myUnetSolution_Higher_parameters_(1).ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7H4PZr7ZB5S4"
      },
      "source": [
        "<h1>Data Preparation<h1>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ABrMe_AxDjDy",
        "outputId": "9839bf07-e2a2-475b-d7a1-12dd5cc0c798"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "_BmapbhGB5S8"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import time\n",
        "# import cv2\n",
        "import imageio.v2 as imageio\n",
        "import torch\n",
        "import torchvision\n",
        "from torchvision import utils as vutils\n",
        "from torchvision import transforms\n",
        "from torch import nn\n",
        "from torch.nn import functional as F\n",
        "from torch.utils import data\n",
        "from torch.optim import SGD, Adam\n",
        "from PIL import Image\n",
        "import matplotlib.pyplot as plt\n",
        "import os\n",
        "import zipfile\n",
        "from statistics import mean\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZJGXvVxfq8rm",
        "outputId": "d6b9d1a2-6a0a-4d78-b83e-4b6102470245"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Contents of ../content/drive/MyDrive/archive 2.zip have been extracted to ./training_data\n"
          ]
        }
      ],
      "source": [
        "# Specify the path to the zip file\n",
        "zip_file_path = '../content/drive/MyDrive/archive 2.zip'\n",
        "\n",
        "# Specify the directory where you want to extract the contents\n",
        "extracted_dir = './training_data'\n",
        "\n",
        "# Create the directory if it doesn't exist\n",
        "os.makedirs(extracted_dir, exist_ok=True)\n",
        "\n",
        "# Open the zip file\n",
        "with zipfile.ZipFile(zip_file_path, 'r') as zip_ref:\n",
        "    # Extract all the contents into the specified directory\n",
        "    zip_ref.extractall(extracted_dir)\n",
        "\n",
        "print(f\"Contents of {zip_file_path} have been extracted to {extracted_dir}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dNrWjiYOB5S-"
      },
      "source": [
        "<h2>Global Variables<h2>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XBzBi7DxB5S-",
        "outputId": "310d5605-a3c5-483d-d261-ff57d1a2b91f"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Experiments running on: cpu\n"
          ]
        }
      ],
      "source": [
        "# Device\n",
        "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
        "print('Experiments running on: {}'.format(device))\n",
        "# Hyperparameters\n",
        "random_seed = 1\n",
        "learning_rate = 0.01\n",
        "num_epochs = 50\n",
        "\n",
        "# Architecture\n",
        "\n",
        "root_dir = 'data'\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "DkAwwqDzB5TA"
      },
      "outputs": [],
      "source": [
        "'''\n",
        "Extract number of positive vs negative samples (where negative samples have an all-zero mask)\n",
        "Check for files without corresponding mask and vice versa\n",
        "'''\n",
        "root = './training_data/archive/lgg-mri-segmentation/kaggle_3m'\n",
        "\n",
        "no_mask = 0\n",
        "no_mask_files = []\n",
        "no_file = 0\n",
        "no_files = []\n",
        "num_empty_masks = 0\n",
        "num_nonempty_masks = 0\n",
        "\n",
        "S = 3 # number of pos/neg samples to take\n",
        "empty_mask_samples = []\n",
        "nonempty_mask_samples = []\n",
        "\n",
        "img_dimensions = []\n",
        "msk_dimensions = []\n",
        "\n",
        "n_files = 0\n",
        "for directory in [os.path.join(root,x) for x in os.listdir(root) if os.path.isdir(os.path.join(root, x))]:\n",
        "    for file in os.listdir(directory):\n",
        "        if not file.endswith(('.jpg', '.jpeg', '.png', '.tif', '.tiff')):\n",
        "            continue  # Skip non-image files\n",
        "        n_files += 1\n",
        "        img_dimensions.append(np.array(imageio.imread(os.path.join(directory, file), format='tif')).shape)\n",
        "        #count files with no mask\n",
        "        if 'mask' not in file:\n",
        "            #check if mask exists\n",
        "            mask_path = os.path.join(directory, file[:file.find('.tif')]+'_mask.tif')\n",
        "            if not os.path.exists(mask_path):\n",
        "                no_mask += 1\n",
        "                no_mask_files.append(os.path.join(directory, file))\n",
        "        else:\n",
        "            msk_dimensions.append(np.array(imageio.imread(os.path.join(directory, file), format='tif')).shape)\n",
        "            #count masks with no file\n",
        "            f_path = os.path.join(directory, file[:file.find('mask')-1]+'.tif')\n",
        "            #check if file exists\n",
        "            if not os.path.exists(f_path):\n",
        "                no_file += 1\n",
        "                no_files.append(os.path.join(directory, file))\n",
        "\n",
        "            #check if mask is empty\n",
        "            j = np.max(imageio.imread(os.path.join(directory, file), format='tif'))\n",
        "            if j > 0:\n",
        "                num_nonempty_masks += 1\n",
        "                if len(nonempty_mask_samples) < S:\n",
        "                    nonempty_mask_samples.append(os.path.join(directory, file))\n",
        "            else:\n",
        "                num_empty_masks += 1\n",
        "                if len(empty_mask_samples) < S:\n",
        "                    empty_mask_samples.append(os.path.join(directory, file))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ynTsXCEDMjpY",
        "outputId": "58eecbb2-9cf9-4a21-9ef9-0f269f39a3f7"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "/bin/bash: line 1: nvidia-smi: command not found\n"
          ]
        }
      ],
      "source": [
        "\n",
        "!nvidia-smi"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "J9rdxNNSB5TB",
        "outputId": "63f48ea6-52d8-4de4-b1ad-bc592efbff86"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Image Dimensions\n",
            "2\n",
            "{(256, 256), (256, 256, 3)}\n",
            "Mask Dimensions\n",
            "1\n",
            "{(256, 256)}\n"
          ]
        }
      ],
      "source": [
        "#check variance in dimensions\n",
        "print('Image Dimensions')\n",
        "print(len(set(img_dimensions)))\n",
        "print(set(img_dimensions))\n",
        "print('Mask Dimensions')\n",
        "print(len(set(msk_dimensions)))\n",
        "print(set(msk_dimensions))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 520
        },
        "id": "CH_eS2j_B5TB",
        "outputId": "dc27ff3a-5c63-44e4-e7d4-af5f8c707564"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "#No Mask: 0\n",
            "#Mask w/ No File: 0\n",
            "#Total Files 7858\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "<BarContainer object of 2 artists>"
            ]
          },
          "execution_count": 8,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "print(f'#No Mask: {no_mask}')\n",
        "print(f'#Mask w/ No File: {no_file}')\n",
        "print(f'#Total Files {n_files}')\n",
        "\n",
        "plt.figure()\n",
        "plt.title('Num Samples with Pos/Neg Diagnosis')\n",
        "plt.bar([0,1], [num_empty_masks, num_nonempty_masks], tick_label=['Negative', 'Positive'])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FezO5zZkB5TB"
      },
      "source": [
        "Luckily no files are missing their counterpart either, as some files have a completely negative diagnoses it may be a problem to train our segmentation model on an unbalanced dataset like this. A few approaches can be taken to deal with this:\n",
        "\n",
        "1. We balance our sampling during training time to avoid biasing the segmentation algorithm, this could still be faulty\n",
        "2. We employ a classifier to make a diagnoses (positive/negative) and only segment positive diagnoses\n",
        "\n",
        "In this notebook we will assume the use of the second approach - sometimes practitioners will make this classifier another output layer of the segmenter so that they can share important features, other times we make use of transfer learning and train something like ResNet50 in order to create a robust classifier to support the segmenter. Here we out goal is mainly to increase the quality of the segmentation, so we will just train the segmenter on only positive samples."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "euwUJUOOB5TC"
      },
      "source": [
        "Since we only need the positive files for training we will forget about the negative files"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "aIfs3uXwB5TC",
        "outputId": "ef97d1e9-5694-43ba-915b-e89dfdff3245"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1500x1500 with 12 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "#display images of negative and positive samples - negative samples have an empty mask\n",
        "fig, axs = plt.subplots(6,2, figsize=(15,15))\n",
        "for ax, col in zip(axs[0], ['MRI', 'Mask']):\n",
        "    ax.set_title(col)\n",
        "\n",
        "display_imgs = empty_mask_samples+nonempty_mask_samples #concat lists for easy plotting\n",
        "#print(display_imgs)\n",
        "for i in range(len(display_imgs)):\n",
        "    mask_path = display_imgs[i]\n",
        "    img_path = mask_path[:mask_path.find('mask')-1]+'.tif'\n",
        "    axs[i,0].imshow(np.array(imageio.imread(img_path)))\n",
        "    axs[i,1].imshow(np.array(imageio.imread(mask_path)))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "p9dnuF74B5TD"
      },
      "source": [
        "<h1>Create Torch Dataset<h1>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6ZxQ_dYuB5TD"
      },
      "source": [
        "Classification"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "nZzAfuJqB5TD"
      },
      "outputs": [],
      "source": [
        "class Brain_MRI_Classification_Dataset(data.Dataset):\n",
        "    def __init__(self, inputs, transform=None):\n",
        "        self.inputs = inputs\n",
        "        self.transform = transform\n",
        "        self.input_dtype = torch.float32\n",
        "        self.target_dtype = torch.float32\n",
        "\n",
        "    def __len__(self):\n",
        "        return len(self.inputs)\n",
        "\n",
        "    def __getitem__(self, index):\n",
        "        #for classification return only the image and the binary label\n",
        "        img_path = self.inputs[index][0]\n",
        "        image = imageio.imread(img_path)\n",
        "        x = torch.from_numpy(np.transpose(np.array(image), (2,0,1))).type(self.input_dtype)\n",
        "        y = torch.from_numpy(np.array(self.inputs[index][2])).type(self.target_dtype)\n",
        "\n",
        "        if self.transform is not None:\n",
        "            x = self.transform(x)\n",
        "\n",
        "        return x,y\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4XU3qvI0B5TD"
      },
      "source": [
        "Segementation"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "2NaVi8yuB5TE"
      },
      "outputs": [],
      "source": [
        "class Brain_MRI_Segmentation_Dataset(data.Dataset):\n",
        "    def __init__(self, inputs, transform=None):\n",
        "        self.inputs = inputs\n",
        "        self.transform = transform\n",
        "        self.input_dtype = torch.float32\n",
        "        self.target_dtype = torch.float32\n",
        "\n",
        "    def __len__(self):\n",
        "        return len(self.inputs)\n",
        "\n",
        "    def __getitem__(self, index):\n",
        "        #for classification return only the image and the binary label\n",
        "        img_path = self.inputs[index][0]\n",
        "        mask_path = self.inputs[index][1]\n",
        "        #mask_img = cv2.normalize(cv2.imread(mask_path), None, alpha=0, beta=1, norm_type=cv2.NORM_MINMAX, dtype=cv2.CV_32F)\n",
        "        mask_img = imageio.imread(mask_path)\n",
        "        x = torch.from_numpy(np.transpose(np.array(imageio.imread(img_path)), (2,0,1))).type(self.input_dtype)\n",
        "        y = torch.from_numpy(np.resize(np.array(mask_img)/255., (1,256,256))).type(self.target_dtype)\n",
        "\n",
        "        if self.transform is not None:\n",
        "            x = self.transform(x)\n",
        "            y = self.transform(y)\n",
        "\n",
        "        return x,y"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "cD_0mdZLB5TE"
      },
      "outputs": [],
      "source": [
        "#store list of train files for dataset creation of the form (train_img, mask_img, pos_neg_result(1/0))\n",
        "#these tuples are fed into the datasets we create below\n",
        "file_list = []\n",
        "for directory in [os.path.join(root,x) for x in os.listdir(root) if os.path.isdir(os.path.join(root, x))]:\n",
        "    for file in os.listdir(directory):\n",
        "        #add files to list\n",
        "        if 'mask' not in file:\n",
        "            result = 0\n",
        "            img_path = os.path.join(directory, file)\n",
        "            mask_path = os.path.join(directory, file[:file.find('.tif')]+'_mask.tif')\n",
        "\n",
        "            #check if mask is nonempty\n",
        "            # Check if the mask file exists\n",
        "            if os.path.exists(mask_path) and np.max(imageio.imread(mask_path)) > 0:\n",
        "                result = 1\n",
        "\n",
        "            file_list.append([img_path, mask_path, result])\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QLmCl9lnolQA",
        "outputId": "1958e46a-b0ac-40a0-858d-ae23e30fb910"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[['./training_data/archive/lgg-mri-segmentation/kaggle_3m/TCGA_FG_5964_20010511/TCGA_FG_5964_20010511_13.tif', './training_data/archive/lgg-mri-segmentation/kaggle_3m/TCGA_FG_5964_20010511/TCGA_FG_5964_20010511_13_mask.tif', 1], ['./training_data/archive/lgg-mri-segmentation/kaggle_3m/TCGA_FG_5964_20010511/TCGA_FG_5964_20010511_14.tif', './training_data/archive/lgg-mri-segmentation/kaggle_3m/TCGA_FG_5964_20010511/TCGA_FG_5964_20010511_14_mask.tif', 1], ['./training_data/archive/lgg-mri-segmentation/kaggle_3m/TCGA_FG_5964_20010511/TCGA_FG_5964_20010511_12.tif', './training_data/archive/lgg-mri-segmentation/kaggle_3m/TCGA_FG_5964_20010511/TCGA_FG_5964_20010511_12_mask.tif', 1], ['./training_data/archive/lgg-mri-segmentation/kaggle_3m/TCGA_FG_5964_20010511/TCGA_FG_5964_20010511_17.tif', './training_data/archive/lgg-mri-segmentation/kaggle_3m/TCGA_FG_5964_20010511/TCGA_FG_5964_20010511_17_mask.tif', 1], ['./training_data/archive/lgg-mri-segmentation/kaggle_3m/TCGA_FG_5964_20010511/TCGA_FG_5964_20010511_15.tif', './training_data/archive/lgg-mri-segmentation/kaggle_3m/TCGA_FG_5964_20010511/TCGA_FG_5964_20010511_15_mask.tif', 1]]\n",
            "1373\n"
          ]
        }
      ],
      "source": [
        "#load data\n",
        "positive_diagnoses = [x for x in file_list if x[2] == 1]\n",
        "print(positive_diagnoses[:5])\n",
        "print(len(positive_diagnoses))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "uEAOpnrKB5TE"
      },
      "outputs": [],
      "source": [
        "\n",
        "\n",
        "mri_dataset = Brain_MRI_Segmentation_Dataset(positive_diagnoses)\n",
        "\n",
        "validation_size = int(0.2 * len(mri_dataset))\n",
        "test_size = int(0.1 * len(mri_dataset))\n",
        "\n",
        "train_set, val_set, test_set = data.random_split(mri_dataset, [len(mri_dataset)-(validation_size+test_size), validation_size, test_size])\n",
        "\n",
        "train_loader = data.DataLoader(dataset=train_set, batch_size=2, shuffle=True, num_workers=2)\n",
        "val_loader = data.DataLoader(dataset=val_set, batch_size=2, shuffle=False, num_workers=2)\n",
        "test_loader = data.DataLoader(dataset=test_set, batch_size=2, shuffle=False, num_workers=2)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pW3qoeCdB5TE"
      },
      "source": [
        "<h3>Losses<h3>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cBCFIYj1B5TF"
      },
      "source": [
        "Dice Loss"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "fMYn2z3gB5TF"
      },
      "outputs": [],
      "source": [
        "#Dice Loss Function - taken from: https://github.com/kevinzakka/pytorch-goodies/blob/master/losses.py\n",
        "def dice_loss(logits, true, eps=1e-7):\n",
        "    \"\"\"Computes the Sørensen–Dice loss.\n",
        "    Note that PyTorch optimizers minimize a loss. In this\n",
        "    case, we would like to maximize the dice loss so we\n",
        "    return the negated dice loss.\n",
        "    Args:\n",
        "        true: a tensor of shape [B, 1, H, W].\n",
        "        logits: a tensor of shape [B, C, H, W]. Corresponds to\n",
        "            the raw output or logits of the model.\n",
        "        eps: added to the denominator for numerical stability.\n",
        "    Returns:\n",
        "        dice_loss: the Sørensen–Dice loss.\n",
        "    \"\"\"\n",
        "    num_classes = logits.shape[1]\n",
        "    if num_classes == 1:\n",
        "        true_1_hot = torch.eye(num_classes + 1, device=device)[true.long().squeeze(1)]\n",
        "        true_1_hot = true_1_hot.permute(0, 3, 1, 2).float()\n",
        "        true_1_hot_f = true_1_hot[:, 0:1, :, :]\n",
        "        true_1_hot_s = true_1_hot[:, 1:2, :, :]\n",
        "        true_1_hot = torch.cat([true_1_hot_s, true_1_hot_f], dim=1)\n",
        "        pos_prob = torch.sigmoid(logits)\n",
        "        neg_prob = 1 - pos_prob\n",
        "        probas = torch.cat([pos_prob, neg_prob], dim=1)\n",
        "    else:\n",
        "        true_1_hot = torch.eye(num_classes)[true.long().squeeze(1)]\n",
        "        true_1_hot = true_1_hot.permute(0, 3, 1, 2).float()\n",
        "        probas = F.softmax(logits, dim=1)\n",
        "    true_1_hot = true_1_hot.type(logits.type())\n",
        "    dims = (0,) + tuple(range(2, true.ndimension()))\n",
        "    intersection = torch.sum(probas * true_1_hot, dims)\n",
        "    cardinality = torch.sum(probas + true_1_hot, dims)\n",
        "    dice_loss = (2. * intersection / (cardinality + eps)).mean()\n",
        "    return (1 - dice_loss)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "MtR0HdDKB5TF"
      },
      "outputs": [],
      "source": [
        "#combined cross entropy and dice loss\n",
        "def bce_dice_loss(output, target):\n",
        "    bce = nn.BCEWithLogitsLoss()\n",
        "    return bce(output, target) + dice_loss(output, target)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "B5gHBOM1B5TF"
      },
      "outputs": [],
      "source": [
        "#Cross entropy loss used in class\n",
        "def Cross_loss(output, target):\n",
        "    loss_func = torch.nn.CrossEntropyLoss()\n",
        "    return loss_func(output, target)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rQw88JHmB5TG"
      },
      "source": [
        "**IoU Metric**\n",
        "Obtained from: https://www.kaggle.com/iezepov/fast-iou-scoring-metric-in-pytorch-and-numpy"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "m1GMelgEB5TG"
      },
      "outputs": [],
      "source": [
        "def iou_pytorch(outputs: torch.Tensor, labels: torch.Tensor, threshold=0.5):\n",
        "    # You can comment out this line if you are passing tensors of equal shape\n",
        "    # But if you are passing output from UNet or something it will most probably\n",
        "    # be with the BATCH x 1 x H x W shape\n",
        "    SMOOTH = 1e-6\n",
        "    # outputs = outputs.squeeze(1)  # BATCH x 1 x H x W => BATCH x H x W\n",
        "    # labels = labels.squeeze(1)\n",
        "\n",
        "    bin_out = torch.where(outputs > threshold, 1, 0).type(torch.int16)\n",
        "    labels = labels.type(torch.int16)\n",
        "\n",
        "    intersection = (bin_out & labels).float().sum((1, 2))  # Will be zero if Truth=0 or Prediction=0\n",
        "    union = (bin_out | labels).float().sum((1, 2))         # Will be zero if both are 0\n",
        "\n",
        "    iou = (intersection + SMOOTH) / (union + SMOOTH)  # We smooth our division to avoid 0/0\n",
        "\n",
        "    thresholded = torch.clamp(20 * (iou - 0.5), 0, 10).ceil() / 10  # This is equal to comparing with thresolds\n",
        "\n",
        "    return thresholded.mean()  # Or thresholded.mean() if you are interested in average across the batch\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "V4IHXBv7B5TG"
      },
      "source": [
        "<h1>Models<h1>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8abnmGrzB5TG"
      },
      "source": [
        "Segnet"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "7Kvz6Rp0B5TG"
      },
      "outputs": [],
      "source": [
        "\n",
        "class ConvBlock(nn.Module):\n",
        "    def __init__(self, in_channels, out_channels):\n",
        "        super(ConvBlock, self).__init__()\n",
        "        self.block = nn.Sequential(\n",
        "            nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1, padding=1, bias=False),\n",
        "            nn.BatchNorm2d(out_channels),\n",
        "            nn.ReLU(inplace=True),\n",
        "            nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=1, padding=1, bias=False),\n",
        "            nn.BatchNorm2d(out_channels),\n",
        "            nn.ReLU(inplace=True)\n",
        "        )\n",
        "\n",
        "    def forward(self, x):\n",
        "        return self.block(x)\n",
        "\n",
        "class Encoder(nn.Module):\n",
        "    def __init__(self, in_channels, out_channels):\n",
        "        super(Encoder, self).__init__()\n",
        "        self.sequence = nn.Sequential(\n",
        "            ConvBlock(in_channels, out_channels),\n",
        "            nn.MaxPool2d(kernel_size=2, stride=2),\n",
        "        )\n",
        "\n",
        "    def forward(self, x):\n",
        "        return self.sequence(x)\n",
        "\n",
        "class Decoder(nn.Module):\n",
        "    def __init__(self, in_channels, skip_channels, out_channels):\n",
        "        super(Decoder, self).__init__()\n",
        "        combined_channels = in_channels + skip_channels\n",
        "        self.sequence = nn.Sequential(\n",
        "            nn.Conv2d(combined_channels, out_channels, kernel_size=3, padding=1),\n",
        "            nn.BatchNorm2d(out_channels),\n",
        "            nn.ReLU(inplace=True),\n",
        "            nn.Conv2d(out_channels, out_channels, kernel_size=3, padding=1),\n",
        "            nn.BatchNorm2d(out_channels),\n",
        "            nn.ReLU(inplace=True),\n",
        "            nn.ConvTranspose2d(out_channels, out_channels, kernel_size=2, stride=2),\n",
        "        )\n",
        "\n",
        "    def forward(self, x):\n",
        "        return self.sequence(x)\n",
        "\n",
        "class Segnet(nn.Module):\n",
        "    def __init__(self, in_channels, out_channels):\n",
        "        super(Segnet, self).__init__()\n",
        "\n",
        "        # Reduced Encoder Layers\n",
        "        self.encoder1 = Encoder(in_channels, 64)\n",
        "        self.encoder2 = Encoder(64, 128)\n",
        "        self.encoder3 = Encoder(128, 256)\n",
        "\n",
        "        # Reduced Decoder Layers\n",
        "        self.decoder1 = Decoder(256, 0, 128)\n",
        "        self.decoder2 = Decoder(128, 128, 64)\n",
        "        self.decoder3 = Decoder(64, 64, out_channels)   # Changed the final decoder layer\n",
        "\n",
        "        # Final Output Convolution\n",
        "        self.output_conv = nn.Conv2d(out_channels, out_channels, kernel_size=1)\n",
        "\n",
        "    def forward(self, x):\n",
        "        # Encoding\n",
        "        e1 = self.encoder1(x)\n",
        "        e2 = self.encoder2(e1)\n",
        "        e3 = self.encoder3(e2)\n",
        "\n",
        "        # Decoding\n",
        "        d1 = self.decoder1(e3)\n",
        "        d2 = self.decoder2(torch.cat([d1, e2], dim=1))\n",
        "        d3 = self.decoder3(torch.cat([d2, e1], dim=1))\n",
        "\n",
        "        # Output\n",
        "        logits = self.output_conv(d3)\n",
        "\n",
        "        return logits\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "uX9rQ3CbB5TH"
      },
      "outputs": [],
      "source": [
        "modelSegnet = Segnet(in_channels=3, out_channels=1)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YSIDStCAB5TH",
        "outputId": "3bc6d145-359d-4ff5-be64-2c4d33deb658"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Segnet(\n",
              "  (encoder1): Encoder(\n",
              "    (sequence): Sequential(\n",
              "      (0): ConvBlock(\n",
              "        (block): Sequential(\n",
              "          (0): Conv2d(3, 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",
              "          (3): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "          (4): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "          (5): ReLU(inplace=True)\n",
              "        )\n",
              "      )\n",
              "      (1): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
              "    )\n",
              "  )\n",
              "  (encoder2): Encoder(\n",
              "    (sequence): Sequential(\n",
              "      (0): ConvBlock(\n",
              "        (block): Sequential(\n",
              "          (0): Conv2d(64, 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",
              "          (3): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "          (4): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "          (5): ReLU(inplace=True)\n",
              "        )\n",
              "      )\n",
              "      (1): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
              "    )\n",
              "  )\n",
              "  (encoder3): Encoder(\n",
              "    (sequence): Sequential(\n",
              "      (0): ConvBlock(\n",
              "        (block): Sequential(\n",
              "          (0): Conv2d(128, 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",
              "          (3): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "          (4): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "          (5): ReLU(inplace=True)\n",
              "        )\n",
              "      )\n",
              "      (1): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
              "    )\n",
              "  )\n",
              "  (decoder1): Decoder(\n",
              "    (sequence): Sequential(\n",
              "      (0): Conv2d(256, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "      (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (2): ReLU(inplace=True)\n",
              "      (3): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "      (4): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (5): ReLU(inplace=True)\n",
              "      (6): ConvTranspose2d(128, 128, kernel_size=(2, 2), stride=(2, 2))\n",
              "    )\n",
              "  )\n",
              "  (decoder2): Decoder(\n",
              "    (sequence): Sequential(\n",
              "      (0): Conv2d(256, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "      (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (2): ReLU(inplace=True)\n",
              "      (3): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "      (4): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (5): ReLU(inplace=True)\n",
              "      (6): ConvTranspose2d(64, 64, kernel_size=(2, 2), stride=(2, 2))\n",
              "    )\n",
              "  )\n",
              "  (decoder3): Decoder(\n",
              "    (sequence): Sequential(\n",
              "      (0): Conv2d(128, 1, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "      (1): BatchNorm2d(1, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (2): ReLU(inplace=True)\n",
              "      (3): Conv2d(1, 1, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "      (4): BatchNorm2d(1, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (5): ReLU(inplace=True)\n",
              "      (6): ConvTranspose2d(1, 1, kernel_size=(2, 2), stride=(2, 2))\n",
              "    )\n",
              "  )\n",
              "  (output_conv): Conv2d(1, 1, kernel_size=(1, 1), stride=(1, 1))\n",
              ")"
            ]
          },
          "execution_count": 21,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "torch.cuda.empty_cache()\n",
        "modelSegnet.to(device)\n",
        "# print(modelSegnet)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "i7AoLiRmB5TH",
        "outputId": "8b05a1a6-c30d-4b3c-eb5c-3e6cf76d64d5"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "1857430\n"
          ]
        }
      ],
      "source": [
        "num_params = sum(p.numel() for p in modelSegnet.parameters() if p.requires_grad)\n",
        "print(num_params)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_zO62WEPSbQj"
      },
      "source": [
        "<h3>DeepLabV2<h3>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "DZ8QGhC2Saot"
      },
      "outputs": [],
      "source": [
        "class ASPP(nn.Module):\n",
        "    def __init__(self, in_channels, out_channels):\n",
        "        super(ASPP, self).__init__()\n",
        "        self.aspp1 = nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=1, padding=0, dilation=1, bias=False)\n",
        "        self.aspp2 = nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1, padding=6, dilation=6, bias=False)\n",
        "        self.aspp3 = nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1, padding=12, dilation=12, bias=False)\n",
        "        self.aspp4 = nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1, padding=18, dilation=18, bias=False)\n",
        "        self.global_avg_pool = nn.Sequential(nn.AdaptiveAvgPool2d((1,1)),\n",
        "                                             nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=1, bias=False),\n",
        "                                             nn.Upsample(size=(in_channels, in_channels), mode='bilinear', align_corners=True))\n",
        "\n",
        "    def forward(self, x):\n",
        "        x1 = self.aspp1(x)\n",
        "        # print(f\"x1 shape: {x1.shape}\")\n",
        "        x2 = self.aspp2(x)\n",
        "        # print(f\"x2 shape: {x2.shape}\")\n",
        "        x3 = self.aspp3(x)\n",
        "        # print(f\"x3 shape: {x3.shape}\")\n",
        "        x4 = self.aspp4(x)\n",
        "        # print(f\"x4 shape: {x4.shape}\")\n",
        "        x5 = self.global_avg_pool(x)\n",
        "        # print(f\"x5 shape: {x5.shape}\")\n",
        "\n",
        "        # Upsample x5 to match the size of x1, x2, x3, and x4\n",
        "        target_size = x1.size()[2:]  # Extract the height and width from x1\n",
        "        x5 = F.interpolate(x5, size=target_size, mode='bilinear', align_corners=True)\n",
        "\n",
        "        return torch.cat([x1, x2, x3, x4, x5], dim=1)\n",
        "\n",
        "\n",
        "class ConvBlock(nn.Module):\n",
        "    def __init__(self, in_channels, out_channels):\n",
        "        super(ConvBlock, self).__init__()\n",
        "        self.block = nn.Sequential(\n",
        "            nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1, padding=1, bias=False),\n",
        "            nn.ReLU(inplace=True),\n",
        "            nn.MaxPool2d(kernel_size=2, stride=2),\n",
        "            nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=1, padding=1, bias=False), # corrected here\n",
        "            nn.ReLU(inplace=True),\n",
        "            nn.MaxPool2d(kernel_size=2, stride=2),\n",
        "            nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=1, padding=1, bias=False),\n",
        "            nn.ReLU(inplace=True)\n",
        "        )\n",
        "    def forward(self, x):\n",
        "        return self.block(x)\n",
        "\n",
        "\n",
        "\n",
        "class Encoder(nn.Module):\n",
        "    def __init__(self, in_channels, mid_channels, out_channels):\n",
        "        super(Encoder, self).__init__()\n",
        "        self.sequence = nn.Sequential(\n",
        "            ConvBlock(in_channels, mid_channels),\n",
        "            ASPP(mid_channels, mid_channels),\n",
        "            nn.Conv2d(mid_channels * 5, out_channels, kernel_size=1, stride=1, padding=0, bias=False),\n",
        "            nn.Upsample(scale_factor=2, mode='bilinear', align_corners=False),\n",
        "        )\n",
        "\n",
        "    def forward(self, x):\n",
        "        return self.sequence(x)\n",
        "\n",
        "\n",
        "class Decoder(nn.Module):\n",
        "    def __init__(self, in_channels, mid_channels, out_channels, output_size=(256, 256)):\n",
        "        super(Decoder, self).__init__()\n",
        "        self.output_size = output_size\n",
        "        self.aspp = ASPP(in_channels, mid_channels)\n",
        "        self.conv1x1 = nn.Conv2d(mid_channels * 5, out_channels, kernel_size=1, stride=1, padding=0, bias=False)\n",
        "\n",
        "        self.encoder = Encoder(in_channels, mid_channels, out_channels)\n",
        "        self.upsample = nn.Upsample(size=self.output_size, mode='bilinear', align_corners=False)\n",
        "\n",
        "        self.final_conv = nn.Conv2d(out_channels * 2, out_channels, kernel_size=3, stride=1, padding=1, bias=False)\n",
        "\n",
        "    def forward(self, x):\n",
        "        x_aspp = self.aspp(x)\n",
        "        x1 = self.conv1x1(x_aspp)\n",
        "        x1 = self.upsample(x1)\n",
        "\n",
        "        x2 = self.encoder(x)\n",
        "        x2 = self.upsample(x2)\n",
        "\n",
        "        # Check and adjust the sizes if they do not match\n",
        "        # if x1.size()[2:] != x2.size()[2:]:\n",
        "            # Adjust x1 or x2 using additional upsampling or downsampling\n",
        "            # For example:\n",
        "            # x1 = F.interpolate(x1, size=x2.size()[2:], mode='bilinear', align_corners=False)\n",
        "\n",
        "        x_concat = torch.cat([x1, x2], dim=1)\n",
        "        x_final = self.final_conv(x_concat)\n",
        "        return x_final\n",
        "\n",
        "\n",
        "class Deeplab(nn.Module):\n",
        "    def __init__(self, in_channels, mid_channels, out_channels):\n",
        "        super(Deeplab, self).__init__()\n",
        "\n",
        "        # Make sure that the channel sizes are consistent\n",
        "        self.encoder = Encoder(in_channels, mid_channels, mid_channels)\n",
        "\n",
        "        # The decoder receives the output from the encoder\n",
        "        self.decoder = Decoder(mid_channels, mid_channels, out_channels)\n",
        "\n",
        "    def forward(self, x):\n",
        "        # Encoder and decoder operations\n",
        "        x = self.encoder(x)\n",
        "        x = self.decoder(x)\n",
        "\n",
        "        return x\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "wdiDyCzhS0Hz"
      },
      "outputs": [],
      "source": [
        "modelDeeplab = Deeplab(3,128, 1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sQaghqoPS9pU",
        "outputId": "a2fec810-dedf-4889-c29d-e5a61ea554a5"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Deeplab(\n",
              "  (encoder): Encoder(\n",
              "    (sequence): Sequential(\n",
              "      (0): ConvBlock(\n",
              "        (block): Sequential(\n",
              "          (0): Conv2d(3, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "          (1): ReLU(inplace=True)\n",
              "          (2): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
              "          (3): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "          (4): ReLU(inplace=True)\n",
              "          (5): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
              "          (6): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "          (7): ReLU(inplace=True)\n",
              "        )\n",
              "      )\n",
              "      (1): ASPP(\n",
              "        (aspp1): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (aspp2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(6, 6), dilation=(6, 6), bias=False)\n",
              "        (aspp3): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(12, 12), dilation=(12, 12), bias=False)\n",
              "        (aspp4): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(18, 18), dilation=(18, 18), bias=False)\n",
              "        (global_avg_pool): Sequential(\n",
              "          (0): AdaptiveAvgPool2d(output_size=(1, 1))\n",
              "          (1): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (2): Upsample(size=(128, 128), mode='bilinear')\n",
              "        )\n",
              "      )\n",
              "      (2): Conv2d(640, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (3): Upsample(scale_factor=2.0, mode='bilinear')\n",
              "    )\n",
              "  )\n",
              "  (decoder): Decoder(\n",
              "    (aspp): ASPP(\n",
              "      (aspp1): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (aspp2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(6, 6), dilation=(6, 6), bias=False)\n",
              "      (aspp3): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(12, 12), dilation=(12, 12), bias=False)\n",
              "      (aspp4): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(18, 18), dilation=(18, 18), bias=False)\n",
              "      (global_avg_pool): Sequential(\n",
              "        (0): AdaptiveAvgPool2d(output_size=(1, 1))\n",
              "        (1): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (2): Upsample(size=(128, 128), mode='bilinear')\n",
              "      )\n",
              "    )\n",
              "    (conv1x1): Conv2d(640, 1, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "    (encoder): Encoder(\n",
              "      (sequence): Sequential(\n",
              "        (0): ConvBlock(\n",
              "          (block): Sequential(\n",
              "            (0): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "            (1): ReLU(inplace=True)\n",
              "            (2): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
              "            (3): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "            (4): ReLU(inplace=True)\n",
              "            (5): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
              "            (6): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "            (7): ReLU(inplace=True)\n",
              "          )\n",
              "        )\n",
              "        (1): ASPP(\n",
              "          (aspp1): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (aspp2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(6, 6), dilation=(6, 6), bias=False)\n",
              "          (aspp3): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(12, 12), dilation=(12, 12), bias=False)\n",
              "          (aspp4): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(18, 18), dilation=(18, 18), bias=False)\n",
              "          (global_avg_pool): Sequential(\n",
              "            (0): AdaptiveAvgPool2d(output_size=(1, 1))\n",
              "            (1): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "            (2): Upsample(size=(128, 128), mode='bilinear')\n",
              "          )\n",
              "        )\n",
              "        (2): Conv2d(640, 1, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (3): Upsample(scale_factor=2.0, mode='bilinear')\n",
              "      )\n",
              "    )\n",
              "    (upsample): Upsample(size=(256, 256), mode='bilinear')\n",
              "    (final_conv): Conv2d(2, 1, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "  )\n",
              ")"
            ]
          },
          "execution_count": 25,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "modelDeeplab.to(device)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "apBEc77g7b1o",
        "outputId": "a496609a-63bb-488e-dece-c5a508b89bb1"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "2249362\n"
          ]
        }
      ],
      "source": [
        "num_params = sum(p.numel() for p in modelDeeplab.parameters() if p.requires_grad)\n",
        "print(num_params)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "337vIB4qolQT"
      },
      "source": [
        "<h3>Unet<h3>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "xV56n7jjolQU"
      },
      "outputs": [],
      "source": [
        "class ConvBlock(nn.Module):\n",
        "    def __init__(self, in_channels, out_channels):\n",
        "        super(ConvBlock, self).__init__()\n",
        "        self.block = nn.Sequential(\n",
        "            nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1, padding=1, bias=False),\n",
        "            nn.BatchNorm2d(out_channels),\n",
        "            nn.ReLU(inplace=True),\n",
        "            nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=1, padding=1, bias=False),\n",
        "            nn.BatchNorm2d(out_channels),\n",
        "            nn.ReLU(inplace=True)\n",
        "        )\n",
        "\n",
        "    def forward(self, x):\n",
        "        return self.block(x)\n",
        "\n",
        "class DownConv(nn.Module):\n",
        "    def __init__(self, in_channels, out_channels):\n",
        "        super(DownConv, self).__init__()\n",
        "        self.sequence = nn.Sequential(\n",
        "            ConvBlock(in_channels, out_channels),\n",
        "            nn.MaxPool2d(kernel_size=2, stride=2)\n",
        "        )\n",
        "\n",
        "    def forward(self, x):\n",
        "        return self.sequence(x)\n",
        "\n",
        "class UpConv(nn.Module):\n",
        "    def __init__(self, in_channels, out_channels):\n",
        "        super(UpConv, self).__init__()\n",
        "        self.sequence = nn.Sequential(\n",
        "            nn.ConvTranspose2d(in_channels, in_channels, kernel_size=2, stride=2),\n",
        "            ConvBlock(in_channels, out_channels)\n",
        "        )\n",
        "\n",
        "    def forward(self, x):\n",
        "        return self.sequence(x)\n",
        "\n",
        "class UNet(nn.Module):\n",
        "    def __init__(self, in_channels=3, out_channels=1):\n",
        "        super(UNet, self).__init__()\n",
        "        #input_dim = 256\n",
        "        self.encoder = nn.ModuleList([\n",
        "            DownConv(in_channels, 64), #128\n",
        "            DownConv(64, 128), #64\n",
        "            DownConv(128, 256), #32\n",
        "            DownConv(256, 512) #16\n",
        "        ])\n",
        "\n",
        "        self.bottleneck = ConvBlock(512, 1024)\n",
        "\n",
        "        #extra channels allow for concatenation of skip connections in upsampling block\n",
        "        self.decoder = nn.ModuleList([\n",
        "            UpConv(512+1024,512), #32\n",
        "            UpConv(256+512,256), #64\n",
        "            UpConv(128+256,128), #128\n",
        "            UpConv(64+128,64) #256\n",
        "        ])\n",
        "\n",
        "        self.output_conv = nn.Conv2d(64, out_channels, kernel_size=1)\n",
        "\n",
        "    def forward(self, x):\n",
        "        skips = []\n",
        "        o = x\n",
        "        for layer in self.encoder:\n",
        "            o = layer(o)\n",
        "            skips.append(o)\n",
        "\n",
        "        o = self.bottleneck(o)\n",
        "\n",
        "        for i, layer in enumerate(self.decoder):\n",
        "            #print(o.size())\n",
        "            o = torch.cat((skips[len(skips)-i-1],o), dim=1)\n",
        "            #print(o.size())\n",
        "            o = layer(o)\n",
        "\n",
        "        return self.output_conv(o)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Im9o0MJNolQV"
      },
      "outputs": [],
      "source": [
        "modelUnet = UNet()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3XwL7Vv_olQW",
        "outputId": "260c2c7e-fb21-4632-85ea-7b56c01fd9df"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "43921473\n"
          ]
        }
      ],
      "source": [
        "num_params = sum(p.numel() for p in modelUnet.parameters() if p.requires_grad)\n",
        "print(num_params)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BLlXbNLDB5TH"
      },
      "source": [
        "<h2>Optimizer<h2>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "TayYGmzEB5TI"
      },
      "outputs": [],
      "source": [
        "# optimizer_SGD_Segnet = torch.optim.SGD(modelSegnet.parameters(), lr=learning_rate)\n",
        "# optimizer_Adam_Segnet = torch.optim.Adam(modelSegnet.parameters(), lr=learning_rate)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_u1SRDGoB5TI"
      },
      "source": [
        "<h1>Train/Validation Loop<h1>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ctnDjHVcB5TI"
      },
      "outputs": [],
      "source": [
        "\n",
        "from torch.cuda.amp import GradScaler, autocast\n",
        "\n",
        "def run_experiment(model_name, model, num_epochs, optimizer, loss_fn, train_loader, val_loader, device='cuda', clear_mem=True, accumulation_steps=1):\n",
        "    # Initialize the GradScaler\n",
        "    scaler = GradScaler()\n",
        "\n",
        "    train_losses = []\n",
        "    val_losses = []\n",
        "    val_scores = []\n",
        "    train_scores = []\n",
        "    train_acc_lst, valid_acc_lst = [], []\n",
        "    train_loss_lst, valid_loss_lst = [], []\n",
        "\n",
        "    best_acc = 0\n",
        "    start_time = time.time()\n",
        "\n",
        "    model.to(device)\n",
        "    print('Model sent to ' + str(device))\n",
        "\n",
        "    for epoch in range(num_epochs):\n",
        "        #######################\n",
        "        # Train model         #\n",
        "        #######################\n",
        "        model.train()\n",
        "        for i, (features, targets) in enumerate(train_loader):\n",
        "            img = features.to(device)\n",
        "            msk = targets.to(device)\n",
        "\n",
        "\n",
        "\n",
        "            # Resets the gradients to zero\n",
        "            optimizer.zero_grad()\n",
        "\n",
        "            # Forward pass with autocasting\n",
        "            # with autocast():\n",
        "            logits = model(img)\n",
        "\n",
        "            loss = loss_fn(logits, msk)\n",
        "\n",
        "            # Scales loss, calls backward() to create scaled gradients\n",
        "            loss.backward()\n",
        "\n",
        "            # Unscales the gradients and calls or skips optimizer.step()\n",
        "            optimizer.step()\n",
        "\n",
        "            # Updates the scale for next iteration\n",
        "            # scaler.update()\n",
        "\n",
        "            iou_score = iou_pytorch(logits.detach(), msk)\n",
        "\n",
        "            train_losses.append(loss.item())\n",
        "            train_scores.append(iou_score)\n",
        "\n",
        "            if i % 50 == 0:\n",
        "                print(f'Epoch: {epoch+1:05d}/{num_epochs:05d} | Batch {i:05d}/{len(train_loader):05d} | Loss: {loss.item():.4f} | IoU: {iou_score:.4f}')\n",
        "\n",
        "        #######################\n",
        "        # Validate model      #\n",
        "        #######################\n",
        "        model.eval()\n",
        "        with torch.no_grad():\n",
        "            for i, (features, targets) in enumerate(val_loader):\n",
        "                img = features.to(device)\n",
        "                msk = targets.to(device)\n",
        "\n",
        "                # Forward pass without autocasting\n",
        "                logits = model(img)\n",
        "                loss = loss_fn(logits, msk)\n",
        "\n",
        "                iou_score = iou_pytorch(logits.detach(), msk)\n",
        "\n",
        "                val_losses.append(loss.item())\n",
        "                val_scores.append(iou_score)\n",
        "\n",
        "                if i % 50 == 0:\n",
        "                    print(f'Validation | Epoch: {epoch+1:05d}/{num_epochs:05d} | Batch {i:05d}/{len(val_loader):05d} | Loss: {loss.item():.4f} |IoU: {iou_score:.4f}')\n",
        "\n",
        "\n",
        "\n",
        "    results = {\n",
        "        'model_name': model_name,\n",
        "        'train_losses': train_losses,\n",
        "        'train_scores': train_scores,\n",
        "        'val_losses': val_losses,\n",
        "        'val_scores': val_scores\n",
        "    }\n",
        "\n",
        "    if clear_mem:\n",
        "        del model\n",
        "        del optimizer\n",
        "        del loss_fn\n",
        "        torch.cuda.empty_cache()\n",
        "\n",
        "    return results\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "7tGENHBtN4zS"
      },
      "outputs": [],
      "source": [
        "def test_model(model, test_loader, device, loss_fn):\n",
        "    model.eval()  # Set the model to evaluation mode\n",
        "    test_losses = []\n",
        "    test_scores = []\n",
        "\n",
        "    with torch.no_grad():  # Disable gradient calculations\n",
        "        for i, (features, targets) in enumerate(test_loader):\n",
        "            img, msk = features.to(device), targets.to(device)\n",
        "            logits = model(img)\n",
        "            loss = loss_fn(logits, msk)\n",
        "            test_losses.append(loss.item())\n",
        "            iou_score = iou_pytorch(logits.detach(), msk)\n",
        "            test_scores.append(iou_score)\n",
        "\n",
        "            if i % 50 == 0:\n",
        "                print(f'Test | Batch {i:05d}/{len(test_loader):05d} | Loss: {loss.item():.4f} |IoU: {iou_score:.4f}')\n",
        "\n",
        "    return test_losses, test_scores"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "YLMREbIEB5TJ"
      },
      "outputs": [],
      "source": [
        "def plot_training_scores(losses, train_scores):\n",
        "\n",
        "    # Convert list to PyTorch tensor or NumPy array\n",
        "    train_scores_tensor = torch.tensor(train_scores)\n",
        "\n",
        "    # Convert tensors to CPU if they are on GPU\n",
        "    if train_scores_tensor.is_cuda:\n",
        "        train_scores_tensor = train_scores_tensor.cpu()\n",
        "\n",
        "    # Plot loss and IoU\n",
        "    fig, axs = plt.subplots(1, 2, figsize=(10, 5))\n",
        "    axs[0].set_title('Train BCE Loss')\n",
        "    axs[0].plot(range(len(losses)), losses)\n",
        "\n",
        "    axs[1].set_title('IoU Score vs Training Step')\n",
        "    axs[1].plot(range(len(train_scores_tensor)), train_scores_tensor.numpy())  # Convert to numpy array here\n",
        "\n",
        "    print(f'MEAN TRAIN IOU: {torch.mean(train_scores_tensor)}')\n",
        "\n",
        "\n",
        "def plot_validation_scores(val_losses, val_scores):\n",
        "    #plot loss and IoU scores - we use a histogram because we care about the distribution of losses, not its progression\n",
        "    fig, axs = plt.subplots(1,2, figsize=(7,7))\n",
        "    axs[0].set_title('BCE Loss on Validation Set')\n",
        "    axs[0].hist(val_losses)\n",
        "\n",
        "    temp = [t.cpu().item() for t in val_scores]\n",
        "    axs[1].set_title('IoU Scores on Validation Set')\n",
        "    axs[1].hist(temp)\n",
        "    axs[1].axvline(np.median(np.array(temp)), color='k', linestyle='dashed', linewidth=1)\n",
        "    print(f'MEAN VAL IOU: {mean(temp)}')\n",
        "\n",
        "\n",
        "def plot_test_scores(test_losses, test_scores):\n",
        "    # Convert list to PyTorch tensor or NumPy array\n",
        "    test_scores_tensor = torch.tensor(test_scores)\n",
        "\n",
        "    # Convert tensors to CPU if they are on GPU\n",
        "    if test_scores_tensor.is_cuda:\n",
        "        test_scores_tensor = test_scores_tensor.cpu()\n",
        "\n",
        "    # Plot loss and IoU\n",
        "    fig, axs = plt.subplots(1, 2, figsize=(10, 5))\n",
        "    axs[0].set_title('Test BCE Loss')\n",
        "    axs[0].plot(range(len(test_losses)), test_losses)\n",
        "\n",
        "    axs[1].set_title('IoU Score vs Test Steps')\n",
        "    axs[1].plot(range(len(test_scores_tensor)), test_scores_tensor.numpy())  # Convert to numpy array here\n",
        "\n",
        "    print(f'MEAN TEST IOU: {torch.mean(test_scores_tensor)}')\n",
        "\n",
        "\n",
        "def visualize_segmentation(model, data_loader, num_samples=5, device='cuda'):\n",
        "    #visualize segmentation on unseen samples\n",
        "    fig, axs = plt.subplots(num_samples, 3, figsize=(60,60))\n",
        "\n",
        "    for ax, col in zip(axs[0], ['MRI', 'Ground Truth', 'Predicted Mask']):\n",
        "        ax.set_title(col)\n",
        "\n",
        "    index = 0\n",
        "    for i,batch in enumerate(data_loader):\n",
        "            img = batch[0].to(device)\n",
        "            msk = batch[1].to(device)\n",
        "\n",
        "            output = model(img)\n",
        "\n",
        "            for j in range(batch[0].size()[0]):  # iterate over batch size\n",
        "                img_cpu = img[j].detach().cpu().numpy()  # Convert to numpy array after moving to CPU\n",
        "                msk_cpu = torch.squeeze(msk[j]).detach().cpu().numpy()\n",
        "                output_cpu = torch.squeeze(output[j]).detach().cpu().numpy()\n",
        "\n",
        "                axs[index, 0].imshow(np.transpose(img_cpu, (1, 2, 0)).astype(np.uint8), cmap='bone', interpolation='none')\n",
        "                axs[index, 1].imshow(np.transpose(img_cpu, (1, 2, 0)).astype(np.uint8), cmap='bone', interpolation='none')\n",
        "                axs[index, 1].imshow(msk_cpu, cmap='Blues', interpolation='none', alpha=0.5)\n",
        "                axs[index, 2].imshow(np.transpose(img_cpu, (1, 2, 0)).astype(np.uint8), cmap='bone', interpolation='none')\n",
        "                axs[index, 2].imshow(output_cpu, cmap='Greens', interpolation='none', alpha=0.5)\n",
        "\n",
        "\n",
        "                index += 1\n",
        "\n",
        "            if index >= num_samples:\n",
        "                break\n",
        "\n",
        "    plt.tight_layout()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "R2q9FL1jB5TJ"
      },
      "source": [
        "<h2>Experiments<h2>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "BJxePh8u7_D0"
      },
      "outputs": [],
      "source": [
        "torch.cuda.empty_cache()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jdDVUvATolQc"
      },
      "source": [
        "<h3>Segnet<h3>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true,
          "base_uri": "https://localhost:8080/"
        },
        "id": "I2TkrSRoHHFp",
        "outputId": "90fd60dc-7eab-4b8d-8ae5-789f1f37ea04"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Model sent to cpu\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/torch/cuda/amp/grad_scaler.py:125: UserWarning: torch.cuda.amp.GradScaler is enabled, but CUDA is not available.  Disabling.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch: 00001/00050 | Batch 00000/00481 | Loss: 0.6023 | IoU: 0.7402\n",
            "Epoch: 00001/00050 | Batch 00050/00481 | Loss: 0.6050 | IoU: 0.7930\n",
            "Epoch: 00001/00050 | Batch 00100/00481 | Loss: 0.6018 | IoU: 0.7910\n",
            "Epoch: 00001/00050 | Batch 00150/00481 | Loss: 0.6018 | IoU: 0.8730\n",
            "Epoch: 00001/00050 | Batch 00200/00481 | Loss: 0.5823 | IoU: 0.8262\n",
            "Epoch: 00001/00050 | Batch 00250/00481 | Loss: 0.5942 | IoU: 0.8887\n",
            "Epoch: 00001/00050 | Batch 00300/00481 | Loss: 0.5508 | IoU: 0.6738\n",
            "Epoch: 00001/00050 | Batch 00350/00481 | Loss: 0.5663 | IoU: 0.8320\n",
            "Epoch: 00001/00050 | Batch 00400/00481 | Loss: 0.5757 | IoU: 0.8418\n",
            "Epoch: 00001/00050 | Batch 00450/00481 | Loss: 0.5573 | IoU: 0.7910\n",
            "Validation | Epoch: 00001/00050 | Batch 00000/00137 | Loss: 0.5653 |IoU: 0.8301\n",
            "Validation | Epoch: 00001/00050 | Batch 00050/00137 | Loss: 0.5722 |IoU: 0.8145\n",
            "Validation | Epoch: 00001/00050 | Batch 00100/00137 | Loss: 0.5842 |IoU: 0.9082\n",
            "Epoch: 00002/00050 | Batch 00000/00481 | Loss: 0.5630 | IoU: 0.8652\n",
            "Epoch: 00002/00050 | Batch 00050/00481 | Loss: 0.5445 | IoU: 0.7715\n",
            "Epoch: 00002/00050 | Batch 00100/00481 | Loss: 0.5647 | IoU: 0.8027\n",
            "Epoch: 00002/00050 | Batch 00150/00481 | Loss: 0.5617 | IoU: 0.8262\n",
            "Epoch: 00002/00050 | Batch 00200/00481 | Loss: 0.5523 | IoU: 0.8301\n",
            "Epoch: 00002/00050 | Batch 00250/00481 | Loss: 0.5488 | IoU: 0.8438\n",
            "Epoch: 00002/00050 | Batch 00300/00481 | Loss: 0.5488 | IoU: 0.8086\n",
            "Epoch: 00002/00050 | Batch 00350/00481 | Loss: 0.5587 | IoU: 0.7930\n",
            "Epoch: 00002/00050 | Batch 00400/00481 | Loss: 0.5245 | IoU: 0.7578\n",
            "Epoch: 00002/00050 | Batch 00450/00481 | Loss: 0.5585 | IoU: 0.9006\n",
            "Validation | Epoch: 00002/00050 | Batch 00000/00137 | Loss: 0.5420 |IoU: 0.8301\n",
            "Validation | Epoch: 00002/00050 | Batch 00050/00137 | Loss: 0.5438 |IoU: 0.8145\n",
            "Validation | Epoch: 00002/00050 | Batch 00100/00137 | Loss: 0.5589 |IoU: 0.9043\n",
            "Epoch: 00003/00050 | Batch 00000/00481 | Loss: 0.5575 | IoU: 0.8672\n",
            "Epoch: 00003/00050 | Batch 00050/00481 | Loss: 0.5335 | IoU: 0.7324\n",
            "Epoch: 00003/00050 | Batch 00100/00481 | Loss: 0.5336 | IoU: 0.8359\n",
            "Epoch: 00003/00050 | Batch 00150/00481 | Loss: 0.5210 | IoU: 0.8379\n",
            "Epoch: 00003/00050 | Batch 00200/00481 | Loss: 0.4985 | IoU: 0.7383\n",
            "Epoch: 00003/00050 | Batch 00250/00481 | Loss: 0.5148 | IoU: 0.7871\n",
            "Epoch: 00003/00050 | Batch 00300/00481 | Loss: 0.5055 | IoU: 0.8105\n",
            "Epoch: 00003/00050 | Batch 00350/00481 | Loss: 0.5029 | IoU: 0.7695\n",
            "Epoch: 00003/00050 | Batch 00400/00481 | Loss: 0.4878 | IoU: 0.7871\n",
            "Epoch: 00003/00050 | Batch 00450/00481 | Loss: 0.5139 | IoU: 0.8693\n",
            "Validation | Epoch: 00003/00050 | Batch 00000/00137 | Loss: 0.5251 |IoU: 0.8301\n",
            "Validation | Epoch: 00003/00050 | Batch 00050/00137 | Loss: 0.5171 |IoU: 0.8145\n",
            "Validation | Epoch: 00003/00050 | Batch 00100/00137 | Loss: 0.5374 |IoU: 0.9082\n",
            "Epoch: 00004/00050 | Batch 00000/00481 | Loss: 0.5159 | IoU: 0.8262\n",
            "Epoch: 00004/00050 | Batch 00050/00481 | Loss: 0.4825 | IoU: 0.7793\n",
            "Epoch: 00004/00050 | Batch 00100/00481 | Loss: 0.4739 | IoU: 0.7816\n",
            "Epoch: 00004/00050 | Batch 00150/00481 | Loss: 0.4611 | IoU: 0.7305\n",
            "Epoch: 00004/00050 | Batch 00200/00481 | Loss: 0.4906 | IoU: 0.7654\n",
            "Epoch: 00004/00050 | Batch 00250/00481 | Loss: 0.4834 | IoU: 0.8904\n",
            "Epoch: 00004/00050 | Batch 00300/00481 | Loss: 0.4482 | IoU: 0.6809\n",
            "Epoch: 00004/00050 | Batch 00350/00481 | Loss: 0.4330 | IoU: 0.7750\n",
            "Epoch: 00004/00050 | Batch 00400/00481 | Loss: 0.4578 | IoU: 0.8580\n",
            "Epoch: 00004/00050 | Batch 00450/00481 | Loss: 0.4122 | IoU: 0.8469\n",
            "Validation | Epoch: 00004/00050 | Batch 00000/00137 | Loss: 0.5092 |IoU: 0.8301\n",
            "Validation | Epoch: 00004/00050 | Batch 00050/00137 | Loss: 0.4727 |IoU: 0.8154\n",
            "Validation | Epoch: 00004/00050 | Batch 00100/00137 | Loss: 0.5043 |IoU: 0.8697\n",
            "Epoch: 00005/00050 | Batch 00000/00481 | Loss: 0.4490 | IoU: 0.9268\n",
            "Epoch: 00005/00050 | Batch 00050/00481 | Loss: 0.4127 | IoU: 0.8547\n",
            "Epoch: 00005/00050 | Batch 00100/00481 | Loss: 0.4931 | IoU: 0.7666\n",
            "Epoch: 00005/00050 | Batch 00150/00481 | Loss: 0.4926 | IoU: 0.7576\n",
            "Epoch: 00005/00050 | Batch 00200/00481 | Loss: 0.3214 | IoU: 0.8244\n",
            "Epoch: 00005/00050 | Batch 00250/00481 | Loss: 0.3987 | IoU: 0.7973\n",
            "Epoch: 00005/00050 | Batch 00300/00481 | Loss: 0.3174 | IoU: 0.8949\n",
            "Epoch: 00005/00050 | Batch 00350/00481 | Loss: 0.2666 | IoU: 0.8506\n",
            "Epoch: 00005/00050 | Batch 00400/00481 | Loss: 0.3227 | IoU: 0.8713\n",
            "Epoch: 00005/00050 | Batch 00450/00481 | Loss: 0.2599 | IoU: 0.8244\n",
            "Validation | Epoch: 00005/00050 | Batch 00000/00137 | Loss: 0.4998 |IoU: 0.8301\n",
            "Validation | Epoch: 00005/00050 | Batch 00050/00137 | Loss: 0.3084 |IoU: 0.8846\n",
            "Validation | Epoch: 00005/00050 | Batch 00100/00137 | Loss: 0.4486 |IoU: 0.7854\n",
            "Epoch: 00006/00050 | Batch 00000/00481 | Loss: 0.4520 | IoU: 0.7074\n",
            "Epoch: 00006/00050 | Batch 00050/00481 | Loss: 0.3405 | IoU: 0.8207\n",
            "Epoch: 00006/00050 | Batch 00100/00481 | Loss: 0.2157 | IoU: 0.8754\n",
            "Epoch: 00006/00050 | Batch 00150/00481 | Loss: 0.4590 | IoU: 0.7695\n",
            "Epoch: 00006/00050 | Batch 00200/00481 | Loss: 0.1972 | IoU: 0.8225\n",
            "Epoch: 00006/00050 | Batch 00250/00481 | Loss: 0.2944 | IoU: 0.8145\n",
            "Epoch: 00006/00050 | Batch 00300/00481 | Loss: 0.2865 | IoU: 0.8564\n",
            "Epoch: 00006/00050 | Batch 00350/00481 | Loss: 0.2125 | IoU: 0.8770\n",
            "Epoch: 00006/00050 | Batch 00400/00481 | Loss: 0.2421 | IoU: 0.8184\n",
            "Epoch: 00006/00050 | Batch 00450/00481 | Loss: 0.1757 | IoU: 0.7730\n",
            "Validation | Epoch: 00006/00050 | Batch 00000/00137 | Loss: 0.5034 |IoU: 0.8301\n",
            "Validation | Epoch: 00006/00050 | Batch 00050/00137 | Loss: 0.1444 |IoU: 0.9195\n",
            "Validation | Epoch: 00006/00050 | Batch 00100/00137 | Loss: 0.3665 |IoU: 0.8109\n",
            "Epoch: 00007/00050 | Batch 00000/00481 | Loss: 0.1124 | IoU: 0.9393\n",
            "Epoch: 00007/00050 | Batch 00050/00481 | Loss: 0.4020 | IoU: 0.7012\n",
            "Epoch: 00007/00050 | Batch 00100/00481 | Loss: 0.1781 | IoU: 0.8063\n",
            "Epoch: 00007/00050 | Batch 00150/00481 | Loss: 0.1227 | IoU: 0.9266\n",
            "Epoch: 00007/00050 | Batch 00200/00481 | Loss: 0.2420 | IoU: 0.8449\n",
            "Epoch: 00007/00050 | Batch 00250/00481 | Loss: 0.1066 | IoU: 0.8680\n",
            "Epoch: 00007/00050 | Batch 00300/00481 | Loss: 0.2099 | IoU: 0.9252\n",
            "Epoch: 00007/00050 | Batch 00350/00481 | Loss: 0.1712 | IoU: 0.8414\n",
            "Epoch: 00007/00050 | Batch 00400/00481 | Loss: 0.2933 | IoU: 0.6967\n",
            "Epoch: 00007/00050 | Batch 00450/00481 | Loss: 0.2652 | IoU: 0.6740\n",
            "Validation | Epoch: 00007/00050 | Batch 00000/00137 | Loss: 0.5060 |IoU: 0.8301\n",
            "Validation | Epoch: 00007/00050 | Batch 00050/00137 | Loss: 0.0995 |IoU: 0.9133\n",
            "Validation | Epoch: 00007/00050 | Batch 00100/00137 | Loss: 0.4071 |IoU: 0.7781\n",
            "Epoch: 00008/00050 | Batch 00000/00481 | Loss: 0.1850 | IoU: 0.8910\n",
            "Epoch: 00008/00050 | Batch 00050/00481 | Loss: 0.0833 | IoU: 0.9516\n",
            "Epoch: 00008/00050 | Batch 00100/00481 | Loss: 0.1061 | IoU: 0.8590\n",
            "Epoch: 00008/00050 | Batch 00150/00481 | Loss: 0.1217 | IoU: 0.8953\n",
            "Epoch: 00008/00050 | Batch 00200/00481 | Loss: 0.2366 | IoU: 0.7865\n",
            "Epoch: 00008/00050 | Batch 00250/00481 | Loss: 0.1854 | IoU: 0.8178\n",
            "Epoch: 00008/00050 | Batch 00300/00481 | Loss: 0.1809 | IoU: 0.9312\n",
            "Epoch: 00008/00050 | Batch 00350/00481 | Loss: 0.1041 | IoU: 0.9246\n",
            "Epoch: 00008/00050 | Batch 00400/00481 | Loss: 0.0711 | IoU: 0.9035\n",
            "Epoch: 00008/00050 | Batch 00450/00481 | Loss: 0.1076 | IoU: 0.8170\n",
            "Validation | Epoch: 00008/00050 | Batch 00000/00137 | Loss: 0.5070 |IoU: 0.8301\n",
            "Validation | Epoch: 00008/00050 | Batch 00050/00137 | Loss: 0.0756 |IoU: 0.9203\n",
            "Validation | Epoch: 00008/00050 | Batch 00100/00137 | Loss: 0.2975 |IoU: 0.8457\n",
            "Epoch: 00009/00050 | Batch 00000/00481 | Loss: 0.1231 | IoU: 0.8342\n",
            "Epoch: 00009/00050 | Batch 00050/00481 | Loss: 0.2740 | IoU: 0.7709\n",
            "Epoch: 00009/00050 | Batch 00100/00481 | Loss: 0.3159 | IoU: 0.7967\n",
            "Epoch: 00009/00050 | Batch 00150/00481 | Loss: 0.1591 | IoU: 0.8719\n",
            "Epoch: 00009/00050 | Batch 00200/00481 | Loss: 0.1234 | IoU: 0.8672\n",
            "Epoch: 00009/00050 | Batch 00250/00481 | Loss: 0.0946 | IoU: 0.8537\n",
            "Epoch: 00009/00050 | Batch 00300/00481 | Loss: 0.3355 | IoU: 0.7969\n",
            "Epoch: 00009/00050 | Batch 00350/00481 | Loss: 0.1188 | IoU: 0.9258\n",
            "Epoch: 00009/00050 | Batch 00400/00481 | Loss: 0.3038 | IoU: 0.7949\n",
            "Epoch: 00009/00050 | Batch 00450/00481 | Loss: 0.2145 | IoU: 0.7193\n",
            "Validation | Epoch: 00009/00050 | Batch 00000/00137 | Loss: 0.5073 |IoU: 0.8301\n",
            "Validation | Epoch: 00009/00050 | Batch 00050/00137 | Loss: 0.0650 |IoU: 0.9223\n",
            "Validation | Epoch: 00009/00050 | Batch 00100/00137 | Loss: 0.3454 |IoU: 0.8041\n",
            "Epoch: 00010/00050 | Batch 00000/00481 | Loss: 0.1866 | IoU: 0.7604\n",
            "Epoch: 00010/00050 | Batch 00050/00481 | Loss: 0.0993 | IoU: 0.9016\n",
            "Epoch: 00010/00050 | Batch 00100/00481 | Loss: 0.0710 | IoU: 0.9193\n",
            "Epoch: 00010/00050 | Batch 00150/00481 | Loss: 0.1465 | IoU: 0.9119\n",
            "Epoch: 00010/00050 | Batch 00200/00481 | Loss: 0.1984 | IoU: 0.8758\n",
            "Epoch: 00010/00050 | Batch 00250/00481 | Loss: 0.1320 | IoU: 0.7770\n",
            "Epoch: 00010/00050 | Batch 00300/00481 | Loss: 0.1553 | IoU: 0.8711\n",
            "Epoch: 00010/00050 | Batch 00350/00481 | Loss: 0.2713 | IoU: 0.8592\n",
            "Epoch: 00010/00050 | Batch 00400/00481 | Loss: 0.3074 | IoU: 0.8680\n",
            "Epoch: 00010/00050 | Batch 00450/00481 | Loss: 0.1946 | IoU: 0.8791\n",
            "Validation | Epoch: 00010/00050 | Batch 00000/00137 | Loss: 0.5077 |IoU: 0.8301\n",
            "Validation | Epoch: 00010/00050 | Batch 00050/00137 | Loss: 0.0627 |IoU: 0.9201\n",
            "Validation | Epoch: 00010/00050 | Batch 00100/00137 | Loss: 0.3027 |IoU: 0.8369\n",
            "Epoch: 00011/00050 | Batch 00000/00481 | Loss: 0.2217 | IoU: 0.9115\n",
            "Epoch: 00011/00050 | Batch 00050/00481 | Loss: 0.0974 | IoU: 0.8955\n",
            "Epoch: 00011/00050 | Batch 00100/00481 | Loss: 0.1285 | IoU: 0.8795\n",
            "Epoch: 00011/00050 | Batch 00150/00481 | Loss: 0.0738 | IoU: 0.9316\n",
            "Epoch: 00011/00050 | Batch 00200/00481 | Loss: 0.1421 | IoU: 0.8475\n",
            "Epoch: 00011/00050 | Batch 00250/00481 | Loss: 0.1699 | IoU: 0.8104\n",
            "Epoch: 00011/00050 | Batch 00300/00481 | Loss: 0.1623 | IoU: 0.7895\n",
            "Epoch: 00011/00050 | Batch 00350/00481 | Loss: 0.0930 | IoU: 0.9127\n",
            "Epoch: 00011/00050 | Batch 00400/00481 | Loss: 0.1392 | IoU: 0.8264\n",
            "Epoch: 00011/00050 | Batch 00450/00481 | Loss: 0.2532 | IoU: 0.8311\n",
            "Validation | Epoch: 00011/00050 | Batch 00000/00137 | Loss: 0.5079 |IoU: 0.8301\n",
            "Validation | Epoch: 00011/00050 | Batch 00050/00137 | Loss: 0.0579 |IoU: 0.9234\n",
            "Validation | Epoch: 00011/00050 | Batch 00100/00137 | Loss: 0.2873 |IoU: 0.8422\n",
            "Epoch: 00012/00050 | Batch 00000/00481 | Loss: 0.2356 | IoU: 0.7752\n",
            "Epoch: 00012/00050 | Batch 00050/00481 | Loss: 0.0766 | IoU: 0.9170\n"
          ]
        }
      ],
      "source": [
        "model = modelSegnet\n",
        "optimizer=torch.optim.SGD(model.parameters(), lr=learning_rate)\n",
        "loss_fn = dice_loss\n",
        "train_loader= train_loader\n",
        "val_loader= val_loader\n",
        "\n",
        "torch.cuda.empty_cache()\n",
        "results = run_experiment(\"Segnet\", model, num_epochs, optimizer, loss_fn, train_loader, val_loader, device=device, clear_mem=True)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ao_Qbu_0PFwe"
      },
      "outputs": [],
      "source": [
        "model=modelSegnet\n",
        "test_loader= test_loader\n",
        "device=device\n",
        "loss_fn = dice_loss\n",
        "\n",
        "test_losses, test_scores = test_model( model, test_loader, device, loss_fn)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "hrrcvp_mcJjb"
      },
      "outputs": [],
      "source": [
        "plot_training_scores(results['train_losses'], results['train_scores'])\n",
        "plot_validation_scores(results['val_losses'], results['val_scores'])\n",
        "plot_test_scores(test_losses, test_scores)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "DvCrhQwrgKrV"
      },
      "outputs": [],
      "source": [
        "\n",
        "model = modelSegnet\n",
        "optimizer=torch.optim.SGD(model.parameters(), lr=learning_rate)\n",
        "loss_fn = dice_loss\n",
        "\n",
        "visualize_segmentation(model, val_loader, num_samples=10, device='cuda')\n",
        "del model\n",
        "del optimizer\n",
        "del loss_fn\n",
        "torch.cuda.empty_cache()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "OTOSQF2sP-pI"
      },
      "outputs": [],
      "source": [
        "\n",
        "model = modelSegnet\n",
        "optimizer=torch.optim.SGD(model.parameters(), lr=learning_rate)\n",
        "loss_fn = dice_loss\n",
        "\n",
        "visualize_segmentation(model, test_loader, num_samples=10, device='cuda')\n",
        "del model\n",
        "del optimizer\n",
        "del loss_fn\n",
        "torch.cuda.empty_cache()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wPvDj8fb6xqy"
      },
      "source": [
        "<h3>DeepLab<h3>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "wuTLP3h0olQi"
      },
      "outputs": [],
      "source": [
        "torch.cuda.empty_cache()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "g15qUvzj64yy"
      },
      "outputs": [],
      "source": [
        "\n",
        "\n",
        "model = modelDeeplab\n",
        "optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate)\n",
        "loss_fn = dice_loss\n",
        "train_loader= train_loader\n",
        "val_loader= val_loader\n",
        "\n",
        "torch.cuda.empty_cache()\n",
        "results = run_experiment(\"Unet\", model, num_epochs, optimizer, loss_fn, train_loader, val_loader, device=device, clear_mem=True)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "vYgmCV5hLvYL"
      },
      "outputs": [],
      "source": [
        "model=modelDeeplab\n",
        "test_loader=test_loader\n",
        "device=device\n",
        "loss_fn = dice_loss\n",
        "\n",
        "test_losses, test_scores = test_model( model, test_loader, device, loss_fn)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "T7Tb41V5M09x"
      },
      "outputs": [],
      "source": [
        "plot_training_scores(results['train_losses'], results['train_scores'])\n",
        "plot_validation_scores(results['val_losses'], results['val_scores'])\n",
        "plot_test_scores(test_losses, test_scores)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "kMHxJicyOzLs"
      },
      "outputs": [],
      "source": [
        "\n",
        "model = modelDeeplab\n",
        "optimizer=torch.optim.SGD(model.parameters(), lr=learning_rate)\n",
        "loss_fn = dice_loss\n",
        "\n",
        "visualize_segmentation(model, val_loader, num_samples=10, device='cuda')\n",
        "del model\n",
        "del optimizer\n",
        "del loss_fn\n",
        "torch.cuda.empty_cache()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "pqVSpyVoPTOV"
      },
      "outputs": [],
      "source": [
        "\n",
        "model = modelDeeplab\n",
        "optimizer=torch.optim.SGD(model.parameters(), lr=learning_rate)\n",
        "loss_fn = dice_loss\n",
        "\n",
        "visualize_segmentation(model, test_loader, num_samples=10, device='cuda')\n",
        "del model\n",
        "del optimizer\n",
        "del loss_fn\n",
        "torch.cuda.empty_cache()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "g3ivZNJNolQn"
      },
      "source": [
        "<h3>UNet<h3>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "H1bixX_UolQn"
      },
      "outputs": [],
      "source": [
        "torch.cuda.empty_cache()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "kcdEPQ92olQn"
      },
      "outputs": [],
      "source": [
        "model = modelUnet\n",
        "optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate)\n",
        "loss_fn = dice_loss\n",
        "train_loader= train_loader\n",
        "val_loader= val_loader\n",
        "\n",
        "\n",
        "results = run_experiment(\"Unet\", model, num_epochs, optimizer, loss_fn, train_loader, val_loader, device=device, clear_mem=True)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "DMyt_2SXolQo"
      },
      "outputs": [],
      "source": [
        "model=modelUnet\n",
        "test_loader=test_loader\n",
        "device=device\n",
        "loss_fn = dice_loss\n",
        "\n",
        "test_losses, test_scores = test_model( model, test_loader, device, loss_fn)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "qOeprF4jolQo"
      },
      "outputs": [],
      "source": [
        "\n",
        "plot_training_scores(results['train_losses'], results['train_scores'])\n",
        "plot_validation_scores(results['val_losses'], results['val_scores'])\n",
        "plot_test_scores(test_losses, test_scores)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "XmoJE9fHolQo"
      },
      "outputs": [],
      "source": [
        "\n",
        "model = modelUnet\n",
        "optimizer=torch.optim.SGD(model.parameters(), lr=learning_rate)\n",
        "loss_fn = dice_loss\n",
        "\n",
        "visualize_segmentation(model, val_loader, num_samples=10, device='cuda')\n",
        "del model\n",
        "del optimizer\n",
        "del loss_fn\n",
        "torch.cuda.empty_cache()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "mhjcxu2oolQq"
      },
      "outputs": [],
      "source": [
        "\n",
        "model = modelUnet\n",
        "optimizer=torch.optim.SGD(model.parameters(), lr=learning_rate)\n",
        "loss_fn = dice_loss\n",
        "\n",
        "visualize_segmentation(model, test_loader, num_samples=10, device='cuda')\n",
        "del model\n",
        "del optimizer\n",
        "del loss_fn\n",
        "torch.cuda.empty_cache()"
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.11.5"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}