=(224,224,3),classes=14\n",
        "      )\n",
        "      \n",
        "m = densenet_.layers[-2].output\n",
        "predictions = Dense(2, activation='softmax')(m)\n",
        "kmodel = Model(inputs=densenet_.inputs, outputs=predictions)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_AxOH_QpO82k"
      },
      "source": [
        "kmodel.load_weights('2_class_weight_3_7.h5')\n",
        "weights=model.get_weights()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1Ynw387pebLl"
      },
      "source": [
        "adam = Adamax(learning_rate=0.00001, beta_1=0.9, beta_2=0.999)\n",
        "kmodel.compile(loss='categorical_crossentropy',optimizer=adam,metrics=['accuracy', precision, recall, fbeta_score])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lWq8HK9MX5S7"
      },
      "source": [
        "def keras_to_pyt(km, pm):\n",
        "    weight_dict = dict()\n",
        "  \n",
        "    for layer in km.layers:\n",
        "        print(layer)\n",
        "        print(layer.get_config()['name'])\n",
        "        print(np.asarray(layer.get_weights()).shape)\n",
        "        if type(layer) is keras.layers.convolutional.Conv2D:\n",
        "            print(len(layer.get_weights()))\n",
        "            print(np.asarray(layer.get_weights()).shape)\n",
        "            weight_dict[layer.get_config()['name'] + '.weight'] = np.transpose(layer.get_weights()[0], (3, 2, 0, 1))\n",
        "            weight_dict[layer.get_config()['name'] + '.bias'] = layer.get_weights()[1]\n",
        "        elif type(layer) is keras.layers.Dense:\n",
        "            weight_dict[layer.get_config()['name'] + '.weight'] = np.transpose(layer.get_weights()[0], (1, 0))\n",
        "            weight_dict[layer.get_config()['name'] + '.bias'] = layer.get_weights()[1]\n",
        "    pyt_state_dict = pm.state_dict()\n",
        "    for key in pyt_state_dict.keys():\n",
        "        pyt_state_dict[key] = torch.from_numpy(weight_dict[key])\n",
        "    pm.load_state_dict(pyt_state_dict)\n",
        "    return pm"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fAmzfaZU9Dbk"
      },
      "source": [
        "from keras import backend as K\n",
        "\n",
        "def binary_accuracy(y_true, y_pred):\n",
        "    return K.mean(K.equal(y_true, K.round(y_pred)))\n",
        "\n",
        "\n",
        "def categorical_accuracy(y_true, y_pred):\n",
        "    return K.mean(K.equal(K.argmax(y_true, axis=-1),\n",
        "                          K.argmax(y_pred, axis=-1)))\n",
        "\n",
        "\n",
        "def sparse_categorical_accuracy(y_true, y_pred):\n",
        "    return K.mean(K.equal(K.max(y_true, axis=-1),\n",
        "                          K.cast(K.argmax(y_pred, axis=-1), K.floatx())))\n",
        "\n",
        "\n",
        "def top_k_categorical_accuracy(y_true, y_pred, k=5):\n",
        "    return K.mean(K.in_top_k(y_pred, K.argmax(y_true, axis=-1), k))\n",
        "\n",
        "\n",
        "def mean_squared_error(y_true, y_pred):\n",
        "    return K.mean(K.square(y_pred - y_true))\n",
        "\n",
        "\n",
        "def mean_absolute_error(y_true, y_pred):\n",
        "    return K.mean(K.abs(y_pred - y_true))\n",
        "\n",
        "\n",
        "def mean_absolute_percentage_error(y_true, y_pred):\n",
        "    diff = K.abs((y_true - y_pred) / K.clip(K.abs(y_true),\n",
        "                                            K.epsilon(),\n",
        "                                            None))\n",
        "    return 100. * K.mean(diff)\n",
        "\n",
        "\n",
        "def mean_squared_logarithmic_error(y_true, y_pred):\n",
        "    first_log = K.log(K.clip(y_pred, K.epsilon(), None) + 1.)\n",
        "    second_log = K.log(K.clip(y_true, K.epsilon(), None) + 1.)\n",
        "    return K.mean(K.square(first_log - second_log))\n",
        "\n",
        "\n",
        "def hinge(y_true, y_pred):\n",
        "    return K.mean(K.maximum(1. - y_true * y_pred, 0.))\n",
        "\n",
        "\n",
        "def squared_hinge(y_true, y_pred):\n",
        "    return K.mean(K.square(K.maximum(1. - y_true * y_pred, 0.)))\n",
        "\n",
        "\n",
        "def categorical_crossentropy(y_true, y_pred):\n",
        "    return K.mean(K.categorical_crossentropy(y_pred, y_true))\n",
        "\n",
        "\n",
        "def sparse_categorical_crossentropy(y_true, y_pred):\n",
        "    return K.mean(K.sparse_categorical_crossentropy(y_pred, y_true))\n",
        "\n",
        "\n",
        "def binary_crossentropy(y_true, y_pred):\n",
        "    return K.mean(K.binary_crossentropy(y_pred, y_true))\n",
        "\n",
        "\n",
        "def kullback_leibler_divergence(y_true, y_pred):\n",
        "    y_true = K.clip(y_true, K.epsilon(), 1)\n",
        "    y_pred = K.clip(y_pred, K.epsilon(), 1)\n",
        "    return K.mean(K.sum(y_true * K.log(y_true / y_pred), axis=-1))\n",
        "\n",
        "\n",
        "def poisson(y_true, y_pred):\n",
        "    return K.mean(y_pred - y_true * K.log(y_pred + K.epsilon()))\n",
        "\n",
        "\n",
        "def cosine_proximity(y_true, y_pred):\n",
        "    y_true = K.l2_normalize(y_true, axis=-1)\n",
        "    y_pred = K.l2_normalize(y_pred, axis=-1)\n",
        "    return -K.mean(y_true * y_pred)\n",
        "\n",
        "\n",
        "def matthews_correlation(y_true, y_pred):\n",
        "    \"\"\"Matthews correlation metric.\n",
        "    It is only computed as a batch-wise average, not globally.\n",
        "    Computes the Matthews correlation coefficient measure for quality\n",
        "    of binary classification problems.\n",
        "    \"\"\"\n",
        "    y_pred_pos = K.round(K.clip(y_pred, 0, 1))\n",
        "    y_pred_neg = 1 - y_pred_pos\n",
        "\n",
        "    y_pos = K.round(K.clip(y_true, 0, 1))\n",
        "    y_neg = 1 - y_pos\n",
        "\n",
        "    tp = K.sum(y_pos * y_pred_pos)\n",
        "    tn = K.sum(y_neg * y_pred_neg)\n",
        "\n",
        "    fp = K.sum(y_neg * y_pred_pos)\n",
        "    fn = K.sum(y_pos * y_pred_neg)\n",
        "\n",
        "    numerator = (tp * tn - fp * fn)\n",
        "    denominator = K.sqrt((tp + fp) * (tp + fn) * (tn + fp) * (tn + fn))\n",
        "\n",
        "    return numerator / (denominator + K.epsilon())\n",
        "\n",
        "\n",
        "def precision(y_true, y_pred):\n",
        "    \"\"\"Precision metric.\n",
        "    Only computes a batch-wise average of precision.\n",
        "    Computes the precision, a metric for multi-label classification of\n",
        "    how many selected items are relevant.\n",
        "    \"\"\"\n",
        "    true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))\n",
        "    predicted_positives = K.sum(K.round(K.clip(y_pred, 0, 1)))\n",
        "    precision = true_positives / (predicted_positives + K.epsilon())\n",
        "    return precision\n",
        "\n",
        "\n",
        "def recall(y_true, y_pred):\n",
        "    \"\"\"Recall metric.\n",
        "    Only computes a batch-wise average of recall.\n",
        "    Computes the recall, a metric for multi-label classification of\n",
        "    how many relevant items are selected.\n",
        "    \"\"\"\n",
        "    true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))\n",
        "    possible_positives = K.sum(K.round(K.clip(y_true, 0, 1)))\n",
        "    recall = true_positives / (possible_positives + K.epsilon())\n",
        "    return recall\n",
        "\n",
        "\n",
        "def fbeta_score(y_true, y_pred, beta=1):\n",
        "    \"\"\"Computes the F score.\n",
        "    The F score is the weighted harmonic mean of precision and recall.\n",
        "    Here it is only computed as a batch-wise average, not globally.\n",
        "    This is useful for multi-label classification, where input samples can be\n",
        "    classified as sets of labels. By only using accuracy (precision) a model\n",
        "    would achieve a perfect score by simply assigning every class to every\n",
        "    input. In order to avoid this, a metric should penalize incorrect class\n",
        "    assignments as well (recall). The F-beta score (ranged from 0.0 to 1.0)\n",
        "    computes this, as a weighted mean of the proportion of correct class\n",
        "    assignments vs. the proportion of incorrect class assignments.\n",
        "    With beta = 1, this is equivalent to a F-measure. With beta < 1, assigning\n",
        "    correct classes becomes more important, and with beta > 1 the metric is\n",
        "    instead weighted towards penalizing incorrect class assignments.\n",
        "    \"\"\"\n",
        "    if beta < 0:\n",
        "        raise ValueError('The lowest choosable beta is zero (only precision).')\n",
        "\n",
        "    # If there are no true positives, fix the F score at 0 like sklearn.\n",
        "    if K.sum(K.round(K.clip(y_true, 0, 1))) == 0:\n",
        "        return 0\n",
        "\n",
        "    p = precision(y_true, y_pred)\n",
        "    r = recall(y_true, y_pred)\n",
        "    bb = beta ** 2\n",
        "    fbeta_score = (1 + bb) * (p * r) / (bb * p + r + K.epsilon())\n",
        "    return fbeta_score\n",
        "\n",
        "\n",
        "def fmeasure(y_true, y_pred):\n",
        "    \"\"\"Computes the f-measure, the harmonic mean of precision and recall.\n",
        "    Here it is only computed as a batch-wise average, not globally.\n",
        "    \"\"\"\n",
        "    return fbeta_score(y_true, y_pred, beta=1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nANAyKJ0Iyen"
      },
      "source": [
        "tensorboard --logdir=lightning_logs"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 459
        },
        "id": "nNQxOu5gTX7b",
        "outputId": "081b4b94-3dc2-41fb-a213-9027e678217a"
      },
      "source": [
        "state_dict = torch.load('2_class_weight_3_7.h5')\n",
        "for k,v in state_dict.items():\n",
        "  print(k)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "UnpicklingError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mUnpicklingError\u001b[0m                           Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-85-86adb53d19f6>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mstate_dict\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'2_class_weight_3_7.h5'\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      2\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mv\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mstate_dict\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m(\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[1;32m      3\u001b[0m   \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/torch/serialization.py\u001b[0m in \u001b[0;36mload\u001b[0;34m(f, map_location, pickle_module, **pickle_load_args)\u001b[0m\n\u001b[1;32m    591\u001b[0m                     \u001b[0;32mreturn\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjit\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mopened_file\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    592\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0m_load\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mopened_zipfile\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmap_location\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpickle_module\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mpickle_load_args\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 593\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0m_legacy_load\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mopened_file\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmap_location\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpickle_module\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mpickle_load_args\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    594\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    595\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/torch/serialization.py\u001b[0m in \u001b[0;36m_legacy_load\u001b[0;34m(f, map_location, pickle_module, **pickle_load_args)\u001b[0m\n\u001b[1;32m    760\u001b[0m             \"functionality.\")\n\u001b[1;32m    761\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 762\u001b[0;31m     \u001b[0mmagic_number\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpickle_module\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mpickle_load_args\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    763\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mmagic_number\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mMAGIC_NUMBER\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    764\u001b[0m         \u001b[0;32mraise\u001b[0m \u001b[0mRuntimeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Invalid magic number; corrupt file?\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mUnpicklingError\u001b[0m: invalid load key, 'H'."
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 340
        },
        "id": "2N2dtnyZUF4T",
        "outputId": "4153e2df-cca5-48dd-cfab-dab2f6f9e699"
      },
      "source": [
        "import pickle\n",
        "from functools import *\n",
        "\n",
        "model = torch.load('modelW.h5')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "RuntimeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-94-926cad392ad6>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mfunctools\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mmodel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'modelW.h5'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/torch/serialization.py\u001b[0m in \u001b[0;36mload\u001b[0;34m(f, map_location, pickle_module, **pickle_load_args)\u001b[0m\n\u001b[1;32m    591\u001b[0m                     \u001b[0;32mreturn\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjit\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mopened_file\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    592\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0m_load\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mopened_zipfile\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmap_location\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpickle_module\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mpickle_load_args\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 593\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0m_legacy_load\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mopened_file\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmap_location\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpickle_module\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mpickle_load_args\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    594\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    595\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/torch/serialization.py\u001b[0m in \u001b[0;36m_legacy_load\u001b[0;34m(f, map_location, pickle_module, **pickle_load_args)\u001b[0m\n\u001b[1;32m    762\u001b[0m     \u001b[0mmagic_number\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpickle_module\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mpickle_load_args\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    763\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mmagic_number\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mMAGIC_NUMBER\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 764\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mRuntimeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Invalid magic number; corrupt file?\"\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    765\u001b[0m     \u001b[0mprotocol_version\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpickle_module\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mpickle_load_args\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    766\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mprotocol_version\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mPROTOCOL_VERSION\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mRuntimeError\u001b[0m: Invalid magic number; corrupt file?"
          ]
        }
      ]
    }
  ]
}