Lung_Cancer_Segmentation_(custom_model).ipynb

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  "metadata": {
    "colab": {
      "provenance": [],
      "collapsed_sections": [
        "gd1sTOflsAeO",
        "HEJiijwssL1J",
        "6by8C2aXociR",
        "3s5tPx5cs48F",
        "OSbyzQp_s9JF",
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        "mKzo9kIRueaH"
      ],
      "authorship_tag": "ABX9TyMi1+KROAVVQ2JCBsm2grdD",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU",
    "gpuClass": "standard"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/quockhanhdao/Lung_Tumours_Segmentation/blob/main/Lung_Cancer_Segmentation_(custom_model).ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Install"
      ],
      "metadata": {
        "id": "gd1sTOflsAeO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install -q \"monai-weekly[tqdm, nibabel, gdown, ignite]\"\n",
        "!pip install neptune-client torch torchvision"
      ],
      "metadata": {
        "id": "eLTHhFe2r-xx"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Import libraries"
      ],
      "metadata": {
        "id": "HEJiijwssL1J"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\") # remove some scikit-image warnings\n",
        "\n",
        "from typing import Optional, Sequence, Union, Tuple\n",
        "\n",
        "import monai\n",
        "\n",
        "from monai.networks.blocks.convolutions import Convolution, ResidualUnit\n",
        "from monai.networks.layers.factories import Act, Norm\n",
        "from monai.networks.layers.simplelayers import SkipConnection\n",
        "\n",
        "from monai.apps import DecathlonDataset\n",
        "from monai.data import DataLoader, decollate_batch\n",
        "from monai.utils import first\n",
        "from monai.networks.layers import Norm\n",
        "from monai.metrics import DiceMetric\n",
        "from monai.inferers import sliding_window_inference\n",
        "from monai.transforms import (\n",
        "    Compose,\n",
        "    LoadImageD,\n",
        "    EnsureChannelFirstD,\n",
        "    OrientationD,\n",
        "    SpacingD,\n",
        "    ScaleIntensityD,\n",
        "    ResizeD,\n",
        "    RandAffineD,\n",
        "    ToTensorD,\n",
        "    AsDiscrete\n",
        ")\n",
        "\n",
        "import torch\n",
        "from torch import nn\n",
        "from torch.nn import (\n",
        "    Conv3d,\n",
        "    ConvTranspose3d,\n",
        "    MaxPool3d,\n",
        "    ModuleList,\n",
        "    ReLU,\n",
        "    functional,\n",
        "    Dropout,\n",
        ")\n",
        "from torchvision.transforms import CenterCrop\n",
        "\n",
        "import math\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "print(torch.cuda.device_count())\n",
        "print(torch.cuda.get_device_name(0))"
      ],
      "metadata": {
        "id": "KHnmYubUsO5L",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3dd6d46c-bd79-4518-e526-c85f5a274b04"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1\n",
            "Tesla T4\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Help function"
      ],
      "metadata": {
        "id": "6by8C2aXociR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def CenterCrop3D(arr, size):\n",
        "  (_, _, h, w, d) = arr.shape\n",
        "  pad_h = math.floor((h - size[0]) / 2)\n",
        "  pad_w = math.floor((w - size[1]) / 2)\n",
        "  pad_d = math.floor((d - size[2]) / 2)\n",
        "  return arr[:, :, (pad_h):(pad_h + size[0]), (pad_w):(pad_w + size[1]), (pad_d):(pad_d + size[2])]\n",
        "\n",
        "class DiceMetric(nn.Module):\n",
        "  def __init__(self, smooth = 0.001, sigmoid = False, softmax = False):\n",
        "    super(DiceMetric, self).__init__()\n",
        "    self.smooth = smooth\n",
        "    self.sigmoid = sigmoid\n",
        "    self.softmax = softmax\n",
        "\n",
        "  def forward(self, preds, targets):\n",
        "    if self.sigmoid:\n",
        "      preds = torch.nn.Sigmoid()(preds)\n",
        "    elif self.softmax:\n",
        "      preds = torch.nn.Softmax(1)(preds)\n",
        "\n",
        "    # Reduce batch-axis and channel-axis (classes-axis)\n",
        "    preds = preds[0, 0]\n",
        "    targets = targets[0, 0]\n",
        "\n",
        "    # Flatten label and prediction tensors\n",
        "    preds = preds.view(-1)\n",
        "    targets = targets.view(-1)\n",
        "    \n",
        "    intersection = (preds * targets).sum()\n",
        "    dice = (2 * intersection + self.smooth) / (preds.sum() + targets.sum() + self.smooth)\n",
        "\n",
        "    return dice"
      ],
      "metadata": {
        "id": "71DFXgGroguh"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Track model (neptune.ai)"
      ],
      "metadata": {
        "id": "3s5tPx5cs48F"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import neptune.new as neptune\n",
        "\n",
        "run = neptune.init(\n",
        "    project = \"khanh.daoquoc/Lung-Tumours-Segmentation\",\n",
        "    api_token = \"eyJhcGlfYWRkcmVzcyI6Imh0dHBzOi8vYXBwLm5lcHR1bmUuYWkiLCJhcGlfdXJsIjoiaHR0cHM6Ly9hcHAubmVwdHVuZS5haSIsImFwaV9rZXkiOiI3Mjg4YTFkMy05ODlmLTQ3OWUtOWJhZS0yYjk5YmJlMmQ2NTMifQ==\",\n",
        ")"
      ],
      "metadata": {
        "id": "GydaDtCGs7Yk",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e6a8fd9e-c611-4de6-a1dd-2a33eef7a10a"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "https://app.neptune.ai/khanh.daoquoc/Lung-Tumours-Segmentation/e/LUNG-17\n",
            "Remember to stop your run once you’ve finished logging your metadata (https://docs.neptune.ai/api-reference/run#.stop). It will be stopped automatically only when the notebook kernel/interactive console is terminated.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Load data"
      ],
      "metadata": {
        "id": "OSbyzQp_s9JF"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Test data"
      ],
      "metadata": {
        "id": "oeKu2--BtBnD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Dowload data, src: http://medicaldecathlon.com/\n",
        "dataset = DecathlonDataset(root_dir = \"./\", \n",
        "                           task = \"Task06_Lung\", section = \"training\", \n",
        "                           transform = None, download = True)\n",
        "print(f\"\\nNumber of subjects: {len(dataset)}.\\nThe first element in the dataset is {dataset[0]}.\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8o2CbXGCtDFG",
        "outputId": "327ebc14-7b2b-4a15-b471-249d8150d60c"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Task06_Lung.tar: 8.53GB [05:53, 25.9MB/s]                            "
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2022-09-08 08:39:27,658 - INFO - Downloaded: Task06_Lung.tar\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2022-09-08 08:40:04,631 - INFO - Verified 'Task06_Lung.tar', md5: 8afd997733c7fc0432f71255ba4e52dc.\n",
            "2022-09-08 08:40:04,633 - INFO - Writing into directory: ..\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Loading dataset: 100%|██████████| 51/51 [00:00<00:00, 130751.53it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Number of subjects: 51.\n",
            "The first element in the dataset is {'image': 'Task06_Lung/imagesTr/lung_014.nii.gz', 'label': 'Task06_Lung/labelsTr/lung_014.nii.gz'}.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Load data to check\n",
        "loader = LoadImageD(keys=(\"image\", \"label\"))\n",
        "data_check = loader({\"image\": \"Task06_Lung/imagesTr/lung_014.nii.gz\",\n",
        "                     \"label\": \"Task06_Lung/labelsTr/lung_014.nii.gz\"})\n",
        "print(f\"Image shape: {data_check['image'].shape} \\nLabel shape: {data_check['label'].shape}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RS3VBOd8tHcw",
        "outputId": "9c7c94d4-7476-48d0-909e-9fcc2bb64240"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Image shape: (512, 512, 589) \n",
            "Label shape: (512, 512, 589)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Visualize check image and label\n",
        "slide = 379\n",
        "plt.figure(\"check\", (12, 6))\n",
        "\n",
        "plt.subplot(1, 2, 1)\n",
        "plt.title(\"image\")\n",
        "plt.imshow(data_check['image'][:, :, slide], cmap = \"gray\")\n",
        "\n",
        "plt.subplot(1, 2, 2)\n",
        "plt.title(\"label\")\n",
        "plt.imshow(data_check['image'][:, :, slide], cmap = \"gray\")\n",
        "plt.imshow(data_check['label'][:, :, slide], cmap = 'jet', alpha = 0.5)\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "1ie3iVHttMh3",
        "outputId": "ad2785fa-8df1-423e-c8de-e8c97e1cd868"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x432 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Load training set"
      ],
      "metadata": {
        "id": "fDPOFGXntQFy"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Set up transform\n",
        "KEYS = [\"image\", \"label\"]\n",
        "\n",
        "transform = Compose([\n",
        "    LoadImageD(keys = KEYS),\n",
        "    EnsureChannelFirstD(keys = KEYS),\n",
        "    OrientationD(KEYS, axcodes='RAS'),\n",
        "    SpacingD(keys = KEYS, \n",
        "             pixdim = (1., 1., 1.), \n",
        "             mode = ('bilinear', 'nearest')),\n",
        "    ScaleIntensityD(keys = \"image\"),\n",
        "    ResizeD(KEYS, \n",
        "            (128, 128, 64), \n",
        "            mode = ('trilinear', 'nearest')),\n",
        "    RandAffineD(keys = KEYS, \n",
        "                spatial_size = (128, 128, 96),\n",
        "                rotate_range = (0, 0, np.pi/12),\n",
        "                scale_range = (0.1, 0.1, 0.1),\n",
        "                mode = ('bilinear', 'nearest'),\n",
        "                prob = 1.0),\n",
        "    ToTensorD(KEYS),\n",
        "])\n",
        "\n",
        "# Load data\n",
        "dataset = DecathlonDataset(root_dir = \"./\",\n",
        "                           task = \"Task06_Lung\", section = \"training\",\n",
        "                           transform = transform, download = True)\n",
        "\n",
        "train_loader = DataLoader(dataset, batch_size = 1, shuffle = True, num_workers = 4)"
      ],
      "metadata": {
        "id": "O4xXIQCKtdwX",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "633b209a-b1f4-4254-991a-1d57590112b8"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2022-09-08 08:41:46,046 - INFO - Verified 'Task06_Lung.tar', md5: 8afd997733c7fc0432f71255ba4e52dc.\n",
            "2022-09-08 08:41:46,048 - INFO - File exists: Task06_Lung.tar, skipped downloading.\n",
            "2022-09-08 08:41:46,051 - INFO - Non-empty folder exists in Task06_Lung, skipped extracting.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Loading dataset: 100%|██████████| 51/51 [10:02<00:00, 11.81s/it]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Check training set\n",
        "print(f\"Length of training set: {len(train_loader)}\")\n",
        "\n",
        "check_training = first(train_loader)\n",
        "training_image, training_label = check_training['image'], check_training['label']\n",
        "print(f\"check data training image shape: {training_image.shape} \\ncheck data training label shape: {training_label.shape}\")"
      ],
      "metadata": {
        "id": "-w6vgfjetpKa",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4ae3c296-5f78-46ce-ff50-b9ed0cbd9888"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Length of training set: 51\n",
            "check data training image shape: (1, 1, 128, 128, 96) \n",
            "check data training label shape: (1, 1, 128, 128, 96)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Visualize training data\n",
        "slide = 60\n",
        "plt.figure(\"Training Data\", (12, 12))\n",
        "\n",
        "plt.subplot(1, 2, 1)\n",
        "plt.title(\"image\")\n",
        "plt.imshow(training_image[0, 0, :, :, slide], cmap = \"gray\")\n",
        "\n",
        "plt.subplot(1, 2, 2)\n",
        "plt.title(\"label\")\n",
        "plt.imshow(training_image[0, 0, :, :, slide], cmap = \"gray\")\n",
        "plt.imshow(training_label[0, 0, :, :, slide], cmap = 'jet', alpha = 0.4)\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "wLxax5S-tzLi",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "01568b38-1c33-4697-c9ae-500457cff3c5"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x864 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Load validation set"
      ],
      "metadata": {
        "id": "eWbjZXRAtTOj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Set up transform\n",
        "val_transform = Compose([\n",
        "    LoadImageD(keys = KEYS),\n",
        "    EnsureChannelFirstD(keys = KEYS),\n",
        "    OrientationD(KEYS, axcodes='RAS'),\n",
        "    SpacingD(keys = KEYS, pixdim = (1., 1., 1.), mode = ('bilinear', 'nearest')),\n",
        "    ScaleIntensityD(keys = \"image\"),\n",
        "    ResizeD(KEYS, (128, 128, 96), mode = ('trilinear', 'nearest')),\n",
        "    ToTensorD(KEYS),\n",
        "])\n",
        "\n",
        "# Load data\n",
        "val_dataset = DecathlonDataset(root_dir = \"./\",\n",
        "                           task = \"Task06_Lung\", section = \"validation\",\n",
        "                           transform = val_transform, download = True)\n",
        "\n",
        "val_loader = DataLoader(val_dataset, batch_size = 1, shuffle = True, num_workers = 4)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "D1dMxsBEt6C2",
        "outputId": "f35ba6d4-b280-483a-b374-8088d990457a"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2022-09-08 08:52:32,465 - INFO - Verified 'Task06_Lung.tar', md5: 8afd997733c7fc0432f71255ba4e52dc.\n",
            "2022-09-08 08:52:32,469 - INFO - File exists: Task06_Lung.tar, skipped downloading.\n",
            "2022-09-08 08:52:32,473 - INFO - Non-empty folder exists in Task06_Lung, skipped extracting.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Loading dataset: 100%|██████████| 12/12 [02:23<00:00, 11.99s/it]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Check validation set\n",
        "print(f\"Length of validation set: {len(val_loader)}\")\n",
        "\n",
        "check_val = first(val_loader)\n",
        "val_image, val_label = check_val['image'], check_val['label']\n",
        "print(f\"check data image shape: {val_image.shape} \\ncheck data label shape: {val_label.shape}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mgGhXUE9t9ga",
        "outputId": "ec67a8ef-62c3-44b0-875f-7e3515a12a0a"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Length of validation set: 12\n",
            "check data image shape: (1, 1, 128, 128, 96) \n",
            "check data label shape: (1, 1, 128, 128, 96)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Visualize validation data\n",
        "slide = 66\n",
        "plt.figure(\"Validation Data\", (12, 12))\n",
        "\n",
        "plt.subplot(1, 2, 1)\n",
        "plt.title(\"image\")\n",
        "plt.imshow(val_image[0, 0, :, :, slide], cmap = \"gray\")\n",
        "\n",
        "plt.subplot(1, 2, 2)\n",
        "plt.title(\"label\")\n",
        "plt.imshow(val_image[0, 0, :, :, slide], cmap = \"gray\")\n",
        "plt.imshow(val_label[0, 0, :, :, slide], cmap = 'jet', alpha = 0.5)\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "e2Lgl_tKuDWq",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "c637215d-36ff-4a19-f2fa-0ab989cfe51d"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x864 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Model, Loss, Optimizer"
      ],
      "metadata": {
        "id": "xIECG47quJ3M"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Creat model version (neptune.ai)"
      ],
      "metadata": {
        "id": "3lRSm1d5QfFH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model_version = neptune.init_model_version(\n",
        "    model = \"LUNG-UNET\",\n",
        "    project = \"khanh.daoquoc/Lung-Tumours-Segmentation\",\n",
        "    api_token = \"eyJhcGlfYWRkcmVzcyI6Imh0dHBzOi8vYXBwLm5lcHR1bmUuYWkiLCJhcGlfdXJsIjoiaHR0cHM6Ly9hcHAubmVwdHVuZS5haSIsImFwaV9rZXkiOiI3Mjg4YTFkMy05ODlmLTQ3OWUtOWJhZS0yYjk5YmJlMmQ2NTMifQ==\",\n",
        ")"
      ],
      "metadata": {
        "id": "USxLk8FLQbYV",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "dc70e783-a106-47e3-802f-5a96bb0a94be"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "https://app.neptune.ai/khanh.daoquoc/Lung-Tumours-Segmentation/m/LUNG-UNET/v/LUNG-UNET-3\n",
            "Remember to stop your model_version once you’ve finished logging your metadata (https://docs.neptune.ai/api-reference/model-version#.stop). It will be stopped automatically only when the notebook kernel/interactive console is terminated.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Construct model"
      ],
      "metadata": {
        "id": "sV6YdRrbuRMf"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Block"
      ],
      "metadata": {
        "id": "a3nAhxEw_q2P"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "class Block(nn.Module):\n",
        "  def __init__(self, in_channels, out_channels):\n",
        "    super().__init__()\n",
        "    # store the convolution and ReLU layers\n",
        "    self.conv1 = Conv3d(in_channels = in_channels, out_channels = out_channels, \n",
        "                        kernel_size = 3, stride = 1, padding = 1)\n",
        "    self.relu = ReLU()\n",
        "    self.conv2 = Conv3d(in_channels = out_channels, out_channels = out_channels, \n",
        "                        kernel_size = 3, stride = 1, padding = 1)\n",
        "    \n",
        "  def forward(self, x):\n",
        "    return self.relu(self.conv2(self.relu(self.conv1(x))))"
      ],
      "metadata": {
        "id": "_ABCtjK1udOb"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Encoder"
      ],
      "metadata": {
        "id": "fL8SMH_e_t-5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "class Encoder(nn.Module):\n",
        "  def __init__(self, drop_out = 0.5, channels = (1, 32, 64, 128)):\n",
        "    super().__init__()\n",
        "    # store the encoder blocks and maxpooling layer\n",
        "    self.encoder_blocks = ModuleList(\n",
        "        [Block(channels[i], channels[i + 1]) for i in range(len(channels) - 1)]\n",
        "    )\n",
        "    self.drop = Dropout(p = drop_out)\n",
        "    self.pool = MaxPool3d(kernel_size = 2)\n",
        "\n",
        "  def forward(self, x):\n",
        "    # initialize an empty list to store the intermediate outputs\n",
        "    block_outputs = []\n",
        "    # loop through the encoder blocks\n",
        "    for block in self.encoder_blocks:\n",
        "      # pass the inputs through the current encoder block\n",
        "      # store the outputs, then apply maxpooling on the output\n",
        "      x = block(x)\n",
        "      block_outputs.append(x)\n",
        "      x = self.pool(x)\n",
        "      x = self.drop(x)\n",
        "    # return the list containing the intermediate outputs\n",
        "    return block_outputs"
      ],
      "metadata": {
        "id": "Q3erHLbL8tc9"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Decoder"
      ],
      "metadata": {
        "id": "lejOJ6hF_wiW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "class Decoder(nn.Module):\n",
        "  def __init__(self, drop_out = 0.5, channels = (128, 64, 32)):\n",
        "    super().__init__()\n",
        "    # initialize the number of channels, upsampler and decoder blocks\n",
        "    self.channels = channels\n",
        "    self.drop = Dropout(p = drop_out)\n",
        "    self.up_convs = ModuleList(\n",
        "        [ConvTranspose3d(in_channels = channels[i], out_channels = channels[i + 1], \n",
        "                         kernel_size = 2, stride = 1) for i in range(len(channels) - 1)]\n",
        "    )\n",
        "    self.decoder_blocks = ModuleList(\n",
        "        [Block(channels[i], channels[i + 1]) for i in range(len(channels) - 1)]\n",
        "    )\n",
        "\n",
        "  def crop(self, encoder_features, x):\n",
        "      # grab the dimensions of input x,\n",
        "      # then crop the encoder features to match those dimensions\n",
        "      (_, _, H, W, D) = x.shape\n",
        "      result = CenterCrop3D(encoder_features, [H, W, D])\n",
        "      # return the cropped features\n",
        "      return result\n",
        "\n",
        "  def forward(self, x, encoder_features):\n",
        "    # loop through the number of channels\n",
        "    for i in range(len(self.channels) - 1):\n",
        "      # pass the inputs through the upsampler blocks\n",
        "      x = self.up_convs[i](x)\n",
        "      # crop the current features from the encoder blocks,\n",
        "      # concatenate them with the curent upsampled features (along the channel dimension),\n",
        "      # pass the concatenated output through the current decoder block\n",
        "      encFeat = self.crop(encoder_features[i], x)\n",
        "      x = torch.cat([x, encFeat], dim = 1) # which dimension???\n",
        "      x = self.drop(x)\n",
        "      x = self.decoder_blocks[i](x)\n",
        "    # return the final decoder output\n",
        "    return x"
      ],
      "metadata": {
        "id": "zhLmnSer8tlo"
      },
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### UNet"
      ],
      "metadata": {
        "id": "Zv_OuE5__y81"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "class UNet(nn.Module):\n",
        "  def __init__(self, drop_out = 0.5, channels = (1, 32, 64, 128)):\n",
        "    super().__init__()\n",
        "    encoder_channels = channels\n",
        "    decoder_channels = []\n",
        "    counter = len(channels) - 1\n",
        "    while counter > 0:\n",
        "      decoder_channels.append(channels[counter])\n",
        "      counter -= 1\n",
        "    # initalize the encoder and decoder\n",
        "    self.encoder = Encoder(drop_out, encoder_channels)\n",
        "    self.decoder = Decoder(drop_out, decoder_channels)\n",
        "    # initialize the regression head and store the class variables\n",
        "    self.out = Conv3d(in_channels = decoder_channels[-1], out_channels = 1,\n",
        "                       kernel_size = 1, stride = 1, padding = 1)\n",
        "  \n",
        "  def forward(self, x):\n",
        "    out_size = x.shape[2:5]\n",
        "    # grab the features from the encoder\n",
        "    encoder_features = self.encoder(x)\n",
        "    # pass the encoder features through decoder making sure that\n",
        "    # their dimensions are suited for concatenation\n",
        "    decoder_features = self.decoder(\n",
        "        encoder_features[-1],\n",
        "        encoder_features[::-1][1:]\n",
        "    )\n",
        "    # pass the decoder features through the regression head\n",
        "    # to obtain the segmentation mask\n",
        "    map = self.out(decoder_features)\n",
        "    # resize the output to match the input shape\n",
        "    map = nn.functional.interpolate(map, out_size)\n",
        "    return map"
      ],
      "metadata": {
        "id": "f2h7lO2q8ttz"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Construct loss function"
      ],
      "metadata": {
        "id": "vySHKMsZ_b4K"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Dice Loss"
      ],
      "metadata": {
        "id": "UFgmVL6b_5-f"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "class DiceLoss(nn.Module):\n",
        "  def __init__(self, smooth = 0.001, sigmoid = False, softmax = False):\n",
        "    super(DiceLoss, self).__init__()\n",
        "    self.smooth = smooth\n",
        "    self.sigmoid = sigmoid\n",
        "    self.softmax = softmax\n",
        "\n",
        "  def forward(self, preds, targets):\n",
        "    if self.sigmoid:\n",
        "      preds = torch.nn.Sigmoid()(preds)\n",
        "    elif self.softmax:\n",
        "      preds = torch.nn.Softmax(1)(preds)\n",
        "\n",
        "    # Reduce batch-axis and channel-axis (classes-axis)\n",
        "    preds = preds[0, 0]\n",
        "    targets = targets[0, 0]\n",
        "\n",
        "    # Flatten label and prediction tensors\n",
        "    preds = preds.view(-1)\n",
        "    targets = targets.view(-1)\n",
        "    \n",
        "    intersection = (preds * targets).sum()\n",
        "    dice = (2 * intersection + self.smooth) / (preds.sum() + targets.sum() + self.smooth)\n",
        "\n",
        "    return 1 - dice"
      ],
      "metadata": {
        "id": "s-ntwJPJ_lYd"
      },
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Configuration"
      ],
      "metadata": {
        "id": "lzE9ybAj23-t"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "config = {\n",
        "    'channels' : (1, 64, 128, 256, 512),\n",
        "    'drop_out' : 0.2,\n",
        "\n",
        "    'learning_rate' : 0.0001,\n",
        "    'decay_lr' : True,\n",
        "    'decay_rate' : 0.1,\n",
        "    'decay_step' : 10,\n",
        "\n",
        "    'epoch' : 100,\n",
        "    'val_interval' : 1\n",
        "}\n",
        "\n",
        "run[\"parameters\"] = config\n",
        "model_version[\"parameters\"] = config"
      ],
      "metadata": {
        "id": "ZeTtsTEN27CX"
      },
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Setup model"
      ],
      "metadata": {
        "id": "VFs8zOQ4uXBU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "device = torch.device(\"cuda:0\") if torch.cuda.is_available() else \"cpu\"\n",
        "\n",
        "model = UNet(drop_out = config['drop_out'], channels = config['channels']).to(device)\n",
        "print(model)\n",
        "\n",
        "loss_function = DiceLoss(sigmoid = True)\n",
        "optimizer = torch.optim.Adam(model.parameters(), lr = config['learning_rate'])\n",
        "dice_metric = DiceMetric(sigmoid = True)\n",
        "if config['decay_lr']:\n",
        "  scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size = config['decay_step'], gamma = config['decay_rate'])"
      ],
      "metadata": {
        "id": "EsKrnLW8udmp",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b484f6c6-836e-4c0a-f634-3d0d1cc93d20"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "UNet(\n",
            "  (encoder): Encoder(\n",
            "    (encoder_blocks): ModuleList(\n",
            "      (0): Block(\n",
            "        (conv1): Conv3d(1, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n",
            "        (relu): ReLU()\n",
            "        (conv2): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n",
            "      )\n",
            "      (1): Block(\n",
            "        (conv1): Conv3d(64, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n",
            "        (relu): ReLU()\n",
            "        (conv2): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n",
            "      )\n",
            "      (2): Block(\n",
            "        (conv1): Conv3d(128, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n",
            "        (relu): ReLU()\n",
            "        (conv2): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n",
            "      )\n",
            "      (3): Block(\n",
            "        (conv1): Conv3d(256, 512, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n",
            "        (relu): ReLU()\n",
            "        (conv2): Conv3d(512, 512, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n",
            "      )\n",
            "    )\n",
            "    (drop): Dropout(p=0.2, inplace=False)\n",
            "    (pool): MaxPool3d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
            "  )\n",
            "  (decoder): Decoder(\n",
            "    (drop): Dropout(p=0.2, inplace=False)\n",
            "    (up_convs): ModuleList(\n",
            "      (0): ConvTranspose3d(512, 256, kernel_size=(2, 2, 2), stride=(1, 1, 1))\n",
            "      (1): ConvTranspose3d(256, 128, kernel_size=(2, 2, 2), stride=(1, 1, 1))\n",
            "      (2): ConvTranspose3d(128, 64, kernel_size=(2, 2, 2), stride=(1, 1, 1))\n",
            "    )\n",
            "    (decoder_blocks): ModuleList(\n",
            "      (0): Block(\n",
            "        (conv1): Conv3d(512, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n",
            "        (relu): ReLU()\n",
            "        (conv2): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n",
            "      )\n",
            "      (1): Block(\n",
            "        (conv1): Conv3d(256, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n",
            "        (relu): ReLU()\n",
            "        (conv2): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n",
            "      )\n",
            "      (2): Block(\n",
            "        (conv1): Conv3d(128, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n",
            "        (relu): ReLU()\n",
            "        (conv2): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n",
            "      )\n",
            "    )\n",
            "  )\n",
            "  (out): Conv3d(64, 1, kernel_size=(1, 1, 1), stride=(1, 1, 1), padding=(1, 1, 1))\n",
            ")Warning: string series 'monitoring/stdout' value was longer than 1000 characters and was truncated. This warning is printed only once per series.\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Model, Loss, Optimizer (2)"
      ],
      "metadata": {
        "id": "sykdfwtKBcEo"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Creat model version (neptune.ai)"
      ],
      "metadata": {
        "id": "rk9U6lO1QW9h"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model_version = neptune.init_model_version(\n",
        "    model = \"LUNG-UNET2\",\n",
        "    project = \"khanh.daoquoc/Lung-Tumours-Segmentation\",\n",
        "    api_token = \"eyJhcGlfYWRkcmVzcyI6Imh0dHBzOi8vYXBwLm5lcHR1bmUuYWkiLCJhcGlfdXJsIjoiaHR0cHM6Ly9hcHAubmVwdHVuZS5haSIsImFwaV9rZXkiOiI3Mjg4YTFkMy05ODlmLTQ3OWUtOWJhZS0yYjk5YmJlMmQ2NTMifQ==\",\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JxhtnGl1QXFN",
        "outputId": "367ef65a-8a22-4c30-a1f0-0d939119834b"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "https://app.neptune.ai/khanh.daoquoc/Lung-Tumours-Segmentation/m/LUNG-UNET2/v/LUNG-UNET2-1\n",
            "Remember to stop your model_version once you’ve finished logging your metadata (https://docs.neptune.ai/api-reference/model-version#.stop). It will be stopped automatically only when the notebook kernel/interactive console is terminated.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Construct model"
      ],
      "metadata": {
        "id": "MTMeVYWTL5Mk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "class UNetM(nn.Module):\n",
        "  def __init__(self,\n",
        "      in_channels: int,\n",
        "      out_channels: int,\n",
        "      channels: Sequence[int],\n",
        "      num_res_units: int,\n",
        "      act: Union[Tuple, str] = Act.PRELU,\n",
        "      norm: Union[Tuple, str] = Norm.INSTANCE,\n",
        "      dropout: float = 0.0,\n",
        "  ) -> None:\n",
        "    \n",
        "    super().__init__()\n",
        "\n",
        "    self.in_channels = in_channels\n",
        "    self.out_channels = out_channels\n",
        "    self.channels = channels\n",
        "    self.num_res_units = num_res_units\n",
        "    self.act = act\n",
        "    self.norm = norm\n",
        "    self.dropout = dropout\n",
        "\n",
        "    def block(\n",
        "        inc: int, outc: int, channels: Sequence[int], is_top: bool\n",
        "    ) -> nn.Module:\n",
        "      c = channels[0]\n",
        "\n",
        "      subblock: nn.Module\n",
        "\n",
        "      if len(channels) > 2:\n",
        "        subblock = block(c, c, channels[1:], False)\n",
        "        upc = c * 2\n",
        "      else:\n",
        "        subblock = self.bottom_layer(c, channels[1])\n",
        "        upc = c + channels[1]\n",
        "\n",
        "      down = self.down_layer(inc, c, 2, is_top)\n",
        "      up = self.up_layer(upc, outc, 2, is_top)\n",
        "\n",
        "      return self.connection_block(down, up, subblock)\n",
        "    \n",
        "    self.model = block(in_channels, out_channels, self.channels, True)\n",
        "\n",
        "  def connection_block(\n",
        "      self, down_path: nn.Module, up_path: nn.Module, subblock: nn.Module\n",
        "  ) -> nn.Module:\n",
        "    return nn.Sequential(down_path, SkipConnection(subblock), up_path)\n",
        "\n",
        "  def down_layer(\n",
        "      self, in_channels: int, out_channels: int, strides: int, is_top: bool\n",
        "  ) -> nn.Module:\n",
        "    mod: nn.Module\n",
        "    if self.num_res_units > 0:\n",
        "      mod = ResidualUnit(\n",
        "          spatial_dims = 3,\n",
        "          in_channels = in_channels,\n",
        "          out_channels = out_channels,\n",
        "          strides = strides,\n",
        "          kernel_size = 3,\n",
        "          subunits = self.num_res_units,\n",
        "          act = self.act,\n",
        "          norm = self.norm,\n",
        "          dropout = self.dropout,\n",
        "      )\n",
        "      return mod\n",
        "    mod = Convolution(\n",
        "        spatial_dims = 3,\n",
        "        in_channels = in_channels,\n",
        "        out_channels = out_channels,\n",
        "        strides = strides,\n",
        "        kernel_size = 3,\n",
        "        act = self.act,\n",
        "        norm = self.norm,\n",
        "        dropout = self.dropout,\n",
        "    )\n",
        "    return mod\n",
        "\n",
        "  def bottom_layer(\n",
        "      self, in_channels: int, out_channels: int\n",
        "  ) -> nn.Module:\n",
        "    return self.down_layer(in_channels, out_channels, 1, False)\n",
        "\n",
        "  def up_layer(\n",
        "      self, in_channels: int, out_channels: int, strides: int, is_top: bool\n",
        "  ) -> nn.Module:\n",
        "    conv: Union[Convolution, nn.Sequential]\n",
        "\n",
        "    conv = Convolution(\n",
        "        spatial_dims = 3,\n",
        "        in_channels = in_channels,\n",
        "        out_channels = out_channels,\n",
        "        strides = strides,\n",
        "        kernel_size = 3,\n",
        "        act = self.act,\n",
        "        norm = self.norm,\n",
        "        dropout = self.dropout,\n",
        "        conv_only = is_top and self.num_res_units == 0,\n",
        "        is_transposed = True,\n",
        "    )\n",
        "\n",
        "    if self.num_res_units > 0:\n",
        "      res = ResidualUnit(\n",
        "          spatial_dims = 3, \n",
        "          in_channels = out_channels,\n",
        "          out_channels = out_channels,\n",
        "          strides = 1,\n",
        "          kernel_size = 3,\n",
        "          subunits = 1,\n",
        "          act = self.act,\n",
        "          norm = self.norm,\n",
        "          dropout = self.dropout,\n",
        "          last_conv_only = is_top,\n",
        "      )\n",
        "      conv = nn.Sequential(conv, res)\n",
        "    \n",
        "    return conv\n",
        "  \n",
        "  def forward(self, x: torch.Tensor) -> torch.Tensor:\n",
        "    x = self.model(x)\n",
        "    return x"
      ],
      "metadata": {
        "id": "GB7gRKMvCYWj"
      },
      "execution_count": 43,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Configuration"
      ],
      "metadata": {
        "id": "RXbbMpZ9LmB5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "config = {\n",
        "    'in_channels' : 1,\n",
        "    'out_channels' : 2,\n",
        "    'channels' : (64, 128, 256, 512),\n",
        "    'drop_out' : 0.2,\n",
        "    'num_res_units' : 2,\n",
        "    'norm' : Norm.BATCH,\n",
        "\n",
        "    'learning_rate' : 0.001,\n",
        "    'decay_lr' : False,\n",
        "    'decay_rate' : 0.1,\n",
        "    'decay_step' : 10,\n",
        "\n",
        "    'epoch' : 100,\n",
        "    'val_interval' : 1\n",
        "}\n",
        "\n",
        "run[\"parameters\"] = config\n",
        "model_version[\"parameters\"] = config"
      ],
      "metadata": {
        "id": "TG_mKT0eLiQ6"
      },
      "execution_count": 49,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Setup model"
      ],
      "metadata": {
        "id": "axe5dPEHL8A7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "device = torch.device(\"cuda:0\") if torch.cuda.is_available() else \"cpu\"\n",
        "\n",
        "model = UNetM(\n",
        "    in_channels = config['in_channels'],\n",
        "    out_channels = config['out_channels'],\n",
        "    channels = config['channels'],\n",
        "    num_res_units = config['num_res_units'],\n",
        "    norm = config['norm'],\n",
        "    dropout = config['drop_out'],\n",
        ").to(device)\n",
        "\n",
        "# print(model)\n",
        "\n",
        "loss_function = monai.losses.DiceLoss(include_background = False, to_onehot_y = True, softmax = True) # using Dice Loss function\n",
        "optimizer = torch.optim.Adam(model.parameters(), lr = config['learning_rate'])\n",
        "dice_metric = monai.metrics.DiceMetric(include_background = False, reduction = \"mean\")\n",
        "if config['decay_lr']:\n",
        "  scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size = config['decay_step'], gamma = config['decay_rate'])"
      ],
      "metadata": {
        "id": "NDFp2XhaMAKf"
      },
      "execution_count": 50,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Start training"
      ],
      "metadata": {
        "id": "XVNWApAPubaC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "loss_values = []\n",
        "metric_values = []\n",
        "best_metric = -1\n",
        "best_metric_epoch = -1\n",
        "post_pred = Compose([AsDiscrete(argmax = True, to_onehot = 2)])\n",
        "post_label = Compose([AsDiscrete(to_onehot = 2)])\n",
        "\n",
        "print(\"Start training the network...\")\n",
        "\n",
        "for epoch in range(config['epoch']):\n",
        "  print(\"-\" * 13)\n",
        "  print(f\"Epoch {epoch + 1}/{config['epoch']}\")\n",
        "\n",
        "  # Turn model to \"train\" mode\n",
        "  model.train()\n",
        "\n",
        "  epoch_loss = 0\n",
        "  step = 0\n",
        "  for batch_data in train_loader:\n",
        "    step += 1\n",
        "\n",
        "    input, label = (\n",
        "        batch_data[\"image\"].to(device),\n",
        "        batch_data[\"label\"].to(device),\n",
        "    )\n",
        "\n",
        "    optimizer.zero_grad()\n",
        "    output = model(input)\n",
        "    loss = loss_function(output, label)\n",
        "    loss.backward() # Compute gradient\n",
        "    optimizer.step() # Update model's parameters\n",
        "\n",
        "    epoch_loss += loss.item()\n",
        "    print(f\"{step}/{len(dataset) // train_loader.batch_size}, \"\n",
        "          f\"train_loss: {loss.item():4f}\")\n",
        "    \n",
        "  epoch_loss /= step\n",
        "  loss_values.append(epoch_loss)\n",
        "  print(f\"epoch {epoch + 1} average loss: {epoch_loss:.4f}\")\n",
        "\n",
        "  # Track train loss\n",
        "  run[\"train/loss\"].log(epoch_loss)\n",
        "  model_version[\"train/loss\"].log(epoch_loss)\n",
        "\n",
        "  # Decay learning rate if decay_lr = True\n",
        "  if config['decay_lr']:\n",
        "    scheduler.step()\n",
        "\n",
        "  if (epoch + 1) % config['val_interval'] == 0:\n",
        "    # Turn model to \"eval\" mode\n",
        "    model.eval()\n",
        "\n",
        "    with torch.no_grad():\n",
        "      metric = 0\n",
        "      step_val = 0\n",
        "      for val_data in val_loader:\n",
        "        step_val += 1\n",
        "\n",
        "        val_input, val_label = (\n",
        "            val_data[\"image\"].to(device),\n",
        "            val_data[\"label\"].to(device),\n",
        "        )\n",
        "        \n",
        "        \"\"\"\n",
        "        val_output = model(val_input)\n",
        "        val_output = torch.round(val_output)\n",
        "        curr_metric = dice_metric(val_output, val_label)\n",
        "        metric += curr_metric.item()\n",
        "        \"\"\"\n",
        "\n",
        "        roi_size = (128, 128, 96)\n",
        "        sw_batch_size = 1\n",
        "\n",
        "        val_output = sliding_window_inference(\n",
        "            val_input, roi_size, sw_batch_size, model\n",
        "        )\n",
        "        val_output = [post_pred(i) for i in decollate_batch(val_output)]\n",
        "        val_label = [post_label(i) for i in decollate_batch(val_label)]\n",
        "\n",
        "        # Compute metric for current iteration\n",
        "        dice_metric(y_pred = val_output, y = val_label)\n",
        "\n",
        "      \"\"\"\n",
        "      metric /= step_val\n",
        "      \"\"\"\n",
        "\n",
        "      # Aggregate the final mean dice result\n",
        "      metric = dice_metric.aggregate().item()\n",
        "\n",
        "      # Reset the status for the next epoch\n",
        "      dice_metric.reset()\n",
        "\n",
        "      metric_values.append(metric)\n",
        "      \n",
        "      if metric > best_metric:\n",
        "        best_metric = metric\n",
        "        best_metric_epoch = epoch + 1\n",
        "        print(\"Saved new best metric model!!!\")\n",
        "        torch.save(model.state_dict(), \"best_metric_model.pth\")\n",
        "\n",
        "      print(\n",
        "          f\"current epoch: {epoch + 1},\"\n",
        "          f\" current mean dice: {metric:.4f},\"\n",
        "          f\" best mean dice: {best_metric:.4f},\"\n",
        "          f\" at epoch: {best_metric_epoch}\"\n",
        "      )\n",
        "\n",
        "      # Track train dice metric\n",
        "      run[\"train/dice\"].log(metric)\n",
        "      model_version[\"train/dice\"].log(metric)\n",
        "\n",
        "print(\n",
        "    f\"train completed, best metric: {best_metric:.4f}\"\n",
        "    f\" at epoch: {best_metric_epoch}\"\n",
        ")"
      ],
      "metadata": {
        "id": "_PsnIAHtud9o",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b30273ec-9b8a-404d-9096-b05cfe319f23"
      },
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1;30;43mKết quả truyền trực tuyến bị cắt bớt đến 5000 dòng cuối.\u001b[0m\n",
            "23/51, train_loss: 0.989723\n",
            "24/51, train_loss: 0.996400\n",
            "25/51, train_loss: 1.000000\n",
            "26/51, train_loss: 0.975219\n",
            "27/51, train_loss: 0.999981\n",
            "28/51, train_loss: 1.000000\n",
            "29/51, train_loss: 0.998250\n",
            "30/51, train_loss: 0.999911\n",
            "31/51, train_loss: 0.999218\n",
            "32/51, train_loss: 0.978144\n",
            "33/51, train_loss: 0.998812\n",
            "34/51, train_loss: 0.964916\n",
            "35/51, train_loss: 0.968797\n",
            "36/51, train_loss: 0.999172\n",
            "37/51, train_loss: 0.999371\n",
            "38/51, train_loss: 0.999994\n",
            "39/51, train_loss: 0.987218\n",
            "40/51, train_loss: 0.996492\n",
            "41/51, train_loss: 0.990632\n",
            "42/51, train_loss: 0.994639\n",
            "43/51, train_loss: 0.998535\n",
            "44/51, train_loss: 0.999960\n",
            "45/51, train_loss: 0.997602\n",
            "46/51, train_loss: 0.997949\n",
            "47/51, train_loss: 0.998858\n",
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            "epoch 10 average loss: 0.9920\n",
            "current epoch: 10, current mean dice: 0.0304, best mean dice: 0.0421, at epoch: 9\n",
            "-------------\n",
            "Epoch 11/100\n",
            "1/51, train_loss: 0.995250\n",
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            "epoch 11 average loss: 0.9888\n",
            "current epoch: 11, current mean dice: 0.0150, best mean dice: 0.0421, at epoch: 9\n",
            "-------------\n",
            "Epoch 12/100\n",
            "1/51, train_loss: 0.979694\n",
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            "epoch 12 average loss: 0.9776\n",
            "Saved new best metric model!!!\n",
            "current epoch: 12, current mean dice: 0.0481, best mean dice: 0.0481, at epoch: 12\n",
            "-------------\n",
            "Epoch 13/100\n",
            "1/51, train_loss: 0.918277\n",
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            "epoch 13 average loss: 0.9515\n",
            "current epoch: 13, current mean dice: 0.0000, best mean dice: 0.0481, at epoch: 12\n",
            "-------------\n",
            "Epoch 14/100\n",
            "1/51, train_loss: 0.999988\n",
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            "epoch 14 average loss: 0.9658\n",
            "Saved new best metric model!!!\n",
            "current epoch: 14, current mean dice: 0.0867, best mean dice: 0.0867, at epoch: 14\n",
            "-------------\n",
            "Epoch 15/100\n",
            "1/51, train_loss: 0.957824\n",
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            "epoch 15 average loss: 0.9585\n",
            "current epoch: 15, current mean dice: 0.0543, best mean dice: 0.0867, at epoch: 14\n",
            "-------------\n",
            "Epoch 16/100\n",
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            "epoch 16 average loss: 0.9455\n",
            "Saved new best metric model!!!\n",
            "current epoch: 16, current mean dice: 0.1397, best mean dice: 0.1397, at epoch: 16\n",
            "-------------\n",
            "Epoch 17/100\n",
            "1/51, train_loss: 1.000000\n",
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            "epoch 17 average loss: 0.8970\n",
            "Saved new best metric model!!!\n",
            "current epoch: 17, current mean dice: 0.1404, best mean dice: 0.1404, at epoch: 17\n",
            "-------------\n",
            "Epoch 18/100\n",
            "1/51, train_loss: 0.999982\n",
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            "epoch 18 average loss: 0.8891\n",
            "current epoch: 18, current mean dice: 0.0114, best mean dice: 0.1404, at epoch: 17\n",
            "-------------\n",
            "Epoch 19/100\n",
            "1/51, train_loss: 0.999880\n",
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            "epoch 19 average loss: 0.9999\n",
            "current epoch: 19, current mean dice: 0.0000, best mean dice: 0.1404, at epoch: 17\n",
            "-------------\n",
            "Epoch 20/100\n",
            "1/51, train_loss: 0.999991\n",
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            "epoch 20 average loss: 0.9996\n",
            "current epoch: 20, current mean dice: 0.0000, best mean dice: 0.1404, at epoch: 17\n",
            "-------------\n",
            "Epoch 21/100\n",
            "1/51, train_loss: 0.999994\n",
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            "epoch 21 average loss: 0.9701\n",
            "Saved new best metric model!!!\n",
            "current epoch: 21, current mean dice: 0.2056, best mean dice: 0.2056, at epoch: 21\n",
            "-------------\n",
            "Epoch 22/100\n",
            "1/51, train_loss: 1.000000\n",
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            "epoch 22 average loss: 0.9070\n",
            "current epoch: 22, current mean dice: 0.1109, best mean dice: 0.2056, at epoch: 21\n",
            "-------------\n",
            "Epoch 23/100\n",
            "1/51, train_loss: 0.989518\n",
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            "epoch 23 average loss: 0.8414\n",
            "current epoch: 23, current mean dice: 0.1944, best mean dice: 0.2056, at epoch: 21\n",
            "-------------\n",
            "Epoch 24/100\n",
            "1/51, train_loss: 0.979980\n",
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            "epoch 24 average loss: 0.8112\n",
            "Saved new best metric model!!!\n",
            "current epoch: 24, current mean dice: 0.2732, best mean dice: 0.2732, at epoch: 24\n",
            "-------------\n",
            "Epoch 25/100\n",
            "1/51, train_loss: 0.983447\n",
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            "epoch 25 average loss: 0.7973\n",
            "current epoch: 25, current mean dice: 0.1415, best mean dice: 0.2732, at epoch: 24\n",
            "-------------\n",
            "Epoch 26/100\n",
            "1/51, train_loss: 0.352950\n",
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            "epoch 26 average loss: 0.7833\n",
            "Saved new best metric model!!!\n",
            "current epoch: 26, current mean dice: 0.3276, best mean dice: 0.3276, at epoch: 26\n",
            "-------------\n",
            "Epoch 27/100\n",
            "1/51, train_loss: 0.924047\n",
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            "epoch 27 average loss: 0.7836\n",
            "current epoch: 27, current mean dice: 0.1797, best mean dice: 0.3276, at epoch: 26\n",
            "-------------\n",
            "Epoch 28/100\n",
            "1/51, train_loss: 0.634150\n",
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            "epoch 28 average loss: 0.7763\n",
            "Saved new best metric model!!!\n",
            "current epoch: 28, current mean dice: 0.3394, best mean dice: 0.3394, at epoch: 28\n",
            "-------------\n",
            "Epoch 29/100\n",
            "1/51, train_loss: 0.648492\n",
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            "epoch 29 average loss: 0.7328\n",
            "Saved new best metric model!!!\n",
            "current epoch: 29, current mean dice: 0.3931, best mean dice: 0.3931, at epoch: 29\n",
            "-------------\n",
            "Epoch 30/100\n",
            "1/51, train_loss: 0.435407\n",
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            "epoch 30 average loss: 0.7139\n",
            "current epoch: 30, current mean dice: 0.3811, best mean dice: 0.3931, at epoch: 29\n",
            "-------------\n",
            "Epoch 31/100\n",
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            "epoch 31 average loss: 0.6839\n",
            "current epoch: 31, current mean dice: 0.3434, best mean dice: 0.3931, at epoch: 29\n",
            "-------------\n",
            "Epoch 32/100\n",
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            "epoch 32 average loss: 0.7016\n",
            "current epoch: 32, current mean dice: 0.3472, best mean dice: 0.3931, at epoch: 29\n",
            "-------------\n",
            "Epoch 33/100\n",
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            "epoch 33 average loss: 0.7428\n",
            "current epoch: 33, current mean dice: 0.3314, best mean dice: 0.3931, at epoch: 29\n",
            "-------------\n",
            "Epoch 34/100\n",
            "1/51, train_loss: 0.934685\n",
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            "epoch 34 average loss: 0.6651\n",
            "Saved new best metric model!!!\n",
            "current epoch: 34, current mean dice: 0.4367, best mean dice: 0.4367, at epoch: 34\n",
            "-------------\n",
            "Epoch 35/100\n",
            "1/51, train_loss: 0.902774\n",
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            "epoch 35 average loss: 0.6999\n",
            "current epoch: 35, current mean dice: 0.4230, best mean dice: 0.4367, at epoch: 34\n",
            "-------------\n",
            "Epoch 36/100\n",
            "1/51, train_loss: 0.595475\n",
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            "epoch 36 average loss: 0.6677\n",
            "current epoch: 36, current mean dice: 0.4321, best mean dice: 0.4367, at epoch: 34\n",
            "-------------\n",
            "Epoch 37/100\n",
            "1/51, train_loss: 0.325105\n",
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            "epoch 37 average loss: 0.6798\n",
            "current epoch: 37, current mean dice: 0.3732, best mean dice: 0.4367, at epoch: 34\n",
            "-------------\n",
            "Epoch 38/100\n",
            "1/51, train_loss: 0.585583\n",
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            "epoch 38 average loss: 0.6515\n",
            "current epoch: 38, current mean dice: 0.3915, best mean dice: 0.4367, at epoch: 34\n",
            "-------------\n",
            "Epoch 39/100\n",
            "1/51, train_loss: 0.273954\n",
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            "epoch 39 average loss: 0.6219\n",
            "current epoch: 39, current mean dice: 0.4050, best mean dice: 0.4367, at epoch: 34\n",
            "-------------\n",
            "Epoch 40/100\n",
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            "epoch 40 average loss: 0.6331\n",
            "current epoch: 40, current mean dice: 0.4242, best mean dice: 0.4367, at epoch: 34\n",
            "-------------\n",
            "Epoch 41/100\n",
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            "epoch 41 average loss: 0.6431\n",
            "current epoch: 41, current mean dice: 0.3696, best mean dice: 0.4367, at epoch: 34\n",
            "-------------\n",
            "Epoch 42/100\n",
            "1/51, train_loss: 0.620491\n",
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            "epoch 42 average loss: 0.6406\n",
            "Saved new best metric model!!!\n",
            "current epoch: 42, current mean dice: 0.4480, best mean dice: 0.4480, at epoch: 42\n",
            "-------------\n",
            "Epoch 43/100\n",
            "1/51, train_loss: 0.316092\n",
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            "epoch 43 average loss: 0.6252\n",
            "current epoch: 43, current mean dice: 0.4081, best mean dice: 0.4480, at epoch: 42\n",
            "-------------\n",
            "Epoch 44/100\n",
            "1/51, train_loss: 0.915730\n",
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            "epoch 44 average loss: 0.6184\n",
            "current epoch: 44, current mean dice: 0.4271, best mean dice: 0.4480, at epoch: 42\n",
            "-------------\n",
            "Epoch 45/100\n",
            "1/51, train_loss: 0.334062\n",
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            "epoch 45 average loss: 0.5891\n",
            "Saved new best metric model!!!\n",
            "current epoch: 45, current mean dice: 0.4640, best mean dice: 0.4640, at epoch: 45\n",
            "-------------\n",
            "Epoch 46/100\n",
            "1/51, train_loss: 0.912718\n",
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            "epoch 46 average loss: 0.6111\n",
            "current epoch: 46, current mean dice: 0.4283, best mean dice: 0.4640, at epoch: 45\n",
            "-------------\n",
            "Epoch 47/100\n",
            "1/51, train_loss: 0.782178\n",
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            "51/51, train_loss: 0.349553\n",
            "epoch 47 average loss: 0.5842\n",
            "Saved new best metric model!!!\n",
            "current epoch: 47, current mean dice: 0.4824, best mean dice: 0.4824, at epoch: 47\n",
            "-------------\n",
            "Epoch 48/100\n",
            "1/51, train_loss: 0.574429\n",
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            "epoch 48 average loss: 0.5549\n",
            "current epoch: 48, current mean dice: 0.4347, best mean dice: 0.4824, at epoch: 47\n",
            "-------------\n",
            "Epoch 49/100\n",
            "1/51, train_loss: 0.912769\n",
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            "epoch 49 average loss: 0.5912\n",
            "current epoch: 49, current mean dice: 0.4643, best mean dice: 0.4824, at epoch: 47\n",
            "-------------\n",
            "Epoch 50/100\n",
            "1/51, train_loss: 1.000000\n",
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            "epoch 50 average loss: 0.5667\n",
            "current epoch: 50, current mean dice: 0.4572, best mean dice: 0.4824, at epoch: 47\n",
            "-------------\n",
            "Epoch 51/100\n",
            "1/51, train_loss: 0.594028\n",
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            "51/51, train_loss: 1.000000\n",
            "epoch 51 average loss: 0.5559\n",
            "Saved new best metric model!!!\n",
            "current epoch: 51, current mean dice: 0.4839, best mean dice: 0.4839, at epoch: 51\n",
            "-------------\n",
            "Epoch 52/100\n",
            "1/51, train_loss: 0.386143\n",
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            "epoch 52 average loss: 0.5594\n",
            "current epoch: 52, current mean dice: 0.4658, best mean dice: 0.4839, at epoch: 51\n",
            "-------------\n",
            "Epoch 53/100\n",
            "1/51, train_loss: 0.535132\n",
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            "epoch 53 average loss: 0.5520\n",
            "Saved new best metric model!!!\n",
            "current epoch: 53, current mean dice: 0.4956, best mean dice: 0.4956, at epoch: 53\n",
            "-------------\n",
            "Epoch 54/100\n",
            "1/51, train_loss: 0.506886\n",
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            "epoch 54 average loss: 0.5710\n",
            "current epoch: 54, current mean dice: 0.4932, best mean dice: 0.4956, at epoch: 53\n",
            "-------------\n",
            "Epoch 55/100\n",
            "1/51, train_loss: 0.663987\n",
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            "epoch 55 average loss: 0.5633\n",
            "Saved new best metric model!!!\n",
            "current epoch: 55, current mean dice: 0.5282, best mean dice: 0.5282, at epoch: 55\n",
            "-------------\n",
            "Epoch 56/100\n",
            "1/51, train_loss: 0.600152\n",
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            "epoch 56 average loss: 0.5635\n",
            "current epoch: 56, current mean dice: 0.4170, best mean dice: 0.5282, at epoch: 55\n",
            "-------------\n",
            "Epoch 57/100\n",
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            "epoch 57 average loss: 0.5756\n",
            "current epoch: 57, current mean dice: 0.4590, best mean dice: 0.5282, at epoch: 55\n",
            "-------------\n",
            "Epoch 58/100\n",
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            "epoch 58 average loss: 0.5492\n",
            "current epoch: 58, current mean dice: 0.3983, best mean dice: 0.5282, at epoch: 55\n",
            "-------------\n",
            "Epoch 59/100\n",
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            "epoch 59 average loss: 0.5215\n",
            "current epoch: 59, current mean dice: 0.4504, best mean dice: 0.5282, at epoch: 55\n",
            "-------------\n",
            "Epoch 60/100\n",
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            "epoch 60 average loss: 0.5351\n",
            "current epoch: 60, current mean dice: 0.4130, best mean dice: 0.5282, at epoch: 55\n",
            "-------------\n",
            "Epoch 61/100\n",
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            "epoch 61 average loss: 0.5290\n",
            "current epoch: 61, current mean dice: 0.4971, best mean dice: 0.5282, at epoch: 55\n",
            "-------------\n",
            "Epoch 62/100\n",
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            "epoch 62 average loss: 0.5163\n",
            "current epoch: 62, current mean dice: 0.4454, best mean dice: 0.5282, at epoch: 55\n",
            "-------------\n",
            "Epoch 63/100\n",
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            "epoch 63 average loss: 0.5266\n",
            "current epoch: 63, current mean dice: 0.4662, best mean dice: 0.5282, at epoch: 55\n",
            "-------------\n",
            "Epoch 64/100\n",
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            "epoch 64 average loss: 0.5558\n",
            "current epoch: 64, current mean dice: 0.4866, best mean dice: 0.5282, at epoch: 55\n",
            "-------------\n",
            "Epoch 65/100\n",
            "1/51, train_loss: 0.525557\n",
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            "epoch 65 average loss: 0.5328\n",
            "current epoch: 65, current mean dice: 0.5109, best mean dice: 0.5282, at epoch: 55\n",
            "-------------\n",
            "Epoch 66/100\n",
            "1/51, train_loss: 0.276635\n",
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            "epoch 66 average loss: 0.5259\n",
            "current epoch: 66, current mean dice: 0.4875, best mean dice: 0.5282, at epoch: 55\n",
            "-------------\n",
            "Epoch 67/100\n",
            "1/51, train_loss: 0.271272\n",
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            "epoch 67 average loss: 0.4802\n",
            "current epoch: 67, current mean dice: 0.4259, best mean dice: 0.5282, at epoch: 55\n",
            "-------------\n",
            "Epoch 68/100\n",
            "1/51, train_loss: 0.507816\n",
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            "epoch 68 average loss: 0.4855\n",
            "current epoch: 68, current mean dice: 0.4748, best mean dice: 0.5282, at epoch: 55\n",
            "-------------\n",
            "Epoch 69/100\n",
            "1/51, train_loss: 0.366118\n",
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            "epoch 69 average loss: 0.5269\n",
            "current epoch: 69, current mean dice: 0.4683, best mean dice: 0.5282, at epoch: 55\n",
            "-------------\n",
            "Epoch 70/100\n",
            "1/51, train_loss: 0.275734\n",
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            "epoch 70 average loss: 0.5069\n",
            "current epoch: 70, current mean dice: 0.4841, best mean dice: 0.5282, at epoch: 55\n",
            "-------------\n",
            "Epoch 71/100\n",
            "1/51, train_loss: 0.635750\n",
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            "51/51, train_loss: 0.406201\n",
            "epoch 71 average loss: 0.5046\n",
            "Saved new best metric model!!!\n",
            "current epoch: 71, current mean dice: 0.5519, best mean dice: 0.5519, at epoch: 71\n",
            "-------------\n",
            "Epoch 72/100\n",
            "1/51, train_loss: 0.530841\n",
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            "epoch 72 average loss: 0.4861\n",
            "current epoch: 72, current mean dice: 0.4207, best mean dice: 0.5519, at epoch: 71\n",
            "-------------\n",
            "Epoch 73/100\n",
            "1/51, train_loss: 0.581676\n",
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            "epoch 73 average loss: 0.5142\n",
            "current epoch: 73, current mean dice: 0.5096, best mean dice: 0.5519, at epoch: 71\n",
            "-------------\n",
            "Epoch 74/100\n",
            "1/51, train_loss: 0.281742\n",
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            "epoch 74 average loss: 0.5208\n",
            "current epoch: 74, current mean dice: 0.4534, best mean dice: 0.5519, at epoch: 71\n",
            "-------------\n",
            "Epoch 75/100\n",
            "1/51, train_loss: 0.759967\n",
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            "epoch 75 average loss: 0.5045\n",
            "current epoch: 75, current mean dice: 0.4582, best mean dice: 0.5519, at epoch: 71\n",
            "-------------\n",
            "Epoch 76/100\n",
            "1/51, train_loss: 0.529326\n",
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            "epoch 76 average loss: 0.4861\n",
            "Saved new best metric model!!!\n",
            "current epoch: 76, current mean dice: 0.5574, best mean dice: 0.5574, at epoch: 76\n",
            "-------------\n",
            "Epoch 77/100\n",
            "1/51, train_loss: 0.259828\n",
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            "epoch 77 average loss: 0.4791\n",
            "current epoch: 77, current mean dice: 0.4310, best mean dice: 0.5574, at epoch: 76\n",
            "-------------\n",
            "Epoch 78/100\n",
            "1/51, train_loss: 0.234269\n",
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            "epoch 78 average loss: 0.4840\n",
            "current epoch: 78, current mean dice: 0.4466, best mean dice: 0.5574, at epoch: 76\n",
            "-------------\n",
            "Epoch 79/100\n",
            "1/51, train_loss: 0.999999\n",
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            "epoch 79 average loss: 0.4939\n",
            "current epoch: 79, current mean dice: 0.5046, best mean dice: 0.5574, at epoch: 76\n",
            "-------------\n",
            "Epoch 80/100\n",
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            "epoch 80 average loss: 0.5135\n",
            "current epoch: 80, current mean dice: 0.4913, best mean dice: 0.5574, at epoch: 76\n",
            "-------------\n",
            "Epoch 81/100\n",
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            "epoch 81 average loss: 0.4776\n",
            "current epoch: 81, current mean dice: 0.4421, best mean dice: 0.5574, at epoch: 76\n",
            "-------------\n",
            "Epoch 82/100\n",
            "1/51, train_loss: 0.375682\n",
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            "epoch 82 average loss: 0.4709\n",
            "current epoch: 82, current mean dice: 0.4649, best mean dice: 0.5574, at epoch: 76\n",
            "-------------\n",
            "Epoch 83/100\n",
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            "epoch 83 average loss: 0.4664\n",
            "current epoch: 83, current mean dice: 0.4582, best mean dice: 0.5574, at epoch: 76\n",
            "-------------\n",
            "Epoch 84/100\n",
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            "epoch 84 average loss: 0.4654\n",
            "current epoch: 84, current mean dice: 0.4524, best mean dice: 0.5574, at epoch: 76\n",
            "-------------\n",
            "Epoch 85/100\n",
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            "epoch 85 average loss: 0.4986\n",
            "current epoch: 85, current mean dice: 0.5134, best mean dice: 0.5574, at epoch: 76\n",
            "-------------\n",
            "Epoch 86/100\n",
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            "epoch 86 average loss: 0.4768\n",
            "current epoch: 86, current mean dice: 0.3894, best mean dice: 0.5574, at epoch: 76\n",
            "-------------\n",
            "Epoch 87/100\n",
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            "epoch 87 average loss: 0.4657\n",
            "current epoch: 87, current mean dice: 0.4463, best mean dice: 0.5574, at epoch: 76\n",
            "-------------\n",
            "Epoch 88/100\n",
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            "epoch 88 average loss: 0.4722\n",
            "current epoch: 88, current mean dice: 0.4187, best mean dice: 0.5574, at epoch: 76\n",
            "-------------\n",
            "Epoch 89/100\n",
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            "epoch 89 average loss: 0.4854\n",
            "current epoch: 89, current mean dice: 0.4867, best mean dice: 0.5574, at epoch: 76\n",
            "-------------\n",
            "Epoch 90/100\n",
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            "epoch 90 average loss: 0.4526\n",
            "current epoch: 90, current mean dice: 0.5252, best mean dice: 0.5574, at epoch: 76\n",
            "-------------\n",
            "Epoch 91/100\n",
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            "epoch 91 average loss: 0.4517\n",
            "current epoch: 91, current mean dice: 0.4370, best mean dice: 0.5574, at epoch: 76\n",
            "-------------\n",
            "Epoch 92/100\n",
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            "epoch 92 average loss: 0.4458\n",
            "current epoch: 92, current mean dice: 0.4703, best mean dice: 0.5574, at epoch: 76\n",
            "-------------\n",
            "Epoch 93/100\n",
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            "epoch 93 average loss: 0.4791\n",
            "current epoch: 93, current mean dice: 0.4531, best mean dice: 0.5574, at epoch: 76\n",
            "-------------\n",
            "Epoch 94/100\n",
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            "epoch 94 average loss: 0.4633\n",
            "current epoch: 94, current mean dice: 0.3773, best mean dice: 0.5574, at epoch: 76\n",
            "-------------\n",
            "Epoch 95/100\n",
            "1/51, train_loss: 0.721743\n",
            "2/51, train_loss: 0.292260\n",
            "3/51, train_loss: 0.202416\n",
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            "50/51, train_loss: 0.522823\n",
            "51/51, train_loss: 0.999979\n",
            "epoch 95 average loss: 0.4784\n",
            "current epoch: 95, current mean dice: 0.4517, best mean dice: 0.5574, at epoch: 76\n",
            "-------------\n",
            "Epoch 96/100\n",
            "1/51, train_loss: 0.623233\n",
            "2/51, train_loss: 0.348759\n",
            "3/51, train_loss: 0.469888\n",
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            "50/51, train_loss: 0.535073\n",
            "51/51, train_loss: 0.517809\n",
            "epoch 96 average loss: 0.4889\n",
            "current epoch: 96, current mean dice: 0.5224, best mean dice: 0.5574, at epoch: 76\n",
            "-------------\n",
            "Epoch 97/100\n",
            "1/51, train_loss: 0.166112\n",
            "2/51, train_loss: 0.523075\n",
            "3/51, train_loss: 0.323321\n",
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            "51/51, train_loss: 0.201492\n",
            "epoch 97 average loss: 0.4470\n",
            "current epoch: 97, current mean dice: 0.3971, best mean dice: 0.5574, at epoch: 76\n",
            "-------------\n",
            "Epoch 98/100\n",
            "1/51, train_loss: 0.349232\n",
            "2/51, train_loss: 0.653283\n",
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            "51/51, train_loss: 0.580797\n",
            "epoch 98 average loss: 0.4488\n",
            "current epoch: 98, current mean dice: 0.5442, best mean dice: 0.5574, at epoch: 76\n",
            "-------------\n",
            "Epoch 99/100\n",
            "1/51, train_loss: 0.365319\n",
            "2/51, train_loss: 0.525862\n",
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            "51/51, train_loss: 0.433116\n",
            "epoch 99 average loss: 0.4602\n",
            "current epoch: 99, current mean dice: 0.4659, best mean dice: 0.5574, at epoch: 76\n",
            "-------------\n",
            "Epoch 100/100\n",
            "1/51, train_loss: 0.529277\n",
            "2/51, train_loss: 0.264121\n",
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            "50/51, train_loss: 0.376791\n",
            "51/51, train_loss: 0.177539\n",
            "epoch 100 average loss: 0.4414\n",
            "current epoch: 100, current mean dice: 0.4060, best mean dice: 0.5574, at epoch: 76\n",
            "train completed, best metric: 0.5574 at epoch: 76\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Track best dice metric and stop 'run'\n",
        "run[\"best dice\"] = best_metric\n",
        "model_version[\"best_dice\"] = best_metric\n",
        "run.stop()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5dW6zPR0rEIc",
        "outputId": "dd83204e-fdab-4214-ebd3-447dddc0de4f"
      },
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Shutting down background jobs, please wait a moment...\n",
            "Done!\n",
            "Waiting for the remaining 2 operations to synchronize with Neptune. Do not kill this process.\n",
            "All 2 operations synced, thanks for waiting!\n",
            "Explore the metadata in the Neptune app:\n",
            "https://app.neptune.ai/khanh.daoquoc/Lung-Tumours-Segmentation/e/LUNG-17\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Check model"
      ],
      "metadata": {
        "id": "mKzo9kIRueaH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "checker = first(val_loader)\n",
        "image, label = checker['image'], checker['label']"
      ],
      "metadata": {
        "id": "9w4Ng9uKuhwy"
      },
      "execution_count": 54,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Visualize training data\n",
        "slide = 71\n",
        "plt.figure(\"Training Data\", (12, 12))\n",
        "\n",
        "plt.subplot(1, 2, 1)\n",
        "plt.title(\"image\")\n",
        "plt.imshow(image[0, 0, :, :, slide], cmap = \"gray\")\n",
        "\n",
        "plt.subplot(1, 2, 2)\n",
        "plt.title(\"label\")\n",
        "plt.imshow(image[0, 0, :, :, slide], cmap = \"gray\")\n",
        "plt.imshow(label[0, 0, :, :, slide], cmap = 'jet', alpha = 0.4)\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "nsMPSgLDuB07"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.cpu()\n",
        "model.load_state_dict(torch.load(\"best_metric_model.pth\"))\n",
        "\n",
        "with torch.no_grad():\n",
        "  output = model(image)\n",
        "  output = torch.nn.Sigmoid()(output)\n",
        "  output = torch.round(output)\n",
        "  print(f\"Output shape: {output.shape}\")\n",
        "\n",
        "  slide = 68\n",
        "  plt.figure(\"Test Model\", (12, 12))\n",
        "\n",
        "  plt.subplot(1, 3, 1)\n",
        "  plt.title(\"Input\")\n",
        "  plt.imshow(image[0, 0, :, :, slide], cmap = \"gray\")\n",
        "\n",
        "  plt.subplot(1, 3, 2)\n",
        "  plt.title(\"Label\")\n",
        "  plt.imshow(image[0, 0, :, :, slide], cmap = \"gray\")\n",
        "  plt.imshow(label[0, 0, :, :, slide], cmap = 'jet', alpha = 0.5)\n",
        "\n",
        "  plt.subplot(1, 3, 3)\n",
        "  plt.title(\"Output\")\n",
        "  plt.imshow(image[0, 0, :, :, slide], cmap = \"gray\")\n",
        "  plt.imshow(output[0, 0, :, :, slide], cmap = 'jet', alpha = 0.5)\n",
        "\n",
        "  plt.show()"
      ],
      "metadata": {
        "id": "O-L_jhDGnPop",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 278
        },
        "outputId": "38caec87-9e77-4087-933d-4289721b5078"
      },
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Output shape: (1, 2, 128, 128, 96)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x864 with 3 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}