Spleen_dict.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Spleen_dict.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "mount_file_id": "1KxRs6swO8JXLsiB10CDnz6j-SluROoN9",
      "authorship_tag": "ABX9TyP7FH7ebaM5tmSqg+tw1NkT",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Meddebma/pyradiomics/blob/master/Spleen_dict.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "z8xY60j1gxdX",
        "outputId": "eb6bd732-dce7-47a7-8ebe-1382cc43aa16"
      },
      "source": [
        "%pip install git+https://github.com/Project-MONAI/MONAI#egg=MONAI\n",
        "#pip install monai==0.3.0\n",
        "#%pip install git+https://github.com/Project-MONAI/MONAI#egg=MONAI\n",
        "%pip install 'monai[all]'\n",
        "%pip install pytorch-ignite\n",
        "%pip install nibabel==3.2.0\n",
        "\n",
        "import logging\n",
        "import os\n",
        "import sys\n",
        "import tempfile\n",
        "from glob import glob\n",
        "\n",
        "import nibabel as nib\n",
        "import numpy as np\n",
        "import torch\n",
        "from torch.utils.data import DataLoader\n",
        "from torch.utils.tensorboard import SummaryWriter\n",
        "\n",
        "import monai\n",
        "from monai.data import create_test_image_3d, list_data_collate\n",
        "from monai.inferers import sliding_window_inference\n",
        "from monai.metrics import DiceMetric\n",
        "from monai.transforms import (\n",
        "    Activations,\n",
        "    AsChannelFirstd,\n",
        "    AsDiscrete,\n",
        "    Compose,\n",
        "    LoadNiftid,\n",
        "    Spacingd,\n",
        "    Orientationd,\n",
        "    CropForegroundd,\n",
        "    RandCropByPosNegLabeld,\n",
        "    ScaleIntensityRanged,\n",
        "    RandRotate90d,\n",
        "    ScaleIntensityd,\n",
        "    ToTensord,\n",
        "    AddChanneld,\n",
        "    KeepLargestConnectedComponent, \n",
        "    LabelToContour\n",
        ")\n",
        "from monai.losses import DiceLoss\n",
        "from monai.metrics import compute_meandice\n",
        "from monai.networks.layers import Norm\n",
        "from monai.utils import first, set_determinism\n",
        "from monai.visualize import plot_2d_or_3d_image"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
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          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "q7aBb3wvhao1"
      },
      "source": [
        "\n",
        "\n",
        "# Import Data and Transform"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5MugHh3Fhjnm",
        "outputId": "313633ef-16d9-4317-e6ef-e8b31f75798b"
      },
      "source": [
        "root= \"/content/drive/My Drive/Task09_Spleen/\"\n",
        "images = sorted(glob(os.path.join(root, \"imagesTr\",\"*.nii.gz\")))\n",
        "segs = sorted(glob(os.path.join(root, \"labelsTr\", \"*.nii.gz\")))\n",
        "\n",
        "train_files = [{\"img\": img, \"seg\": seg} for img, seg in zip(images[:20], segs[:20])]\n",
        "val_files = [{\"img\": img, \"seg\": seg} for img, seg in zip(images[-20:], segs[-20:])]\n",
        "print (len(images))\n",
        "print (len(segs))\n",
        "\n",
        "train_transforms = Compose(\n",
        "    [\n",
        "        LoadNiftid(keys=[\"img\", \"seg\"]),\n",
        "        AddChanneld(keys=[\"img\", \"seg\"]),\n",
        "        Spacingd(keys=[\"img\", \"seg\"], pixdim=(1.5, 1.5, 2.0), mode=(\"bilinear\", \"nearest\")),\n",
        "        Orientationd(keys=[\"img\", \"seg\"], axcodes=\"RAS\"),\n",
        "        ScaleIntensityRanged(\n",
        "            keys=[\"img\"], a_min=-57, a_max=164, b_min=0.0, b_max=1.0, clip=True,\n",
        "        ),\n",
        "        CropForegroundd(keys=[\"img\", \"seg\"], source_key=\"img\"),\n",
        "        RandCropByPosNegLabeld(\n",
        "            keys=[\"img\", \"seg\"],\n",
        "            label_key=\"seg\",\n",
        "            spatial_size=(96, 96, 96),\n",
        "            pos=1,\n",
        "            neg=1,\n",
        "            num_samples=4,\n",
        "            image_key=\"img\",\n",
        "            image_threshold=0,\n",
        "        ),\n",
        "        # user can also add other random transforms\n",
        "        # RandAffined(keys=['image', 'label'], mode=('bilinear', 'nearest'), prob=1.0, spatial_size=(96, 96, 96),\n",
        "        #             rotate_range=(0, 0, np.pi/15), scale_range=(0.1, 0.1, 0.1)),\n",
        "        ToTensord(keys=[\"img\", \"seg\"]),\n",
        "    ]\n",
        ")\n",
        "val_transforms = Compose(\n",
        "    [\n",
        "        LoadNiftid(keys=[\"img\", \"seg\"]),\n",
        "        AddChanneld(keys=[\"img\", \"seg\"]),\n",
        "        Spacingd(keys=[\"img\", \"seg\"], pixdim=(1.5, 1.5, 2.0), mode=(\"bilinear\", \"nearest\")),\n",
        "        Orientationd(keys=[\"img\", \"seg\"], axcodes=\"RAS\"),\n",
        "        ScaleIntensityRanged(\n",
        "            keys=[\"img\"], a_min=-57, a_max=164, b_min=0.0, b_max=1.0, clip=True,\n",
        "        ),\n",
        "        CropForegroundd(keys=[\"img\", \"seg\"], source_key=\"img\"),\n",
        "        ToTensord(keys=[\"img\", \"seg\"]),\n",
        "    ]\n",
        ")\n",
        "\n",
        "# define dataset, data loader\n",
        "check_ds = monai.data.Dataset(data=train_files, transform=train_transforms)\n",
        "# use batch_size=2 to load images and use RandCropByPosNegLabeld to generate 2 x 4 images for network training\n",
        "check_loader = DataLoader(check_ds, batch_size=2, num_workers=4, collate_fn=list_data_collate)\n",
        "check_data = monai.utils.misc.first(check_loader)\n",
        "print(check_data[\"img\"].shape, check_data[\"seg\"].shape)\n",
        "\n",
        "    # create a training data loader\n",
        "train_ds = monai.data.Dataset(data=train_files, transform=train_transforms)\n",
        "    # use batch_size=2 to load images and use RandCropByPosNegLabeld to generate 2 x 4 images for network training\n",
        "train_loader = DataLoader(\n",
        "        train_ds,\n",
        "        batch_size=2,\n",
        "        shuffle=True,\n",
        "        num_workers=4,\n",
        "        collate_fn=list_data_collate,\n",
        "        pin_memory=torch.cuda.is_available(),\n",
        "    )\n",
        "    # create a validation data loader\n",
        "val_ds = monai.data.Dataset(data=val_files, transform=val_transforms)\n",
        "val_loader = DataLoader(val_ds, batch_size=1, num_workers=4, collate_fn=list_data_collate)\n",
        "dice_metric = DiceMetric(include_background=True, reduction=\"mean\")\n",
        "post_trans = Compose([Activations(sigmoid=True), AsDiscrete(threshold_values=True)])\n"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "41\n",
            "41\n",
            "torch.Size([8, 1, 96, 96, 96]) torch.Size([8, 1, 96, 96, 96])\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dUIsBtJ1iCjV"
      },
      "source": [
        "# Create Model (UNET)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "B19i3mL1iLAL"
      },
      "source": [
        "# create UNet, DiceLoss and Adam optimizer\n",
        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
        "model = monai.networks.nets.UNet(\n",
        "        dimensions=3,\n",
        "        in_channels=1,\n",
        "        out_channels=1,\n",
        "        channels=(16, 32, 64, 128, 256),\n",
        "        strides=(2, 2, 2, 2),\n",
        "        num_res_units=2,\n",
        "    ).to(device)\n",
        "loss_function = monai.losses.DiceLoss(sigmoid=True)\n",
        "optimizer = torch.optim.Adam(model.parameters(), 1e-3)"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "j_pT2JIQqkhB"
      },
      "source": [
        "# Train the Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 686
        },
        "id": "jP6BTEQkzUyc",
        "outputId": "381fb7b4-56a7-4d32-f9e1-6798b45717f3"
      },
      "source": [
        "val_interval = 2\n",
        "best_metric = -1\n",
        "best_metric_epoch = -1\n",
        "epoch_loss_values = list()\n",
        "metric_values = list()\n",
        "writer = SummaryWriter()\n",
        "for epoch in range(5):\n",
        "        print(\"-\" * 10)\n",
        "        print(f\"epoch {epoch + 1}/{5}\")\n",
        "        model.train()\n",
        "        epoch_loss = 0\n",
        "        step = 0\n",
        "        for batch_data in train_loader:\n",
        "            step += 1\n",
        "            inputs, labels = batch_data[\"img\"].to(device), batch_data[\"seg\"].to(device)\n",
        "            optimizer.zero_grad()\n",
        "            outputs = model(inputs)\n",
        "            loss = loss_function(outputs, labels)\n",
        "            loss.backward()\n",
        "            optimizer.step()\n",
        "            epoch_loss += loss.item()\n",
        "            epoch_len = len(train_ds) // train_loader.batch_size\n",
        "            print(f\"{step}/{epoch_len}, train_loss: {loss.item():.4f}\")\n",
        "            writer.add_scalar(\"train_loss\", loss.item(), epoch_len * epoch + step)\n",
        "        epoch_loss /= step\n",
        "        epoch_loss_values.append(epoch_loss)\n",
        "        print(f\"epoch {epoch + 1} average loss: {epoch_loss:.4f}\")\n",
        "\n",
        "        if (epoch + 1) % val_interval == 0:\n",
        "            model.eval()\n",
        "            with torch.no_grad():\n",
        "                metric_sum = 0.0\n",
        "                metric_count = 0\n",
        "                val_images = None\n",
        "                val_labels = None\n",
        "                val_outputs = None\n",
        "                for val_data in val_loader:\n",
        "                    val_images, val_labels = val_data[\"img\"].to(device), val_data[\"seg\"].to(device)\n",
        "                    roi_size = (96, 96, 96)\n",
        "                    sw_batch_size = 4\n",
        "                    val_outputs = sliding_window_inference(val_images, roi_size, sw_batch_size, model)\n",
        "                    val_outputs = post_trans(val_outputs)\n",
        "                    value, _ = dice_metric(y_pred=val_outputs, y=val_labels)\n",
        "                    metric_count += len(value)\n",
        "                    metric_sum += value.item() * len(value)\n",
        "                metric = metric_sum / metric_count\n",
        "                metric_values.append(metric)\n",
        "                if metric > best_metric:\n",
        "                    best_metric = metric\n",
        "                    best_metric_epoch = epoch + 1\n",
        "                    torch.save(model.state_dict(), \"best_metric_model_segmentation3d_dict.pth\")\n",
        "                    print(\"saved new best metric model\")\n",
        "                print(\n",
        "                    \"current epoch: {} current mean dice: {:.4f} best mean dice: {:.4f} at epoch {}\".format(\n",
        "                        epoch + 1, metric, best_metric, best_metric_epoch\n",
        "                    )\n",
        "                )\n",
        "                writer.add_scalar(\"val_mean_dice\", metric, epoch + 1)\n",
        "                # plot the last model output as GIF image in TensorBoard with the corresponding image and label\n",
        "                plot_2d_or_3d_image(val_images, epoch + 1, writer, index=0, tag=\"image\")\n",
        "                plot_2d_or_3d_image(val_labels, epoch + 1, writer, index=0, tag=\"label\")\n",
        "                plot_2d_or_3d_image(val_outputs, epoch + 1, writer, index=0, tag=\"output\")\n",
        "\n",
        "print(f\"train completed, best_metric: {best_metric:.4f} at epoch: {best_metric_epoch}\")\n",
        "writer.close()"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "----------\n",
            "epoch 1/5\n",
            "1/10, train_loss: 0.9752\n",
            "2/10, train_loss: 0.9662\n",
            "3/10, train_loss: 0.9767\n",
            "4/10, train_loss: 0.9447\n",
            "5/10, train_loss: 0.8629\n",
            "6/10, train_loss: 0.9310\n",
            "7/10, train_loss: 0.9057\n",
            "8/10, train_loss: 0.8681\n",
            "9/10, train_loss: 0.8662\n",
            "10/10, train_loss: 0.8858\n",
            "epoch 1 average loss: 0.9183\n",
            "----------\n",
            "epoch 2/5\n",
            "1/10, train_loss: 0.9648\n",
            "2/10, train_loss: 0.9296\n",
            "3/10, train_loss: 0.9504\n",
            "4/10, train_loss: 0.9616\n",
            "5/10, train_loss: 0.8899\n",
            "6/10, train_loss: 0.8126\n",
            "7/10, train_loss: 0.8574\n",
            "8/10, train_loss: 0.8784\n",
            "9/10, train_loss: 0.8575\n",
            "10/10, train_loss: 0.8194\n",
            "epoch 2 average loss: 0.8922\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "error",
          "ename": "ValueError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-10-6170c070439a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     41\u001b[0m                     \u001b[0mval_outputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msliding_window_inference\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mval_images\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mroi_size\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msw_batch_size\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     42\u001b[0m                     \u001b[0mval_outputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpost_trans\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mval_outputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 43\u001b[0;31m                     \u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdice_metric\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_pred\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mval_outputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mval_labels\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     44\u001b[0m                     \u001b[0mmetric_count\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     45\u001b[0m                     \u001b[0mmetric_sum\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mValueError\u001b[0m: not enough values to unpack (expected 2, got 1)"
          ]
        }
      ]
    }
  ]
}