FH_PS_AOP_Challenge.ipynb

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  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# FH-PS-AOP Challenge: Fetal Head Segmentation and Angle of Progression Estimation"
      ],
      "metadata": {
        "id": "QvIIdMWkQ18C"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Anna Putina,\n",
        "Universitat Politècnica De Catalunya"
      ],
      "metadata": {
        "id": "-1stXWKeeeyU"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Overview:\n",
        "\n",
        "This Jupyter notebook presents a solution to the FH-PS-AOP Grand Challenge, focusing on the automatic segmentation of Fetal Head to Pubic Symphysis (FH-PS) structures in transperineal ultrasound images.\n",
        "\n",
        "## Challenge Description:\n",
        "\n",
        "The FH-PS-AOP Challenge aims to advance automated techniques for FH-PS segmentation, providing a benchmark dataset for objective method comparison.\n",
        "\n",
        "## Motivation:\n",
        "\n",
        "- **Development of Automated Techniques:** Encourage the development and application of state-of-the-art automated methods for FH-PS segmentation in transperineal ultrasound images.\n",
        "\n",
        "- **Benchmark Dataset:** Establish a benchmark dataset for fair and transparent comparisons of different segmentation methods, contributing to advancements in biomedical image analysis.\n",
        "\n",
        "- **AOP Estimation:** Stimulate the creation of novel, general-purpose methods for the estimation of the Angle of Progression (AOP) in the context of fetal head descent.\n",
        "\n",
        "## Implementation:\n",
        "\n",
        "This notebook provides a step-by-step solution to the challenge using the Colab environment. It includes data preprocessing, model development, training, and evaluation on Training Set. The final model can be exported for integration into a Docker container, aligning with the challenge guidelines."
      ],
      "metadata": {
        "id": "YXHmogxeefMN"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Imports"
      ],
      "metadata": {
        "id": "LwHjpwQoefZp"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Installing the necessary libraries"
      ],
      "metadata": {
        "id": "ySbJDOna-1bN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install segmentation-models-pytorch"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "btugSDfdQ9Sz",
        "outputId": "af15bbb0-ab1e-424d-c2bd-38c60138023b"
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      "execution_count": 1,
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            "Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.10/dist-packages (from sympy->torch->efficientnet-pytorch==0.7.1->segmentation-models-pytorch) (1.3.0)\n",
            "Building wheels for collected packages: efficientnet-pytorch, pretrainedmodels\n",
            "  Building wheel for efficientnet-pytorch (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for efficientnet-pytorch: filename=efficientnet_pytorch-0.7.1-py3-none-any.whl size=16428 sha256=16d63344b17c2b7c44990f911201282936d42d7871e7ee4d563670900e7a58e5\n",
            "  Stored in directory: /root/.cache/pip/wheels/03/3f/e9/911b1bc46869644912bda90a56bcf7b960f20b5187feea3baf\n",
            "  Building wheel for pretrainedmodels (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for pretrainedmodels: filename=pretrainedmodels-0.7.4-py3-none-any.whl size=60945 sha256=2147359d0c20f7996a01fbd7867548fe98aefd25d6ed2f1c4e577e90295fcd34\n",
            "  Stored in directory: /root/.cache/pip/wheels/35/cb/a5/8f534c60142835bfc889f9a482e4a67e0b817032d9c6883b64\n",
            "Successfully built efficientnet-pytorch pretrainedmodels\n",
            "Installing collected packages: munch, efficientnet-pytorch, timm, pretrainedmodels, segmentation-models-pytorch\n",
            "Successfully installed efficientnet-pytorch-0.7.1 munch-4.0.0 pretrainedmodels-0.7.4 segmentation-models-pytorch-0.3.3 timm-0.9.2\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install torchinfo"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VeUD2O9GJJQg",
        "outputId": "eb53934e-137a-40ac-cc7b-c56459b584ac"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting torchinfo\n",
            "  Downloading torchinfo-1.8.0-py3-none-any.whl (23 kB)\n",
            "Installing collected packages: torchinfo\n",
            "Successfully installed torchinfo-1.8.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install SimpleITK"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7Et7DeM9SALR",
        "outputId": "f04312e9-04cb-4b8c-fdd6-76e679cacabb"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting SimpleITK\n",
            "  Downloading SimpleITK-2.3.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (52.7 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m52.7/52.7 MB\u001b[0m \u001b[31m13.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hInstalling collected packages: SimpleITK\n",
            "Successfully installed SimpleITK-2.3.1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Import libraries\n",
        "import os\n",
        "import glob\n",
        "import random\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "from sklearn import metrics\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score\n",
        "\n",
        "# Image manipulation\n",
        "from PIL import Image\n",
        "import cv2\n",
        "\n",
        "import SimpleITK as sitk\n",
        "from skimage.util import montage as montage2d\n",
        "\n",
        "# PyTorch libraries\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "from torch.utils.data import Dataset, DataLoader\n",
        "import torchvision.transforms as T\n",
        "import torchvision\n",
        "import torch.nn.functional as F\n",
        "from torch.autograd import Variable\n",
        "import torch.optim as optim\n",
        "from torchinfo import summary\n",
        "from torch.optim.lr_scheduler import ReduceLROnPlateau\n",
        "from torchvision.utils import save_image\n",
        "\n",
        "import albumentations as A\n",
        "from albumentations.pytorch import ToTensorV2\n",
        "\n",
        "from tqdm.auto import tqdm\n",
        "\n",
        "import segmentation_models_pytorch as smp\n",
        "import segmentation_models_pytorch.utils.metrics\n",
        "from segmentation_models_pytorch.losses import DiceLoss, JaccardLoss, SoftBCEWithLogitsLoss"
      ],
      "metadata": {
        "id": "Yr7NqqKeJKx2"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Secondary functions such as training, calculating metrics, and plotting have been moved to separate files to avoid cluttering the code. To use them, one need to upload them to Colab."
      ],
      "metadata": {
        "id": "F9AmXyXeaodX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# From local helper files\n",
        "from evaluation import compute_accuracy_loss\n",
        "from metrics import check_metrics\n",
        "from train import train_model\n",
        "from plotting import plot_loss_accuracy,  plot_img_mask_pred\n",
        "from aop_estimation import angle_of_progression_estimation"
      ],
      "metadata": {
        "id": "uJbXght6OXR4"
      },
      "execution_count": 102,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Parameters"
      ],
      "metadata": {
        "id": "PPHPhY2dOYkc"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Parameters for dataloaders, model training, saving and loading"
      ],
      "metadata": {
        "id": "WE0gp72tqcOW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "LEARNING_RATE = 1e-4\n",
        "DEVICE = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
        "BATCH_SIZE = 16\n",
        "NUM_EPOCHS = 10\n",
        "NUM_WORKERS = 4\n",
        "IMAGE_HEIGHT = 256\n",
        "IMAGE_WIDTH = 256"
      ],
      "metadata": {
        "id": "S7K1ao5oOY5u"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "DEVICE"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "r2GE_VYpO6pG",
        "outputId": "63457def-593d-4c57-822c-6c13b34f067a"
      },
      "execution_count": 8,
      "outputs": [
        {
          "data": {
            "text/plain": [
              "device(type='cuda')"
            ]
          },
          "execution_count": 8,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Data loading"
      ],
      "metadata": {
        "id": "stZxwlJtPCwe"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "To upload the data, first one should upload both folders (one with images and another with masks) to Google Drive, and then mount Google Drive to Colab."
      ],
      "metadata": {
        "id": "79Qr2tuJR--7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ISnYaMo0PB2e",
        "outputId": "ba707e19-d4b4-46e4-fb6c-3e83d0efabd3"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Here, the names of the directories for images and ground truth are specified."
      ],
      "metadata": {
        "id": "-eSVY9CjhvcZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Define the base directory where images and ground truth data are stored\n",
        "base_dir = '/content/drive/My Drive/'\n",
        "\n",
        "challenge_folder = \"Pubic Symphysis-Fetal Head Segmentation and Angle of Progression/\"\n",
        "\n",
        "# Define the subdirectories for images and ground truth\n",
        "image_folder = challenge_folder + \"image_mha/\"\n",
        "gt_folder = challenge_folder + \"label_mha/\""
      ],
      "metadata": {
        "id": "F1mU6C6vJah6"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Combine the base directory with subdirectories\n",
        "image_dir = os.path.join(base_dir, image_folder)\n",
        "gt_dir = os.path.join(base_dir, gt_folder)\n",
        "\n",
        "# Get paths to the training and target images\n",
        "image_paths = sorted([os.path.join(image_dir, fname)\n",
        "                      for fname in os.listdir(image_dir)\n",
        "                      if fname.endswith(\".mha\")])\n",
        "gt_paths = sorted([os.path.join(gt_dir, fname)\n",
        "                    for fname in os.listdir(gt_dir)\n",
        "                    if fname.endswith(\".mha\")])\n",
        "\n",
        "# Check if dimensions are equal\n",
        "print(\"Number of samples in the image folder:\", len(image_paths))\n",
        "print(\"Number of samples in the mask folder:\", len(gt_paths))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1xq0Ik-rStZd",
        "outputId": "c8df095f-ef7c-43e3-ec14-cca5c627ee40"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of samples in the image folder: 4000\n",
            "Number of samples in the mask folder: 4000\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Visualisation of an image with its corresponding mask"
      ],
      "metadata": {
        "id": "wnpsXNvfSK0I"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "image = sitk.ReadImage(base_dir + image_folder + '00001.mha')"
      ],
      "metadata": {
        "id": "pXfvdbM6JbOa"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Image characteristics\n",
        "print(image.GetDimension())\n",
        "print(image.GetWidth())\n",
        "print(image.GetHeight())\n",
        "print(image.GetSize())\n",
        "print(image.GetNumberOfComponentsPerPixel())\n",
        "print(image.GetDepth())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZN3_nZuJVaE6",
        "outputId": "30d7fbec-4aa9-40e3-cdbf-1088c7eae8ce"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "3\n",
            "256\n",
            "256\n",
            "(256, 256, 3)\n",
            "1\n",
            "3\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Conversion between numpy and SimpleITK\n",
        "np_image = sitk.GetArrayFromImage(image)\n",
        "print(np_image.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "crrSLt5pW-Ia",
        "outputId": "4fe4ef3b-f273-4521-91e3-6511c399f60e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(3, 256, 256)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "image_xslice = sitk.GetArrayViewFromImage(image)[0,:,:]\n",
        "image_yslice = sitk.GetArrayViewFromImage(image)[1,:,:]\n",
        "image_zslice = sitk.GetArrayViewFromImage(image)[2,:,:]"
      ],
      "metadata": {
        "id": "3Xqsdl-6W_e1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "fig = plt.figure(figsize=(10,3))\n",
        "\n",
        "fig.add_subplot(1,3,1)\n",
        "plt.imshow(image_xslice, cmap='gray')\n",
        "plt.title('xslice', fontsize=10)\n",
        "plt.axis('off')\n",
        "\n",
        "fig.add_subplot(1,3,2)\n",
        "plt.imshow(image_yslice, cmap='gray');\n",
        "plt.title('yslice', fontsize=10)\n",
        "plt.axis('off')\n",
        "\n",
        "fig.add_subplot(1,3,3)\n",
        "plt.title('zslice', fontsize=10)\n",
        "plt.imshow(image_zslice, cmap='gray')\n",
        "plt.axis('off');"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 284
        },
        "id": "6LO9JOrqW_p-",
        "outputId": "bb77a4a3-8fbb-4719-eb4e-8a1a088c6863"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x300 with 3 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "mask = sitk.ReadImage(base_dir + gt_folder + '00001.mha')"
      ],
      "metadata": {
        "id": "z7YPnS4CXF9M"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Mask characteristics\n",
        "print(mask.GetDimension())\n",
        "print(mask.GetWidth())\n",
        "print(mask.GetHeight())\n",
        "print(mask.GetSize())\n",
        "print(mask.GetNumberOfComponentsPerPixel())\n",
        "print(mask.GetDepth())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9tQAGKrLW_0r",
        "outputId": "22d283cd-27bd-422b-c403-9e7ee5d074b0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2\n",
            "256\n",
            "256\n",
            "(256, 256)\n",
            "1\n",
            "0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Conversion between numpy and SimpleITK\n",
        "mask_xslice = sitk.GetArrayViewFromImage(mask)"
      ],
      "metadata": {
        "id": "r0ClJJJxXXVR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "fig = plt.figure(figsize=(10,3))\n",
        "\n",
        "fig.add_subplot(1,1,1)\n",
        "plt.imshow(mask_xslice)\n",
        "plt.title('default colormap mask', fontsize=10)\n",
        "plt.axis('off')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 305
        },
        "id": "ehk84_JeXeO_",
        "outputId": "3e3d0afb-5990-4c52-bc8c-cbbd131779bf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(-0.5, 255.5, 255.5, -0.5)"
            ]
          },
          "metadata": {},
          "execution_count": 26
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x300 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def image_reader(img_path, isMask):\n",
        "  img = sitk.ReadImage(img_path)\n",
        "  np_image = sitk.GetArrayFromImage(img)\n",
        "\n",
        "  if isMask:\n",
        "    img_rgb = Image.fromarray(np_image,'L')\n",
        "    img_array = np_image\n",
        "  else:\n",
        "    img_array = np.transpose(np_image, (1,2,0))\n",
        "    img_rgb = Image.fromarray(img_array,'RGB')\n",
        "\n",
        "  return img_rgb, img_array"
      ],
      "metadata": {
        "id": "h4aFqr3bjWeZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "index = 100"
      ],
      "metadata": {
        "id": "6SlPgtS2XeZa"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "image, img_np = image_reader(base_dir + image_folder + f'{index:05d}.mha', False)\n",
        "mask, mask_np = image_reader(base_dir + gt_folder + f'{index:05d}.mha', True)"
      ],
      "metadata": {
        "id": "6EjdQikcXec0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Showing image and mask in the same plot\n",
        "fig = plt.figure(figsize=(5,5))\n",
        "\n",
        "fig.add_subplot(1,2,1)\n",
        "plt.imshow(img_np)\n",
        "plt.title('example colormap image', fontsize=10)\n",
        "plt.axis('off')\n",
        "\n",
        "fig.add_subplot(1,2,2)\n",
        "plt.imshow(mask_np);\n",
        "plt.title('example colormap mask', fontsize=10)\n",
        "plt.axis('off')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 250
        },
        "id": "JsZim2PzXefk",
        "outputId": "d7f464dc-9641-4516-e4b7-b2fe9357ff5c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(-0.5, 255.5, 255.5, -0.5)"
            ]
          },
          "metadata": {},
          "execution_count": 32
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x500 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Isolating FH, PS, and background in the mask\n",
        "mask_tensor = torch.tensor(mask_np)\n",
        "mask_dict = {0:'background', 1: 'PS', 2:'FH'}\n",
        "mask_np = mask_tensor.type(torch.LongTensor)\n",
        "main_mask = F.one_hot(mask_np, len(mask_dict))\n",
        "main_mask = np.asarray(main_mask)\n",
        "main_mask.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FK16gA5EYE2F",
        "outputId": "e95d464c-0451-4102-b657-1bebe0daf7e5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(256, 256, 3)"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fig = plt.figure(figsize=(10,3))\n",
        "\n",
        "fig.add_subplot(1,3,1)\n",
        "plt.imshow(main_mask[:,:,0])\n",
        "plt.title('default colormap Background', fontsize=10)\n",
        "plt.axis('off')\n",
        "\n",
        "fig.add_subplot(1,3,2)\n",
        "plt.imshow(main_mask[:,:,1]);\n",
        "plt.title('default colormap PS', fontsize=10)\n",
        "plt.axis('off')\n",
        "\n",
        "fig.add_subplot(1,3,3)\n",
        "plt.title('default colormap FH', fontsize=10)\n",
        "plt.imshow(main_mask[:,:,2])\n",
        "plt.axis('off');"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 284
        },
        "id": "HDictXl6YE83",
        "outputId": "e20279b8-d702-49f5-dbe3-7a6453337dea"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x300 with 3 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Creating datasets and dataloaders"
      ],
      "metadata": {
        "id": "2YkHEMwHYPP-"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Initially, we divide the dataset into training, validation, and test sets."
      ],
      "metadata": {
        "id": "z8tdiYsHr82D"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "percentage_test = 0.1\n",
        "percentage_validation = 0.15\n",
        "\n",
        "# Set your random seed for reproducibility\n",
        "random_seed = 142\n",
        "\n",
        "# Split your data into training, validation, and test sets\n",
        "image_paths_trainval, image_paths_test, mask_paths_trainval, mask_paths_test = train_test_split(image_paths, gt_paths, test_size=percentage_test, random_state=random_seed)\n",
        "image_paths_train, image_paths_val, mask_paths_train, mask_paths_val = train_test_split(image_paths_trainval, mask_paths_trainval, test_size=percentage_validation, random_state=random_seed)"
      ],
      "metadata": {
        "id": "weKQIOOIYFAR"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Classes in the ground truth mask"
      ],
      "metadata": {
        "id": "eomQbeEntO1c"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "CLASSES = ['background', 'PS', 'FH']"
      ],
      "metadata": {
        "id": "N9TRzJlZYFIi"
      },
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Image and mask transform operations\n",
        "\n",
        " Image data augmentation is performed using the Albumentations library as part of the preprocessing pipeline. Two sets of augmentation operations were defined: `train_transform` for training images and `val_transform` for validation images. The `train_transform` included resizing, random rotations, horizontal flips, and pixel value normalization, with the aim of enhancing the diversity of the training data and improving the model's generalization ability. On the other hand, the `val_transform` focused primarily on resizing and normalization to ensure consistency in the validation set."
      ],
      "metadata": {
        "id": "BIr73nd6H6q6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "train_transform = A.Compose(\n",
        "    [\n",
        "        A.Resize(height=IMAGE_HEIGHT, width=IMAGE_WIDTH),\n",
        "        A.CoarseDropout(\n",
        "            max_holes = 4, # Maximum number of regions to zero out. (default: 8)\n",
        "            max_height = 4, # Maximum height of the hole. (default: 8)\n",
        "            max_width = 4, # Maximum width of the hole. (default: 8)\n",
        "            min_holes=None, # Minimum number of regions to zero out. (default: None, which equals max_holes)\n",
        "            min_height=None, # Minimum height of the hole. (default: None, which equals max_height)\n",
        "            min_width=None, # Minimum width of the hole. (default: None, which equals max_width)\n",
        "            fill_value=0, # Value for dropped pixels.\n",
        "            mask_fill_value=None, # Fill value for dropped pixels in mask.\n",
        "            always_apply=False,\n",
        "            p=0.5\n",
        "            ),\n",
        "        A.Rotate(limit=25, p=1.0),\n",
        "        A.HorizontalFlip(p=0.5),\n",
        "        A.VerticalFlip(p=0.3),\n",
        "        A.Normalize(\n",
        "            mean=[0.0, 0.0, 0.0],\n",
        "            std=[1.0, 1.0, 1.0],\n",
        "            max_pixel_value=255.0,\n",
        "        ),\n",
        "        ToTensorV2(),\n",
        "     ],\n",
        ")"
      ],
      "metadata": {
        "id": "eGXTkVasbyur"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "val_transform = A.Compose(\n",
        "    [\n",
        "        A.Resize(height=IMAGE_HEIGHT, width=IMAGE_WIDTH),\n",
        "        A.Normalize(\n",
        "            mean=[0.0, 0.0, 0.0],\n",
        "            std=[1.0, 1.0, 1.0],\n",
        "            max_pixel_value=255.0,\n",
        "        ),\n",
        "        ToTensorV2(),\n",
        "     ],\n",
        ")"
      ],
      "metadata": {
        "id": "41Ru4cUkH24W"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Dataset Class**\n",
        "\n",
        "The mask is divided into three parts, all contained within the same torch tensor, which is responsible for the background, pubic symphysis, and fetal head."
      ],
      "metadata": {
        "id": "k5Hc1vcPuElb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def image_reader(img_path, isMask):\n",
        "  img = sitk.ReadImage(img_path)\n",
        "  np_image = sitk.GetArrayFromImage(img)\n",
        "\n",
        "  if isMask:\n",
        "    img_rgb = Image.fromarray(np_image,'L')\n",
        "    img_array = np_image\n",
        "  else:\n",
        "    img_array = np.transpose(np_image, (1,2,0))\n",
        "    img_rgb = Image.fromarray(img_array,'RGB')\n",
        "\n",
        "  return img_rgb, img_array"
      ],
      "metadata": {
        "id": "LtWMRwNz_hWR"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class HeadSymphysisDataset(Dataset):\n",
        "    def __init__(self, image_paths, mask_paths, transform=None):\n",
        "        self.image_paths = image_paths\n",
        "        self.mask_paths = mask_paths\n",
        "        self.transform = transform\n",
        "\n",
        "    def __len__(self):\n",
        "        return len(self.image_paths)\n",
        "\n",
        "\n",
        "    def __getitem__(self, idx):\n",
        "        img_name = self.image_paths[idx]\n",
        "        mask_name = self.mask_paths[idx]\n",
        "\n",
        "        _, img = image_reader(img_name, False)\n",
        "        _, mask = image_reader(mask_name, True)\n",
        "\n",
        "        if self.transform:\n",
        "            augmentations = self.transform(image=img, mask=mask)\n",
        "            img = augmentations[\"image\"]\n",
        "            mask = augmentations[\"mask\"]\n",
        "\n",
        "        mask = mask.type(torch.LongTensor)\n",
        "        mask_onehot = F.one_hot(mask, len(CLASSES))\n",
        "        mask_stack = torch.stack((mask_onehot[:,:,0], mask_onehot[:,:,1], mask_onehot[:,:,2]))\n",
        "        mask_stack = mask_stack.type(torch.IntTensor)\n",
        "\n",
        "        return img, mask_stack"
      ],
      "metadata": {
        "id": "BFKgdXGViYVj"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_dataset = HeadSymphysisDataset(image_paths_train, mask_paths_train, transform = train_transform)\n",
        "val_dataset = HeadSymphysisDataset(image_paths_val, mask_paths_val, transform = val_transform)\n",
        "test_dataset = HeadSymphysisDataset(image_paths_test, mask_paths_test, transform = val_transform)"
      ],
      "metadata": {
        "id": "T1Ksd2-jRWKS"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "len(train_dataset)\n",
        "len(val_dataset)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ySvKk_r0TPFL",
        "outputId": "9e40193d-c74f-4a0f-ec8c-8be6fd5c2446"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "540"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Dataloaders**"
      ],
      "metadata": {
        "id": "VBmGTUJBurA7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "train_dataloader = DataLoader(train_dataset, batch_size=BATCH_SIZE, num_workers=NUM_WORKERS, shuffle=True)\n",
        "val_dataloader = DataLoader(val_dataset, batch_size=BATCH_SIZE, num_workers=NUM_WORKERS, shuffle=False)\n",
        "test_dataloader = DataLoader(test_dataset, batch_size=1, num_workers=NUM_WORKERS, shuffle=False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vAnxTCRE5R65",
        "outputId": "ae238cb8-d83e-459a-a35f-b0c32cc91bd8"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py:557: UserWarning: This DataLoader will create 4 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n",
            "  warnings.warn(_create_warning_msg(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(len(train_dataloader))\n",
        "print(len(val_dataloader))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2pK-g2SRYFbS",
        "outputId": "59f25c9e-18c4-4225-f13b-a871bbfa4087"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "192\n",
            "34\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Plotting Images and Masks from Datasets"
      ],
      "metadata": {
        "id": "KNXH6b1ju-dJ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "In the plotting function `plot_img_mask_pred`, a random sample is chosen to be plotted."
      ],
      "metadata": {
        "id": "gkNGjtpMvA7c"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plot_img_mask_pred(train_dataset)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 228
        },
        "id": "u5LRMDhDu9ne",
        "outputId": "2adacd65-aba3-4943-b351-943cd822299b"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x400 with 4 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We could see that data transformation techniques are applied only to training set."
      ],
      "metadata": {
        "id": "XE0df0XU3KYP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plot_img_mask_pred(val_dataset)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 228
        },
        "id": "PUI_nnYovIj2",
        "outputId": "b47acadf-958a-48d3-f03d-d7be21cb9f0f"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x400 with 4 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plot_img_mask_pred(test_dataset)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 228
        },
        "id": "wtlToRQ7vIv8",
        "outputId": "9fd907cc-5edb-4fda-c255-4f5271814dd4"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x400 with 4 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## U-Net Model"
      ],
      "metadata": {
        "id": "aAnn0YOVcHjQ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "I am experimenting with various encoders, including `ResNet18`, `MIT-B0`, `DenseNet121`, `TIMM-ResNeSt14d`, and `TIMM-ResNeSt50d`, while also exploring different parameters such as `dropout` rate.\n",
        "\n"
      ],
      "metadata": {
        "id": "kj4x4p0k6HG7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "ENCODER = 'mit_b0'\n",
        "ENCODER_WEIGHTS = 'imagenet'\n",
        "ACTIVATION = 'softmax2d'\n",
        "\n",
        "AUX_PARAMS=dict(\n",
        "    pooling='max',\n",
        "    dropout=0.2,\n",
        "    activation='softmax',\n",
        "    classes=len(CLASSES),\n",
        ")"
      ],
      "metadata": {
        "id": "axveuZsnfqYd"
      },
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = smp.Unet(\n",
        "    encoder_name=ENCODER,\n",
        "    encoder_weights=ENCODER_WEIGHTS,\n",
        "    decoder_attention_type=None,\n",
        "    classes=len(CLASSES),\n",
        "    activation=ACTIVATION,\n",
        "    aux_params=AUX_PARAMS\n",
        ")\n",
        "\n",
        "preprocessing = smp.encoders.get_preprocessing_fn(ENCODER, ENCODER_WEIGHTS)"
      ],
      "metadata": {
        "id": "bGOTjgV-gEvp"
      },
      "execution_count": 43,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.classification_head = None"
      ],
      "metadata": {
        "id": "GuuD3q3DjrWG"
      },
      "execution_count": 44,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "summary(model)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kxUiwZAagVeJ",
        "outputId": "e7898903-963e-43ce-8b00-48279ed736c4"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "======================================================================\n",
              "Layer (type:depth-idx)                        Param #\n",
              "======================================================================\n",
              "Unet                                          --\n",
              "├─MixVisionTransformerEncoder: 1-1            --\n",
              "│    └─OverlapPatchEmbed: 2-1                 --\n",
              "│    │    └─Conv2d: 3-1                       4,736\n",
              "│    │    └─LayerNorm: 3-2                    64\n",
              "│    └─OverlapPatchEmbed: 2-2                 --\n",
              "│    │    └─Conv2d: 3-3                       18,496\n",
              "│    │    └─LayerNorm: 3-4                    128\n",
              "│    └─OverlapPatchEmbed: 2-3                 --\n",
              "│    │    └─Conv2d: 3-5                       92,320\n",
              "│    │    └─LayerNorm: 3-6                    320\n",
              "│    └─OverlapPatchEmbed: 2-4                 --\n",
              "│    │    └─Conv2d: 3-7                       368,896\n",
              "│    │    └─LayerNorm: 3-8                    512\n",
              "│    └─ModuleList: 2-5                        --\n",
              "│    │    └─Block: 3-9                        79,616\n",
              "│    │    └─Block: 3-10                       79,616\n",
              "│    └─LayerNorm: 2-6                         64\n",
              "│    └─ModuleList: 2-7                        --\n",
              "│    │    └─Block: 3-11                       118,272\n",
              "│    │    └─Block: 3-12                       118,272\n",
              "│    └─LayerNorm: 2-8                         128\n",
              "│    └─ModuleList: 2-9                        --\n",
              "│    │    └─Block: 3-13                       418,560\n",
              "│    │    └─Block: 3-14                       418,560\n",
              "│    └─LayerNorm: 2-10                        320\n",
              "│    └─ModuleList: 2-11                       --\n",
              "│    │    └─Block: 3-15                       800,000\n",
              "│    │    └─Block: 3-16                       800,000\n",
              "│    └─LayerNorm: 2-12                        512\n",
              "├─UnetDecoder: 1-2                            --\n",
              "│    └─Identity: 2-13                         --\n",
              "│    └─ModuleList: 2-14                       --\n",
              "│    │    └─DecoderBlock: 3-17                1,549,312\n",
              "│    │    └─DecoderBlock: 3-18                516,608\n",
              "│    │    └─DecoderBlock: 3-19                129,280\n",
              "│    │    └─DecoderBlock: 3-20                27,776\n",
              "│    │    └─DecoderBlock: 3-21                6,976\n",
              "├─SegmentationHead: 1-3                       --\n",
              "│    └─Conv2d: 2-15                           435\n",
              "│    └─Identity: 2-16                         --\n",
              "│    └─Activation: 2-17                       --\n",
              "│    │    └─Softmax: 3-22                     --\n",
              "======================================================================\n",
              "Total params: 5,549,779\n",
              "Trainable params: 5,549,779\n",
              "Non-trainable params: 0\n",
              "======================================================================"
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model = model.to(DEVICE)"
      ],
      "metadata": {
        "id": "GDRk4-MrgE6C"
      },
      "execution_count": 46,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Training"
      ],
      "metadata": {
        "id": "YMP_ekql7G9w"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "I explored several loss functions, including `DiceLoss`, `JaccardLoss`, `SoftBCEWithLogitsLoss` (from the SMP library), as well as `CrossEntropyLoss` and `BCEWithLogitsLoss` (from torch.nn)."
      ],
      "metadata": {
        "id": "oE7zRELv7LPk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "loss = SoftBCEWithLogitsLoss()\n",
        "\n",
        "optimizer = optim.Adam(model.parameters(), lr=LEARNING_RATE)\n",
        "\n",
        "scheduler = ReduceLROnPlateau(optimizer, factor=0.1, patience=5, mode='min', verbose=True)"
      ],
      "metadata": {
        "id": "w27m2ttegE-K"
      },
      "execution_count": 47,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_loss_lst, valid_loss_lst, valid_acc_lst =  [], [], []"
      ],
      "metadata": {
        "id": "3t0pW4LU8ThY"
      },
      "execution_count": 48,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_loss_lst, valid_loss_lst, valid_acc_lst = train_model(model, NUM_EPOCHS, train_dataloader, val_dataloader,\n",
        "                                                loss, optimizer, DEVICE, scheduler, 'train_loss')"
      ],
      "metadata": {
        "id": "iImBpfd1Cf2g",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
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        },
        "outputId": "5828000d-28b6-4f12-df52-3846d931fadf"
      },
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/10 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "0e93b4df2ec1440ab42738a5285b794c"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "d36e9f1fbfbe4317a9725558848f8ce5"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch: 01/10 | Train Loss: 0.6154 | Validation Loss: 0.5879 | Validation Accuracy: 97.9%\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "4d21d83c0c1849babbb7437a5e7d6ef6"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch: 02/10 | Train Loss: 0.5818 | Validation Loss: 0.5777 | Validation Accuracy: 98.6%\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "8712c5ac3411428c8260c6dd6753dc24"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch: 03/10 | Train Loss: 0.5770 | Validation Loss: 0.5748 | Validation Accuracy: 98.7%\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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              "  0%|          | 0/192 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "d0db422bd46c47db8459c653c95ed926"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Exception ignored in: <function _MultiProcessingDataLoaderIter.__del__ at 0x7d66ae064280>\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py\", line 1478, in __del__\n",
            "    self._shutdown_workers()\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py\", line 1461, in _shutdown_workers\n",
            "    if w.is_alive():\n",
            "  File \"/usr/lib/python3.10/multiprocessing/process.py\", line 160, in is_alive\n",
            "    assert self._parent_pid == os.getpid(), 'can only test a child process'\n",
            "AssertionError: can only test a child process\n",
            "Exception ignored in: <function _MultiProcessingDataLoaderIter.__del__ at 0x7d66ae064280>\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py\", line 1478, in __del__\n",
            "    self._shutdown_workers()\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py\", line 1461, in _shutdown_workers\n",
            "    if w.is_alive():\n",
            "  File \"/usr/lib/python3.10/multiprocessing/process.py\", line 160, in is_alive\n",
            "    assert self._parent_pid == os.getpid(), 'can only test a child process'\n",
            "AssertionError: can only test a child process\n",
            "Exception ignored in: <function _MultiProcessingDataLoaderIter.__del__ at 0x7d66ae064280>\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py\", line 1478, in __del__\n",
            "    self._shutdown_workers()\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py\", line 1461, in _shutdown_workers\n",
            "    if w.is_alive():\n",
            "  File \"/usr/lib/python3.10/multiprocessing/process.py\", line 160, in is_alive\n",
            "    assert self._parent_pid == os.getpid(), 'can only test a child process'\n",
            "AssertionError: can only test a child process\n",
            "Exception ignored in: <function _MultiProcessingDataLoaderIter.__del__ at 0x7d66ae064280>\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py\", line 1478, in __del__\n",
            "    self._shutdown_workers()\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py\", line 1461, in _shutdown_workers\n",
            "    if w.is_alive():\n",
            "  File \"/usr/lib/python3.10/multiprocessing/process.py\", line 160, in is_alive\n",
            "    assert self._parent_pid == os.getpid(), 'can only test a child process'\n",
            "AssertionError: can only test a child process\n",
            "Exception ignored in: <function _MultiProcessingDataLoaderIter.__del__ at 0x7d66ae064280>\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py\", line 1478, in __del__\n",
            "    self._shutdown_workers()\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py\", line 1461, in _shutdown_workers\n",
            "    if w.is_alive():\n",
            "  File \"/usr/lib/python3.10/multiprocessing/process.py\", line 160, in is_alive\n",
            "    assert self._parent_pid == os.getpid(), 'can only test a child process'\n",
            "AssertionError: can only test a child process\n",
            "Exception ignored in: <function _MultiProcessingDataLoaderIter.__del__ at 0x7d66ae064280>\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py\", line 1478, in __del__\n",
            "    self._shutdown_workers()\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py\", line 1461, in _shutdown_workers\n",
            "    if w.is_alive():\n",
            "  File \"/usr/lib/python3.10/multiprocessing/process.py\", line 160, in is_alive\n",
            "    assert self._parent_pid == os.getpid(), 'can only test a child process'\n",
            "AssertionError: can only test a child process\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch: 04/10 | Train Loss: 0.5748 | Validation Loss: 0.5733 | Validation Accuracy: 98.9%\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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              "  0%|          | 0/192 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "ec852b0292964698b00fe4ea33acacf0"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch: 05/10 | Train Loss: 0.5737 | Validation Loss: 0.5722 | Validation Accuracy: 99.1%\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/192 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "cb14719798c44d04855855204faaeda6"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch: 06/10 | Train Loss: 0.5731 | Validation Loss: 0.5721 | Validation Accuracy: 99.0%\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/192 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "4ab22535728f4dd4a6a9c57a92d4223b"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch: 07/10 | Train Loss: 0.5725 | Validation Loss: 0.5715 | Validation Accuracy: 99.1%\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/192 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "59eea10a13094c16a6954f11da57a4a2"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch: 08/10 | Train Loss: 0.5722 | Validation Loss: 0.5716 | Validation Accuracy: 99.1%\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/192 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "8f6d694269b04ebda9892c3e9b0b0454"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch: 09/10 | Train Loss: 0.5719 | Validation Loss: 0.5709 | Validation Accuracy: 99.2%\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/192 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a134b3af941b4632ba5aed6aa28137ee"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch: 10/10 | Train Loss: 0.5717 | Validation Loss: 0.5710 | Validation Accuracy: 99.1%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The plots I received for Loss and Accuracy"
      ],
      "metadata": {
        "id": "Jyq0guBG-Az7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plot_loss_accuracy(train_loss_lst, valid_loss_lst, valid_acc_lst)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 407
        },
        "id": "u9llFZ_89_eH",
        "outputId": "3535db96-9f83-454e-ba75-53efda3c61c3"
      },
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x400 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Saving model"
      ],
      "metadata": {
        "id": "Mc-WGrAkw41s"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model_name = \"best_performance.pt\"\n",
        "save_path = base_dir + challenge_folder + f'/Saved Models/'\n",
        "checkpoint_name = save_path + model_name"
      ],
      "metadata": {
        "id": "PO7xXdJPeANy"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "torch.save(model, checkpoint_name)"
      ],
      "metadata": {
        "id": "Zl0GZ2G8KQsJ"
      },
      "execution_count": 52,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Evaluating model"
      ],
      "metadata": {
        "id": "_byD4ywQxAwJ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Load the model if needed"
      ],
      "metadata": {
        "id": "8cTySIKq9Yh2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "loaded_model = torch.load(checkpoint_name)\n",
        "loaded_model.eval()\n",
        "loaded_model = loaded_model.to(DEVICE)\n"
      ],
      "metadata": {
        "id": "nC-rq_WcxBGd"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.eval()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AV5WC8779lNW",
        "outputId": "ad534186-42d3-4ef6-c93c-f85dd311f76b"
      },
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Unet(\n",
              "  (encoder): MixVisionTransformerEncoder(\n",
              "    (patch_embed1): OverlapPatchEmbed(\n",
              "      (proj): Conv2d(3, 32, kernel_size=(7, 7), stride=(4, 4), padding=(3, 3))\n",
              "      (norm): LayerNorm((32,), eps=1e-05, elementwise_affine=True)\n",
              "    )\n",
              "    (patch_embed2): OverlapPatchEmbed(\n",
              "      (proj): Conv2d(32, 64, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))\n",
              "      (norm): LayerNorm((64,), eps=1e-05, elementwise_affine=True)\n",
              "    )\n",
              "    (patch_embed3): OverlapPatchEmbed(\n",
              "      (proj): Conv2d(64, 160, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))\n",
              "      (norm): LayerNorm((160,), eps=1e-05, elementwise_affine=True)\n",
              "    )\n",
              "    (patch_embed4): OverlapPatchEmbed(\n",
              "      (proj): Conv2d(160, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))\n",
              "      (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
              "    )\n",
              "    (block1): ModuleList(\n",
              "      (0): Block(\n",
              "        (norm1): LayerNorm((32,), eps=1e-06, elementwise_affine=True)\n",
              "        (attn): Attention(\n",
              "          (q): Linear(in_features=32, out_features=32, bias=True)\n",
              "          (kv): Linear(in_features=32, out_features=64, bias=True)\n",
              "          (attn_drop): Dropout(p=0.0, inplace=False)\n",
              "          (proj): Linear(in_features=32, out_features=32, bias=True)\n",
              "          (proj_drop): Dropout(p=0.0, inplace=False)\n",
              "          (sr): Conv2d(32, 32, kernel_size=(8, 8), stride=(8, 8))\n",
              "          (norm): LayerNorm((32,), eps=1e-05, elementwise_affine=True)\n",
              "        )\n",
              "        (drop_path): Identity()\n",
              "        (norm2): LayerNorm((32,), eps=1e-06, elementwise_affine=True)\n",
              "        (mlp): Mlp(\n",
              "          (fc1): Linear(in_features=32, out_features=128, bias=True)\n",
              "          (dwconv): DWConv(\n",
              "            (dwconv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=128)\n",
              "          )\n",
              "          (act): GELU(approximate='none')\n",
              "          (fc2): Linear(in_features=128, out_features=32, bias=True)\n",
              "          (drop): Dropout(p=0.0, inplace=False)\n",
              "        )\n",
              "      )\n",
              "      (1): Block(\n",
              "        (norm1): LayerNorm((32,), eps=1e-06, elementwise_affine=True)\n",
              "        (attn): Attention(\n",
              "          (q): Linear(in_features=32, out_features=32, bias=True)\n",
              "          (kv): Linear(in_features=32, out_features=64, bias=True)\n",
              "          (attn_drop): Dropout(p=0.0, inplace=False)\n",
              "          (proj): Linear(in_features=32, out_features=32, bias=True)\n",
              "          (proj_drop): Dropout(p=0.0, inplace=False)\n",
              "          (sr): Conv2d(32, 32, kernel_size=(8, 8), stride=(8, 8))\n",
              "          (norm): LayerNorm((32,), eps=1e-05, elementwise_affine=True)\n",
              "        )\n",
              "        (drop_path): DropPath(drop_prob=0.014)\n",
              "        (norm2): LayerNorm((32,), eps=1e-06, elementwise_affine=True)\n",
              "        (mlp): Mlp(\n",
              "          (fc1): Linear(in_features=32, out_features=128, bias=True)\n",
              "          (dwconv): DWConv(\n",
              "            (dwconv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=128)\n",
              "          )\n",
              "          (act): GELU(approximate='none')\n",
              "          (fc2): Linear(in_features=128, out_features=32, bias=True)\n",
              "          (drop): Dropout(p=0.0, inplace=False)\n",
              "        )\n",
              "      )\n",
              "    )\n",
              "    (norm1): LayerNorm((32,), eps=1e-06, elementwise_affine=True)\n",
              "    (block2): ModuleList(\n",
              "      (0): Block(\n",
              "        (norm1): LayerNorm((64,), eps=1e-06, elementwise_affine=True)\n",
              "        (attn): Attention(\n",
              "          (q): Linear(in_features=64, out_features=64, bias=True)\n",
              "          (kv): Linear(in_features=64, out_features=128, bias=True)\n",
              "          (attn_drop): Dropout(p=0.0, inplace=False)\n",
              "          (proj): Linear(in_features=64, out_features=64, bias=True)\n",
              "          (proj_drop): Dropout(p=0.0, inplace=False)\n",
              "          (sr): Conv2d(64, 64, kernel_size=(4, 4), stride=(4, 4))\n",
              "          (norm): LayerNorm((64,), eps=1e-05, elementwise_affine=True)\n",
              "        )\n",
              "        (drop_path): DropPath(drop_prob=0.029)\n",
              "        (norm2): LayerNorm((64,), eps=1e-06, elementwise_affine=True)\n",
              "        (mlp): Mlp(\n",
              "          (fc1): Linear(in_features=64, out_features=256, bias=True)\n",
              "          (dwconv): DWConv(\n",
              "            (dwconv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=256)\n",
              "          )\n",
              "          (act): GELU(approximate='none')\n",
              "          (fc2): Linear(in_features=256, out_features=64, bias=True)\n",
              "          (drop): Dropout(p=0.0, inplace=False)\n",
              "        )\n",
              "      )\n",
              "      (1): Block(\n",
              "        (norm1): LayerNorm((64,), eps=1e-06, elementwise_affine=True)\n",
              "        (attn): Attention(\n",
              "          (q): Linear(in_features=64, out_features=64, bias=True)\n",
              "          (kv): Linear(in_features=64, out_features=128, bias=True)\n",
              "          (attn_drop): Dropout(p=0.0, inplace=False)\n",
              "          (proj): Linear(in_features=64, out_features=64, bias=True)\n",
              "          (proj_drop): Dropout(p=0.0, inplace=False)\n",
              "          (sr): Conv2d(64, 64, kernel_size=(4, 4), stride=(4, 4))\n",
              "          (norm): LayerNorm((64,), eps=1e-05, elementwise_affine=True)\n",
              "        )\n",
              "        (drop_path): DropPath(drop_prob=0.043)\n",
              "        (norm2): LayerNorm((64,), eps=1e-06, elementwise_affine=True)\n",
              "        (mlp): Mlp(\n",
              "          (fc1): Linear(in_features=64, out_features=256, bias=True)\n",
              "          (dwconv): DWConv(\n",
              "            (dwconv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=256)\n",
              "          )\n",
              "          (act): GELU(approximate='none')\n",
              "          (fc2): Linear(in_features=256, out_features=64, bias=True)\n",
              "          (drop): Dropout(p=0.0, inplace=False)\n",
              "        )\n",
              "      )\n",
              "    )\n",
              "    (norm2): LayerNorm((64,), eps=1e-06, elementwise_affine=True)\n",
              "    (block3): ModuleList(\n",
              "      (0): Block(\n",
              "        (norm1): LayerNorm((160,), eps=1e-06, elementwise_affine=True)\n",
              "        (attn): Attention(\n",
              "          (q): Linear(in_features=160, out_features=160, bias=True)\n",
              "          (kv): Linear(in_features=160, out_features=320, bias=True)\n",
              "          (attn_drop): Dropout(p=0.0, inplace=False)\n",
              "          (proj): Linear(in_features=160, out_features=160, bias=True)\n",
              "          (proj_drop): Dropout(p=0.0, inplace=False)\n",
              "          (sr): Conv2d(160, 160, kernel_size=(2, 2), stride=(2, 2))\n",
              "          (norm): LayerNorm((160,), eps=1e-05, elementwise_affine=True)\n",
              "        )\n",
              "        (drop_path): DropPath(drop_prob=0.057)\n",
              "        (norm2): LayerNorm((160,), eps=1e-06, elementwise_affine=True)\n",
              "        (mlp): Mlp(\n",
              "          (fc1): Linear(in_features=160, out_features=640, bias=True)\n",
              "          (dwconv): DWConv(\n",
              "            (dwconv): Conv2d(640, 640, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=640)\n",
              "          )\n",
              "          (act): GELU(approximate='none')\n",
              "          (fc2): Linear(in_features=640, out_features=160, bias=True)\n",
              "          (drop): Dropout(p=0.0, inplace=False)\n",
              "        )\n",
              "      )\n",
              "      (1): Block(\n",
              "        (norm1): LayerNorm((160,), eps=1e-06, elementwise_affine=True)\n",
              "        (attn): Attention(\n",
              "          (q): Linear(in_features=160, out_features=160, bias=True)\n",
              "          (kv): Linear(in_features=160, out_features=320, bias=True)\n",
              "          (attn_drop): Dropout(p=0.0, inplace=False)\n",
              "          (proj): Linear(in_features=160, out_features=160, bias=True)\n",
              "          (proj_drop): Dropout(p=0.0, inplace=False)\n",
              "          (sr): Conv2d(160, 160, kernel_size=(2, 2), stride=(2, 2))\n",
              "          (norm): LayerNorm((160,), eps=1e-05, elementwise_affine=True)\n",
              "        )\n",
              "        (drop_path): DropPath(drop_prob=0.071)\n",
              "        (norm2): LayerNorm((160,), eps=1e-06, elementwise_affine=True)\n",
              "        (mlp): Mlp(\n",
              "          (fc1): Linear(in_features=160, out_features=640, bias=True)\n",
              "          (dwconv): DWConv(\n",
              "            (dwconv): Conv2d(640, 640, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=640)\n",
              "          )\n",
              "          (act): GELU(approximate='none')\n",
              "          (fc2): Linear(in_features=640, out_features=160, bias=True)\n",
              "          (drop): Dropout(p=0.0, inplace=False)\n",
              "        )\n",
              "      )\n",
              "    )\n",
              "    (norm3): LayerNorm((160,), eps=1e-06, elementwise_affine=True)\n",
              "    (block4): ModuleList(\n",
              "      (0): Block(\n",
              "        (norm1): LayerNorm((256,), eps=1e-06, elementwise_affine=True)\n",
              "        (attn): Attention(\n",
              "          (q): Linear(in_features=256, out_features=256, bias=True)\n",
              "          (kv): Linear(in_features=256, out_features=512, bias=True)\n",
              "          (attn_drop): Dropout(p=0.0, inplace=False)\n",
              "          (proj): Linear(in_features=256, out_features=256, bias=True)\n",
              "          (proj_drop): Dropout(p=0.0, inplace=False)\n",
              "        )\n",
              "        (drop_path): DropPath(drop_prob=0.086)\n",
              "        (norm2): LayerNorm((256,), eps=1e-06, elementwise_affine=True)\n",
              "        (mlp): Mlp(\n",
              "          (fc1): Linear(in_features=256, out_features=1024, bias=True)\n",
              "          (dwconv): DWConv(\n",
              "            (dwconv): Conv2d(1024, 1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=1024)\n",
              "          )\n",
              "          (act): GELU(approximate='none')\n",
              "          (fc2): Linear(in_features=1024, out_features=256, bias=True)\n",
              "          (drop): Dropout(p=0.0, inplace=False)\n",
              "        )\n",
              "      )\n",
              "      (1): Block(\n",
              "        (norm1): LayerNorm((256,), eps=1e-06, elementwise_affine=True)\n",
              "        (attn): Attention(\n",
              "          (q): Linear(in_features=256, out_features=256, bias=True)\n",
              "          (kv): Linear(in_features=256, out_features=512, bias=True)\n",
              "          (attn_drop): Dropout(p=0.0, inplace=False)\n",
              "          (proj): Linear(in_features=256, out_features=256, bias=True)\n",
              "          (proj_drop): Dropout(p=0.0, inplace=False)\n",
              "        )\n",
              "        (drop_path): DropPath(drop_prob=0.100)\n",
              "        (norm2): LayerNorm((256,), eps=1e-06, elementwise_affine=True)\n",
              "        (mlp): Mlp(\n",
              "          (fc1): Linear(in_features=256, out_features=1024, bias=True)\n",
              "          (dwconv): DWConv(\n",
              "            (dwconv): Conv2d(1024, 1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=1024)\n",
              "          )\n",
              "          (act): GELU(approximate='none')\n",
              "          (fc2): Linear(in_features=1024, out_features=256, bias=True)\n",
              "          (drop): Dropout(p=0.0, inplace=False)\n",
              "        )\n",
              "      )\n",
              "    )\n",
              "    (norm4): LayerNorm((256,), eps=1e-06, elementwise_affine=True)\n",
              "  )\n",
              "  (decoder): UnetDecoder(\n",
              "    (center): Identity()\n",
              "    (blocks): ModuleList(\n",
              "      (0): DecoderBlock(\n",
              "        (conv1): Conv2dReLU(\n",
              "          (0): Conv2d(416, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "          (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "          (2): ReLU(inplace=True)\n",
              "        )\n",
              "        (attention1): Attention(\n",
              "          (attention): Identity()\n",
              "        )\n",
              "        (conv2): Conv2dReLU(\n",
              "          (0): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "          (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "          (2): ReLU(inplace=True)\n",
              "        )\n",
              "        (attention2): Attention(\n",
              "          (attention): Identity()\n",
              "        )\n",
              "      )\n",
              "      (1): DecoderBlock(\n",
              "        (conv1): Conv2dReLU(\n",
              "          (0): Conv2d(320, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "          (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "          (2): ReLU(inplace=True)\n",
              "        )\n",
              "        (attention1): Attention(\n",
              "          (attention): Identity()\n",
              "        )\n",
              "        (conv2): Conv2dReLU(\n",
              "          (0): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "          (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "          (2): ReLU(inplace=True)\n",
              "        )\n",
              "        (attention2): Attention(\n",
              "          (attention): Identity()\n",
              "        )\n",
              "      )\n",
              "      (2): DecoderBlock(\n",
              "        (conv1): Conv2dReLU(\n",
              "          (0): Conv2d(160, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "          (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "          (2): ReLU(inplace=True)\n",
              "        )\n",
              "        (attention1): Attention(\n",
              "          (attention): Identity()\n",
              "        )\n",
              "        (conv2): Conv2dReLU(\n",
              "          (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "          (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "          (2): ReLU(inplace=True)\n",
              "        )\n",
              "        (attention2): Attention(\n",
              "          (attention): Identity()\n",
              "        )\n",
              "      )\n",
              "      (3): DecoderBlock(\n",
              "        (conv1): Conv2dReLU(\n",
              "          (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "          (1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "          (2): ReLU(inplace=True)\n",
              "        )\n",
              "        (attention1): Attention(\n",
              "          (attention): Identity()\n",
              "        )\n",
              "        (conv2): Conv2dReLU(\n",
              "          (0): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "          (1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "          (2): ReLU(inplace=True)\n",
              "        )\n",
              "        (attention2): Attention(\n",
              "          (attention): Identity()\n",
              "        )\n",
              "      )\n",
              "      (4): DecoderBlock(\n",
              "        (conv1): Conv2dReLU(\n",
              "          (0): Conv2d(32, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "          (1): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "          (2): ReLU(inplace=True)\n",
              "        )\n",
              "        (attention1): Attention(\n",
              "          (attention): Identity()\n",
              "        )\n",
              "        (conv2): Conv2dReLU(\n",
              "          (0): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "          (1): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "          (2): ReLU(inplace=True)\n",
              "        )\n",
              "        (attention2): Attention(\n",
              "          (attention): Identity()\n",
              "        )\n",
              "      )\n",
              "    )\n",
              "  )\n",
              "  (segmentation_head): SegmentationHead(\n",
              "    (0): Conv2d(16, 3, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "    (1): Identity()\n",
              "    (2): Activation(\n",
              "      (activation): Softmax(dim=1)\n",
              "    )\n",
              "  )\n",
              "  (classification_head): None\n",
              ")"
            ]
          },
          "metadata": {},
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The metrics that are used to analyze the performance of the model for this task are fundamental for image segmentation models.\n",
        "\n",
        "1. **Sensitivity (True Positive Rate, Recall):**\n",
        "   - Sensitivity measures the ability of a segmentation model to correctly identify positive instances.\n",
        "   - Formula: Sensitivity = (True Positives) / (True Positives + False Negatives)\n",
        "\n",
        "2. **Specificity:**\n",
        "   - Specificity measures the ability of a segmentation model to correctly identify negative instances.\n",
        "   - Formula: Specificity = (True Negatives) / (True Negatives + False Positives)\n",
        "\n",
        "3. **Pixel Accuracy:**\n",
        "   - Pixel accuracy assesses the overall correctness of pixel-wise predictions.\n",
        "   - Formula: Pixel Accuracy = (Correctly Classified Pixels) / (Total Pixels)\n",
        "\n",
        "4. **Jaccard Score (Intersection over Union, IoU):**\n",
        "   - The Jaccard Score calculates the intersection over the union of the predicted and ground truth segmentation masks.\n",
        "   - Formula: IoU = (Intersection) / (Union)\n",
        "\n",
        "5. **Dice Score (F1 Score):**\n",
        "   - The Dice Score measures the similarity between the predicted and ground truth masks.\n",
        "   - Formula: Dice Score = (2 * True Positives) / (2 * True Positives + False Positives + False Negatives)"
      ],
      "metadata": {
        "id": "n6iYOvPU-4Vq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sensetivity, specificity, accuracy, jaccard, dice = check_metrics(test_dataloader, model, device=DEVICE)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9afenWyHsqK7",
        "outputId": "2b601597-c899-4ff9-f058-10e298a31ffc"
      },
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Sensitivity: 0.986095\n",
            "Specificity: 0.993068\n",
            "Pixel Accuracy: 0.990744\n",
            "Jaccard Score: 0.972709\n",
            "Dice Score: 0.986115\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The graphs below demonstrate the performance of the model for one of the test samples. They provide a visual comparison between the predicted mask and the ground truth one."
      ],
      "metadata": {
        "id": "ezlFoDwX_KLP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plot_img_mask_pred(test_dataset, plot_pred=True, model=model, device = DEVICE)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 571
        },
        "id": "DxIjO6-052T9",
        "outputId": "9d5a3ba8-8b0c-4d59-e4ae-7aca0eef661f"
      },
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x800 with 8 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Angle of Progression Estimation"
      ],
      "metadata": {
        "id": "Ffio8cqR611F"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Random pair image-mask from the test dataset"
      ],
      "metadata": {
        "id": "oGxzbKpyE6Zt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "index = random.randint(0, len(test_dataset) - 1)"
      ],
      "metadata": {
        "id": "yuw0tVRybEuz"
      },
      "execution_count": 130,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "image = test_dataset[index][0].permute(1,2,0)\n",
        "mask = test_dataset[index][1].permute(1,2,0)"
      ],
      "metadata": {
        "id": "GERiPLIRcBHd"
      },
      "execution_count": 131,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Prediction"
      ],
      "metadata": {
        "id": "pVzE5nT2FBhd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "img_to_pred = test_dataset[index][0].unsqueeze(0).type(torch.float32).to(DEVICE)\n",
        "pred = loaded_model(img_to_pred)\n",
        "pred = pred.squeeze(0).cpu().detach().permute(1,2,0)\n",
        "pred[pred < 0.5]=0\n",
        "pred[pred > 0.5]=1"
      ],
      "metadata": {
        "id": "wXLp0207enFf"
      },
      "execution_count": 132,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "To numpy, change the shape"
      ],
      "metadata": {
        "id": "nO8igTfkFEqb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "image = image.numpy()\n",
        "mask = (mask[:,:,1] + mask[:,:,2]*2).numpy()\n",
        "pred = (pred[:,:,1] + pred[:,:,2]*2).numpy()"
      ],
      "metadata": {
        "id": "3iPlV3jtFMy1"
      },
      "execution_count": 133,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Angle of Progression estimation"
      ],
      "metadata": {
        "id": "g-hCxX_4GJdJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "gt_aop, gt_aop_img = angle_of_progression_estimation(mask, return_img=True)\n",
        "pred_aop, pred_aop_img = angle_of_progression_estimation(pred, return_img=True)"
      ],
      "metadata": {
        "id": "neAUpH1RakrL"
      },
      "execution_count": 134,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(8, 8))\n",
        "plt.subplot(2, 2, 1)\n",
        "plt.imshow(mask)\n",
        "plt.title('Ground Truth')\n",
        "\n",
        "\n",
        "plt.subplot(2, 2, 2)\n",
        "plt.imshow(pred)\n",
        "plt.title(\"Predicted Mask\")\n",
        "\n",
        "plt.subplot(2, 2, 3)\n",
        "plt.imshow(gt_aop_img)\n",
        "plt.title(f'Ground Truth AOP: {round(gt_aop, 2)}')\n",
        "\n",
        "plt.subplot(2, 2, 4)\n",
        "plt.imshow(pred_aop_img)\n",
        "plt.title(f'Predicted AOP: {round(pred_aop, 2)}')\n",
        "\n",
        "# Show the plots\n",
        "plt.tight_layout()\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 807
        },
        "id": "z07jS-HiCJLY",
        "outputId": "6bca24cf-54de-4593-e441-c5a0babeadeb"
      },
      "execution_count": 135,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x800 with 4 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}