pTOP.ipynb

{
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        "id": "view-in-github",
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        "<a href=\"https://colab.research.google.com/github/t-willi/Simula/blob/main/pTOP.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
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            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
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          "text": [
            "\u001b[34m\u001b[1mwandb\u001b[0m: Appending key for api.wandb.ai to your netrc file: /root/.netrc\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: Downloading large artifact ecg_25000.zip:v0, 1383.92MB. 1 files... \n",
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          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<torch._C.Generator at 0x7f514d5b62f0>"
            ]
          },
          "metadata": {},
          "execution_count": 1
        }
      ],
      "source": [
        "!pip install wandb\n",
        "!pip install ecg_plot\n",
        "import wandb\n",
        "import pandas as pd\n",
        "import torch\n",
        "from torch import nn\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "from torch.utils.data import Dataset, DataLoader\n",
        "import glob\n",
        "import torch.optim as optim\n",
        "from random import shuffle\n",
        "from tqdm.auto import tqdm\n",
        "import requests\n",
        "import zipfile\n",
        "from pathlib import Path\n",
        "\n",
        "if torch.cuda.is_available()==True:\n",
        "  device=\"cuda:0\"\n",
        "else:\n",
        "  device =\"cpu\"\n",
        "api = wandb.Api()\n",
        "artifact = api.artifact('ecg_simula/setup_weights and biases/ecg_25000.zip:v0')\n",
        "artifact.download()\n",
        "torch.manual_seed(42)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "CD2sbAqZ1i_Y"
      },
      "outputs": [],
      "source": [
        "# previous_model = api.artifact('ecg_simula/AE_pTOP/Model:latest', type='Model')\n",
        "# previous_model.download()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "BpY8EHOt-Syy"
      },
      "outputs": [],
      "source": [
        "def request(path=None,name=None):\n",
        "  import requests\n",
        "  from pathlib import Path\n",
        "  request = requests.get(path)\n",
        "  name=name+\".py\"\n",
        "  with open(name,\"wb\") as f:\n",
        "    f.write(request.content)\n",
        "\n",
        "unzip_git_dir=\"https://raw.githubusercontent.com/t-willi/NeuralNetworks/main/unzip.py\"\n",
        "Dataloader_git_dir=\"https://raw.githubusercontent.com/t-willi/NeuralNetworks/main/dataset_and_loader.py\"\n",
        "#model_git_dir=\"https://raw.githubusercontent.com/t-willi/NeuralNetworks/main/Networks.py\"\n",
        "get_pred_no_reshape_git_dir=\"https://raw.githubusercontent.com/t-willi/NeuralNetworks/main/get_predictions_no_reshape.py\"\n",
        "get_pred_git_dir=\"https://raw.githubusercontent.com/t-willi/NeuralNetworks/main/get_predictions.py\"\n",
        "plt_ECG_git_dir=\"https://raw.githubusercontent.com/t-willi/NeuralNetworks/main/plot_ECG.py\"\n",
        "\n",
        "request(unzip_git_dir,\"Unzip\")\n",
        "from Unzip import unzip\n",
        "\n",
        "request(Dataloader_git_dir,\"dataset_and_loader\")\n",
        "from dataset_and_loader import Custom_dataset as CD\n",
        "from dataset_and_loader import make_loader as ml\n",
        "\n",
        "#Download and instantialize model\n",
        "# request(model_git_dir,\"Networks\")\n",
        "# from Networks import ECG_stacked_AE\n",
        "# model=ECG_AE_v1()\n",
        "\n",
        "#download prediction generator\n",
        "request(get_pred_no_reshape_git_dir,\"get_predictions\")\n",
        "from get_predictions import get_pred\n",
        "#download ECG plotter\n",
        "request(plt_ECG_git_dir,\"plot_ECG\")\n",
        "from plot_ECG import plotECG\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZjYlL-CE-Z8i",
        "outputId": "dbc7aaf9-fe98-4a77-c881-07c8ad6169d9"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Did not find custom_data/train directory, creating one...\n",
            "Unzipping data to folder...\n",
            "unzip is finished\n"
          ]
        }
      ],
      "source": [
        "unzip(save_path=\"custom_data/train\",zip_path=\"/content/artifacts/ecg_25000.zip:v0/ecg_25000.zip\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "s7u8WUctU59p"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "RXCOPIZO8CcI"
      },
      "outputs": [],
      "source": [
        "import torch\n",
        "import pandas as pd\n",
        "\n",
        "def get_pred(dataset=None,model=None):\n",
        "  \"\"\"\n",
        "  Function takes a Tensor Dataset as input,first a random file from the dataset is selected,\n",
        "  then the Tensor pair is recombined and shaped into a df-->df_Input. \n",
        "  X is used afterwards as input into the model. The predictions are safed as --> df_output.\n",
        "  Both dataframes are now unscaled by 5011, the max value of the whole dataset.\n",
        "  Then a tuple pair of input and output is returned.\n",
        "  \"\"\"\n",
        "  import random\n",
        "  limit=len(dataset)\n",
        "  rand_idx=random.randint(0,limit)\n",
        "  X,y=dataset[rand_idx]\n",
        "  #need to combine tensors to make dataframe for plotting input and output side by side\n",
        "  full_tensor=torch.cat((X,y.squeeze()))\n",
        "  full_tensor=full_tensor.numpy()\n",
        "  df_input=pd.DataFrame(full_tensor).T\n",
        "  df_input.columns = [\"R1\",\"R2\",\"Rv1\",\"Rv2\",\"Rv3\",\"Rv4\",\"Rv5\",\"Rv6\"]\n",
        "  model.to(\"cpu\")\n",
        "  model.eval()\n",
        "  with torch.inference_mode():\n",
        "    output=model(X)\n",
        "  output=output.detach().numpy()\n",
        "  output=output.squeeze().T\n",
        "  #unscale data\n",
        "  df_output = pd.DataFrame(output,columns=[\"F2\",\"Fv1\",\"Fv2\",\"Fv3\",\"Fv4\",\"Fv5\",\"Fv6\"])*5011\n",
        "  df_input=df_input*5011\n",
        "  return df_input,df_output"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "import pandas as pd\n",
        "from pathlib import Path\n",
        "def plotECG(df1=None,df2=None,title=None,pad_df2=True,path=None):\n",
        "  \"\"\"\n",
        "  takes two dataframes with identical columns, concats them and plots them as ecg using ecg_plot\n",
        "  it also takes the first column of df1 and ads it to df1 if pad_df2 is True\n",
        "  \"\"\"\n",
        "  index=[\"real1\",\"realR2\",\"realv1\",\"realv2\",\"realv3\",\"realv4\",\"realv5\",\"realv6\",\"real_lead1\",\n",
        "         \"pred2\",\"predv1\",\"predv2\",\"predv3\",\"predv4\",\"predv5\",\"predv6\"]\n",
        "\n",
        "  ecg_path=path\n",
        "  if Path(ecg_path).is_dir():\n",
        "      print(f\"{ecg_path} directory exists.\")\n",
        "  else:\n",
        "      print(f\"Did not find {ecg_path} directory, creating one...\")\n",
        "      Path(ecg_path).mkdir(parents=True, exist_ok=True)\n",
        "  import ecg_plot\n",
        "  if pad_df2 is True:\n",
        "    if len(df1.columns)>len(df2.columns):\n",
        "      df2.insert(0, 'real_lead1', df1[\"R1\"])\n",
        "  frames=[df1/1000,df2/1000]\n",
        "  combined_df=pd.concat(frames,axis=1,join=\"outer\",)\n",
        "  ecg_plot.plot(combined_df.values.T, sample_rate = 500,title = title,\n",
        "                     lead_index = index )\n",
        "  ecg_plot.save_as_png('ecg',ecg_path+\"/\")\n",
        "  return combined_df\n",
        "\n"
      ],
      "metadata": {
        "id": "4xO1Bqv4YjhL"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "id": "UjiBtneQ5dow"
      },
      "outputs": [],
      "source": [
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "import torch.utils.data\n",
        "\n",
        "class Transpose1dLayer(nn.Module):\n",
        "    def __init__(self, in_channels, out_channels, kernel_size, stride, padding=11, upsample=None, output_padding=1):\n",
        "        super(Transpose1dLayer, self).__init__()\n",
        "        self.upsample = upsample\n",
        "        self.upsample_layer = torch.nn.Upsample(scale_factor=upsample)\n",
        "        reflection_pad = kernel_size // 2\n",
        "        self.reflection_pad = nn.ConstantPad1d(reflection_pad, value=0)\n",
        "        self.conv1d = torch.nn.Conv1d(in_channels, out_channels, kernel_size, stride)\n",
        "        self.Conv1dTrans = nn.ConvTranspose1d(in_channels, out_channels, kernel_size, stride, padding, output_padding)\n",
        "\n",
        "    def forward(self, x):\n",
        "        if self.upsample:\n",
        "            #x = torch.cat((x, in_feature), 1)\n",
        "            return self.conv1d(self.reflection_pad(self.upsample_layer(x)))\n",
        "        else:\n",
        "            return self.Conv1dTrans(x)\n",
        "\n",
        "\n",
        "class Pulse2pulseGenerator(nn.Module):\n",
        "    def __init__(self,latent_dim=100, post_proc_filt_len=512,upsample=True):\n",
        "        super(Pulse2pulseGenerator, self).__init__()\n",
        "        # \"Dense\" is the same meaning as fully connection.\n",
        "        stride = 4\n",
        "        if upsample:\n",
        "            stride = 1\n",
        "            upsample = 5\n",
        "        # if upsample is anything but none Transpose1dLayer will do\n",
        "        # self.conv1d(self.reflection_pad(self.upsample_layer(x)))\n",
        "        # which is a 1d convolution on padded and upsampled data x\n",
        "        self.deconv_1 = Transpose1dLayer(250 , 250, 25, stride, upsample=upsample)\n",
        "        self.deconv_2 = Transpose1dLayer(250, 150, 25, stride, upsample=upsample)\n",
        "        self.deconv_3 = Transpose1dLayer(150, 50, 25, stride, upsample=upsample)\n",
        "        self.deconv_4 = Transpose1dLayer( 50, 25, 25, stride, upsample=2)\n",
        "        self.deconv_5 = Transpose1dLayer( 25, 10, 25, stride, upsample=upsample)\n",
        "        self.deconv_6 = Transpose1dLayer(  10, 7, 25, stride, upsample=2)\n",
        "\n",
        "        #new convolutional layers\n",
        "        self.conv_1 = nn.Conv1d(1, 10, 25, stride=2, padding=25 // 2)\n",
        "        self.conv_2 = nn.Conv1d(10, 25, 25, stride=5, padding= 25 // 2)\n",
        "        self.conv_3 = nn.Conv1d(25, 50 , 25, stride=2, padding= 25 // 2)\n",
        "        self.conv_4 = nn.Conv1d(50, 150 , 25, stride=5, padding= 25 // 2)\n",
        "        self.conv_5 = nn.Conv1d(150, 250 , 25, stride=5, padding= 25 // 2)\n",
        "        self.conv_6 = nn.Conv1d(250, 250 , 25, stride=5, padding= 25 // 2)\n",
        "\n",
        "        for m in self.modules():\n",
        "            if isinstance(m, nn.ConvTranspose1d) or isinstance(m, nn.Linear):\n",
        "                nn.init.kaiming_normal_(m.weight.data)\n",
        "\n",
        "    def forward(self, x):\n",
        "        if x.ndim==2:\n",
        "          x=x.unsqueeze(0)\n",
        "        x = F.leaky_relu(self.conv_1(x)) #(1,1,5000 --> 1, 10, 2500)\n",
        "        x = F.leaky_relu(self.conv_2(x)) #( --> 1, 25, 500)\n",
        "        x = F.leaky_relu(self.conv_3(x)) #(--> 1, 50, 250)\n",
        "        x = F.leaky_relu(self.conv_4(x)) # --> 1, 150, 50)\n",
        "        x = F.leaky_relu(self.conv_5(x)) #(--> 1, 250, 10)\n",
        "        x = F.leaky_relu(self.conv_6(x)) #(--> 1, 250, 2)\n",
        "        x = F.relu(self.deconv_1(x)) #(--> 1, 250, 10)\n",
        "        x = F.relu(self.deconv_2(x)) #(--> 1, 150, 50)\n",
        "        x = F.relu(self.deconv_3(x)) #( --> 1, 50, 250)\n",
        "        x = F.relu(self.deconv_4(x)) #(--> 1, 25, 500)\n",
        "        x = F.relu(self.deconv_5(x)) #(--> 1, 10, 2500)\n",
        "        x = torch.tanh(self.deconv_6(x)) #(1, 7, 5000)\n",
        "        x=x.squeeze()\n",
        "        return x\n",
        "\n",
        "model=Pulse2pulseGenerator().to(device)\n",
        "# PATH=\"/content/artifacts/Model:v3/model\"\n",
        "# model.load_state_dict(torch.load(PATH))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "id": "M4ClDwMi8EM_"
      },
      "outputs": [],
      "source": [
        "import glob\n",
        "import pandas as pd\n",
        "import torch\n",
        "class Custom_dataset():\n",
        "    def __init__(self, data_dir,max_value=5011,column=3,split=True,target=\"train\",size=1):\n",
        "      #get all files from directory loaded in all_files list\n",
        "      self.column=column\n",
        "      self.max_value=max_value\n",
        "      self.size=size\n",
        "      #should shuffle the data here?\n",
        "      self.files = glob.glob(data_dir + '/*.asc')\n",
        "      self.len=int((len(self.files))*self.size)\n",
        "      #print(f\"len:{self.len}\")\n",
        "      self.cut1=int(self.len*0.8)\n",
        "      #print(f\"cut1:{self.cut1}\")\n",
        "      self.cut2=int(self.len*0.9)\n",
        "      #print(f\"cut2:{self.cut2}\")\n",
        "      self.train_files=self.files[0:self.cut1]\n",
        "      self.test_files=self.files[self.cut1:self.cut2]\n",
        "      self.val_files=self.files[self.cut2:self.len]\n",
        "      self.target=target\n",
        "      self.split=split\n",
        "\n",
        "    def __len__(self):\n",
        "      if self.split is True:\n",
        "        if self.target is \"train\":\n",
        "          return len(self.train_files)\n",
        "        if self.target is \"test\":\n",
        "          return len(self.test_files)\n",
        "        if self.target is \"val\":\n",
        "          return len(self.val_files)\n",
        "      if self.split is not True:\n",
        "        return len(self.files)\n",
        "\n",
        "    def __getitem__(self,idx):\n",
        "      header = [\"1\",\"2\",\"v1\",\"v2\",\"v3\",\"v4\",\"v5\",\"v6\"]\n",
        "      #turn list of dataframes into Tensor\n",
        "      if self.split is True:\n",
        "        if self.target is \"train\":\n",
        "          temp_df=pd.read_csv(self.train_files[idx],sep=\" \", names = header)\n",
        "        if self.target is \"test\":\n",
        "          temp_df=pd.read_csv(self.test_files[idx],sep=\" \", names = header)\n",
        "        if self.target is \"val\":\n",
        "          temp_df=pd.read_csv(self.val_files[idx],sep=\" \", names = header)\n",
        "      if self.split is not True:\n",
        "        temp_df=pd.read_csv(self.files[idx],sep=\" \", names = header)\n",
        "      temp_df/=self.max_value\n",
        "      #load input tensor\n",
        "      \n",
        "      temp_list_in=temp_df.iloc[:,0]\n",
        "      #temp_list_in=normalize([temp_list_in], norm=\"max\")\n",
        "      temp_tensor_in = torch.tensor(temp_list_in,dtype=torch.float32)\n",
        "      temp_tensor_in=temp_tensor_in.unsqueeze(0)\n",
        "      #load label Tensor\n",
        "      temp_list_out=temp_df.iloc[:,1:9].values\n",
        "      #temp_list_out=normalize([temp_list_out], norm=\"max\")\n",
        "      temp_tensor_out=torch.tensor(temp_list_out,dtype=torch.float32)\n",
        "      #temp_tensor_out=temp_tensor_out.unsqueeze(0)\n",
        "      temp_tensor_out=torch.permute(temp_tensor_out,(1,0))\n",
        "      #combine input and label and output\n",
        "      temp_tensor_pair= temp_tensor_in,temp_tensor_out\n",
        "      return temp_tensor_pair\n",
        "\n",
        "\n",
        "def make_loader(dataset,batch_size):\n",
        "  from torch.utils.data import DataLoader\n",
        "  loader = DataLoader(dataset,\n",
        "                      batch_size=batch_size,\n",
        "                      shuffle=True,\n",
        "                      drop_last=True\n",
        "                      )\n",
        "  return loader\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# data_dir=\"custom_data/train\"\n",
        "# test_dataset = Custom_dataset(data_dir=data_dir,split=True,target=\"train\")\n",
        "# # x,y=test_dataset[0]\n",
        "# df_in,df_out=get_pred(test_dataset,model)\n",
        "# combined_df=plotECG(df_in,df_out,path=\"/content/Test_ecg\")"
      ],
      "metadata": {
        "id": "Z2FYyplNS4F5"
      },
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# #df_in,df_out=get_pred(test_dataset,model)\n",
        "# model=Pulse2pulseGenerator().to(device)\n",
        "# PATH=\"/content/artifacts/Model:v3/model\"\n",
        "# model.load_state_dict(torch.load(PATH))\n",
        "# model.eval()\n",
        "# with torch.inference_mode():\n",
        "#   output=model(x)\n",
        "# output.shape"
      ],
      "metadata": {
        "id": "AsOuTgBSY-MN"
      },
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# x=pd.DataFrame(x.numpy().T)\n",
        "# y=pd.DataFrame(y.numpy().T)\n",
        "# output=(pd.DataFrame(output.numpy().T))*5011/1000\n"
      ],
      "metadata": {
        "id": "Jg5YTHLKTRXS"
      },
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# import ecg_plot\n",
        "# ecg_plot.plot(y.values.T, sample_rate = 500, title = 'ECG 12')\n",
        "# ecg_plot.show()"
      ],
      "metadata": {
        "id": "n75annuhU_pO"
      },
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# df=plotECG(df_in,df_out,path=\"/content/Test_ecg\")"
      ],
      "metadata": {
        "id": "p88BAMJwTn5Y"
      },
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "metadata": {
        "id": "2jN6NII07yAd"
      },
      "outputs": [],
      "source": [
        "# data_dir=\"custom_data/train\"\n",
        "# train_dataset = Custom_dataset(data_dir=data_dir,split=True,target=\"train\")\n",
        "# train_loader=ml(train_dataset,128)\n",
        "# x,y=train_dataset[0]\n",
        "# x.shape,y.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "metadata": {
        "id": "25b0RZBWD3V1"
      },
      "outputs": [],
      "source": [
        "# x_loader,y_loader=next(iter(train_loader))\n",
        "# x_loader.shape,y_loader.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "metadata": {
        "id": "oX2d6JQZDsp2"
      },
      "outputs": [],
      "source": [
        "# model=Pulse2pulseGenerator()\n",
        "# t=torch.rand(1,5000)\n",
        "# output=model(x)\n",
        "# output.shape\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JayETpOEvw_L",
        "outputId": "db1e3baa-d28e-4196-dca8-0a1b13142faa"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\u001b[34m\u001b[1mwandb\u001b[0m: \u001b[33mWARNING\u001b[0m Calling wandb.login() after wandb.init() has no effect.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ],
      "source": [
        "import wandb\n",
        "\n",
        "wandb.login()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "metadata": {
        "id": "9GWE7duKhBOk"
      },
      "outputs": [],
      "source": [
        "# 🐝 initialise a wandb run\n",
        "config = dict(\n",
        "    epochs=51,\n",
        "    batch_size=32,\n",
        "    learning_rate=0.0001,)  #learing rate from puls to puls paper\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 38,
      "metadata": {
        "id": "kb_VpAKpNeoF"
      },
      "outputs": [],
      "source": [
        "def model_pipeline(hyperparameters,model=model):\n",
        "    # tell wandb to get started\n",
        "    wandb.init(project=\"AE_pTOP_serious_try2\", config=hyperparameters)\n",
        "    # access all HPs through wandb.config, so logging matches execution!\n",
        "    config = wandb.config\n",
        "    # make the model, data, and optimization problem\n",
        "    train_loader, val_loader,test_dataset, criterion, optimizer,val_dataset = make(config)\n",
        "    # and use them to train the model\n",
        "    train(model, train_loader,val_loader,test_dataset, criterion, optimizer,val_dataset,config)\n",
        "    return model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 39,
      "metadata": {
        "id": "vpjHI6FGNmCc"
      },
      "outputs": [],
      "source": [
        "def make(config):\n",
        "    # Make the data\n",
        "    print(\"making data\")\n",
        "    data_dir=\"custom_data/train\"\n",
        "    train_dataset = Custom_dataset(data_dir=data_dir,split=True,target=\"train\")\n",
        "    val_dataset = Custom_dataset(data_dir=data_dir,split=True,target=\"val\")\n",
        "    test_dataset = Custom_dataset(data_dir=data_dir,split=True,target=\"test\")\n",
        "    train_loader = ml(train_dataset, batch_size=config.batch_size)\n",
        "    val_loader = ml(val_dataset, batch_size=config.batch_size)\n",
        "    \n",
        "\n",
        "    # Make the loss and optimizer\n",
        "    criterion = nn.MSELoss()\n",
        "    optimizer = torch.optim.Adam(model.parameters(), lr=config.learning_rate)\n",
        "    \n",
        "    return train_loader, val_loader,test_dataset, criterion, optimizer,val_dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 40,
      "metadata": {
        "id": "T8HI7EZEXZD7"
      },
      "outputs": [],
      "source": [
        "def train(model, train_loader,val_loader,test_dataset, criterion, optimizer,val_dataset, config):\n",
        "  # Tell wandb to watch what the model gets up to: gradients, weights, and more!\n",
        "  wandb.watch(model, criterion, log=\"all\")\n",
        "  for epoch in tqdm(range(config.epochs)):\n",
        "    train_loss=0\n",
        "    for batch,(X,y) in tqdm(enumerate(train_loader)):\n",
        "      # Forward pass ➡\n",
        "      X, y = X.to(device), y.to(device)\n",
        "      #print(f\"shape of input{x.shape},shape of label_y{y.shape}\") \n",
        "      model.train()\n",
        "      output=model(X)\n",
        "      #print(f\"shape of model_output_raw{output.shape}\") \n",
        "      # output=torch.reshape(output,(config.batch_size, 1, 7, 5000))\n",
        "      loss = criterion(output,y)\n",
        "      train_loss += loss\n",
        "      # Backward pass ⬅\n",
        "      optimizer.zero_grad()\n",
        "      loss.backward()\n",
        "      # Step with optimizer\n",
        "      optimizer.step()\n",
        "    #average loss per batch\n",
        "    train_loss /= len(train_loader)\n",
        "\n",
        "\n",
        "    val_loss = 0\n",
        "    model.eval()\n",
        "    with torch.inference_mode():\n",
        "      for batch,(X,y) in enumerate(val_loader):\n",
        "        #print(\"doing test loop\")\n",
        "        X, y = X.to(device), y.to(device)\n",
        "        val_pred = model(X)\n",
        "        # val_pred=torch.reshape(val_pred,(config.batch_size, 1, 7, 5000))\n",
        "        loss=criterion(val_pred,y)\n",
        "        val_loss += loss\n",
        "      val_loss /= len(val_loader)  \n",
        "      wandb.log({\"train_loss\": train_loss, \n",
        "                 \"val_loss\": val_loss})\n",
        "      \n",
        "\n",
        "    if epoch % 1==0:\n",
        "      #setup folder for outputs\n",
        "      ecg_path=Path(\"/content/ECG/\")\n",
        "      if ecg_path.is_dir():\n",
        "        print(\"creating ECG table\")\n",
        "      else:\n",
        "        ecg_path.mkdir()\n",
        "      df_input,df_output=get_pred(test_dataset,model)\n",
        "      model.to(device)\n",
        "      #plotting the ECG and creating the combined DF\n",
        "      combined_df=plotECG(df_input,df_output,path=\"/content/ECG/\")\n",
        "      #saving combined DF as table on wandB\n",
        "      input_prediction_table = wandb.Table(dataframe=combined_df)\n",
        "      #calling png:to_jpag function in order to save the ECG plot\n",
        "      png_to_jpg()\n",
        "      #logging the ECG plot and the csv table\n",
        "      wandb.log({\"ECG\": wandb.Image(\"/content/ECG/ECG.jpg\")})\n",
        "      wandb.log({\"Input and predictions\": input_prediction_table}) \n",
        "      \n",
        "    if epoch % 1==0:\n",
        "    #Save the model in the exchangeable ONNX format\n",
        "      model_dir=Path(\"/content/saved_model\")\n",
        "      if model_dir.is_dir():\n",
        "        print(\"Saving model\")  \n",
        "      else:\n",
        "        model_dir.mkdir(parents=True, exist_ok=True)\n",
        "      trained_model_artifact = wandb.Artifact(\"AE\",\n",
        "                                              type=\"model\",\n",
        "                                              description=\"Simple AE to start with training on ECG data translation\")\n",
        "      print(\"one\")\n",
        "      torch.save(model.state_dict(), \"/content/saved_model/model\")\n",
        "      print(\"two\")\n",
        "      wandb.log_artifact(\"/content/saved_model/model\", name='Model', type='Model') \n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 41,
      "metadata": {
        "id": "fV8nOkCu2HyT"
      },
      "outputs": [],
      "source": [
        "import cv2 as cv\n",
        "from PIL import Image\n",
        "def png_to_jpg(path=None):\n",
        "  im = cv.imread(\"/content/ECG/ecg.png\")\n",
        "  im = Image.fromarray(im)\n",
        "  im.save(\"/content/ECG/ECG.jpg\")\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 42,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
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        "id": "xpkY2SmatuJP",
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      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
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            "text/html": [
              "Finishing last run (ID:3tlzmt50) before initializing another..."
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        {
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              "Waiting for W&B process to finish... <strong style=\"color:green\">(success).</strong>"
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        {
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              "<style>\n",
              "    table.wandb td:nth-child(1) { padding: 0 10px; text-align: left ; width: auto;} td:nth-child(2) {text-align: left ; width: 100%}\n",
              "    .wandb-row { display: flex; flex-direction: row; flex-wrap: wrap; justify-content: flex-start; width: 100% }\n",
              "    .wandb-col { display: flex; flex-direction: column; flex-basis: 100%; flex: 1; padding: 10px; }\n",
              "    </style>\n",
              "<div class=\"wandb-row\"><div class=\"wandb-col\"><h3>Run history:</h3><br/><table class=\"wandb\"><tr><td>train_loss</td><td>▁</td></tr><tr><td>val_loss</td><td>▁</td></tr></table><br/></div><div class=\"wandb-col\"><h3>Run summary:</h3><br/><table class=\"wandb\"><tr><td>train_loss</td><td>0.00164</td></tr><tr><td>val_loss</td><td>0.00159</td></tr></table><br/></div></div>"
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            "text/html": [
              "Synced <strong style=\"color:#cdcd00\">treasured-spaceship-1</strong>: <a href=\"https://wandb.ai/ecg_simula/AE_pTOP_serious_try2/runs/3tlzmt50\" target=\"_blank\">https://wandb.ai/ecg_simula/AE_pTOP_serious_try2/runs/3tlzmt50</a><br/>Synced 5 W&B file(s), 2 media file(s), 2 artifact file(s) and 0 other file(s)"
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            "text/html": [
              "Find logs at: <code>./wandb/run-20221029_124701-3tlzmt50/logs</code>"
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        {
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              "Syncing run <strong><a href=\"https://wandb.ai/ecg_simula/AE_pTOP_serious_try2/runs/36v0a9i8\" target=\"_blank\">glowing-eon-2</a></strong> to <a href=\"https://wandb.ai/ecg_simula/AE_pTOP_serious_try2\" target=\"_blank\">Weights & Biases</a> (<a href=\"https://wandb.me/run\" target=\"_blank\">docs</a>)<br/>"
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          "text": [
            "making data\n"
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            "text/plain": [
              "  0%|          | 0/51 [00:00<?, ?it/s]"
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          "text": [
            "creating ECG table\n",
            "/content/ECG/ directory exists.\n",
            "Saving model\n",
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        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "0it [00:00, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "b5ee0b1d61314e5b8c47dafc5d4564fa"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "creating ECG table\n",
            "/content/ECG/ directory exists.\n",
            "Saving model\n",
            "one\n",
            "two\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "0it [00:00, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "ae1e0a450ce44a1db6cfbf31be575e26"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "creating ECG table\n",
            "/content/ECG/ directory exists.\n",
            "Saving model\n",
            "one\n",
            "two\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "0it [00:00, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "7feedd23ec1047dfa0c61d95b4dca6a0"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "creating ECG table\n",
            "/content/ECG/ directory exists.\n",
            "Saving model\n",
            "one\n",
            "two\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "0it [00:00, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "6df9b651b1e04bda84fdc7c1b2e8e337"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "creating ECG table\n",
            "/content/ECG/ directory exists.\n",
            "Saving model\n",
            "one\n",
            "two\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "0it [00:00, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "729063b97ba14a3da65eb358ccce44c6"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "creating ECG table\n",
            "/content/ECG/ directory exists.\n",
            "Saving model\n",
            "one\n",
            "two\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "0it [00:00, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "113cf4c96a3240f8ba13492a53078381"
            }
          },
          "metadata": {}
        },
        {
          "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-42-78fbf03a599a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# Build, train and analyze the model with the pipeline\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mmodel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel_pipeline\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mconfig\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<ipython-input-38-6cc596cfee6a>\u001b[0m in \u001b[0;36mmodel_pipeline\u001b[0;34m(hyperparameters, model)\u001b[0m\n\u001b[1;32m      7\u001b[0m     \u001b[0mtrain_loader\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mval_loader\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mtest_dataset\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcriterion\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moptimizer\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mval_dataset\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmake\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mconfig\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m     \u001b[0;31m# and use them to train the model\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 9\u001b[0;31m     \u001b[0mtrain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrain_loader\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mval_loader\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mtest_dataset\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcriterion\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moptimizer\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mval_dataset\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mconfig\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     10\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m<ipython-input-40-bb5babbcc8b0>\u001b[0m in \u001b[0;36mtrain\u001b[0;34m(model, train_loader, val_loader, test_dataset, criterion, optimizer, val_dataset, config)\u001b[0m\n\u001b[1;32m      4\u001b[0m   \u001b[0;32mfor\u001b[0m \u001b[0mepoch\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mtqdm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mconfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mepochs\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      5\u001b[0m     \u001b[0mtrain_loss\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[0;32m----> 6\u001b[0;31m     \u001b[0;32mfor\u001b[0m \u001b[0mbatch\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mtqdm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrain_loader\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[0m\u001b[1;32m      7\u001b[0m       \u001b[0;31m# Forward pass ➡\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m       \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdevice\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/tqdm/notebook.py\u001b[0m in \u001b[0;36m__iter__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    257\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[1;32m    258\u001b[0m             \u001b[0mit\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtqdm_notebook\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__iter__\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--> 259\u001b[0;31m             \u001b[0;32mfor\u001b[0m \u001b[0mobj\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mit\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    260\u001b[0m                 \u001b[0;31m# return super(tqdm...) will not catch exception\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    261\u001b[0m                 \u001b[0;32myield\u001b[0m \u001b[0mobj\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/tqdm/std.py\u001b[0m in \u001b[0;36m__iter__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1193\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1194\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-> 1195\u001b[0;31m             \u001b[0;32mfor\u001b[0m \u001b[0mobj\u001b[0m \u001b[0;32min\u001b[0m \u001b[0miterable\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   1196\u001b[0m                 \u001b[0;32myield\u001b[0m \u001b[0mobj\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1197\u001b[0m                 \u001b[0;31m# Update and possibly print the progressbar.\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/utils/data/dataloader.py\u001b[0m in \u001b[0;36m__next__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    679\u001b[0m                 \u001b[0;31m# TODO(https://github.com/pytorch/pytorch/issues/76750)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    680\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_reset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# type: ignore[call-arg]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 681\u001b[0;31m             \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_next_data\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    682\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_num_yielded\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    683\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_dataset_kind\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0m_DatasetKind\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mIterable\u001b[0m \u001b[0;32mand\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m\\\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/utils/data/dataloader.py\u001b[0m in \u001b[0;36m_next_data\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    719\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_next_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\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    720\u001b[0m         \u001b[0mindex\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_next_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# may raise StopIteration\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 721\u001b[0;31m         \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_dataset_fetcher\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfetch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# may raise StopIteration\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    722\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_pin_memory\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    723\u001b[0m             \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_utils\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpin_memory\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpin_memory\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_pin_memory_device\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/utils/data/_utils/fetch.py\u001b[0m in \u001b[0;36mfetch\u001b[0;34m(self, possibly_batched_index)\u001b[0m\n\u001b[1;32m     47\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mfetch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpossibly_batched_index\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     48\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mauto_collation\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 49\u001b[0;31m             \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0midx\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0midx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mpossibly_batched_index\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     50\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     51\u001b[0m             \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mpossibly_batched_index\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/utils/data/_utils/fetch.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m     47\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mfetch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpossibly_batched_index\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     48\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mauto_collation\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 49\u001b[0;31m             \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0midx\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0midx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mpossibly_batched_index\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     50\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     51\u001b[0m             \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mpossibly_batched_index\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m<ipython-input-27-e466f353660b>\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, idx)\u001b[0m\n\u001b[1;32m     38\u001b[0m       \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msplit\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     39\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtarget\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;34m\"train\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 40\u001b[0;31m           \u001b[0mtemp_df\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain_files\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0midx\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0msep\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\" \"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnames\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mheader\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     41\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtarget\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;34m\"test\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     42\u001b[0m           \u001b[0mtemp_df\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtest_files\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0midx\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0msep\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\" \"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnames\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mheader\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/pandas/util/_decorators.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    309\u001b[0m                     \u001b[0mstacklevel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mstacklevel\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    310\u001b[0m                 )\n\u001b[0;32m--> 311\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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    312\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    313\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mwrapper\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/pandas/io/parsers/readers.py\u001b[0m in \u001b[0;36mread_csv\u001b[0;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, encoding_errors, dialect, error_bad_lines, warn_bad_lines, on_bad_lines, delim_whitespace, low_memory, memory_map, float_precision, storage_options)\u001b[0m\n\u001b[1;32m    584\u001b[0m     \u001b[0mkwds\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkwds_defaults\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    585\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 586\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0m_read\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwds\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    587\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    588\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/io/parsers/readers.py\u001b[0m in \u001b[0;36m_read\u001b[0;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[1;32m    486\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    487\u001b[0m     \u001b[0;32mwith\u001b[0m \u001b[0mparser\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 488\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mparser\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnrows\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    489\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    490\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/io/parsers/readers.py\u001b[0m in \u001b[0;36mread\u001b[0;34m(self, nrows)\u001b[0m\n\u001b[1;32m   1057\u001b[0m             \u001b[0mnew_rows\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1058\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1059\u001b[0;31m         \u001b[0mdf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mDataFrame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcol_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mindex\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   1060\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1061\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_currow\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0mnew_rows\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/pandas/core/frame.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, data, index, columns, dtype, copy)\u001b[0m\n\u001b[1;32m    612\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdict\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    613\u001b[0m             \u001b[0;31m# GH#38939 de facto copy defaults to False only in non-dict cases\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 614\u001b[0;31m             \u001b[0mmgr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdict_to_mgr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtyp\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmanager\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    615\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mMaskedArray\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    616\u001b[0m             \u001b[0;32mimport\u001b[0m \u001b[0mnumpy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmrecords\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mmrecords\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
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            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36mreindex\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   4804\u001b[0m         if all(\n\u001b[1;32m   4805\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_axis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0midentical\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4806\u001b[0;31m             \u001b[0;32mfor\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0max\u001b[0m \u001b[0;32min\u001b[0m \u001b[0maxes\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[0m\n\u001b[0m\u001b[1;32m   4807\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0max\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4808\u001b[0m         ):\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m<genexpr>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m   4805\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_axis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0midentical\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4806\u001b[0m             \u001b[0;32mfor\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0max\u001b[0m \u001b[0;32min\u001b[0m \u001b[0maxes\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[0m\n\u001b[0;32m-> 4807\u001b[0;31m             \u001b[0;32mif\u001b[0m \u001b[0max\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   4808\u001b[0m         ):\n\u001b[1;32m   4809\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mcopy\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/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36midentical\u001b[0;34m(self, other)\u001b[0m\n\u001b[1;32m   4817\u001b[0m         \"\"\"\n\u001b[1;32m   4818\u001b[0m         return (\n\u001b[0;32m-> 4819\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mequals\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mother\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   4820\u001b[0m             and all(\n\u001b[1;32m   4821\u001b[0m                 \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mother\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\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/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mequals\u001b[0;34m(self, other)\u001b[0m\n\u001b[1;32m   4803\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mother\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mequals\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4804\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4805\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0marray_equivalent\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_values\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mother\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_values\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   4806\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4807\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mfinal\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/pandas/core/dtypes/missing.py\u001b[0m in \u001b[0;36marray_equivalent\u001b[0;34m(left, right, strict_nan, dtype_equal)\u001b[0m\n\u001b[1;32m    440\u001b[0m     \u001b[0;31m# Object arrays can contain None, NaN and NaT.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    441\u001b[0m     \u001b[0;31m# string dtypes must be come to this path for NumPy 1.7.1 compat\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 442\u001b[0;31m     \u001b[0;32mif\u001b[0m \u001b[0mis_string_dtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mleft\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mis_string_dtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mright\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\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    443\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0m_array_equivalent_object\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mleft\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mright\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstrict_nan\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    444\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/core/dtypes/common.py\u001b[0m in \u001b[0;36mis_string_dtype\u001b[0;34m(arr_or_dtype)\u001b[0m\n\u001b[1;32m    559\u001b[0m     \"\"\"\n\u001b[1;32m    560\u001b[0m     \u001b[0;31m# TODO: gh-15585: consider making the checks stricter.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 561\u001b[0;31m     \u001b[0;32mdef\u001b[0m \u001b[0mcondition\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mbool\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    562\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkind\u001b[0m \u001b[0;32min\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34m\"O\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"S\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"U\"\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mis_excluded_dtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    563\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
          ]
        }
      ],
      "source": [
        "# Build, train and analyze the model with the pipeline\n",
        "model = model_pipeline(config)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 43,
      "metadata": {
        "id": "7mlPtCKdQXaO",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bd8eef42-cc33-4093-acb4-1cdf62839ed3"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saving model\n",
            "one\n",
            "two\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<wandb.sdk.wandb_artifacts.Artifact at 0x7f507bdda6d0>"
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ],
      "source": [
        "# data_dir=\"custom_data/train\"\n",
        "# test_dataset = Custom_dataset(data_dir=data_dir,split=True,target=\"test\")\n",
        "# df_input,df_output=get_pred(test_dataset,model)\n",
        "# model.to(device)\n",
        "# #plotting the ECG and creating the combined DF\n",
        "# combined_df=plotECG(df_input,df_output,path=\"/content/ECG/\")\n",
        "# #saving combined DF as table on wandB\n",
        "# input_prediction_table = wandb.Table(dataframe=combined_df)\n",
        "# #calling png:to_jpag function in order to save the ECG plot\n",
        "# png_to_jpg()\n",
        "# #logging the ECG plot and the csv table\n",
        "# wandb.log({\"ECG\": wandb.Image(\"/content/ECG/ECG.jpg\")})\n",
        "# wandb.log({\"Input and predictions\": input_prediction_table}) \n",
        "\n",
        "#Save the model in the exchangeable ONNX format\n",
        "model_dir=Path(\"/content/saved_model\")\n",
        "if model_dir.is_dir():\n",
        "  print(\"Saving model\")  \n",
        "else:\n",
        "  model_dir.mkdir(parents=True, exist_ok=True)\n",
        "trained_model_artifact = wandb.Artifact(\"AE\",\n",
        "                                        type=\"model\",\n",
        "                                        description=\"Simple AE to start with training on ECG data translation\")\n",
        "model.to(\"cpu\")\n",
        "print(\"one\")\n",
        "torch.save(model.state_dict(), \"/content/saved_model/model\")\n",
        "print(\"two\")\n",
        "wandb.log_artifact(\"/content/saved_model/model\", name='Model', type='Model') "
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data_dir=\"custom_data/train\"\n",
        "test_dataset = Custom_dataset(data_dir=data_dir,split=True,target=\"test\")\n",
        "model.to(\"cpu\")\n",
        "model.eval()\n",
        "with torch.inference_mode():\n",
        "  "
      ],
      "metadata": {
        "id": "BAG719Lkupbj"
      },
      "execution_count": null,
      "outputs": []
    }
  ],
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    "gpuClass": "standard",
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      "name": "python3"
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