AE_V1_linear.ipynb

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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/t-willi/Simula/blob/main/AE_V1_linear.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 481,
      "metadata": {
        "id": "8eUpzrSTCodl"
      },
      "outputs": [],
      "source": [
        "import torch\n",
        "from torch import nn\n",
        "import matplotlib.pyplot as plt\n",
        "import torchvision\n",
        "from torchvision import datasets, models, transforms\n",
        "from torchvision.transforms import ToTensor\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "from torch.utils.data import Dataset, DataLoader\n",
        "from timeit import default_timer as timer \n",
        "import glob\n",
        "import torch.optim as optim\n",
        "\n",
        "\n",
        "if torch.cuda.is_available()==True:\n",
        "  device=\"cuda:0\"\n",
        "else:\n",
        "  device =\"cpu\""
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# t1 = torch.rand(1,2,3)\n",
        "# t1.shape\n",
        "# t1=t1.permute(2,1,0)\n",
        "# t1.shape"
      ],
      "metadata": {
        "id": "AcUwTfSGn0yC"
      },
      "execution_count": 482,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# data_dir=\"/content/CSV_data\"\n",
        "# Data = []\n",
        "# #turn list of dataframes into Tensor\n",
        "# files = glob.glob(data_dir + '/*.csv')\n",
        "# for f in files:\n",
        "#   temp_df=pd.read_csv(f,sep=\" \")\n",
        "#   #print(temp_df)\n",
        "#   temp_list_in=temp_df.iloc[:,1]\n",
        "#   #print(temp_list_in)\n",
        "#   temp_tensor_in = torch.tensor(temp_list_in,dtype=torch.int32)\n",
        "#   #print(temp_tensor_in.dtype)\n",
        "#   temp_list_out=temp_df.iloc[:,2].values\n",
        "#   #print(temp_list_out)\n",
        "#   temp_tensor_out=torch.tensor(temp_list_out,dtype=torch.int32)\n",
        "#   #print(temp_tensor_out.dtype)\n",
        "#   temp_tensor_pair=[temp_tensor_in,temp_tensor_out]\n",
        "#   #print(temp_tensor_pair.dtype)\n",
        "#   Data.append(temp_tensor_pair)\n",
        "\n",
        "# X,y=Data[0]\n",
        "# X.dtype"
      ],
      "metadata": {
        "id": "CQMLS-Jben-2"
      },
      "execution_count": 483,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# def get_tensors(data_dir=\"/content/CSV_data\"):\n",
        "#   Data = []\n",
        "#   #turn list of dataframes into Tensor\n",
        "#   files = glob.glob(data_dir + '/*.csv')\n",
        "#   for f in files:\n",
        "#     temp_df=pd.read_csv(f,sep=\" \")\n",
        "#     #print(temp_df)\n",
        "#     temp_list_in=temp_df.iloc[:,1]\n",
        "#     #print(temp_list_in)\n",
        "#     temp_tensor_in = torch.tensor(temp_list_in,dtype=torch.int32)\n",
        "#     #print(temp_tensor_in.dtype)\n",
        "#     temp_list_out=temp_df.iloc[:,2].values\n",
        "#     #print(temp_list_out)\n",
        "#     temp_tensor_out=torch.tensor(temp_list_out,dtype=torch.int32)\n",
        "#     #print(temp_tensor_out.dtype)\n",
        "#     temp_tensor_pair=[temp_tensor_in,temp_tensor_out]\n",
        "#     #print(temp_tensor_pair.dtype)\n",
        "#     Data.append(temp_tensor_pair)\n",
        "#   return Data"
      ],
      "metadata": {
        "id": "tNobyic6WS_7"
      },
      "execution_count": 484,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Create custom dataset class to load ECG data into dataset, containing \n",
        "# input tensor with lead 1 and output tensor with desired other leads\n",
        "class Custom_dataset(Dataset):\n",
        "    def __init__(self, data_dir):\n",
        "      #get all files from directory loaded in all_files list\n",
        "      self.all_files = []\n",
        "      self.files = glob.glob(data_dir + '/*.csv')\n",
        "      for f in self.files:\n",
        "        temp_df = pd.read_csv(f,sep=\" \")\n",
        "        self.all_files.append(temp_df)\n",
        "\n",
        "    def __len__(self):\n",
        "      return len(self.all_files)\n",
        "\n",
        "    def __getitem__(self,idx):\n",
        "      Data = []\n",
        "      #turn list of dataframes into Tensor\n",
        "      for f in self.files:\n",
        "        temp_df=pd.read_csv(f,sep=\" \")\n",
        "        #print(temp_df)\n",
        "        temp_list_in=temp_df.iloc[:,1]\n",
        "        #print(temp_list_in)\n",
        "        temp_tensor_in = torch.tensor(temp_list_in,dtype=torch.float32)\n",
        "        temp_tensor_in=temp_tensor_in.unsqueeze(0)\n",
        "        #print(temp_tensor_in.dtype)\n",
        "        temp_list_out=temp_df.iloc[:,2].values\n",
        "        #print(temp_list_out)\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=temp_tensor_out.permute(1,0)\n",
        "        #print(temp_tensor_out.dtype)\n",
        "        temp_tensor_pair= temp_tensor_in,temp_tensor_out\n",
        "        #print(temp_tensor_pair.dtype)\n",
        "        #Data.append(temp_tensor_pair)\n",
        "      return temp_tensor_pair\n"
      ],
      "metadata": {
        "id": "cal55bVVRw1N"
      },
      "execution_count": 485,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_dataset = Custom_dataset(data_dir=\"/content/train_CSV_data\")\n",
        "test_dataset = Custom_dataset(data_dir=\"/content/test_CSV_data\")\n",
        "\n"
      ],
      "metadata": {
        "id": "lNtnRLsjUMIp"
      },
      "execution_count": 486,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x,y=train_dataset[0]\n",
        "x"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UP88T4BDvW0e",
        "outputId": "a5dde5a9-5691-4543-c74c-dbef2afe16a4"
      },
      "execution_count": 532,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[-137., -106.,  -54.,  ...,   82.,  100.,   64.]])"
            ]
          },
          "metadata": {},
          "execution_count": 532
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from torch.utils.data.dataloader import DataLoader\n",
        "BATCH_SIZE = 10\n",
        "#turn datasets into iterables\n",
        "train_dataloader = DataLoader(train_dataset,\n",
        "                              batch_size=BATCH_SIZE,\n",
        "                              shuffle=True\n",
        "                              )\n",
        "test_dataloader = DataLoader(test_dataset,\n",
        "                              batch_size=BATCH_SIZE,\n",
        "                              shuffle=True\n",
        "                              )"
      ],
      "metadata": {
        "id": "sA756IN7LuVK"
      },
      "execution_count": 487,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\"\"\"\n",
        "Here, we define the autoencoder model.This model is taken from \"https://github.com/L1aoXingyu/pytorch-beginner/blob/master/08-AutoEncoder/simple_autoencoder.py\"\n",
        "\"\"\"\n",
        "class ECG_AE_v1(nn.Module):\n",
        "    def __init__(self):\n",
        "        super(ECG_AE_v1, self).__init__()\n",
        "        self.AE = nn.Sequential(\n",
        "            nn.Linear(4999, 128),\n",
        "            nn.ReLU(),\n",
        "            nn.Linear(128,20),\n",
        "            nn.ReLU(),\n",
        "            nn.Linear(20,10),\n",
        "            nn.ReLU(),\n",
        "            nn.Linear(10,20),\n",
        "            nn.ReLU(),\n",
        "            nn.Linear(20,128),\n",
        "            nn.ReLU(),\n",
        "            nn.Linear(128,4999),\n",
        "        )\n",
        "\n",
        "\n",
        "    def forward(self, x):\n",
        "        x = self.AE(x)\n",
        "        return x\n",
        "\n",
        "\n",
        "model = ECG_AE_v1().to(device)\n",
        "criterion = nn.MSELoss()\n",
        "optimizer = torch.optim.Adam(model.parameters(), lr=0.001)\n",
        "\n"
      ],
      "metadata": {
        "id": "t5WfPAwVIWSB"
      },
      "execution_count": 538,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eYaklXRV8FkT",
        "outputId": "8293de6b-5944-4b7c-9995-842581d158f6"
      },
      "execution_count": 489,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "ECG_AE_v1(\n",
              "  (AE): Sequential(\n",
              "    (0): Linear(in_features=4999, out_features=128, bias=True)\n",
              "    (1): ReLU()\n",
              "    (2): Linear(in_features=128, out_features=20, bias=True)\n",
              "    (3): ReLU()\n",
              "    (4): Linear(in_features=20, out_features=10, bias=True)\n",
              "    (5): ReLU()\n",
              "    (6): Linear(in_features=10, out_features=20, bias=True)\n",
              "    (7): ReLU()\n",
              "    (8): Linear(in_features=20, out_features=128, bias=True)\n",
              "    (9): ReLU()\n",
              "    (10): Linear(in_features=128, out_features=4999, bias=True)\n",
              "  )\n",
              ")"
            ]
          },
          "metadata": {},
          "execution_count": 489
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X,y=next(iter(train_dataloader))\n",
        "X.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WOxxoaE0g5xn",
        "outputId": "54cb0bf4-8d5b-4d3b-8789-4baebf2bde52"
      },
      "execution_count": 490,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "torch.Size([10, 1, 4999])"
            ]
          },
          "metadata": {},
          "execution_count": 490
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# X,y = train_dataset[0]\n",
        "# X"
      ],
      "metadata": {
        "id": "J0IvpJAl_niF"
      },
      "execution_count": 491,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "output = model(X)\n",
        "output.shape\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "z1DdGj-Q_e8C",
        "outputId": "8491370b-b7b9-4638-bb82-4462c94dcc27"
      },
      "execution_count": 492,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "torch.Size([10, 1, 4999])"
            ]
          },
          "metadata": {},
          "execution_count": 492
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y.shape\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hw_p1P3aKHqZ",
        "outputId": "dbb5112b-3b72-4375-b9bf-31aeb4322750"
      },
      "execution_count": 493,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "torch.Size([10, 1, 4999])"
            ]
          },
          "metadata": {},
          "execution_count": 493
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nC1ZABFWKLTD",
        "outputId": "7c41557c-a131-48f3-c263-b6ce6b3f9bd9"
      },
      "execution_count": 494,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "torch.Size([10, 1, 4999])"
            ]
          },
          "metadata": {},
          "execution_count": 494
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "qFydq9zYLydh"
      },
      "execution_count": 494,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "try: \n",
        "    import torchinfo\n",
        "except:\n",
        "    !pip install torchinfo\n",
        "    import torchinfo\n",
        "\n",
        "from torchinfo import summary\n",
        "summary(model, input_size=[1,4999]) # do a test pass through of an example input size "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QD39NrScQxlq",
        "outputId": "dd404b40-ca1d-4999-d40f-44f2ef15ff88"
      },
      "execution_count": 495,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "==========================================================================================\n",
              "Layer (type:depth-idx)                   Output Shape              Param #\n",
              "==========================================================================================\n",
              "ECG_AE_v1                                [1, 4999]                 --\n",
              "├─Sequential: 1-1                        [1, 4999]                 --\n",
              "│    └─Linear: 2-1                       [1, 128]                  640,000\n",
              "│    └─ReLU: 2-2                         [1, 128]                  --\n",
              "│    └─Linear: 2-3                       [1, 20]                   2,580\n",
              "│    └─ReLU: 2-4                         [1, 20]                   --\n",
              "│    └─Linear: 2-5                       [1, 10]                   210\n",
              "│    └─ReLU: 2-6                         [1, 10]                   --\n",
              "│    └─Linear: 2-7                       [1, 20]                   220\n",
              "│    └─ReLU: 2-8                         [1, 20]                   --\n",
              "│    └─Linear: 2-9                       [1, 128]                  2,688\n",
              "│    └─ReLU: 2-10                        [1, 128]                  --\n",
              "│    └─Linear: 2-11                      [1, 4999]                 644,871\n",
              "==========================================================================================\n",
              "Total params: 1,290,569\n",
              "Trainable params: 1,290,569\n",
              "Non-trainable params: 0\n",
              "Total mult-adds (M): 1.29\n",
              "==========================================================================================\n",
              "Input size (MB): 0.02\n",
              "Forward/backward pass size (MB): 0.04\n",
              "Params size (MB): 5.16\n",
              "Estimated Total Size (MB): 5.22\n",
              "=========================================================================================="
            ]
          },
          "metadata": {},
          "execution_count": 495
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from tqdm.auto import tqdm\n",
        "torch.manual_seed(42)\n",
        "#train and test loop\n",
        "Epochs = 20\n",
        "for epoch in tqdm(range(Epochs)):\n",
        "  print(f\"Epoch:{epoch}\")\n",
        "  train_loss=0\n",
        "  for batch, (X,y) in enumerate(train_dataloader):  \n",
        "    model.train()\n",
        "    output=model(X)\n",
        "    loss = criterion(output,y)\n",
        "    train_loss += loss\n",
        "    optimizer.zero_grad()\n",
        "    loss.backward()\n",
        "    optimizer.step()\n",
        "  #average loss per batch\n",
        "  train_loss /= len(train_dataloader)\n",
        "  #start testing\n",
        "  test_loss = 0\n",
        "  model.eval()\n",
        "  with torch.inference_mode():\n",
        "    for X,y in test_dataloader:\n",
        "      test_pred = model(X)\n",
        "      test_loss += criterion(test_pred,y)    \n",
        "    test_loss /= len(test_dataloader)\n",
        "\n",
        "  ##print what is happening\n",
        "  print(f\"\\nTrain loss: {train_loss:.5f} | Test loss: {test_loss:.5f}\")\n",
        "    \n",
        "\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
          "referenced_widgets": [
            "cba1c9910c134c00a21f5e57c0f7fea7",
            "7c5903081c574d62b7a2d6d80ece31dd",
            "ec58e340b64d4e8bb6d9260b38a43785",
            "5a2dd32325bf424e93bf1009968fdd4c",
            "a8ad2661448241e3867eae4413af0709",
            "25359f5d6bd342a283daf19ddfd5fdf6",
            "d397ee0b13274672848fc68d9ba55a06",
            "a8c1693ccfb94133901cdf38771e73fe",
            "aac9f26a00984905b3866df5ea121ca0",
            "f69db0e75a294aa8b701fe967a966711",
            "c92d8569436b465b965e3e5d80444be8"
          ]
        },
        "id": "3uMREYqwckwg",
        "outputId": "699e9ef4-c436-4cf2-b5ab-0c60d220ecd8"
      },
      "execution_count": 543,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/20 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "cba1c9910c134c00a21f5e57c0f7fea7"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch:0\n",
            "\n",
            "Train loss: 13972.85547 | Test loss: 14060.51855\n",
            "Epoch:1\n",
            "\n",
            "Train loss: 10102.03223 | Test loss: 14062.77051\n",
            "Epoch:2\n",
            "\n",
            "Train loss: 6032.00977 | Test loss: 14064.44238\n",
            "Epoch:3\n",
            "\n",
            "Train loss: 3480.73267 | Test loss: 14063.74609\n",
            "Epoch:4\n",
            "\n",
            "Train loss: 1747.41614 | Test loss: 14063.75098\n",
            "Epoch:5\n",
            "\n",
            "Train loss: 733.15015 | Test loss: 14063.66113\n",
            "Epoch:6\n",
            "\n",
            "Train loss: 347.59903 | Test loss: 14064.07715\n",
            "Epoch:7\n",
            "\n",
            "Train loss: 179.43135 | Test loss: 14064.67773\n",
            "Epoch:8\n",
            "\n",
            "Train loss: 85.75521 | Test loss: 14064.46191\n",
            "Epoch:9\n",
            "\n",
            "Train loss: 45.30025 | Test loss: 14064.12109\n",
            "Epoch:10\n",
            "\n",
            "Train loss: 24.79506 | Test loss: 14064.13379\n",
            "Epoch:11\n",
            "\n",
            "Train loss: 12.79756 | Test loss: 14064.16602\n",
            "Epoch:12\n",
            "\n",
            "Train loss: 7.12266 | Test loss: 14064.26270\n",
            "Epoch:13\n",
            "\n",
            "Train loss: 3.91577 | Test loss: 14064.31934\n",
            "Epoch:14\n",
            "\n",
            "Train loss: 2.16624 | Test loss: 14064.28906\n",
            "Epoch:15\n",
            "\n",
            "Train loss: 1.16974 | Test loss: 14064.26172\n",
            "Epoch:16\n",
            "\n",
            "Train loss: 0.59651 | Test loss: 14064.26074\n",
            "Epoch:17\n",
            "\n",
            "Train loss: 0.33089 | Test loss: 14064.28320\n",
            "Epoch:18\n",
            "\n",
            "Train loss: 0.18462 | Test loss: 14064.29297\n",
            "Epoch:19\n",
            "\n",
            "Train loss: 0.20657 | Test loss: 14064.29492\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "input,output = train_dataset[0]\n",
        "input=input.tolist()\n",
        "data=input[0]\n",
        "data\n",
        "plt.plot(data)\n",
        "plt.title(\"Input ECG lead1\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 298
        },
        "id": "X1iZv_5gFGh3",
        "outputId": "6676fa82-5fc5-4ad5-ca7c-4faf3c47bddf"
      },
      "execution_count": 548,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Input ECG lead1')"
            ]
          },
          "metadata": {},
          "execution_count": 548
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X,y=test_dataset[0]\n",
        "model.eval()\n",
        "with torch.inference_mode():\n",
        "  output=model(X)\n",
        "output=output.tolist()\n",
        "data=output[0]\n",
        "plt.plot(data)\n",
        "plt.title(\"Output ECG trained for 20epochs on 50 datapoints\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 298
        },
        "id": "Nx5cRtALrX_4",
        "outputId": "4a06c573-b8c9-48f4-e862-d7ad0c9e3df8"
      },
      "execution_count": 545,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Output ECG trained for 20epochs on 50 datapoints')"
            ]
          },
          "metadata": {},
          "execution_count": 545
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "scCSpwBAwHNk"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}